dataesr/scientific-paragraphs-categorization huggingface dataset:
Those tools are further discussed in this article alongside the result they provide. In this work, we present StrainFLAIR, a novel method and its implementation that uses variation graph representation of gene sequences for strain identification and quantification. We proposed novel algorithmic and statistical solutions for managing ambiguous alignments and computing an adequate abundance metric at the graph node level. Results on simulated data and on real sequencing data have shown that we could correctly identify and quantify strains present in a sample. Notably, in the controlled experimental design that we investigated, we could also detect the existence of a strain close to, but absent from those in the reference. StrainFLAIR is available at http://github. com/kevsilva/StrainFLAIR.
RESULTS We validated our method on both a simulated and a real dataset. All computations were performed using StrainFLAIR, version 0. 0. 1, with default parameters. The relative abundances estimation was based on the mean of the specific gene abundances, computed by taking into account all the nodes (including non-covered nodes), and using a 50% threshold on the proportion of detected specific genes. The presented results are compared to Kraken2 considered as one of the state-of-theart tool dedicated to the characterization of read set content, and based on flat sequences as references. Read counts given by Kraken2 were normalized by the genome length and converted into relative abundances. Other tested tools either suffer from unfair comparisons as their features differ from StrainFLAIR (DESMAN, PanPhlan and StrainPhlan) or show weaker results than those obtained by Kraken2 (StrainEst, DiTASiC, KrakenUniq and mixtureS).
Validation on a real dataset We used a mock dataset available on EBI-ENA repository under accession number PRJEB42498, in order to validate our method on real sequencing data from samples composed of various species and strains. The mock dataset is composed of 91 strains of bacterial species for which complete genomes or sets of contigs are available, including plasmids. Among the species, two of them contained each two different strains. Three mixes had been generated from the mock, and we used the "Mix1A" in the following results. Even though 20 out of 91 strains were absents in this mix, we indexed the full set of 91 genomes. This was done in order to mimic a controlled StrainFLAIR use case where the the reference graph contains a mix of strains present and absent in the queried data. The metagenomic sample was sequenced using Illumina HiSeq 3000 technology and resulted in 21,389,196 short paired-end reads.
1%), but no specific gene was identified. Considering that StrainFLAIR uses a permissive definition of detected gene (at least one read maps on the gene), having strictly no specific genes detected for Desulfovibrio desulfuricans ND 132 suggests that this strain might in fact be absent from Mix1A. This is also supported by the result from Kraken2 which estimated a relative abundance of ≈9E-5, almost 500 times lower than the theoretical result. As in the simulated dataset validation, Kraken2 affected non-null abundances to all the references.
Our intention was in the one hand to test whether or not indexing highly similar genomes in a graph enables to characterize queried samples at the strain level, and, in the other hand, to provide a end-user tool able to perform the indexing of genomes and the query of reads including the analyses of mapping results. The method exploits state-of-the art-tools additionally to novel algorithmic and statistical solutions. By indexing microbial species and/or strains in a graph, it enables the identification and quantification of strains from a sequenced sample, mapped onto this graph. Albeit in a controlled experiment simplifying the complex reality, we have demonstrated on simulated and on real datasets the ability of our method to identify and correctly estimate the abundance of microbial strains in metagenomic samples.
In this regard, StrainFLAIR integrated a threshold on the proportion of specific genes detected that can be further explored to refine which strain abundances are set to zero. Importantly, results also showed that our graph-based tool had no false positive call, contrary to general purpose tool Kraken2 that detected 100% of strains that were indexed but absent from queried reads. From the validation on real datasets, we showed that StrainFLAIR was still able to correctly estimate the relative abundances in a more complex context mixing both different species and different strains, without being biased by references absent in the sample. Our methodology taking into account all mapped reads and imposing a threshold that sets some strains abundances to zero seems more adequate and closer to what is expected (experimental data or ground truth) compared to other tools.
Moreover, being able to detect some queried strains as absent is particularly interesting in the metagenomics context. Unlike mock datasets that are of controlled and known compositions, no prior knowledge is available for real metagenomic samples. They require the most exhaustive references-including unnecessary genomes-hence strains absent from the sample. StrainFLAIR is a new step towards the objective to take into account those unnecessary genomes without biasing the downstream analysis. Measured computation time performances show that StrainFLAIR enables to analyse million reads in a few hours. Even if this opens the doors to routine analyses of small read sets, new development efforts will be made for reducing computation time in order to scale-up to very large datasets. Additionally, although StrainFLAIR showed convincing results on simulated and real datasets, exploring more complex situations is still necessary.
First, the mock represented a controlled sample with prior knowledge for building the reference set. While this can be reproduced in a real situation by pre-filtering a genome database (using Kraken2 for example), further work might be needed to evaluate the scalability of our method with larger reference sets. However, we also showed that even by adding unnecessary genomes (absent from the queried sample) StrainFLAIR was able to correctly define them as absent strains. Secondly, we presented a case of one unknown strain in a mixture close to one of the reference strain. Future works will aim to address the issue of having several unknown strains close to the same reference or a mix of known and unknown strains close to the same reference, which StrainFLAIR can not distinguish yet. Genomic plasticity and diversity is of increasing importance in microbiology, and lead to the field of pangenomics.
Dam Locations Dams are located in the HTU graph according to their geo-referenced information. The Global Reservoirs and Dams (GRanD) database provides for each of the 6,872 selected dams the coordinates of the dam and the upstream area  (Lehner et al. , 2011). The coordinates alone are not sufficient to locate the dam on Hy-droSHEDS as the upstream area of the infrastructure also needs to be correct. To place the dam on the Hy-droHEDS grid, the location is moved from its initial coordinates downstream following flow direction until the difference of upstream area between HydroSHEDS and GRanD is minimal. Should the upstream area be larger in HydroSHEDS than GRanD, no attempt is made to correct as many upstream locations could exist which minimize the error. This procedure allows to ensure that the infrastructures provided by the database are consistent with the river graph.
Water Bodies (Lakes and Reservoirs) The information of the water bodies is derived from the Global Reservoirs and Dams Database  (Lehner et al. , 2011, GRanD). If the water body ends with a dam, the water body is considered to be managed, else the water body is considered a natural lake. In the case of a natural lake the water above the capacity will flow out with a given time constant:          1 ,1 4 2 max ,0 i i i V Vref Re g (4)    1 ,2 4 2 0 i Re (5)  1 , 2 i k Re is the outflow from HTU i with the water class k. V i is the current water volume stored in the lake i. Vref i is the reference water storage, defined as the maximum water storage below the lake outlet. g 4 is the time constant of the water in the lake (unit: day km -1 ). We propose to set it equal to time constant of the slow aquifer for the moment (g 3 ).
Implementation of the Model The proposed model for human water management has been implemented in ORCHIDEE's routing module. Given the high computation requirement, the LSM was run uncoupled from its routing module to simulate the water inputs (runoff and drainage) which drive lateral transports of water and irrigation demands. Simulating the rivers without the LSM facilitates and speeds-up development, but has implications. The main implication is that we assume for the purpose of this study that all the water abstracted for irrigation reaches the atmosphere and that the return to the river is negligible. In other words, agricultural demand is assumed to be fully consumptive here. Once the routing scheme is integrated into ORCHIDEE this assumption will not be needed any more as the irrigated water not used by the plants will leave soil moisture through drainage and return to the rivers.
It is then distributed to the 1 km pixels by the percentage of the irrigation area at the HTU level as described in Section 2. 1. 2. Note that in ORCHIDEE, the estimation of the irrigation demands is more complex as it considers explicitly the transpiration's soil moisture stress thus taking into account the state of the vegetation when the dry spell arrives  (Guimberteau, Laval, et al. , 2012)  and it is only applied to the crop fraction of the grid. The updated irrigation module, including flood and paddy irrigation technologies, was developed in the ORCHIDEE-CROP LSM which describes crop phenology and growth  (Yin et al. , 2020). The simulations have been validated with observations and census data over China from 1982 to 2014. To estimate the runoff and drainage needed for the routing module, ORCHIDEE was driven by the WFDEI_ CMA forcing.
The Global Reservoir and Dams (GRanD) database reports 49 dams over YRB with 9 of them located along the mainstream. The dams have significantly altered the river discharge in magnitude and phasing. Other information sources for the region shows that additional dams exist, but they are either small or have been completed since the global database was assembled. Taking into account the human activities (e. g. , irrigation and dam regulation) is a necessary step to accurately represent the river discharge over the Yellow River and in order to explore the potential interactions of climate change and water management. There are a number of gauging stations along the Yellow River where observed monthly river discharge are available (Figure  2b ). Moreover, the second and most recent national water resources survey provides the naturalized river discharge in which the impact of consumed water and flows modifications by dams have been removed  (Zhou et al.
B2. Water bodies Lakes or reservoirs can cover large areas and thus be distributed over a number of HTUs or vertices in the graph. It is thus necessary to include the formation and expansion of the water bodies within the graph. For each water body, the maximum storage capacity is known (Vref i ). This information is important as it will determine where these water bodies can form and needs to be simulated. The GLWD database  (Lehner & Döll, 2004)  provides the volume and a polygon to define the water body. This information is used to identify which vertices in the graph are part of the lake/reservoir (Vref i > 0) and divide the volume over these vertices according to their area. Elsewhere the surface storage volume (Svol i ) is zero. We can thus calculate the storage capacity and the current storage of the water body flowing out at i as: If Svol i > 0 and Svol i+1 = 0: The vertices which contain the dam or the sill which controls the water body are characterized by Svol i+1 = 0 and Svol i > 0.
Acknowledgments This study was supported by the  National Natural Science Foundation of China  (grant no. 41561134016 ); the  CHINA-TREND-STREAM French national project (ANR grant  no. ANR-15-CE01-00L1-0L ); and the  China Scholarship Council  (CSC, 201506710042). The work was supported by computing resource of the  IPSL ClimServ cluster at École Polytechnique, France. The authors gratefully acknowledge  Prof. Feng Zhou  for sharing river flow data over the Yellow river basin.
Data Availability Statement The present study uses data from previously published sources or data which are distributed by the public organizations which have commissioned them: The codes and explanation for its usage can be accessed from https://gitlab. in2p3. fr/ipsl/lmd/intro/humanwatermanagement. Atmospheric forcing WFDEI-CMA:  Zhou et al. (2018). Areas equipped for irrigation:  Siebert et al. (2013). ESA land cover map:  Lamarche et al. (2017).
IPFS leverages a DHT system, in that all IPFS nodes "advertise" content items stored in the DHT and this results in a distributed dictionary used for looking up content. Various applications integrating IPFS with ledger and blockchain technologies have already been reported in the literature for transactions recording  [9]  and secure file sharing with decentralized user authentication, access control and group key management mechanisms  [16, 21]. In the approach described in the next section, we leverage IPFS to provide increased capacity for managing large datasets in a decentralized manner and complement the throughput limitations of distributed ledger technologies. Distributed Ledger. It is a distributed database architecture that records transactions on a P2P network and enables multiple members to maintain their own identical copy of a shared ledger without the need for validation from a central entity.
Our approach We propose a novel eGovernance model that creates new digital governance pathways through the integration of emerging technologies and breakthrough cross-sector services. We deploy decentralized applications (dApps) to deliver efficient, reliable and secure data sharing, auditing mechanisms and communication channels for the eGovernment sector. Our overall approach is digital by default, transparent and interoperable by design, and fully adheres to the once only priority. All the individual modules are designed to be built on top of the IPFS and a distributed ledger, as illustrated in Fig. 1. This extended combination of distributed technologies and infrastructures constitutes the backbone of our solution, which is capable of efficiently addressing the complexity of the processes, providing at the same time the security, trustworthiness, immutability and auditability required by contemporary public services.
Hence, providing resilience and a continuum of service. In addition, a middleware layer includes the following modules: (i) a Secure Gateway Channel on-the-fly to assure a secure intercommunication among systems, databases, apps, etc; (ii) an AI Data Schema Transformer, which is based on well-defined libraries and AI models (i. e. GPT2  [19] ) and encompasses the EU interoperability standards to effectively identify and transform data schemas, models and structures, and enable machine-to-machine communication among different types of systems across the EU. Synthetic data collections that map and simulate real data types (i. e. citizens ID, passport, birth certificate, criminal record, etc. ) for different EU countries provide the backbone of this module's perception; (iii) a Transactions-based Analytics Module that runs as a back-end service on the network, gathering transaction histories and provide insights to users through a user interface developed and delivered as a dApp; and (iv) a Browser Service Module that acts as a search engine and facilitator of the network and other modules (i.
We apply data fusion techniques to meaningfully integrate heterogeneous information from multiple data sources that would otherwise remain uncorrelated and unexploited. To build a highly tuned system tailored to the specific needs of eGovernment services, we identify the data distributions and examine possible optimizations in the index structures to identify data patterns. The key idea is that a model can learn the sort order or structure of lookup keys and use this signal to effectively predict the position or existence of the associated records. This holistic framework creates a novel eGovernance mechanism for communication, data sharing and information retrieval among centralized systems and decentralized/distributed applications. Stakeholders are able to use the single sign-on Wallet and the individual modules running on the network, while also having unrestricted access to the dApp ecosystem.
Acknowledgements: This publication has been produced in the context of the  EU H2020  Project " GLASS -SinGLe Sign-on eGovernAnce paradigm based on a distributed file exchange network for Security , transparency, cost effectiveness and truSt", which is co-funded by the  European Commission  under the Grant agreement ID: 959879. This publication reflects only the authors' views and the Community is not liable for any use that may be made of the information contained therein.
Treatment with nucleos(t)ide analogues (NAs) may be stopped after 1-3 years of hepatitis B virus DNA suppression in hepatitis B e antigen (HBeAg)-negative patients according to Asian Pacific Association for the Study of Liver and European Association for the Study of Liver guidelines. However, virological relapse (VR) occurs in most patients. We aimed to analyze soluble immune markers (SIMs) and use machine learning to identify SIM combinations as predictor for early VR after NA discontinuation. A validation cohort was used to verify the predictive power of the SIM combination. In a post hoc analysis of a prospective, multicenter therapeutic vaccination trial (ABX-203, NCT02249988), hepatitis B surface antigen, hepatitis B core antigen, and 47 SIMs were repeatedly determined before NA was stopped.
CoHoRt The derivation cohort was selected from the ABX 203-002 study. The ABX 203-002 trial was a phase 2b-3, open-label, randomized, comparative study that assessed the efficacy of the ABX 203 vaccine to maintain control of HBV infection after cessation of antiviral treatment with NAs in adult HBeAg-negative patients with chronic hepatitis B in the Asia Pacific region. The design, inclusion, and exclusion criteria were as described previously. (19) For the present analysis, we included only patients who were HBeAgnegative before the start of NA treatment. Subjects in the study also had to have HBV DNA < 40 IU/mL as well as ALT and aspartate transaminase levels less than or equal to the upper limit of normal each, at least 1 year before screening. As additional serum samples were needed for the measurement of SIMs, only patients who participated in the additional serum sampling with sufficient clinical data and serum samples at all three time points during therapy were included (weeks 0, 12, and 24).
To eliminate possible confounders for SIM measurement, we excluded patients who received the investigational vaccine. A detailed description is shown in Fig. 1A. The validation cohort was derived from the Chang Gung Memorial Hospital (CGMH) off-Nuc cohort. (20) Briefly, mono-infected, HBeAg-negative patients had stopped NA therapy after demonstration of undetectable HBV DNA on three occasions, each more than 6 months apart according to Asian Pacific Association for the Study of Liver (APASL) guidelines. (20, 21) After end of therapy, patients were monitored every 1-1. 5 months in the first 3 months, then every 3 months, along with HBV-DNA assay for a total of 1 year and every 3-6 months thereafter (more frequently in cases of virological or clinical relapse). (20) he procedures of this study were in accordance with the Declaration of Helsinki and approved by the ethics committee (ClinicalTrials.
Correlations among Cytokines, Viral markers, and early Relapse Pearson correlation coefficients were obtained with the function pearsonr() in the scipy. stats module (https://docs. scipy. org/doc/scipy/ refer ence/stats. html) and then used to assess correlations among cytokines, viral markers (HBsAg and HBcAg), and the target variable of early VR and nonrelapse at each time point.
Hyperparameters of the SVC model were optimized on each individual time point with cross-validation, and the resulting accuracy was reported. In this study, we always considered 5-fold cross-validation repeated 20 times, and hyperparameter optimization was performed using the GridSearchCV function in sklearn, which is the most widely used exhaustive grid search algorithm with cross-validation. (25) To report the predictive performance of the classification procedure, receiver operating characteristic (ROC) curves that plot the trade-offs between sensitivity and specificity were computed by means of the function roc_curve() of the sklearn. metrics module. The mean area under the curve (AUC), SDs, and 95% confidence intervals (CIs) resulting from cross-validation were reported.
(19, 41, 42) In future studies, it would be very interesting to investigate whether a combination of SIMs could not only work in a long-term follow-up cohort, but also help to predict HBsAg loss after stopping NA therapy. Second, the handling of the serum samples between the derivation (frozen and shipped to Europe) and the validation cohort (measured locally) were different, possibly influencing cytokine levels. Additionally, the derivation cohort is based on a multicenter study that included patients across Southeast Asia and Oceania, whereas the validation cohort is from Taiwan and reflects a possibly more homogenous study group. HBV genotypes were not assessed and may be different. Also, data on antiviral resistance to ETV or TDF are not available. Differences in HBV genotypes, type of infection (perinatal vs.
Infection Research (DZIF) and the  National Science Council, Taiwan  ( NMRPD1F1531-3  and  NMRPD1J0431-3 ) and  Chang Gung Memorial Hospital, Linkou  branch ( CMRPG3A0901-3  and  CMRPG3G1281-2 ). M. W. and the study were supported by the ' KlinStrucMed' program  funded by the  Else-Kröner-Fresenius Foundation. *These senior authors contributed equally to this work. **These  authors contributed equally to this work. received grants from  Fujirebio. He consults for and is in the speakers' bureau for  Roche. Dr. Wedemeyer  consults for, advises, is on the speakers' bureau for, received grants from, and is an investigator for AbbVie. He consults for and advises  Abbott, BTG, Dicerna , and  Siemens. He consults for, advises, and is an investigator for Altimmune and MYR GmbH. He consults for, advises, is on the speakers' bureau for, and received grants from  Biotest.
The manuscript is organised in two major parts. In sections II to VI we discuss our physical results. We define the model, establish basic notations, and give a minimal explanation of the METTS method in section II. The METTS method allows investigating typical states at a given temperature, which we show in section III. Results on charge and magnetic ordering are then presented in section IV. We discuss the nature of the spin and charge gaps in section V and present results on the specific heat and magnetic susceptibility in section VI. In the second part in sections VII to XI we explain and demonstrate the more technical aspects of our simulations. We explain the details of the algorithms we employ for our simulations in section VII. We discuss the accuracy of the imaginary-time evolution algorithms we employ in section VIII and investigate the METTS entanglement section IX.