dataesr/scientific-paragraphs-categorization huggingface dataset:
Param eterization of organic molecules based on SAPT All SAPT2+ data, Cartesian coordinate structure and parameter files are also included on the SI. As described in our previous work, the S108×7 dimer set was systematically constructed and the interaction energy was decomposed using SAPT2+ method. Briefly, the S108×7 set contains 108 organic dimers, each with seven intermolecular separations, namely, at 0. 70, 0. 80, 0. 90, 0. 95, 1. 00, 1. 05, and 1. 10 times of the equilibrium distances. All the 38 molecules in S108 set are listed in Figure  1. The interaction energy components of SAPT2+ can be expressed as (16)   We first parametrized AMOEBA+ model (Eq. 3) from QM calculations of the monomer and energy components by SAPT2+. The detailed one-to-one mappings are E SAPT2 + = E electrostatics + E induction + E exchange -repulsion + E dispersion E electrostatics AMOEBA + E electrostatics SAPT2 + E polarization AMOEBA + + E charge -trans f er AMOEBA + E induction SAPT2 + E van der Waals AMOEBA + E exchange -repulsion SAPT2 + + E dispersion SAPT2 + (17) The atomic multipoles were derived for the molecules in S108 dataset in a previous work using a systematic procedure,  41  where the DMA approach was used to derive the initial multipoles which were then optimized to high-level ESP.
The CP parameters of the organic molecules have been derived for S101 dataset  42  and here we directly expanded to S108 set without further optimization. We modified the direct damping factor from the original 0. 75 in our previous publication  68  to 0. 70 in this work. It was found that this change leads to better agreement in many-body energy of water clusters with MP2 values and only has a subtle influence on organic compounds. After subtracting polarization energy from total induction energy of SAPT2+, we parametrized CT model with the remaining energy as the target. In the parametrization of the vdW model, we experimented both the HHG and W-H combining rules for ε ij. The cost function in the optimization of the vdW model was designed as the root mean square deviation (RMSE) of the model predicted vdW energy and that from SAPT2+.
The objective (cost) function was designed as the sum of weighted mean square errors from gas and liquid targets. Weights were applied among different fitting targets and physical properties within a target (Table  2 ). In this work, the optimization was carried out using the trust-radius Newton-Raphson algorithm with an adaptive trust radius. The algorithm requires the first and second derivatives of the objective function in the parameter space. We tested both DMA and ISA-based multipoles in developing water models. POLTYPE tool  97  was used to derive the initial DMA multipoles. We detail the computational procedure for deriving ISA multipoles in SI. In FB optimization, ANALYZE program of Tinker was used to compute the energy of water clusters and liquid boxes from MD simulations. DYNAMIC_OMM program was used to perform liquid phase NPT simulations.
Interm olecular interactions for organic molecules The three non-bonded interactions of AMOEBA+ (electrostatics, CT and vdW) were parameterized separately using the SAPT2+ S108 database as the training set. Polarization parameters were derived separately using the MP2 interaction energy with many-body expansion. 68 As explained above, the sum of AMOEBA+ polarization and CT energy is compared to SAPT induction energy. The performance of AMOEBA+ for energy components are summarized in Table  3  and the correlations between AMOEBA+ vs. SAPT2+ are shown in Figure  2. To validate the transferability, these parameters were further tested on additional SAPT2+ data published by others. In total there are 707 data points in the training set and 309 in the testing set.
S108×7 database (training set) Electrostatics. : The multipole parameters were previously derived based on the DMAfitting procedure. 58 The CP model this work used was previously developed and parametrized on S101 dataset. 60 In this work, we directly applied CP on the S108 dataset without re-optimizing the parameters. Using transferable, expanded element-based parameters, the CP-corrected point multipoles are able to reproduce SAPT2+ electrostatic energy for 108 homo-and heterodimers at 7 intermolecular distances, with an RMSE of 1. 34 kcal•mol -1 (Table  3 ). Excellent agreement was found for the medium-distance regions (0. 90~1. 10 panel in Table  3  and blue dots in Figure  2 ), with an RMSE of 0. 61 kcal•mol -1. It is worth to mention that for several organic molecules we studied (not shown here), the CP parameters derived from DMA-based multipoles can also be directly applied on the ISA multipoles without adjusting the CP parameters, which indicates the robustness and transferability of the CP model and parameters.
AMOEBA+ water model for gas and liquid-phase Using water as an example, we illustrate an approach where we integrate experimental measurements with ab initio data in training AMOEBA+ parameters. In addition, to systematically evaluate different multipole methods (DMA vs ISA) and combining rules for ε in vdW potential (HHG vs W-H), we optimized four sets of water parameters, i. e. , DMA/W-H, ISA/W-H, DMA/HHG and ISA/HHG combinations. For clarity, hereafter we will report the results from the "DMA/W-H" model and refer it to AMOEBA+ in the remaining sections. The discussion will also be made on the other three models, for which we provided the detailed results in SI. Parameters of AMOEBA+ water model are shown in Table  5. The initial non-bonded parameters were derived within the S108 dataset, as described above, and the initial bonded parameters were taken from the AMOEBA03 water model.
Liquid properties-Thermodynamic properties of water calculated by AMOEBA + model include density, enthalpy of vaporization, thermal expansion coefficient, isothermal compressibility, static dielectric constant, and isobaric heat capacity. Structural (radial distribution function) and dynamic properties (self-diffusion constant) that were not included as the parametrization targets but only used as validation are also presented. Density. : One of the anomalous properties of water is its maximum density at 4 °C. The density-temperature profile simulated by AMOEBA+ model shows excellent agreement with experiment with small deviations up to 0. 6% at very high temperature (369 K). Compared to the AMOEBA14 model, AMOEBA+ shows better agreement with experiment in the neardensity-maximum temperatures and similar agreement in the ambient temperature (numerical data shown in SI).
Summary of AMOEBA+ and AM OEBA water models. : The first AMOEBA water model was published in 2003 and the parametrization relied particularly on high-level ab initio data of small water clusters, and limited liquid properties (density and heat of vaporization) at only room temperature. 16 It was encouraging that various bulk properties can be reasonably reproduced by AMOEBA03 water model even beyond the ambient conditions. 42 AMOEBA14 model was a parameter optimization effort explicitly utilizing a wide range of liquid properties at various temperatures, with every single parameter allowed to be optimized. Nevertheless, the gas phase properties were sacrificed somewhat during the optimization. More importantly, the physical meaning of the parameter, e. g. atomic charges, after optimization becomes unclear.
The key improvements are the inclusion of short-range charge penetration and charge transfer effects and modification of direct polarization damping and vdW combining rules. We adopted a unique approach to separate the CT energy out of SAPT induction energy by first determining the many-body polarization energy independent of ab initio EDA such as SAPT. The AMOEBA+ framework was then successfully demonstrated on a set of common organic molecules, where we showed that a classical potential with limited, element-based parameters reproduced SAPT2+ intermolecular energies and their components for homo-and heterodimers at various configurations and separations. The accuracy and transferability of AMOEBA+ potential were further validated on the testing database generated by other researchers. Direct application ab initio potential to condensedphase is limited by the accuracy of quantum mechanical methods, as well as approximations and simplifications made in classical potentials.
Thus the most effective approach to extend ab initio potentials to condensed-phase is to directly incorporate accurate liquid thermodynamic properties that are widely available. Following this strategy, we have derived a new water model based on the AMOEBA+ framework, by incorporating both ab initio data and experimental thermodynamic properties. The optimization against liquid properties lead to subtle changes in the parameters, however, the agreement with initial gas-phase SAPT2+ EDA remains excellent for a relatively simple classical potential. To develop a general AMOEBA+ FF beyond water, we will extend this approach to other molecular species in the future, where the AMOEBA+ parameters derived in this study will be further refined by using additional cluster QM data and well established experimental properties.
Whole-genome sequence contigs homologous to the chicken rDNA repetitive unit were identified based on the Gallus_gallus-5. 0 assembly with BLAST. We used the Geneious 9. 0. 5 and Mega software, maximum likelihood method and Chickspress project for sequence evolution analysis, phylogenetic tree construction and analysis of the raw transcriptome data. Results: Three complete IGS sequences in the White Leghorn chicken genome and one IGS sequence in the red junglefowl contig AADN04001305. 1 (Gallus_gallus-5. 0) were detected. They had various lengths and contained three groups of tandem repeats (some of them being very GC rich) that form highly organized arrays. Initiation and termination sites of rDNA transcription were located within small and large unique regions (SUR and LUR), respectively.
After performing a magnetic bead-loading step (OCPW), the SMRTbell library was sequenced on one SMRTcell (RSII instrument at 0. 25 nM with a 360 min movie). In fact, the WAG137G04 BAC clone DNA was sequenced in a pool with 12 other BAC clones. We obtained a mean read length of 9 kb for the pool of sequences and 7168 reads were obtained for the WAG137G04 BAC (NCBI SRA: PRJNA577229). The quality value was 48 as scored by the PacBio SMRT ® Analysis software suite that calculates this value after the assembly step and predicts the error probability of a basecall, based on Phred quality score.
PacBio RS II assembly PacBio RS II reads were assembled following the HGAP workflow (https ://githu b. com/Pacifi cBio scien ces/Bioin forma tics-Train ing/wiki/HGAP). The SMRT ® Analysis (v2. 2. 0) software suite was used for HGAP implementation. The longest contig WAG137G4_utg0 (100,614 bp) was obtained by the HGAP3 method with an average coverage of 500X. However, the coverage was not regular, with two regions of repeated sequences with a high coverage (see Additional file 1: Figure  S1 ). First, reads were aligned using BLASR against "Escherichia coli str. K12 substr. DH10B, complete genome". The detected E. coli reads and low-quality reads (read quality < 0. 75 and read length < 500 bp) were removed from the data. The filtered reads (8390 reads, 78,347,167 bp) were then preassembled to generate long and highly accurate sequences (776 reads, 5,031,668 bp).
IGS analysis and annotation Nucleotide sequence alignment, identification and annotation of IGS specific repeats in the chicken IGS sequence, analysis of the distribution of CpG islands and IGS transcription were carried out with the Geneious 9. 0. 5 software package (https ://www. genei ous. com/). WGS contigs that are homologous to the chicken rRNA repetitive unit were searched in the Gallus_gallus-5. 0 assembly of the red junglefowl genome (NCBI Assembly: GCA_000002315. 3, GCF_000002315. 4) (NCBI WGS: AADN00000000. 4) using BLAST (https ://blast. ncbi. nlm. nih. gov/Blast. cgi). Homologous repetitive sequences were identified by NCBI BLAST search and Repbase Update library (https ://www. girin st. org/repba se/) search.
Phylogenetic analysis of IGS specific repeats The Molecular Evolutionary Genetics Analysis (MEGA) version 7. 0 software  [24]  was used for a statistical analysis of molecular evolution (see Additional file 2: Table  S1  and Additional file 3: Table  S2 ) and reconstruction of the genetic links between IGS specific repeats. A phylogenetic tree was constructed by using the maximum likelihood method combined with the evolutionary nucleotide replacement Kimura 2-parameter model with a Gamma distribution [5 categories (+G, parameter = 2. 8698)]  [24, 25]. To assess the reliability of the tree topology, we applied a Bootstrap test (500 replications).
Transcriptome data analysis To determine whether the IGS is transcribed in chicken, we analyzed raw transcriptomic data that were available from the Chickspress project (NCBI BioProject: PRJEB4677; PRJNA204941; http://genea tlas. arl. arizo na. edu/), in which RNA libraries were created from red junglefowl total RNA extracted with the miRNeasy Mini Kit (Qiagen)  [26]. We used the transcriptomes of chicken testis (NCBI SRA: ERX321399), ovary (NCBI SRA: ERX321403), kidney (NCBI SRA: ERX321415), liver (NCBI SRA: ERX321417) and heart (NCBI SRA: ERX321421). The IGS sequence (15,241 bp) flanked by the 3′ETS (335 bp) and 5′ETS (1836 bp) sequences from the WAG137G4_utg0 contig was used as a reference.
In chicken, we were unable to identify any known functional sites within the IGS inner repeats and any known motifs similar to the repeated 10-18 bp Sal boxes that have been shown to be a termination signal sequence in human and mouse IGS  [29]. One of the Elena repeats (EL2A) showed 100% similarity to a 24-bp chicken sequence of a microRNA that is deposited in GenBank (NCBI Nucleotide: AM691130. 1). Both EL2D and EL2F were similar to a repeated sequence that is related to antiviral activity (NCBI Nucleotide: AB124589, AB124590. 1). To date, no function is known for the majority of the repeats in chicken IGS. Our alignment of transcriptome reads from different tissues (Chickspress project: http://genea tlas. arl. arizo na. edu/) against IGS sequences indicates that, in chicken, the pre-rRNA transcription starts in the small unique region (SUR) followed by the 5′ETS sequence.
Acknowledgements We are sincerely grateful to  Alissa Gousseva  for her help in English editing. The project was financed by the  Russian Foundation for Basic Research  (project  18-04-01276A ) and  Saint Petersburg State University 's  Programme 4  (project  1. 40. 1625. 2017 ). The technical support was provided by  "Chromas" Resource Center of Saint Petersburg State University.
It differs from mixed erosion and deposition models such as  Kooi and Beaumont (1994)  and  Davy and Lague (2009)  because those models split the divergence of the sediment flux into two terms: a rate of erosion and a rate of deposition. Here, instead I assume that the sediment flux is a function of water flux and slope. Equation (  1 ) is solved with a finite-element scheme written using Python and the FEniCS libraries (I will call the code "fLEM"; see the "Code availability" section). The equations are solved on a Delaunay mesh, where the mesh is made up of predominantly equilateral triangles with an opening angle of 60 •. Model boundary conditions are initially of fixed elevation on the sides normal to the x axis and a zero gradient on the sides normal to the y axis. The model aspect ratio is 4 to 1.
Acknowledgements. This work was inspired by a series of meetings organized by the Facsimile working group and by a visit to the Riu Bergantes catchment in Spain in October 2017. John Armitage  is funded through the  French Agence National de la Recherche ,  Accueil de Chercheurs de Haut Niveau call , grant "InterRift". I would like to thank  Kosuke Ueda  and  Liran Goren  for help in running DAC. I would also like to thank  Liran Goren  and  Andrew Wickert  for their reviews. Edited by:  Jean Braun Reviewed  by:  Liran Goren ,  Andrew Wickert , and one anonymous referee.
Code availability. The code fLEM is available from the following repository: https://bitbucket. org/johnjarmitage/flem/ (Armitage,  2019a). The valley wavelength Python script and DAC input files are available from the following repository: https://bitbucket. org/ johnjarmitage/dac-scripts/  (Armitage, 2019b). DAC was developed by Liran Goren; see https://gitlab. ethz. ch/esd_public/DAC_release/ wikis/home (last access: 16 January 2019).
3 mg/dl over 48 h. 7 This is in contrast to chronic kidney disease (CKD), in which derangements must persist for three months or more. 8 By definition, this distinction relies on historical results, which may not be available. Detection of AKI will prompt a search for causes as well as the taking of corrective action, which may include fluid resuscitation and renal replacement therapy. In contrast, stable chronic kidney disease does not mandate aggressive treatment. Thus, decision making may be encumbered by lack of historical data. Machine learning, a branch of artificial intelligence that allows computers to perform pattern recognition on datasets, is increasingly applicable in medical contexts. 9 0] [21]  Such efforts have addressed the challenge of predicting the next value or event in patients who are already in a monitored setting.
66 to 0. 80. The AUC for the "simple model" of assuming no change from admission value was reduced for hemoglobin and improved for creatinine with this cohort (Table  2   Fig. 2 ). The machine learning models' superior performance compared to the "simple model" for both full and limited cohorts is also evident when examining precision-recall curves (Supplementary Figs 3-6), which take into account for the prevalence of true positives and true negatives in the dataset.
We suspect this finding was produced by a cohort of  otherwise-healthy younger patients admitted for uncontrolled type diabetes. The capacity for model interpretability can help to address valid concerns around bias and discrimination in datasets, and how these may have unseen impacts on the output of uninterpretable machine learning models. 32, 33 We hypothesize that this interpretability may ultimately increase the acceptance and clinical utility of machine learning, by allowing providers to understand and Fig. 4  Representative optimal classification tree for the hemoglobin classification task. In this tree, "Predict 0" indicates the model's prediction that the baseline hemoglobin is <10 g/dl, and "Predict 1" indicates a prediction that the baseline hemoglobin is 10 g/dl or greater. Relevant features generate branch points, and each terminal node (or "leaf") represents the final model prediction.
Please see full explanation of the tree diagram in Fig. 4. Cr creatinine (mg/dl), bicarbonate (mmol/L), white blood cells (thousand/μL), HR Heart Rate (beats per minute), TEMP temperature (degrees celsius), RR respiratory rate (breaths per minute), sodium (mEq/dl), SPO2 oxygen saturation (%), INR(PT) international normalized ratio of prothrombin time, mean average, min minimum, sd standard deviation. gain trust in the models' methodology and outputs. While this work focused on information from a de-identified and structured clinical database, it uses values that are commonly charted. Deploying an algorithm within the EHR, in which the record might report "predicted" as well as "measured" laboratory values, is a future possibility. Further, existing illness severity scores, e. g. , SOFA  34  or APACHE II,  35  operate on the premise that the patient's biomarkers were normal prior to illness.
37 Attention to these details will be important future work for any machine learning deployed in the healthcare setting, where bias is well known to affect patient-health system interactions. 38, 39 his study has several limitations. Only patients with prior-toadmission labs were included, potentially yielding a sicker cohort compared to patients without these outpatient healthcare contacts, which may limit generalizability. However, the shorter length of stay and lower mortality in the group with outpatient lab draws argues against this cohort being significantly sicker. The fact that MIMIC-III is a single-center database also limits external validity of the results. Our machine learning workflow for predicting baseline labs, however, may be adapted for other institutions, allowing the re-training of models to fit different populations.
This work assumes a state of stability or wellness when labs are drawn in the outpatient setting. Although the state of health required to present for outpatient blood test versus that requiring admission to the ICU are highly divergent, the former may not represent a "stable" baseline for all patients. We chose two hours of ICU time as a clinically reasonable cut point for data capture prior to model training and testing. This is a trade-off, as longer data-capture could produce a more accurate prediction, but the clinical utility of the prediction wanes the longer it is delayed. Further, as MIMIC-III is an ICU database, we were not able to incorporate vital sign data from the emergency department or non-ICU wards. If such data were available, it might allow predictions of similar accuracy to be made at an earlier time point.
METHODS This study is reported in accordance with the STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) statement. 40 The project was approved by the Institutional Review Board of the Beth Israel Deaconess Medical Center (IRB Protocol #2001P001699) and was granted a waiver of informed consent. We used retrospective data from the Medical Information Mart for Intensive Care (MIMIC-III, version 1. 4) database; a single center, publically available, de-identified high-resolution database of ICU stays, built and maintained by the Laboratory for Computational Physiology at Massachusetts Institute of Technology (MIT). MIMIC-III is available at http://mimic. physionet. org/. MIMIC-III includes 46,520 patients with intensive care unit admissions between 2001 and 2012 at Beth Israel Deaconess Medical Center in Boston.
Derivation and validation cohorts Using the MIMIC-III database, we identified patients aged 15 to 90 admitted to intensive care who also had prior-to-admission blood tests drawn for hemoglobin or creatinine in the outpatient setting between three and 30 days prior to admission. The outpatient setting was used as a proxy for the patient being in a well state, and the prior to admission value was taken to represent the patient's recent "baseline". Ninety was chosen as an age cut-off, as patients older than 90 have their ages masked for confidentiality in MIMIC-III. For patients with more than one ICU stay, only the first ICU stay was used. Patients were excluded if the ICU stay was shorter than four hours, and if they did not have the lab of interest (hemoglobin or creatinine) tested within two hours before or after ICU admission.
Two final cohorts of 6139 and 4643 patients were identified for the hemoglobin and creatinine prediction tasks respectively (Fig. 6 ). As hemoglobin and creatinine are often tested together, 4331 patients appeared in both groups. In total 75% of each cohort was used for model training. The remaining 25% was used for validation and testing, and all Fig. 6  Cohort selection process from the MIMIC-III database.
ACKNOWLEDGEMENTS Software: This research made use of the community-developed statistical software R, 43 community-developed core Python and Julia packages including IPython, 49 Matplotlib, 50 Scikit-learn, 51 SciPy, 52 Pandas, 45 and packages developed by the  Operations Research Center of MIT including OptImpute , and OptimalTrees. 23,24This paper was composed by participants in the HST. 953 course at the  Massachusetts Institute of Technology, Fall 2018. For more information about the course, please visit: https://criticaldata. mit. edu/course/.