dataesr/scientific-paragraphs-categorization huggingface dataset:
Phenotype microarray analysis The carbon utilization profile of strain S5. 2 was assessed using the 96-well PM1 and PM2A plates (Biolog, USA). In brief, the overnight cultured bacterial colonies were inoculated into IF-0a GN/GP base inoculating fluid (Biolog, USA) to reach 85% transmittance (T) according to the manufacturer's protocol. Aliquots (100 µl) of cell suspension and 1× Biolog redox dye mix A were inoculated into each well of the plates respectively, followed by incubation at 28 °C. The utilization and growth of 192 different carbon substrates from the plates were monitored for 48 h with readings taken at 15 min intervals. The kinetic information was recorded and quantified using OmniLog OL_FM_12 kinetic software (Biolog, USA) followed by data analysis  (Bochner et al. 2001 ). In the event of bacterial growth, photographic readings of colour intensity resulted in dye reduction were represented in OmniLog units (OU)  (Khatri et al.
0 using the PacBio SMRT portal. Gepard dotplot program  (Krumsiek et al. 2007 ) was employed to verify the circularity of the resulted contigs followed by circularization of overlapping ends using minimus2 pipeline in AMOS software package  (Treangen et al. 2011). Genome annotation was subsequently performed using Rapid Prokaryotic Genome Annotation (PROKKA)  (Seemann 2014).
Results Properties of P. mendocina strain S5. 2 SEM showed that cells of strain S5. 2 were 1. 5-2. 5 µm in length and 0. 8-1. 0 µm in width (Additional file 1: Figure  S1 ). Pairwise similarity analysis on EzBioCloud database followed by phylogenetic analysis using complete nucleotide sequences of 16S rRNA showed that strain S5. 2 was closely related to P. mendocina NBRC 14162 T (Additional file 2: Figure  S2 ). This observation was later verified with ANIb analysis (Additional file 3: Table  S1 ).
Funding This research was supported by the  University of Malaya  through  HIR Grants awarded  to  KGC  ( UM-MOHE HIR Grant UM C/625/1/HIR/MOHE/CHAN/14/1 , No. H-50001-A000027 ;  UM-MOHE HIR Grant UM C/625/1/HIR/MOHE/ CHAN/01 , No. A000001-50001 ). This research was also funded by  Postgraduate Research Fund (PPP)  (Grant No. PG080-2015B ) awarded to TMC.
Background/Aims: Reports regarding the role of androgen in breast cancer (BC) are conflicting. Some studies suggest that androgen could lead to undesirable responses in the presence of certain BC tumor characteristics. We have shown that androgen induces C-X-C motif chemokine 12 (CXCL12) in BC cell lines. Our aim was to identify the mechanisms regulating the phenotypic effects of androgen-induced CXCL12 on Androgen Receptor (AR) positive BC cell lines. Methods: We analyzed the expression of CXCL12 and its receptors with qPCR and ELISA and the role of Nuclear Receptor Coactivator 1 (NCOA1) in this effect. AR effects on the CXCL12 promoter was studied via Chromatin-immunoprecipitation. We also analyzed publically available data from The Cancer Genome Atlas to verify AR-CXCL12 interactions and to identify the effect or Aromatase Inhibitors (AI) therapy on CXCL12 expression and disease progression in AR positive cases.
Public microarray data analysis Co-expression of AR and CXCL12 was studied in the METABRIC breast cancer data  [34, 35]  through the online tool cbioportal (http://www. cbioportal. org/) [36, 37]. Furthermore, 1448 TCGA normalized gene expression (based on RNAseq data) cases from 12 different pathologies were downloaded from http://compgenome. org/TCGA/software. html. These cases have been described elsewhere  [38]. From these data, 151 breast cancer patients were extracted. AR status was assigned positive or negative based on the retrieval of a positive or negative value after z-transformation. Clinical data of breast cancer cases, including treatment applied, ER, PR, Her2 status, tumor status and disease relapse were retrieved from TCGA by the use of the TCGA assembler software in R  [39]  v1.
0. 3 and manually curated to match the Zodiac identified breast cancer cases. Finally, we extracted data from the GEO-deposited GDS3116 study  [40]. In this series of breast cancer patients, microarray analysis was performed on samples obtained before and after 14 days of treatment with letrozole. Normalized matrix data were downloaded from the GEO-dataset site and all probe sets related to the AR, CXCL12, CXCR4, CXCR7, and ESR1 were isolated. In case of multiple probe sets, the maximal value was retained for further analysis.
Androgen induces CXCL12 gene expression and protein production from androgen receptor (AR) positive breast cancer cell lines In a previous work [GSE18146, 27], we have reported that T47D and MDA-MB-231 cells express CXCL12 (probe sets 203666_at and 209687_at). In an ongoing work, we have also confirmed the expression of CXCL12 by MCF-7 and SKBR3 cells (Fig. 1A ). We have previously reported [GSE18146, 27] that testosterone activated an early (3h) induction of transcripts and subsequently pathways related to cytokine/chemokine signaling and immune-related genes, including CXCL12, in T47D but not in MDA-MB-231 cells. Here, we report that testosterone induces CXCL12 in ER-positive MCF-7 and T47D cells, but not in the SKBR3 or the MDA-MB-231 cell line by qRT-PCR (Fig. 1B ). As this differential expression might be related to the presence of active androgen receptors, we studied their expression in these four breast cancer cell lines.
AR expression is correlated with CXCL12 expression in breast cancer and AI therapy in ERα/ AR positive tumors is related to increased CXCL12 expression and increased recurrence compared to non-AI treated ERα positive cases Our in vitro data show that AR stimulation by testosterone increases CXCL12 expression and secretion together with CXCR4 expression, in a NCOA1 and p53-related manner, and this enhancement of expression increases cell migration. In order to investigate whether a similar situation occurs in vivo, we investigated the expression of CXCL12 and its relation to AR expression in several publicly available datasets. We first analyzed data from the 2509 breast cancer cases of the METABRIC study  [34, 35]  and found a clear correlation between the expression of AR and CXCL12 (p<0.
As shown in Fig. 7A , in the whole cohort of patients (n=1448), CXCL12 and CXCR4 expression are positively correlated (β=4. 75, FDR<0. 0001) and additionally AR protein expression is positively correlated with CXCL12 gene expression (β=4. 92, FDR<0. 0001), verifying our in vitro data and extending them to pathologies beyond breast cancer. Finally, we downloaded transcription and proteomic data from the Zodiac site and extracted normalized data values only for the breast cancer patients (n=151), together with their clinical data, as described in Material and Methods. Analysis of these 151 cases identified 37 ER+/AR+ and 18 ER+/AR-cases that received AI therapy, alone or in combination with SERMs and/or chemotherapy. Analysis of the cases with known tumor status (Fig. 7B ) revealed that in the AR+ cases there were 4 disease recurrences (2 cancer-related deaths, both in the disease recurrence cases) in the AI treated group and 0 recurrences (3 cancer unrelated deaths) in the AI non-treated group (one tailed chi square p=0.
One of the major differences between these cell lines is their different p53 status with MCF-7 bearing the intact, while T47D cells expressing mutant p53 (http://p53. free. fr/Database/Cancer_cell_lines/p53_ cell_lines. html), although whether mutant p53 in these cells is dominant-negative or has distinct pro-survival effects is still under investigation  [57]. Wild-type p53 has been found to suppress CXCR4 expression in MCF-7 cells  [50]. Our finding of reversal of CXCR4 inhibition by androgen in this cell line, cells via the inhibition of p53, is in accordance with this previous result. Furthermore, the fact that p53 loss or non-functional mutation can revert action on CXCR4 expression from inhibitory to stimulatory has been reported previously in MCF-7 cells that were transfected with dominant-negative V143A p53 mutant or cancer-specific p53 mutants, R175H or R280K  [50].
clinicaltrials. gov). Interestingly, although most studies involve antiandrogen therapy in TNBC and metastatic breast cancer, androgen anabolic treatments are also being studied in TNBC, but also in ER positive breast cancer cases, as well as in metastatic cases. Our work provides another element of androgen action in the breast, namely the enhanced production/secretion of the chemoattractant chemokine CXCL12 and the autocrine stimulation of its receptor CXCR4, through the activation of AR, findings that could be of value in the analysis of the abovementioned studies or in the design of new ones. Our analysis of the public GDS3116 study and TCGA data shows that AR status could modify the effectiveness of AI therapy in breast cancer. AIs block the conversion of androgen to estrogen, thus increasing free testosterone and DHEA-S  [70].
The opposite approach, androgen therapy in breast cancer has already been studied before as stated above and the combination of androgen and antiestrogen as a potential strategy is a concept that has been also discussed in the literature. There seems to be a missing key, possibly a molecular signature that defines the pro-or antioncogenic response of breast cancer cells to androgen. A phase II study (NCT02000375) investigating the safety and activity of a combination of DHEA and an aromatase inhibitor in pre-treated ER+/AR+ postmenopausal patients was terminated early, before reaching its recruitment target, but its data have not been reported. Personalized therapy for breast cancer has always been an important target since such therapies can be less toxic and more effective than chemotherapy.
Acknowledgements This work was supported by the  European Union Programs Regional Potential/ Translational Potential  Grant  285948  (to GN and EC) and by the  University of Crete   Special account for Research grant  No  3505  (to GN) and by the  European Union - FP7 Marie Curie Actions-Career Reintegration Grants   PCIG-GA-2011-303723  (to VP).
These data were used to approximate the amount of charge transfer in complexes of Cl 2 , Br 2 , BrCl and ICl with a series of electron donors. 8 The charge transfer in these systems is only moderate, despite a very strong interaction in some of the systems. This raises the question how the charge transfer (CT) contributes to the energetics of halogen bonding. This information cannot be obtained experimentally but it can be isolated in a calculation. Multiple different approaches have been employed and the published estimates vary widely. The original concept of s-hole bonding proposed by Politzer et al. a Institute of Organic Chemistry and Biochemistry, Czech Academy of Sciences, 166 10 Prague, Czech Republic. E-mail: rezac@uochb. cas. cz b UniversiteParis Sud, Orsay Cedex 91405, France † Electronic supplementary information (ESI) available: Tables of charge-transfer energies calculated using the alternative population schemes (Tables  S1  and S2 ), charge-transfer energies for hydrogen bonds from the S66 dataset (Table  S3 ) and complete DFT-SAPT decomposition for X40 (Table  S4 ) and S66 (Table  S5 ).
These differences have to be kept in mind when comparing charge-transfer energies obtained using different methods. The spatial definition of charge transfer is close to the intuitive understanding of the phenomenon, so that it may provide a reasonable explanation of the role of charge transfer in halogen bonding. In this work, we apply this methodology to a systematic study of halogen bonds ranging from weak to very strong ones. To allow for a comparison with interactions of other types, we work with the X40 dataset  26  of noncovalent complexes of halogenated molecules, which features a wide range of interaction motifs. There also exist much stronger halogen bonds that are not included in the X40 set, and we have added several such systems to our study. The trends in chargetransfer energies and their relation to the overall interaction energies and other characteristics of the systems are used for the rationalization of the role of charge transfer in halogen bonding.
Charge-transfer energy should be discussed not only in relation to the overall interaction energy but also with respect to other components of the interaction energy. Most importantly, the effect of the Pauli repulsion should be separated and the charge-transfer energy should be compared to the remaining (attractive) components, namely electrostatics, induction and dispersion. To obtain these components, we use the DFT-based symmetry-adapted perturbation theory  27, 28  (DFT-SAPT), which provides a well-defined and accurate decomposition of the interaction energy. We provide DFT-SAPT results for the whole X40 dataset as these data may be valuable for other applications. Previously, our implementation of the cDFT methodology 29 was limited to generalized gradient approximation (GGA) DFT functionals, where the charge transfer could be overestimated due to the self-interaction error, which leads to the overdelocalization of electrons.
DFT calculation setup We want to study the series of halogens up to iodine, but our present implementation of the cDFT method cannot work with pseudopotentials which are commonly used in calculations of such a heavy element. It was necessary to find an all-electron basis set and verify its applicability to noncovalent interactions of iodine. At the level of interaction energies calculated using the B-LYP functional, we have compared the def2-QZVP basis set,  38  which was developed to work with relativistic pseudopotentials, with all-electron calculations where the AQZP basis set  39, 40  is used for iodine (while the def2-QZVP is used for the remaining elements). Both basis sets are of quadruple-z size. The root-mean-square deviation of the interaction energies over the iodine-containing systems from the X40 dataset was found to be 0.
Auxiliary calculations One of the quantities that determine the magnitude of the charge transfer is the energy difference between the highest occupied molecular orbital (HOMO) of the electron donor and the lowest unoccupied molecular orbital (LUMO) of the acceptor molecule, DE HOMO-LUMO (the donor and acceptor molecules are identified in the charge transfer calculation). Since DFT does not yield reliable energies of unoccupied orbitals, this term was calculated at the Hartree-Fock (HF) level in the cc-pVTZ basis set (cc-pVTZ-PP with the corresponding pseudopotential was used for iodine). The DFT-SAPT calculations were carried out in the Molpro 2015 package  42, 43  using the recommended setup. The monomer wavefunctions were obtained using the asymptotically corrected PBE0 functional. The calculations were performed in the aug-cc-pVTZ basis set, using the aug-cc-pVTZ-PP version  44  along with the corresponding pseudopotentials for bromine and iodine.
The systems studied In this work, we use three established datasets of model systems related to the topic. For testing the methodology, we use the set of charge-transfer complexes developed by Karthikeyan et al. ,  45  which we used already in our previous study. It features eleven systems exhibiting charge transfer ranging from moderate to extremely strong. To include an example of a hydrogen bond, we have added the water dimer in the geometry taken from the S66 dataset. 46 or the study of halogen bonding, we used the X40 dataset, 26 which covers various classes of interactions involving halogenated molecules. The X40 set maps halogen bonding between Cl, Br and I interacting with nitrogen, oxygen and, where applicable, also sulfur. In addition, there are halogen-p interactions and the set also allows a comparison with hydrogen bonds, where the halogens play various roles.
To compare the charge transfer in halogen and hydrogen bonds, we have also calculated the hydrogen-bonded complexes from the S66 dataset, removing the symmetrical ones where charge transfer cannot be calculated using this methodology and system 11, where the geometry minimum is not a proper H-bond (it is using complexes 1-10, 12-16, 18 and 19 from the set). Two additional systems to complement the X40 dataset as well as the complexes from ref. 8 were prepared using a protocol similar to the one used in the construction of the datasets: the geometries were optimized at the MP2/cc-pVTZ level and the interaction energies were calculated using a composite CCSD(T)/CBS scheme with counterpoise correction. The CCSD(T)/CBS was constructed from MP2/CBS extrapolated  47  from aug-cc-pVTZ and aug-cc-pVQZ basis sets and the CCSD(T) correction was calculated in aug-cc-pVDZ (the -PP variant of the basis set with a pseudopotential was used for iodine).
The accuracy of this scheme with respect to the true CBS limit was estimated to be about 2%. 48, 49 6 The software used The cDFT calculations were carried out in a modified version of the deMon2k program. 33, 34 The code is available to licensed users of deMon2k upon request. The DFT-D3 calculation of interaction energies, the HF calculations of orbital energies, MP2 optimizations, CCSD(T) calculations and the NBO analysis were performed in Turbomole version 6. 6. 50, 51 The Cuby framework  52, 53  was used to automate the calculations of the datasets; all the sets used in this work are distributed as a part of Cuby.
To select a single functional for further discussion, we also looked at the interaction energies in this dataset. DFT-D3 interaction energies  57  (calculated in the def2-QZVP basis set, using the BJ damping  58  ) were compared with the benchmark CCSD(T)/ CBS interaction energies; 20 the resulting errors are listed in Table  3. All the functionals overestimate the strength of the interaction, which can be attributed to overestimated chargetransfer effects due to the self-interaction error. The error decreases with the amount of exact exchange in the functional and the best results are achieved with BH-LYP, where the systematic error (evaluated as the mean signed error, MSE) is only À0. 41 kcal mol À1. Although this functional is not the best one for general use, it is the best choice for charge-transfer complexes.
Alternative population schemes The fragment-based Voronoi deformation density and Becke deformation density schemes were tested on charge-transfer energies in the dataset of charge-transfer complexes. The calculations were performed using B-LYP, B3-LYP, BH-LYP, PBE and PBE0 functionals in the def2-QZVP basis set. The results obtained with different functionals are very similar. Here we will discuss only BH-LYP CT energies, which are plotted in Fig. 2  (the corresponding tables are available in the ESI, † as Tables  S1  and S2 ). Since the FVDD and FBDD schemes are very similar in principle, it is no surprise that they produce almost identical results. What is more important is the comparison with the fragment-based Hirshfeld scheme that we devised in our previous work. 20 n the complexes with weaker charge transfer, the difference between FVDD/FBDD and FBH schemes is small and its distribution is practically random.
Charge transfer in the X40 dataset We have calculated charge transfer in the whole X40 dataset; before we focus on the halogen bonds, we will discuss all the classes of interactions featured in the set. For the purpose of this analysis, the interactions in the set are divided into the following groups: weak interactions governed by dispersion or non-specific electrostatics (systems 1-10), p-p stacking (systems 11 and 12), halogen bonds (13-26), halogen-p interactions (27-30) and hydrogen bonds (31-40). The chargetransfer energies were again calculated using B-LYP, B3-LYP, BH-LYP, PBE and PBE0 functionals. The results are summarized in Table  4  as average charge-transfer energies in the groups. The relative importance of charge transfer in the groups was calculated as the ratio of DE CT and DE int (unless noted otherwise, the benchmark CCSD(T)/CBS interaction energies are used), averaged over each group.
Instead, it is found in an analogous complex of HCl with methylamine, which is weaker (DE int À11. 42 kcal mol À1 ) because HCl is less polar but the charge transfer is exceptionally strong, with DE CT À5. 64 kcal mol À1. Both these hydrogen bonds are substantially stronger than analogous complexes with the oxygen from methanol serving as the electron donor. The large charge-transfer energies in the H-bonds from the X40 dataset warrant a comparison with hydrogen bonds in organic molecules involving oxygen and nitrogen. We have used a set of single H-bonds from the S66 dataset (see the Methods for details), for which we calculated DE CT using the same setup (the charge-transfer energies are available in the ESI, † in Table  S3 ). The average interaction energy in this set, À6. 42 kcal mol À1 , is not far from the one in the H-bonds from the X40 set, À7.
DFT-SAPT calculations on the X40 dataset A comparison of the charge-transfer energy with the interaction energy provides some insight into the importance of charge transfer in the complexes studied, but it has one major drawback. Charge transfer as well as the other attractive forces are opposed by the Pauli repulsion. The larger the interaction energy, the stronger the components that cancel each other. More valuable information for the assessment of the importance of the charge transfer in the interaction is provided by its comparison only with the other attractive terms. To achieve that, we have performed the interaction energy decomposition by means of DFT-SAPT. All the individual components are provided in the ESI, † in Tables  S4  and S5 ; for the purpose of our analysis, we have grouped them as follows.
The first-order exchange repulsion E  (1)  exch is set aside and only the remaining (in this case always attractive) terms are considered. The first-order electrostatic term E  (1)  elst is used as it is; although this is only an approximation, it is calculated on non-symmetrized wavefunctions of the monomers and it does not include the exchange energy resulting from the symmetrization that is part of the E  (1)  exch. The electrostatic contribution compensated for exchange effects would be slightly weaker, but it cannot be calculated using the present methodology. In the second and higher orders, we group the terms into induction/charge transfer and dispersion components, which Table  4  The average charge-transfer energies in groups of systems featuring interactions of a different type from the X40 dataset and hydrogen bonds from the S66 set, calculated using various DFT functionals.
3 and 0. 2%, respectively. In halogen bonds from the X40 set, dispersion and electrostatics are the most important contributions and are of almost the same size, making 46 and 43% of the overall attraction (it has to be reminded that the electrostatic component is Table  5  Results for the X40 dataset: the benchmark CCSD(T)/CBS interaction energy, cDFT charge-transfer energy and the partial charge of the first molecule in the complex calculated with the BH-LYP functional, and the energy difference between the HOMO of the electron donor and the LUMO of the acceptor  overestimated in this decomposition scheme). When counted this way, the contribution of charge transfer becomes even smaller at 3. 1% while induction contributes by 8%. The halogenp interactions feature a similar amount of charge-transfer energy at 2.
The halogen bonds involving oxygen and sulfur are weaker than the ones of nitrogen, which correlate well with the charge-transfer energies. The X40 dataset features two analogous series of halobenzenes interacting with acetone and trimethylamine; in the latter series, the charge-transfer energies are more than twice strong and the iodobenzeneÁ Á Átrimethylamine halogen bond is the strongest one in the set. This difference can be attributed to the lower DE HOMO-LUMO in the trimethylamine complexes (0. 47 a. u. as compared to 0. 52 a. u. in analogous complexes with acetone). However, the CT energies themselves are too small to explain such large differences in interaction energies. The DFT-SAPT decomposition averaged over halogen bonds with different acceptors (Fig. 3 ) shows that the electrostatic interaction is also stronger in the complexes with nitrogen.