dataesr/scientific-paragraphs-categorization huggingface dataset:
In sbal it is possible to represent the natural numbers N p = {0,. , p} using a type of the form ∀α ≤ q. ! x<p (α(x) α(x + 1)) α(0) α(p). In practical programming languages it is more common to find such data types as primitive base types. Intml removes polymorphism for simplicity and adds primitive data types instead. After removing polymorphism from sbal, one is left with types of the form X ⊗ Y, X Y and ! p X. The now missing type of natural numbers may be added as a primitive type of the form [N p ], which is to be understood like [int] in intml. The bounded exponential ! p X corresponds to the subexponential N p • X in intml. Indeed, if we consider N p as a base language type, then t he interactive interpretation of [N p ], X ⊗ Y, X Y and N p • X described in this paper is essentially the same as the interactive interpretation of sbal in  [42].
But adding a base type [N p ] alone is not enough. We also need to add terms to be able to work with it. For example, one may expect there to be an addition constant add : [N p ] N p • [N p ] [N p+q ]. But a good choice of primitive constants is not obvious. Since values of type N p can be stored in space log p using a binary encoding, they can be stored fully in memory. The programmer should expect to be able to work with them without any language constraint. At the same time the language must guarantee that the use of large data does not lead beyond logarithmic space. Allowing unconstrained manipulation of small values that fully fit in memory was one motivation for making the base language visible to the programmer in intml. That intml uses base types in place of polynomials is also a result of a simplification.
While it is possible to formulate intml just with types of the form [N p ], X ⊗ Y, X Y and N p • X, the need for manipulation of polynomials would make the language more complicated. Irrelevant encoding details would become visible in the type system. For example, the intml type system works with (A × B) • X instead of A • (B • X). If we allow only base types of the form N p , then N p • (N q • X) could become N p•q • X, as it is possible to represent pairs of numbers in N p and N q by a single number in N p•q. But in an implementation one may like to use a fast and easy-to-implement pairing function that just concatenates the bits of the two numbers. Then one would need to replace N p•q by N r•q , where r is an upper bound for next the power of two that is larger than p (it is not even obvious how to write a good bound as a polynomial).
This means that small changes in the implementation would influence the type system. It seems more natural to separate concerns by writing (N p × N q ) • X in the type system and by handling the encoding details on the base language level. Dual Light Affine Logic. The concrete definition of the interactive language of intml is inspired by Baillot and Terui's Dual Light Affine Logic (dlal)  [6]. intml treats the subexponential A • X much like dlal treats the exponential modality !X. Just as the exponential modality may only appear in negative positions in dlal, i. e. one can have !X Y but not X !Y, intml only accounts for types of the form A • X Y. The restriction to negative occurrences of A • X much simplifies the type system without being limiting in applications. Subexponentials. We use the term subexponential by analogy with the subexponentials introduced by Nigam and Miller  [37].
Nigam and Miller study a logic with labelled subexponentials. Depending on the label L, weakening and contraction may or may not be allowed for ! L and it may be possible to go from two subexponentials with a certain labels ! L ! K X to one with a third label ! R X. In intml, the subexponential A • X is labelled with a base type A. All intml subexponentials allow weakening. Since our base language does not contain infinite types, they do not allow contraction. If one added a base type N of unbounded natural number, then N • X would support contraction. Moreover, the phenomenon of combining two subexponentials into a third one appears in that A • (B • X) becomes (A × B) • X. Rule Struct also allows conversion between different subexponentials.
One may see intml as a programming language for implementing the (possibly ill-formed) raw strategies of ajm-games. Geometry of Synthesis. The way to see λ-terms as circuits is similar to Ghica's Geometry of Synthesis  [14] , which is defined on bSCI as described in Section 4. 2. The main difference to our approach is the treatment of duplication. In intml, duplication is handled through subexponential types, similarly to what happens with indexes in Abramsky-Jagadeesan-Malacaria (ajm) games. This does not require circuits to have internal states, but forces tokens to be more complex: whenever you interact with an object which could possibly be duplicated, you need to specify which specific copy of it you are querying. In Ghica's work, on the other hand, the target language consists of so-called handshake circuits, which do have an internal state.
Then, one can use T (1) for all subexponentials and use the Struct to maintain this form after each rule application. Thus, arbitrary copying can be allowed. Of course, the type system then does not yield interesting space bounds anymore. In this paper we have shown that this approach to copying can be refined and controlled, so that space bounds can be proven. Defunctionalization. We have developed intml directly from from an analysis of the demand-driven computation that one finds in logarithmic space algorithms. However, it turns out that the translation from the interactive language to the base language is closely related to the more traditional compilation techniques of cps-translation  [22]  and defunctionalization  [40]. These two techniques allow the translation of a higher-order functional language to a firstorder language with tail recursion.
Given a higher-order language, one may first apply a cps-translation to fix the evaluation order and then use defunctionalization to reduce the resulting higher-order program to a first-order program. In intml we have used as the base language a simple first-order language with a loop construct. It is equally possible to use a first-order language with tail recursion instead. For each message passing node and each incoming wire we could define a first-order function that takes the incoming message as argument, that computes the output message and that then invokes the first-order function of the destination node with the resulting output message with a tail call. The translation from the higher-order interactive language to the first-order language with tail recursion that one obtains in this way is closely related to the program one obtains by call-by-name cps-translation  [22]  followed by a particular defunctionalization procedure  [7].
Implicit Characterisations of Logarithmic Space The work in this paper builds on earlier work on implicit characterisations of logarithmic space complexity classes. While implicit characterisations of complexity classes often focus on minimal formal systems, such as function algebras  [36, 38, 39]  or while-languages  [26] , the results are useful in obtaining a better understanding of the resource usage properties of programming languages. Bonfante  [8]  shows how to capture logspace by a firstorder functional language. In intml we work towards extending such results to higher-order functional programming languages. For example, we believe that Møller-Neergaard's function algebra BC - ε can be implemented in intml. The logspace soundness of this function algebra depends on a complicated implementation of recursion with computational amnesia  [36].
Time Lapse Microscopy and Bright-Field Image Analysis of Micropatterned Spheroids in 96-Well Plates For growth determination, time lapse images were acquired every 2 days at 10× magnification for 21 days using a DMI6000 inverted microscope equipped with a light-emitting diode (LED) transmitted light illuminator and environmental control incubator (Leica, Wetzlar, Germany). Between each acquisition, the 96-well plate containing the spheroids was kept in a cell culture incubator. Images were subsequently imported, assembled, and analyzed automatically using a software suite of tools developed in-house called CYTOOls, which is integrated into the open-source ImageJ framework as a plug-in. 24 ore specifically, the tool named Spheroid Analyzer is dedicated to growth determination, handles bright-field and phase contrast images, and extracts the number of spheroids per well, measures of size, and morphological parameters.
2 µM for up to 2 h and SYTOX Green (ex/em 504/523 nm; Life Technologies) incubated overnight at 0. 2 µM. Bright-field and fluorescent images were captured with the Operetta imaging platform (PerkinElmer, Waltham, MA) using a 2×, 0. 08 NA, 6. 2 mm working distance objective. Acquisition parameters were based on optimal excitation and emission wavelengths of the fluorescence probes. Images were analyzed with a script developed using Acapella software (PerkinElmer). Size and morphological features (diameter, area, and roundness) were determined based on object identification of bright-field or Hoechst 33342 stained structures. The image analysis algorithm also quantifies cytotoxicity across the whole spheroid or in specific regions of the spheroid using the total or average PI fluorescent intensity, providing information about levels of cell death.
Semiautomated Software for Nondestructive Analysis of Spheroid Growth over Time Because spheroids are adhered to micropatterns, it is easy to renew the total culture medium multiple times during extended culture periods, allowing chronic exposure to drug candidates and long-term spheroid growth without losing or disrupting structures. Use of cell-adhesive micropatterns has the additional benefit of fixing the location of each spheroid throughout the stages of cell seeding, growth, and treatment, enabling the continuous monitoring and tracking of the same individual spheroids over time, as presented in Figure  3A. To this end, Spheroid Analyzer was developed as part of a software suite of processing tools called CYTOOls to extract and measure spheroid objects on bright-field mounted images at the individual spheroid level, enabling the study of spheroid populations on a well-by-well basis.
Thus, various multiplexed functional assay options are possible, assuming well-separated emission spectra and appropriate filter combinations. In the particular analysis performed here, Hoechst was used during image processing to identify and segment spheroid objects and explore subtle changes in size and morphology, whereas PI was used to quantitatively measure cell health in different zones of the spheroid using scripts developed in Acapella software. A multiradial spheroid analysis was applied to describe the regionalization of the effects of the anticancer drugs (Fig. 5B ). Here, two key measurements termed mean peripheral spheroid fluorescence (MPSF) and mean center spheroid fluorescence (MCSF) were calculated from the contour of the spheroid demarcated by Hoechst fluorescence, separating each spheroid into peripheral and central zones for quantitative analysis of PI intensity levels, the dead cell indicator.
Funding The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was financed by  Bpifrance as part of France's Industrial Strategic Innovation (ISI)  grant program under the project entitled Evolved Tissue Inspired Cell Systems (ETICS) with contract number  1107018W. The Intravital Microscopy Grenoble platform was partly funded by the  French Investissement d'Avenir program.
Large-scale identification of native binding orientations is crucial for understanding the role of protein-protein interactions in their biological context. Measuring binding free energy is the method of choice to estimate binding strength and reveal the relevance of particular conformations in which proteins interact. In a recent study, we successfully applied coarsegrained molecular dynamics simulations to measure binding free energy for two protein complexes with similar accuracy to full-atomistic simulation, but 500-fold less time consuming. Here, we investigate the efficacy of this approach as a scoring method to identify stable binding conformations from thousands of docking decoys produced by protein docking programs. To test our method, we first applied it to calculate binding free energies of all protein conformations in a CAPRI (Critical Assessment of PRedicted Interactions) benchmark dataset, which included over 19000 protein docking solutions for 15 benchmark targets.
The achieved speedup now opens up the possibility to combine the docking searching strategy to produce a set of near-native binding decoys, which is quick and inexpensive, with estimated binding free energies from coarsegrained MD simulations, as a relatively expensive but accurate scoring method. We find this approach to achieve a good balance between efficiency and effectiveness for protein binding orientation prediction. To illustrate this, we here evaluate the capacity of estimated binding free energy from coarse-grained MD simulations to locate near-native binding poses amongst conformations produced by docking programs. We present a proof of concept for the approach of using Free Energy calculation to score binding orientations from protein-protein docking. The benchmark dataset we used is the CAPRI Score_set, which contains over 19,000 structures from 15 published targets from the CAPRI docking experiment  [13].
Force fields and Software To calculate the coarse-grained (CG) potential of mean force (PMF) for our proteins of interest, we used the procedure developed in our previous work  [21]. In short, we used the MAR-TINI force field  [22]  with default time step of 20fs to perform the CG simulations. The Coarsegraining of protein structures was done using the MARTINI model which maps about 4 heavy atoms into a single interaction center. All 20 amino acids are considered as four different types of particles: polar (P), nonpolar (N), apolar (C), or charged (Q)  [23]. GROMACS 4. 0. 5  [24]  was employed to perform all MD simulations, using parameters for the MD simulations as described previously  [21]. A tool that calculates the PMF from a single starting conformation in a PDB file was developed in Python.
Datasets To assess the ability of our method to distinguish near-native from incorrect binding modes, we used the CAPRI Score_set as a benchmark dataset  [13]. CAPRI is well known as a community-wide experiment on the critical assessment of prediction of interactions. The CAPRI Scor-e_set, which comprises around 19,000 complexed structures from multiple rounds of CAPRI experiments, is published as a realistic benchmark dataset for protein docking scoring functions. The interest of this dataset is that it does not contain the true target (crystal) structures, but includes many 'real life' non-occuring or incorrect, acceptable and native-like docking poses. The dataset collects structures from 15 published CAPRI targets predicted by 47 different predictor groups, which represent the state of the art of protein docking  [13].
The CAPRI benchmark set used in this study is annotated regarding structural correctness, and as such does not reflect a typical research situation. However, an individual researcher may generate an input decoy dataset of any size using various docking servers that are readily available online. In this dataset, about 10% of the structures represent acceptable quality or better, based on the three measures used in CAPRI: • f nat : fraction of native ligand-receptor contacts; • L-rms: Root mean square deviation (RMSD) over the ligand backbone atoms with both of the receptors fitted on each other (docking versus solved structures); • I-rms: RMSD of backbone atoms of interface residues between docking structures and the solved structures. Using these measures, each structure is assigned to one of four categories based on the classification used in CAPRI: As a measure for target difficulty, we take the fraction of docked structures of acceptable or better quality.
Sampling procedure First, binding free energy (4G off ) of each structure is calculated from a single MD simulation (replicate) at each distance (in  May et al. , 2014 [21] , 10 or 20 replicates per distance were used). Based on these free energies, we then rank the binding orientations for each target under the assumption that a lower free energy is more stable. Using this ranking, we selected the best (lowest) 50% structures for each target. For this subset, we ran four more replicates per distance to increase sampling and obtain a more accurate estimate of 4G off. Based on the average 4G off over the five replicates (for the best 50% structures), we picked the top 1, 5, 10, 20 and 100 lowest binding free energies for each target. These sets we analysed and compared to the benchmark dataset.
Previously we showed that our method can be used to approximate experimental binding free energies  [21]. The separation shown in Fig  1  demonstrates that binding free energies calculated are also accurate enough to be used as a scoring function for ranking docked conformations. Fig 1  also  shows that there is a large 4G off variation among low I_rms structures. This might reflect true differences in stability, even for very similar structures, however, we expect this to be mainly the uncertainty in our calculated 4G off which could be a limiting factor when applying our method to rank docking binding orientations. The 4G off calculations might be not accurate enough. To obtain more accurate binding free energies, more simulations are needed as already observed by May et al, 2014  [21]. To obtain some estimate of this accuracy, we selected the first target, Target 29, to run two replicate simulations for each structure in the Score_set and compare the rank position differences between the two replicates (data not shown).
S1 Table. The performance of all docking scoring functions participating CAPRI for all 15 Targets in CAPRI Score_set. All docking scoring functions participating the CAPRI competitions for the 15 targets and the number of acceptable or better quality models selected by these methods are listed. For each group, the number of submitted correct models of each category are indicated: high accuracy ( ÃÃÃ ), medium accuracy ( ÃÃ ), and acceptable ( Ã ). Data obtained from  [26]  and  [27]. (PDF) S1 File. The PMF calculation results for all 15 targets (S1_File. zip). (ZIP).
Acknowledgments We thank  Siewert-Jan Marrink ,  Xavier Periole  and  Tsjerk Wassenaar  for stimulating and very insightful discussions on this work. The authors thank  Netherlands Organisation for Scientific Research (NWO)  for awarding 800. 000 core-hours of computational time. We also thank  Kamil Krystian Belau  for help with the Coarse-grained MD simulations. QH is supported by  Chinese Scholarship Council  (No. 2011627127 ). MFL acknowledges support from the  CNRS FR3688 FRABio Research Federation "Structural and Functional Biochemistry of Biomolecular Assemblies ".
09 change in the charge on the atom to which it was attached. As special cases, the models for base-conjugated hypermodification, such as the side chains of carbamoyladenosines (HNA, 26A, 66A, t6A, 12A, and 6GA) had benzene substituting the adenine. For bonded terms the model compounds were built to maintain simplicity while including all atoms necessary for the parameters being targeted. Thus, some model compounds were the same as those used in the charge optimization, and some were fragments from modified bases, whose charges were either well predicted by the CGenFF program or already optimized in the previous step (Fig. 2 ). The parametrized small model compounds were then assembled into the full molecule. In cases where both the base and ribose were involved in the target dihedral, the model compound contained sugar ring parameters transferred from NA36 with the base treated by CGenFF.
In total, 53 target model compounds for the charges and 95 for the bonded and dihedral terms were optimized (see Supporting Information [SDD]). 3D structures of modified nucleosides were taken from Aduri et al. [39]  or created using Maestro9. 3 (Schr€ odinger, LLC, New York, NY, 2012) based on the information in the RNA Modification Database (http://mods. rna. albany. edu). If the modification had multiple protonation states and/or tautomers, the physiologically dominant state was treated as the default species. For example, the primary and secondary aliphatic amines had the protonated state as the main species, and the carboxylate was in the ionized, negatively charged state; other accessible states were included in the force field as shown in the Supporting Information SDD. Nomenclature.
We use three types of names for the modified nucleosides: the IUPAC common name, the symbol used in the text, and the three-letter code used in the force field toppar files. The symbol designations are consistent with the RNA Modification Database, which have been widely accepted in the literature. In most cases, the three-letter codes adopted the AMBER convention  [39]  to avoid confusion for users, except for some nucleotides for which the names used in the Protein Data Bank (PDB) were used instead of the AMBER names. This was done to avoid problems where AMBER names were already taken by compounds previously defined in CGenFF, and to keep the users from having to edit frequently occurring The charge models include acetate (acet), which was available in CGenFF, and 5-methoxyuracil (MOU) that had been optimized prior to OAU as part of the present study.
17). In each H 2 O-4MC complex, the minimum interaction energy and distance between interacting atoms (O w ÁÁÁH 4MC or H w ÁÁÁO 4MC ) were calculated as DE5 E complex 2E 4MC 2E water À Á and R5jr 4MC 2r water j, where r x represents the coordinates of the two directly interacting atoms. As previously described,  [59]  the QM interaction energy of the neutral compounds was multiplied by 1. 16  [44, 45]  since 4MC is a neutral polar compound, and an offset 20. 2 was applied to the QM polar interaction (hydrogen bond) distances (i. e. , the O w ÁÁÁH 5 /H 6 /H 41,3 interactions were excluded). The starting atomic charges were generated by the CGenFF program using the ParamChem website. The atomic charges were then adjusted iteratively until the interaction energy and geometry differences (DDE 5 1.
4b ). However, the interaction energies showed significant improvement in the agreement with QM energies after the optimization in a number of cases. From inspection of the results the poorest initial predictions mainly belonged to the protonated amines, positively charged heterocycles, and wyosines (hypermodified guanosines), as analogs of these species are not yet available in CGenFF. For the remaining compounds the initial energy RMS difference was typically less than 1. 5 kcal/mol and in some cases less than 0. 5 kcal/mol. Thus, the CGenFF program assigned reasonable charges in a number of cases, though for cases in which compounds similar to those being presently studied were not included in the CGenFF training set, significant disagreement with the QM data was obtained. We note that for these compounds the CGenFF penalties for the charges were typically quite large (around 150), indicating the need for further optimization, as previously described.