dataesr/scientific-paragraphs-categorization huggingface dataset:
One important difference is that our definition of partial semantics in terms of a partialization function is generic and can be easily instantiated for different languages. On the contrary previous works defined "trace semantics" as separate relations with many rules, making the proofs to correlate partial and complete semantics more involved. Moreover, by focusing on trace properties (instead of observational equivalence) composition and decomposition can be proved using standard simulations à la CompCert, which is easier than previous proof techniques for fully abstract "trace semantics. " Theorem 4. 2 (Partial Semantics). The source language and compartmentalized machine partial semantics defined as described above provide decomposition and composition (Assumptions 4 and 5). Blame. We prove Assumption 7 by noting that the behavior of the context C S can only depend on its own state and on the events emitted by the program.
The Return instruction is translated to an aligned sequence: pop from the protected stack and jump to the retrieved address. This sequence also fits entirely in a sixteen-unit block. The protection of the addresses on the stack itself is realized by the instrumentation of all the Store and Jump instructions in the program. We used the QuickChick property-based testing tool  [62]  for Coq to test the three compartmentalization invariants described at the beginning of the subsection. For each invariant, we implemented a test that executes the following steps: (i) randomly generates a valid compartmentalized machine program; (ii) compiles it; (iii) executes the resulting target code in a simulator and records a propertyspecific trace; and (iv) analyzes the trace to verify if the property has been violated.
This enforcement mechanism has been shown flexible enough to implement a broad range of tag-based reference monitors, and for many of them it has a relatively modest impact on runtime (typically under 10%) and power ceiling (less than 10%), in return for some increase in energy (typically under 60%) and chip area (110%)  [26]. Moreover, the mechanism is simple enough so that the security of the reference monitors can be verified formally  [12] [13] [14] [15]. The micro-policy machine targeted by our compartmentalizing back end builds on a "symbolic machine" that Azevedo de Amorim et al. used to prove the correctness and security of several micro-policies in Coq  [12, 14, 15]. The code generation and static linking parts of the micro-policy back end are much simpler than for the SFI one. The Call and Return instructions are mapped to Jal and Jump.
This could be useful, for example, when hand-optimizing assembly produced by a secure compiler. In another thread of work, Devriese et al. [24]  proved modular full abstraction by approximate back-translation for a compiler from simply typed to untyped λ-calculus. This work also introduces a complete Coq formalization for the original (non-modular) full abstraction proof of Devriese et al. [22]. Beyond Good and Evil. The closest related work is that of Juglaret et al. [43] , who also aim at protecting mutually distrustful components written in an unsafe language. They adapt fully abstract compilation to components, but observe that defining observational equivalence for programs with undefined behavior is highly problematic. For instance, is the partial program "int buf  [5] ; return buf  [42] " equivalent to "int buf  [5] ; return buf  [43] "? Both encounter undefined behavior by accessing a buffer out of bounds, so at the source level they cannot be distinguished.
Proof Techniques. Abate et al. [6]  observe that, to prove RSC, it suffices to back-translate finite execution prefixes, and recently they propose such a proof for a stronger criterion where multiple such executions are involved. In recent concurrent work, Patrignani and Garg  [66]  also construct such a proof for RSC. The main advantages of our RSC DC MD proof are that (1) it applies to unsafe languages with undefined behavior and (2) it directly reuses a compiler correctness result à la CompCert. For safe source languages or when proof reuse is not needed our proof could be further simplified. Even as it stands though, our proof technique is simple and scalable compared to previous full abstraction proofs. While many proof techniques have been previously investigated  [3, 5, 10, 11, 24, 29, 40, 61] , fully abstract compilation proofs are notoriously difficult, even for very simple languages, with apparently simple conjectures surviving for decades before being finally settled  [23].
That work only considers the correctness of low-level mechanisms in isolation, not how a secure compilation chain makes use of these mechanisms to provide security reasoning principles for code written in a higher-level programming language with components. However, more work in this direction seems underway, with Wilke et al. [82]  working on a variant of CompCert with SFI, based on previous work by Kroll et al. [50] ; we believe RSCC or RSC DC could provide good top-level theorems for such an SFI compiler. In most work on verified compartmentalization  [14, 58, 85] , communication between low-level compartments is done by jumping to a specified set of entry points; the model considered here is more structured and enforces the correct return discipline. Skorstengaard et al. have also recently investigated a secure stack-based calling convention for a simple capability machine  [71] ; they plan to simplify their calling convention using a notion of linear return capability  [70]  that seems similar to the one used in our micro-policy from §4.
CONCLUSION AND FUTURE WORK We introduced RSCC, a new formal criterion for secure compilation providing strong security guarantees despite the dynamic compromise of components with undefined behavior. This criterion gives a precise meaning to informal terms like dynamic compromise and mutual distrust used by proponents of compartmentalization, and it offers a solid foundation for reasoning about security of practical compartmentalized applications and secure compiler chains. Formally Secure Compartmentalization for C. Looking ahead, we hope to apply RSCC to the C language by developing a provably secure compartmentalizing compiler chain based on the CompCert compiler. Scaling up to the whole of C will certainly entail further challenges such as defining a variant of C with components and efficiently enforcing compartmentalization all the way down.
Acknowledgments We thank the anonymous reviewers for their valuable feedback. Yannis Juglaret  first studied with us the compartmentalization micro-policy from  §4. 5  and described it in an unpublished technical report [44]. Jérémy Thibault helped us correctly extract the micro-policies back end to OCaml. We are also grateful to  Danel Ahman ,  Éric Tanter ,  Steven Schäfer , and  William Bowman , for insightful discussions and thoughtful feedback on earlier drafts. This work is in part supported by  ERC Starting  Grant  SECOMP (715753 ), by  NSF  award  Micro-Policies  ( 1513854 ), and by  DARPA  grant  SSITH/HOPE  ( FA8650-15-C-7558 ).
The relapse of patient 3, which comprised 10% blasts, did not show evidence for the FLT3 indels in any of the 125 reads of the relapse sample, allowing assignment to the second group of relapses. All alterations of driver genes were controlled by stringent visual inspection of the primary sequencing data. Whenever DNA was left for analysis, several clonal oncogenic alterations were confirmed by Sanger sequencing (n = 5) or allelic discrimination (n = 1). In all cases, the method was adapted to the sensitivity needed to detect the alteration. Sequence alignment and variant calling. Raw sequence alignment and variant calling were carried out using Illumina CASAVA 1. 8 software. CASAVA performs the alignment of reads to the human reference genome (hg19) using the alignment algorithm ELANDv2, and then calls single-nucleotide variants and short insertions and deletions (indels) based on allele calls and read depth.
We used an Integragen in-house pipeline to annotate each variant according to its presence in the 1000Genome  60  , Exome Variant Server (EVS)  61  or Integragen database, and according to its functional category (synonymous, missense, nonsense, splice variant, frameshift, or in-frame indels). To detect the common FLT3 internal duplications that may be missed by classical variant calling algorithms, we used bam-readcount (https://github. com/genome/bam-readcount) to determine the number of mutated bases in an extended region around the known duplication site (chr13: 28608150-28608349). Samples with mutated bases across the regions were then screened visually using the Integrative Genomics Viewer  62. Somatic coding variants at diagnosis and relapse. We considered only variants located within the exome capture baits and we applied stringent filters to keep only reliable variants sequenced in ≥10 reads, with ≥5 variant calls and a QPHRED score ≥20 for both SNP detection and genotype calling (≥30 for indels).
Somatic status was first defined for each leukemic sample (diagnosis and relapse) individually: we considered a mutation to be somatic if the variant allele fraction (VAF) was ≥0. 15 in the tumor and <0. 05 in the remission sample. To identify variants that may be missed in one of the two tumor samples of a same patient due to clonality or technical differences, we then recovered variants that were detected as somatic in one tumor and displayed a VAF ≥0. 05 or at least two mutated reads in the second tumor sample. We excluded known germline variants with a minor allele frequency >1% in 1000Genomes, EVS, or Integragen proprietary database. All mutations in recurrently mutated genes (≥5 overall variants or ≥2 relapse-specific variants) or critical to reconstruct evolutionary trees were validated by stringent visual control using the Integrative Genomics Viewer  62.
We used the Palimpsest R package (https://github. com/FunGeST/Palimpsest) to estimate the CCF of each somatic mutation, i. e. , the proportion of tumor cells harboring the mutation, taking into account the VAF and local copy-number estimates, as previously described  63, 64. A mutation was considered subclonal if the upper boundary of the 95% confidence interval of the CCF was smaller than 0. 95. Copy-number analysis. To identify CNAs in diagnostic and relapse samples, we identified germline single-nucleotide polymorphisms (SNPs) in each sample and we calculated the coverage log ratio (LRR) and B allele frequency (BAF) at each SNP site. Genomic profiles were divided into homogeneous segments by applying the circular binary segmentation algorithm, as implemented in the Bioconductor package DNAcopy  65  , to both LRR and BAF values.
Acknowledgements The H. d. T. laboratory is supported by the  Ligue Nationale contre le Cancer, INSERM ,  CNRS ,  University Paris Diderot ,  Collège de France ,  Institut National du Cancer , the  Association pour la Recherche contre le Cancer  ( Prix Griffuel ),  European Research Council  Senior Grant 268729-STEMAPL (to H. d. T. ),  TRANSCAN  ( DRAMA  project),  French National Research Agency "Investissements d'Avenir" Programs   ANR-11-PHUC-002  and  ANR-10-IHUB-0002. We warmly thank medical doctors who provided samples from patients and clinical data:  J. P. Marolleau ,  M. Uzunov ,  C. Gervais ,  N. Gachard ,  E. Delabesse ,  E. Lippert ,  S. Raynaud ,  H. Lapillonne ,  M. Hunault ,  C. Ferrand ,  A. Guerci ,  F. Mugneret. We thank  A. Bazarbachi  for critical reading of the manuscript.
Impedance Measurements and Physiological Data Acquisition The skin impedance measurements were acquired using a potentiostat (Autolab equipped with FRA module, Metrohm B. V. , Herisau, Switzerland), using a three electrodes configuration  [22]. The reference electrode was placed on the elbow. The working and counter electrodes were placed 2 cm apart on the forearm. The reference and counter electrodes were Ag/AgCl electrodes (Ambu Blue Sensor N, N-00-S/25, Ballerup, Denmark). 0. 95 cm diameter contact area) and the working electrode (WE) was the one under study  [16]. Impedance was measured within a range of frequencies Polymers 2018, 10, 989 4 of 12 (10 -1 -10 5 Hz) and the measurements were repeated two times. Electrocardiography (ECG) recordings were performed on volunteers. An RHD2216 amplifier chip from Intan Technologies (Los Angeles, CA, USA) was used to record the ECG signals.
Renal epidemiology and evidence-based nephrology The ERA-EDTA Registry is a spearhead of the Society and the major promoter of epidemiology research and education in nephrology in Europe. Initially having focused on end-stage renal disease (ESRD) only, over the last two decades, the Registry has extended its area of interest from ESRD to CKD and from 2004 onwards it holds first class, itinerant and on-site educational activities that have allowed the formation of several renal epidemiologists in diverse European countries. The educational activities of the Registry have gained worldwide recognition, and the Registry has received invitations to organize courses in South America and in other non-European countries. Members of the staff of the Registry and of the Registry Committee are all respected clinical epidemiologists linked to a vast network extended to national and regional registries, thus forming a powerful resource for data collection and elaboration and for the production of large-breadth documents that may inform prevention and clinical practice.
Oversight of scientific activities and Scientific Advisory Board In 2006, the ERA-EDTA created a special Scientific Advisory Board (SAB) whose mission is to enhance the integration of existing kidney research in Europe and to promote translational research in nephrology. The SAB has been key for the selection process and the management of the ERA-EDTA competitive research grants distributed during past years and the SAB chair has always represented this Board in the scientific committee overseeing the annual Congress. Due to limitation in financial resources, 3 years ago, the ERA-EDTA stopped issuing research grants. This de facto limited the range of action of SAB to the ERA-EDTA fellowships. To enhance the integration of the ERA-EDTA activities, the ERA-EDTA fellowships will be assigned to competitive research proposals by the Working Groups (WGs).
2 ). Lactate production was the highest in 'Domestic 2' , 'Mix Eastern Europe' and 'Mix Europe/North America' groups. Acetic acid was the lowest in strains belonging to 'Hawaii/California' group, and their acetate yields and fermentation purity (FP) were also low (Fig. 2 ). The levels of malic acid in 'Canada trees' and ' Americas' wines were higher than in all other groups, and pyruvate concentrations were higher in 'Domestic 2' and 'Mix Eastern Europe' than in most other groups (Fig. 2 ). Volatile metabolome of L. thermotolerans wines. The obtained Chardonnay wines were also analysed for their volatile composition. Out of 90 analysed volatiles, 58 compounds were affected at a strain level (Fig. 3 ; Supplementary Table  S2 ). The majority of these compounds (35/58) were successfully identified in the NIST database via corresponding mass spectra, Kovats' RI indices and, when available, comparison with pure compounds (Supplementary Table  S2 ).
Phenotypic differentiation of L. thermotolerans genetic groups. To determine whether the obtained metabolic dataset discriminated the L. thermotolerans genetic groups, 107 variables were subjected to linear discriminant analysis (LDA). These analysed variables included fermentation kinetics parameters, concentrations of main metabolites, volatile compounds and pH. LDA revealed a clear separation of the 'Canada trees'   and 'Domestic 1' groups from all remaining groups (Fig. 4a ). Albeit less resolved, a suitable partitioning of strains belonging to the remaining genetic groups of strains was also obtained, and the co-localisation of 'Mix Eastern Europe' and 'Mix Europe/North America' groups was congruent with their genetic proximity  4. The 'Canada trees' group were differentiated from other groups based on the low production of the unknown volatile compounds F85, which represented the most important variable for discriminating genetic groups (Fig.
Several volatile compounds significantly accounted for both sugar consumption and glycerol production. Ethyl acetate and acetaldehyde together explained 38% of acetic acid concentration, and lactate contributed with an additional 8%. The correlation between pH and lactate was further confirmed by Spearman's correlation test (Fig. 6 ), which was also used to assess the inter-relationships between consumed sugar concentrations and main fermentation by-products in L. thermotolerans fermentations, (i. e. ethanol, lactate, glycerol and acetate) within each genetic group (except 'Other') and the entire dataset (Supplementary Figs  S5-S14 ). Interestingly, negative correlations were observed between the extent of sugar consumption and lactate production for certain genetic groups, but not globally (Supplementary Fig.
S6 ). Similarly, correlations between lactate and glycerol were detected only within certain groups; positive within four and negative within one ('Hawaii/California'; Supplementary Fig. S12 ). Both lactate and ethanol, and glycerol and ethanol showed weak negative correlations within the whole dataset, and for several genetic groups individually (Supplementary Figs  S9, S10 ). No correlations whatsoever were detected for acetate and glycerol production, while acetate and lactate displayed weak positive correlations within the whole dataset and for two genetic groups (Supplementary Figs  S13, S14 ).
Discussion There is a growing interest in the selection and characterisation of non-Saccharomyces yeasts to be used in winemaking to build 'complexity' and diversify styles. However, surprisingly few studies systematically explored the concepts of their intra-specific phenotypic variability  [26] [27] [28]. To our knowledge, the scale and range of this work represents the broadest oenological characterisation of phenotypic variability within a population of a non-Saccharomyces yeast. It was designed to assess to what extent strains of L. thermotolerans vary in traits of key importance for fermentation outcome, i. e. microbial growth and sugar consumption kinetics, production of volatile and non-volatile metabolites and the resultant (de)acidification. The comprehensive phenotyping dataset was comprised of 114 measured/derived parameters for triplicate fermentations of 94 recently genotyped L.
GC-MS data processing. The GC-MS data was subjected to multivariate curve resolution alternating least squares (MCR-ALS) analysis according to Schmidtke et al. 36  in MATLAB R2017b (Mathworks, Natic, MA, USA). The total ion chromatograms were manually inspected prior to alignment, resulting in 50 time windows. The pre-processing of chromatograms included smoothing and elimination of contamination ions prior to deconvolution. The 90 features (peaks) retained for further analysis were integrated, and their areas were normalised to the geometric mean of the internal standard peak areas. An offset of 1 was applied to each feature peak area prior to logarithmic transformation (base10), mean centring and Pareto scaling were then applied to the block-scaled peak area matrix to obtain the format used for the statistical analysis.
Data analysis. Data was analysed with custom-made scripts in R  49. The microbial growth data (OD 600 readings) were fitted into a logistic model as per Albertin et al. 50 , allowing for the extraction of four population dynamics parameters: lag phase duration (lag OD, h), the maximum growth rate (r OD, maximum number of division/h), the maximum growth (max OD), and the growth time without the lag phase (growth time, h). The sugar consumption data was fitted using a Local Polynomial Regression (loess function) to estimate the time required for consumption of 5% (lag AF, h), 30% without the lag AF (T30, h) and 50% without the lag AF (T50, h) of initial sugars, and maximum sugar consumption rate (max flux; maximum g/L sugar consumed per h). Growth and sugar consumption parameters were used to identify outliers (outlier function; package psych).
19) was obtained for four clusters (x = 2, y = 2). For sugar consumption kinetics, the lowest p-value (3. 10e-5) corresponded to a 4-cluster map (x = 1, y = 4). The variation in each measured and derived parameter (114 parameters) was tested following two factors: strain factor and genetic group factor as determined in Hranilovic et al. 4. For each parameter, the factor effect was tested by either ANOVA (to estimate the percentage of variation explained by each factor) or Kruskal-Wallis (KW) to determine the significance groups (R package agricolae). For both factors and both analyses, the p-values were corrected for multiple tests (p. adjust function, Benjamini & Hochberg correction). LDA was performed using lda function (R package MASS). Since collinear variables may blur the analysis, combined variables (e.
g. G/F, yields) were excluded and the data matrix for LDA thus contained 282 rows (experiments) and 107 variables. A classification algorithm was used (random forest implemented on cforest function, R package party) to identify which variables accounted the most for genetic group discrimination, and the importance of each variable was computed using the varimp function (R package party). Multiple linear regression analysis was performed to examine the relationships between parameters of interest and the remaining variables. An initial model was performed (lm function) containing all explaining variables, followed by a stepwise algorithm (step function, mode in both direction), which was used to choose a model based on the Akaike Information Criterion (AIC). Correlations between metabolites of interest were assessed using Spearman's test.
Acknowledgements We kindly thank  Joseph Schacherer ,  Benedetta Turchetti ,  Benoit Colonna-Ceccaldi ,  Eveline Bartowsky ,  Francisco Carrau ,  Gábor Péter ,  Guiseppe Spano ,  Isora Alonso ,  James Osborne ,  James Swezey ,  Ksenija Lopandic ,  Kyria Boundy-Mills ,  Marc-André Lachance ,  Matthias Sipiczki ,  Steve James ,  Vittorio Capozzi , AEB Group and  Chr. Hansen  for the acquired strains. Tommaso Watson  and  Federico Tondini  are thanked for their assistance in fermentation trial set-up and operation of the Tee-bot v2. 0, and  Anaїs Mourembles ,  Emily Nicholson  and  Sue Maffei  for their help in the sample preparation for the SPME-GC-MS analysis.
Medical History Collect information on: Medical background; childhood illnesses and vaccines; allergies and adverse drug reactions; surgical interventions (previous anesthesia); medications not related to the basic process; previous transfusion history, family tree, and family history valuable; in women, menarche/menopause, pregnancy and childbirth, contraceptive methods, date last rule, and gynecological checkups Travel to malaria, trypanosomiasis, and HTLV-I/II endemic areas Previous relevant infections Data about the current illness: • Start date and initial symptomatology • Diagnostic methodology used (staging) • Chemotherapy and radiotherapy treatments (doses and dates) • Complications from such treatments • Result of these treatments • Recurrences and their treatment.
EBMT Machine Learning Algorithm The variables included in the model are age, Karnofsky (≥80; <80), diagnostic (AML; ALL), disease stage (CR1; CR2; all other stages), interval diagnostic-HSCT (<142 days; ≥142 days), donor-recipient CMV status (both (sero +); both (sero -); any other combination), donor type (MSD; MUD), conditioning (MAC; RIC), and annual allo-HSCT performed in the center (<20; ≥21). The total +100 NRM and 2-year NRM, LFS, and OS could be obtained through a web page: http://bioinfo. lnx. biu. ac. il/~bondi/web1. html. Recently this algorithm has also been validated by an independent set of data from GITMO  (Shouval et al. 2017).