dataesr/scientific-paragraphs-categorization huggingface dataset:
The structures of all calcium channels and receptors mentioned here were poorly exploited from the point of view of their partner proteins even though most of them have been resolved with different depth resolutions  [156] [157] [158] [159] [160] [161]. To date, in addition to the data from cryo-electron microscopy, that has emerged as one of the most effective techniques, advancements in structure-based computational tools also have to be used in order to fully understand the macromolecular membrane protein assemblies and their interactions. In this respect, tools such as homology modeling, docking and virtual screening, virtual amino acid scanning, and structure-based peptide design, can be coupled with the growing public database containing high resolution structures of ion channels and their partners for more efficient peptide development and drug discovery.
The CPC apparatus was equipped with a PuriFlash1000 (Interchim, Montluçon, France) providing the quaternary pump, UV detector set at 280 nm, an automatic sample injection valve fitted with a sample loop (1. 08 mL for small scale, 18. 9 mL for largescale experiments), and a fraction collector. The HPLC system used for the analysis of all fractions was an Alliance 2690 from Waters (Saint-Quentin-en-Yvelines, France) using a binary pump, an autosampler, and a DAD detector Waters 996. Waters EmPower software was used for data acquisition.
ACKNOWLEDGMENTS The authors are grateful to the administration and staff of the  University of Kimpa Vita in Uíge  for logistical support during the stay of T. L. in Angola. The fieldwork in Angola was supported by a travel grant from the  German Academic Exchange Service (DAAD). These published results were obtained in collaboration with the  Instituto Nacional da Biodiversidade e Áreas de Conservação (INBAC) of the Ministério do Ambiente da República de Angola.
, 2011; Fagan et al. , 2014). So far, fewer than 70 small (usually well below 1 g) samples of Apollo rocks have been studied for paleomagnetism, compared to a total of 1402 rock samples available in the Apollo collection (this latter number is a personal communication by Ryan Zeigler). The aim of this study is to obtain a rough estimate of the paleomagnetic potential of a larger fraction of the Apollo rock collection (fraction that span the entire age range that the lunar dynamo has been previously investigated) using non-destructive methods. We measured the natural remanent magnetization (NRM) and the magnetic susceptibility (χ) of whole Apollo samples. These data increase the database of lunar rocks magnetic properties. The measurement of these two properties on the same sample also allows a qualitative assessment of the paleomagnetic potential of these samples.
These methods, in addition to being nondestructive, are fast, easy to implement and allow us to measure whole samples. The NRM intensity is a crude indication of whether a rock can be targeted for paleomagnetic study. χ is a proxy for the metal content. The ratio between NRM and χ can be viewed as a crude proxy for the intensity of the paleofield recorded by a rock, enabling the selection of samples for detailed laboratory demagnetization follow-up studies that address specific questions (timing of the dynamo, maximum field recognized in the weak-field epoch, etc). A high NRM/χ may also be an indication of suitable paleomagnetic intrinsic properties for laboratory follow-up studies. Finally, these analyses increase the database of lunar rock magnetic properties for the purposes of modeling the lunar crustal magnetic field (e.
Methods All measurements were performed directly in the Apollo storage vault at the Lunar Sample Laboratory Facility (LSLF) at Johnson Space Center (JSC), NASA, Houston. A specific susceptibility meter and a spinner magnetometer were used to be assembled in the LSLF and which satisfy curatorial constraints. The samples were left in their original packaging for this study. Sample packaging varied from sample to sample. Samples kept in steel containers could not be studied. We measured those stored in Teflon bags, aluminum containers, or a combination of both. Prior to measurements, the samples were placed at the center of a hollow cubic sample holder made of acrylic plastic. They were maintained in place using padding material made of acrylic and Teflon. The magnetometer and associated data processing methods are described in  Uehara et al.
Magnetic properties and lithology The NRM intensity spreads over two orders of magnitude (S1, Table  S1 ) and shows a strong lithological dependence, in ascending order of NRM: feldspathic rocks, basalts, breccias and regolith breccia (Figure  1a , Table  1 ). This corroborates previous results, although on a much larger dataset. Magnetic susceptibility is also lithology-dependent, with impact-processed rocks being more magnetic (i. e. , higher NRM and χ) (Figure  1b ). This confirms that lunar impact processes are the main factors controlling the amount of ferromagnetic minerals in lunar rocks, with a magnetic enhancement due to incorporation of meteoritic metal (e. g. ,  Rochette et al. , 2010).
3. 2. Comparison with other measurements The NRM and the magnetic susceptibility values averaged by lithology are given in  Table 1 , both for this study and for literature data. The literature dataset consists of 109 NRM estimates (from 61 different Apollo rocks), and 105 susceptibility estimates (from 73 different Apollo rocks). A comparison of both datasets shows that the NRM intensities are larger by an average factor of ~2. 7 in small sample studies (Figure  2a ). This discrepancy (slope of 2. 74 and R 2 =0. 84) is evidence of the NRM directional heterogeneity within some lunar rocks and/or of possible magnetic contamination of small samples during preparation and handling. The two datasets provide similar values for the susceptibility (R 2 = 0. 90) with a slope of 0. 66 (Figure  2b ).
As an example, samples 65315 and 60215 had diamagnetic χ values of -1. 4 x 10 -9 m 3. kg -  and -5. 4 × 10 -9 m 3. kg -  , respectively, in the small sample study of  Cournède et al. (2012)  and 20 x 10 -9 m 3. kg -  and 228 × 10 -9 m 3. kg -  , respectively, in this study. In Figure  5 , we compare our susceptibility results with measurements performed at JSC on another set of large samples (mean mass 19. 8 g) from the returned sample collection  (Macke et al. , 2014). These measurements were performed using a SM30 instrument calibrated following  Gattacceca et al. (2004)  and are provided in section S7, Table  S3. The coefficient of correlation of 0. 70, together with a slope of 0. 998 between the two independent dataset confirm that the scatter observed between our measurements and the small sample laboratory measurements is due to the intrinsic properties of lunar rocks (mostly small scale heterogeneities) rather than instrumental issues.
Paleomagnetic potential of this dataset The ratio of NRM to magnetic susceptibility, noted ß hereafter, may convey information about lunar paleomagnetism. Indeed, high ß values may point to more single domain like behavior (for a given paleofield) or to higher paleofields (for a given magnetic mineralogy). The paleointensity of 23 selected samples measured in this study have already been determined in previous laboratory studies (part S8, Table  S4 ). ß appears broadly correlated with the paleointensity (Figure  6 ). However, a trend line does not intercept at the origin (B,ß)=(0,0). Indeed probably because of secondary magnetization components, hardly any samples have ß below 1. Qualitatively, we estimate that ß values below 4 have no real paleointensity significance. Selecting samples with ß ≥4, and forcing the x-intercept of the linear fitting to ß=4, we have a relation B = 9.
7 * (ß-4) (R 2 =0. 63) with B in µT and ß in A/m. This relation indicates that the overall ß dataset may be informative about the overall evolution of lunar paleointensities and allows using ß as a crude paleointensity proxy. However, there are various reasons why ß may not be a good proxy for the paleointensity. First, the minerals that carry the magnetic susceptibility signal are not necessarily the same ones that carry the NRM. This may introduce a mineralogical bias in the interpretation. Unfortunately, the formation ages of the various lithologies are clustered in discrete and mostly non overlapping periods so that this lithological bias is difficult to circumvent. Second, it would be better to normalize NRM by ferromagnetic susceptibility only, since the paramagnetic minerals contribute to the susceptibility but not to the remanence.
g. ,  Tikoo et al. , 2015) , the result of shock remagnetization would be a drastic decrease of ß regardless of the intensity of the lunar surface field at the time of impact. Other explanation for low ß could be post NRM acquisition "cold" brecciation (without resetting of radiometric ages but clast-scale randomization of the NRM) and recent contamination by metal rich regolith. With all these caveats in mind, the dataset presented in Figure  8  allows selecting samples for laboratory studies addressing specific questions about the lunar surface field evolution.
Conclusions Using two portable instruments specifically designed for this project, we obtained magnetic susceptibility and NRM measurements on 154 and 123 Apollo whole samples, respectively. This dataset represents a significant increase in number (+52% for NRM and +64% for susceptibility) and in mass (80 kg against 20. 5 g for the literature dataset) of the existing database of lunar magnetic properties. Our lithology-averaged NRM values may be more representative of the large-scale magnetic parameters than the values measured on small samples usually well below 1 gram that appears overestimated by an average factor of ~2. 7. The NRM values obtained in this work are therefore more suitable to model and interpret the lunar magnetic field anomalies. Conversely, our magnetic susceptibility estimates may be overestimated for some weakly magnetic lithologies because of coating by glass or regolith material on bulk samples.
Overall, magnetic susceptibility is strongly controlled by lithology and is clearly enhanced by impact processing and associated incorporation of meteoritic metal. The ratio of natural remanence and magnetic susceptibility may be used as a crude paleointensity proxy. Its overall evolution with time is coherent with the existence of the high field and weak field epochs defined in the literature. However our data raise the possibility that the paleointensity during the high field epoch may have been more variable and lower on average than previously suggested. Although this ratio cannot provide robust paleointensities, our dataset represents a valid tool to select samples of interest for detailed paleomagnetic laboratory study. S2. S2 , with all literature data. S2. S4. The right axis indicates the approximate paleointensity for ß > 4 (broken line, see section 3.
Analysis of AhR Expression in BC at the mRNA Level As a first attempt to correlate AhR expression with prognosis in BC, we used the Kaplan-Meier Plotter online database to perform Kaplan-Meier analyses on several BC transcriptomic datasets. As shown in Figure  1 , the meta-analysis showed that the levels of AhR mRNA (assessed using the 202820_at probeset) were not significantly associated with prognosis (n = 3951 patients). Interestingly, the correlation was highly significant in LN-positive patients (n = 1133, p = 0. 008), and stronger than in LN-negative patients (n = 2020, p = 0. 041). Using the BreastMark algorithm (Supplementary Figure  S1 ), AhR mRNA levels were significantly associated with OS only in LN-positive patients (n = 473, p = 0. 002). These results suggest that AhR expression may influence patient survival according to the LN status.
Immunodetection of AhR in BC We then analyzed AhR expression at the protein level by immunohistochemistry using a cohort of 302 BC tissues from 297 primary BC patients, with clinical data shown in Table  1. Among the 297 patients, five were bilateral primary BC, so we dealt with the tumors as individual cases (n = 302). Follow-up ranged from 10-12 years and the median age was 57. 5 years old. Several analyses have been performed previously in our group on this cohort, with results dealing with N-Cadherin (NCAD) and CD133  [14] , nuclear receptors  [15, 16] , and their transcriptional coregulators RIP140 and LCoR  [17]. Staining for AhR was obtained for 302 primary BC tissues and observed in the nucleus and/or cytoplasm of tumor cells. The evaluation in each subcellular location was performed with a standardized immunoreactive score (IRS) ranging from 0 to 12  [18].
Correlation of Gene Expression with Patient Survival The Kaplan-Meier plotter (http://kmplot. com) allows meta-analysis-based biomarker assessment using a background database which is manually curated  [31]. To analyze the prognostic value of a particular gene, the patient samples are split into two groups according to median expressions. The two patient cohorts are compared by a Kaplan-Meier survival plot, and the hazard ratio with 95% confidence intervals and a log rank p-value are calculated. The 202820_at probe set was used for AhR mRNA and relapse-free survival was taken as the endpoint (Figure  1 ). BreastMark (http://glados. ucd. ie/BreastMark) is an algorithm allowing the identification of genes that are associated with disease progression in BC  [32]. The search was performed at a low cut-off threshold with OS as the survival endpoint.
Collective We used a well-characterized collective of 302 paraffin-embedded breast tumor tissue samples, from 297 primary BC patients (five of them are bilateral BC). All patient data were fully anonymized and all diagnostic procedures had already been fully completed when samples were collected for the study. Authors were blinded from the clinical information during the experimental analysis. The study was approved by the Ethical Committee of the Ludwig Maximilians University of Munich, Germany (LMU, approval number 048-08 Date: 18. 03. 2008) and informed consent was obtained from all patients. Tissues were sampled between 2000 and 2002 from patients treated for BC at the Department of Obstetrics and Gynaecology of the LMU. Tumor stage at primary diagnosis was histologically evaluated using the UNICC tumor node metastasis (TNM) classification, which includes the size and extent of tumors (primary tumor size, or pT, classified as: pT1, pT2, pT3, pT4), involvement of regional LN (pN), and presence or absence of metastasis (M).
Immunohistochemistry Expression of ERα, PR, and HER2 was determined at diagnosis, in all BC samples of this cohort at the LMU Department of Pathology, Germany. ERα and PR expression were evaluated by immunohistochemistry, as described previously  [33]. Samples showing nuclear staining in more than 10% of tumor cells were considered as hormone receptor-positive, in agreement with the guidelines at the time of the analysis  (2000) (2001) (2002). HER2 expression was analyzed with an automated staining system (Ventana; Roche, Mannheim, Germany), according to the manufacturer's instructions. Data on VDR, RXR, and THRα1 expression in these BC samples were extracted from a previously published study  [34] , as well as for NCAD, CD133  [14] , and RIP140 and LCoR  [17]. For AhR analysis, samples were processed as previously described  [14, 17, 35].
Data Analysis For all proteins, status was classified by evaluation of the percentage of brown stained tumor cells and staining intensity, allowing the assessment of an IRS, as a product of multiplication between the positive cells proportion score (0-4) and staining intensity score (0-3)  [18]. For the percentage score of stained tumor cells, we considered no cells stained (score = 0), <10 % stained (score = 1), 10-50 % stained (score = 2), 51-80 % stained (score = 3), and 81-100 % stained (score = 4). Staining intensity was evaluated as no (score = 0), weak (score = 1), moderate (score = 2), and strong (score = 3) stainings. In doubtful cases, slides were evaluated by two or three independent examiners with consent at the end. As previously described for LCoR and RIP140 [17], AhR cytoplasmic and nuclear stainings were evaluated in parallel, with the determination of both cytoplasmic IRS and nuclear IRS separately.
Statistical and Survival Analysis Statistical analyses were performed using SPSS 24 (IBMSPSS Statistics, IBM Corp. , Armonk, NY, USA). For all analyses, p values below 0. 05 (*), 0. 01 (**), or 0. 001 (***) were considered statistically significant. To present the mean immunoreactivity levels described by the IRS in Table  2 , the groups were divided into low-vs. high-expressing AhR cases and according to the LN status. We then performed receiver operating characteristic curve (ROC) analyses to calculate the optimal cut-off values between low and high AhR expression, based upon the maximal differences of sensitivity and specificity. The threshold determined regarding OS was an IRS ≥ 1 for either nuclear or cytoplasmic AhR, and ≥ 2 for total AhR. These thresholds were used to determine the percentages of tumors expressing low or high AhR levels described in Table  2 , besides the OS analysis detailed below.
FUNDING BC and JT labs were recently founded by  Fondation pour la Recherche Médicale  (Equipe FRM  DEQ20140329495 ),  Agence Nationale de la Recherche   ANR-15-CE15-0024 ,  Deutsche Forschungsgemeinschaft   DFG TU 220/13-1  and  DFG INST 40/492-2 SFB 1149 , and  Hubert Curien PROCOPE/Deutscher Akademischer Austauschdienst (DAAD)  exchange program.
Image from  [44]. ing through syncing/swarming (coalescing) and growing/aggregating (developing), they present the three key components of their system, MapDevo3D, a multicellular soft-robot-growing platform: • Cells (represented as a swarm) can adhere through elastic forces, • Positional information (morphogenes) can diffuse in the environment for cell-cell communication, • An artificial gene regulatory network can control cells. With this system, the authors grew a 3D multicellular organism composed of hundreds of cells of 27 different types, organized in "tissues" (i. e. modules) able to walk across the environment and climb stairs. Figure  20  illustrates this experiment. Disset et al. proposed in 2014 to simplify this fitness function and build a virtual environment complex enough in order to allow creatures with complex behaviors to emerge  [39].
The human genetic diversity of the Americas has been affected by several events of gene flow that have continued since the colonial era and the Atlantic slave trade. Moreover, multiple waves of migration followed by local admixture occurred in the last two centuries, the impact of which has been largely unexplored. Here, we compiled a genome-wide dataset of $12,000 individuals from twelve American countries and $6,000 individuals from worldwide populations and applied haplotype-based methods to investigate how historical movements from outside the New World affected (1) the genetic structure, (2) the admixture profile, (3) the demographic history, and (4) sex-biased gene-flow dynamics of the Americas. We revealed a high degree of complexity underlying the genetic contribution of European and African populations in North and South America, from both geographic and temporal perspectives, identifying previously unreported sources related to Italy, the Middle East, and to specific regions of Africa.
For instance, Gouveia et al. [18]  have recently performed a detailed analysis of the African regional ancestry and its dynamics in several populations from North and South America and the Caribbean. However, a more comprehensive and systematic investigation considering multiple ancestries across the two continents is still missing  [10] [11] [12] [13]. The recently increased availability of genome-wide data offers now the chance to capture the complexity of historical and demographic events that affected the recent history of the Americas by studying the recent admixture profile of many modern populations. With this in mind, we have first assembled a genome-wide dataset of 17,722 individuals, including $12,000 from North, Central, and South America and $6,000 from Africa, Europe, Asia, and Oceania (Figure  S1A ; Data S1A and S1B) and then we harnessed haplotype-based and allele frequency methods to (1) reconstruct the fine-scale ancestry composition, (2) evaluate the time of admixture, (3) assess the extent and magnitude of sex-biased gene-flow dynamics, and (4) explore the demographic evolution of different continental ancestries after the admixture.
8%). This discrepancy might be due, at least in part, to the fact that our dataset is mostly composed by Brazilian individuals, which have been documented to have a smaller Jewish ancestry  [25]. Only 2. 5% of analyzed individuals contain more than 5% of Jewish or Middle Eastern ancestry (Salvador: 0. 8%, Bambui: 3. 2%, Pelotas: 2. 9%). In contrast, this proportion is higher in the non-Brazilian populations (CLM [Colombians from Medellin]: 8%, Colombian: 3. 8%, Peru: 2. 3%, Mexican: 5. 4%, MXL: 11%, Chile: 16%, Argentina: 12%). Similar proportions were found for Caribbean populations (ACB: 1. 4%, Caribbean: 6. 8%, Dominican: 3. 7%, Puerto: 3. 9%, PUR: 1. 4%). Interestingly, we found a relatively high proportion of individuals showing more than 5% contributions close to ''Jewish'' sources also in AfroAme (3.
Given the computational complexity of this process, the estimation of these two parameters was obtained by averaging the values calculated from an analysis performed on a subset of six hundred individuals from all the analyzed populations, with sample sizes mirroring the global composition of the dataset for five randomly selected chromosomes  (3, 7, 10, 18 and 22). Second, we analyzed the painted dataset using fineSTRUCTURE  [19] , in order to identify homogeneous clusters. We ran the software in three subsequent steps: the first, also called ''greedy,'' infers in a fast way a rough clustering summarizing the relationships among individuals, and it is usually used when the number of samples is large (> 5000 individuals); the second, starting from the greedy clustering, performs 1 million MCMC iterations thinned every 10,000 and preceded by 100,000 burn in iterations.
Estimation of admixture dates In order to provide a temporal characterization of the admixture events in the Americas, we estimated times and most closely related putative sources using population-based and individual-based painting profiles. In the ''population'' approach, given the high demand of computational resources requested for the analysis, we have used fastGLOBETROTTER, which, based on GLOBETROTTER  [77] , implements several optimizations in performance, making it suitable for large datasets. In detail, we first harnessed the painting profiles obtained by CHROMOPAINTER by testing for any evidence of admixture using the options null. ind = 1, prop. ind = 1, and performing 100 bootstrap iterations. For each of the admixture events inferred, we considered only those characterized by bootstrap values for time of admixture between 1 and 400.
Subsequently, we estimated time of admixture repeating the same procedure with options null. ind = 0 and prop. ind = 1 (Data S2C). For the individual analysis we estimated admixture times with GLOBETROTTER, applying the prop. ind = 1, null. ind = 0 approach to the 11,607 target individuals. In order to remove individuals with ''unusual'' painting profiles, only those falling in the 2. 5%-97. 5% admixture time confidence interval were retained. We tested significant differences in times of admixture involving specific African or European clusters by applying a Wilcoxon test using R and setting alternative to ''greater. ''.