dataesr/scientific-paragraphs-categorization huggingface dataset:
Fortunately, data for the predictive variables we employed are likely to become more widely available in future. This is because there is increasing interest in developing and sharing open databases that relate to crop nutrients (https://www. precis ioncropnutrition. net/data/). A standardized and open crop nutrient dataset will allow users to apply the statistical and machine learning methods described in this study to the open sources of data. This is in contrast to using the mean HI and N concentration values from Table  3  in this study. Use of mean values shown in Table  3  for (say) country scale N balances has the disadvantage that values will only be reflective of the conditions from the included trials. This means they may not reflect actual farmer practice in each region. The mixed-effects and random forest prediction methods described in this study when applied to the open datasets will allow estimates of HI and N concentration of maize to vary based on region and by what were seen to be important predictor variables such as CPY for HI, and Ypot and FN for N concentration of crop products and crop residues.
Application of values (for predictor variables) that are representative of farmers practice in each location should provide more locally relevant estimates compared with the mean values from the field experiments. For instance, Ypot values should represent the availability of water at each location with water-limited yield potential (Yw) values used for rainfed locations, and non-water limited (irrigated) yield potential (Yp) values used for irrigated locations. As we expand the open dataset these estimates will be improved. The authors' are therefore interested in receiving data from any crop. This is because contributions of data will help improve the accuracy and granularity of the predictor variables which in turn will improve estimates of HI, CPN and CRN. This would provide more locally relevant estimates of crop nutrient offtake to improve the accuracy of nutrient balances at a global level and provide opportunities for more sustainable nutrient management.
Predictions from both the mixed-effects model and random forest method were shown to provide reasonable levels of accuracy with the random forest method having the greater accuracy. Random forest can also handle larger datasets and can improve predictions over time with the addition of a wide range of variables, and as more data become available. However, prediction accuracy should not be the only metric used to decide what prediction model to use. An advantage of using the linear mixed-effects models for predictions is the fact that is easier to interpret. Unlike random forest, linear mixed-effects models can provide tangible equations which make explicit the positive or negative correlations between the predictor and dependent variables. The methods used in this study were only applied to maize, but could equally be applied to other crops and other nutrients which we now plan to do, focusing first on macronutrients in maize, wheat, rice and soybean.
o Soil variables of dominant soil type in that CZ (ISRIC wise database, https://data. isric. org/geonetwork/srv/eng/catalog. search #/metadata/d9eca770-29a4-4d95-bf93-f32e1ab419c3) o Total available water capacity for the default soil depth of 100 cm for deep soil units, to 10 cm for shallow Lithosols, and to 30 cm for Rankers and Rendzinas (TAWC, 9 classes).
Regression. We used a random forest regression model. Analysis was done in R using the randomForest function and for cross-validation the rf. cross-Validation function (from the randomForest and the rfUtilities package, respectively) was used. For the random forest regression and the crossvalidation the proportion of data withheld was 0. 10 (default value), 99 cross validations (default value), and the number of trees to grow was set to 500. Additional information for estimation of Yp. In GYGA for rainfed crops when estimating Yw, at the same time Yp is also estimated (for the growing season and cultivar of rainfed crops). Normally this is only used for the purpose of obtaining the degree of water limitation for that specific climate zone. Here, we made use of this estimated Yp. For the present study it means that we included some CZs with irrigated cereals, while they do not feature in GYGA as their areas were considered too small (instead only rainfed cereals were considered).
Acknowledgements This work was supported by the  International Fertilizer Association (IFA). The authors wish to acknowledge all those who have contributed raw field experiment data for maize included in this study. Those include  Fernando Garcia  ( Facultad de Ciencias Agrarias ,  Universidad Nacional de Mar del Plata) ,  Rachel Fields  and  Timothy Lewis  (AngloAmerican),  Tek Saptoka  (CIMMYT) and  Maryam Rahimi Jahangirlou. The authors also wish to acknowledge those who helped converting data from the literature into a standardized format including,  Joost Krooshof ,  Bert Rijk ,  Tobias Bader  and  Victoria Miles-Hildago.
Patients and samples We retrospectively included cHCC-CCA samples from patients who underwent surgical resection or liver transplantation in five University Hospitals (Henri Mondor, Cr eteil, France; Beaujon, Clichy, France; Piti e-Salpêtri ere, Paris, France; Reims University Hospital, Reims, France; National Cancer Hospital, Hanoi, Vietnam; Table  1 ). Inclusion criteria were as follows: (i) available baseline data and histologic material, (ii) unequivocal histologic features of cHCC-CCA, and (iii) lack of preoperative antitumor treatment. The following clinical, biological characteristics were systematically recorded: age, gender, etiologies of liver disease [hepatitis B (HBV) or hepatitis C (HCV) infection, alcohol intake, and metabolic syndrome], follow-up (overall survival), alpha-fetoprotein serum levels, tumor size, multinodularity, satellite nodules, vascular invasion (macrovascular/microvascular), and surgical margins.
The Nanostring nSolver 4. 0 software (NanoString Technologies) and "NanoStringNorm" package in R were used to control and normalize raw data. In particular, data were normalized against the geometric mean of 20 housekeeping genes in combination with a positive control normalization, which uses the geometric mean of six synthetic positive targets, to control technical sources of variability. The obtained gene expression values were then log 2 transformed. Gene sets to investigate gene expression of different immune signatures and immune cell subsets were established on the basis of gene annotations of nCounter PanCancer IO 360 Panel (NanoString Technologies) and the web-based application Gorilla (http://cbl-gorilla. cs. technion. ac. il; ref. 23).
After DNA and RNA quantification, 50 pmol/L of each library was multiplexed and clonally amplified on ion-sphere particles (ISP) by emulsion PCR performed on Ion Chef (Thermo Fisher Scientific), according to the manufacturer's instructions. The ISP templates were loaded onto an Ion-540 chip and sequenced on an Ion S5 sequencer with the Ion 540 Kit-Chef, according to the manufacturer's instructions. Run performance was assessed and data analyzed with the Ion ReporterTM Software (Thermo Fisher Scientific) using specific stringent filters (allele frequency between 5% and 90%; only exonic location, read depth greater than 300Â).
Digital pathology and quantification of main immune cell subsets Slides were scanned in brightfield mode using the NanoZoomer HT scanning device (Hamamatsu Photonics) at a 20Â magnification and a 0. 45 mm/pixel resolution. Whole-slide images were further analyzed using the Qupath digital software 0. 1. 2 version (https://qupath. github. io/; ref. 26). Tumoral (CT) and non-tumoral (NT) liver areas were manually annotated by a pathologist (C. T. Nguyen). Optical density sum and Cytoplasm DAB OD mean were applied as parameters to detect positive cells in both CT and NT. Cell densities of immune cells were expressed as the mean number of positive cells per mm 2.
Statistical analysis Data visualization and statistical analysis were performed using R software version 4. 0. 2 (R Foundation for Statistical Computing. https://www. R-project. org) and Bioconductor version 3. 4. Unsupervised hierarchical clustering was done using Cosine distance and Ward's linkage method. Principal component analysis (PCA) using the main two components was also performed in a series of matched tumor/NT sample pairs. Heatmaps were generated using the R packages "ComplexHeatmap"  (27). Bioconductor limma package was used to test for differential gene expression between two groups  (28). Differentially expressed genes were defined by a q-value threshold of ≤0. 05 (according to the FDR method from Benjamini-Hochberg) and a log fold-change >|1|. Data were compared using Wilcoxon rank test or Fisher exact test according to the type of variable.
The comparison of multiple dependent variables in two groups was performed using a multivariate ANOVA (MANOVA). To test the differences regarding the activated immune pathways and the specific gene signatures, we extracted gene lists from the nCounter PanCancer IO 360 Panel -Annotations and Sangro and colleagues  (16) , respectively. For each gene list, we then defined a score as the average gene expression. Scores in the two groups were compared using the Wilcoxon rank test. To compare the gene expression profile of cHCC-CCA and HCC and CCA, we also included in an integrated analysis 355 HCC samples from TCGA-LIHC and 36 CCA samples from TCGA-CHOL (29). RNA-sequencing data from The Cancer Genome Atlas (TCGA) were obtained using the "TCGAbiolinks" package in R  (30). Then, we standardized gene expression separately to have mean 0 and SD 1 per gene in each dataset.
Only genes in common between the three datasets were considered for the analysis. Unsupervised hierarchical clustering of the integrated data was performed as described above. Survival analysis was performed in patients treated by R0 liver resection with available survival data (n ¼ 73). Overall survival was defined by the interval between surgery (resection or liver transplantation) and death. Survival curves were represented using the Kaplan-Meier method compared with log-rank statistics. Univariate analysis was performed using the Cox proportional-hazards model and variables with a P value < 0. 05 were selected for multivariate analysis. All tests were two tailed and P values < 0. 05 were considered significant.
1A ). IH1 cases accounted for approximately half of all IH cHCC-CCA (n 26/55, 47%) and, compared to sub-cluster IH2, showed overexpression of genes related to innate immunity and natural killer (NK) cells (IFNA1, KIR3DL2, KIR2DL3, KIR3DL1; Supplementary Table  S3 ). Comparison between the subclusters IL1 (left side of the main IL cluster) and subcluster IL2 (right side of the main cluster) showed overexpression of cancer-testis antigens (MAGEA3/A6, MAGEA4 MAGEC1, MAGEC2; Supplementary Table  S4 ). Further understanding of this heterogeneity will however require the study of additional cases. To compare the immune profile of cHCC-CCA with that of conventional HCC and CCA, we further performed an integrative transcriptomic analysis with two independent datasets from TCGA (TCGA-LIHC n ¼ 355 and TCGA-CHOL n ¼ 36) and showed that cHCC-CCA retained their immune class and clustered close to HCC and CCA with similar transcriptomic features (Supplementary Fig.
S2;  refs. 31, 32). We then aimed to validate our findings on a public dataset of 20 cases of cHCC-CCA  (33). As observed in our series, unsupervised hierarchical clustering also showed two main immune clusters (high and low), with two subclusters of each main class (Supplementary Fig. S3 ). We observed in the IH subset upregulation of genes involved in various immune processes such as adaptive immunity or antigen processing (Supplementary Table  S5 ), due to the low number of cases of this dataset, differences remained significant in only nine genes after adjustment for multiple tests). Compared with IH2, the IH1 cluster interestingly showed trends toward upregulation of genes related to NK cells, as identified in our series (Supplementary Table  S6 ). We also observed trends toward dysregulation of cancer-testis antigen genes between IL1 and IL2 (Supplementary Table  S7 ), due to the very low number of cases no difference remained significant after correction for multiple tests).
IL2, P ¼ 0. 003; IH1 vs. IH2, P ¼ 0. 004; Supplementary Fig. S4 ). We also aimed to confirm the increased immune infiltration in IH tumors by two distinct approaches: gene expression profiling and digital image analysis of IHC stainings. We first showed that gene sets related to T cells (P < 0. 0001), CD8 T cells (P < 0. 0001), B cells (P < 0. 0001), exhausted CD8 T cells (P < 0. 0001), macrophages (P < 0. 0001), dendritic cells (P < 0. 0001), neutrophils (P < 0. 0001), and NK cells (P < 0. 0001) were upregulated in IH cHCC-CCA (Fig. 3A ). Consistent with the intracluster heterogeneity described above, we observed that some IH cHCC-CCA show a lack of particular immune cell subsets, such B cells, neutrophils, or NK cells (Fig. 3A ). We further performed IHC stainings of main immune cell subsets in 67 randomly selected cases and quantified the densities using Qupath software on the scanned digital slides.
Clinical impact of cHCC-CCA immune subtype Finally, we assessed the prognostic impact of the cHCC-CCA immune subtype. Survival data were available for 73 patients, the median follow-up was 25 months (interquartile range, 12. 2-48). Patients with non-curative resection were excluded (R1 or R2). Twenty-one deaths were recorded. The overall survival rates at 24 months, 60 months were 90%, 80% for patients with the IH subtype and 71%, 67% for patients with the IL subtype, respectively. Kaplan-Meier curves of survival times according to the immune subtype are represented in Fig. 6. Univariate analysis showed that IH subtype [HR ¼ 0. 37, 95% confidence interval (CI) ¼ 0. 14-0. 96, P ¼ 0. 04], satellite nodules (HR ¼ 2. 96, 95% CI ¼ 1. 08-8. 10, P ¼ 0. 034), and HBV infection (HR ¼ 2. 97, 95% CI ¼ 1.
Acknowledgments This study was supported by the  Bristol-Myers Squibb Foundation for Research in Immuno-Oncology. J. Calderaro  is supported by  Fondation ARC, Bayer , and  Institut National du Cancer. The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U. S. C. Section 1734 solely to indicate this fact. Received April 2, 2021; revised June 18, 2021; accepted November 9, 2021; published first November 16, 2021.
These data suggest that the peptidergic neurons of the EWcp are always recruited during nest building but significantly more during pregnancy, while suggesting that other populations in the region may also respond to nesting. We next tested which EWcp neuropeptide was involved in regulating nesting during pregnancy. We injected in the EWcp nucleus of wild-type C57BL/6NRj mice AAVs expressing saCas9 and one of three guide RNAs targeting either Ucn1, Cartpt, or Cck. We validated the knockdowns by immunolabeling and in situ hybridization on sections (Figures  4A  and S3A ). The Ucn1 knockdown was complete. In the CART+ knockdown, only a very few CART+ neurons remained (n = 1. 7 ± 0. 4 versus 22. 4 ± 2. 7 neurons/slice). In the Cck knockdown, 48% of Ucn1+ neurons still expressed Cck (n = 2; 1,089 cells counted).
To evaluate which projection sites are functionally activated, we mapped Fos induction throughout the brain after activating these neurons with the excitatory DREADD hM3Dq in pregnant females (Figure  8E ). CNO injection in hM3Dq expressing Cart-Cre females induced a strong Fos expression in EWcp neurons (Figures  8F  and 8G ) and many other brain regions (Figures  8G  and 8J ). To find the regions whose activity was changed by both preparatory nesting and chemogenetic activations of EWcp CART+ neurons, while also receiving dense projections from them, we combined three whole-brain mapping datasets (Figures  8H  and 8I ): Fos expression elicited by nesting over control objects, elicited by CNO in HM3Dq over controls, and EWcp CART+ projections. The overlap between these datasets showed a distributed array of nuclei among which the preoptic area and suprachiasmatic nuclei, the zona incerta, the central, basolateral, and basomedial amygdala, the ventrolateral periaqueductal gray, and the brainstem reticular formation (Figure  8J ).
0005%). For all experiments, DMSO never exceeded 0. 001% and was systematically present at the same concentration when different solutions were tested on the same cell. 5 cells in 5 mice were recorded with DMSO, and 9 cells in 6 mice with progesterone. All drugs were obtained from Hello Bio (Bristol, UK) or Merck (Sigma). In vitro slice electrophysiology: Data acquisition and analysis Signals were amplified with a Multiclamp 700B amplifier (Molecular devices), sampled at 20 kHz and filtered at 2 kHz in voltage clamp mode, sampled at 20 or 50 kHz for spontaneous or evoked membrane voltage, respectively, and filtered at 10 kHz in current clamp mode. Data were analyzed using pClamp (Molecular Devices), Prism (GraphPad) software and custom written scripts (MATLAB, Math works). Glutamatergic synaptic currents were detected with custom written software (Wdetecta, courtesy of J.
R. Huguenard, Stanford University). In voltage-clamp experiments, access resistance was on average <20 MU and carefully monitored with small voltage steps (-5 mV for 50 ms; repeated 50 times). Data were discarded from analysis if resistance changes exceeded 20% during the recorded period. Spontaneously occurring excitatory postsynaptic current (sEPSC) were recorded at a holding potential of -70 mV (13 cells in 6 mice (virgins) and 18 cells in 6 mice (pregnant). For each cell, the amplitude of individual sEPSC were measured over a 1 to 3 min time window (with at least 200 events per cell) and the mean amplitude and frequency calculated over this period. In current-clamp experiments, input resistance was monitored with small current steps (À10 pA for 500 ms; repeated 30 times) and cells were excluded if it changed by > 25%.
For calcium imaging during the evoked firing protocol, images were acquired with a Nikon DS-Qi2 camera at 10 fps. The analysis of calcium signals was performed with a custom python script: a region of interest (ROI), enclosing the soma of the recorded cell, was determined, and the mean pixel value was computed, over time, within the ROI. To account for photobleaching, the signal was corrected: the baseline signal was determined using the asymmetric least squares smoothing algorithm and subtracted from the original signal. The corrected signal was then standardized and compared between the different firing behaviors.
Computing Resources The data were automatically transferred every day from the acquisition computer to a Lustre server for storage. The processing with ClearMap was done on local workstations, either Dell Precision T7920 or HP Z840. Each workstation was equipped with 2 Intel Xeon Gold 6128 3. 4G 6C/12T CPUs, 512Gb of 2666MHz DDR4 RAM, 4x1Tb NVMe Class 40 Solid State Drives in a RAID0 array (plus a separate system disk), and an NVIDIA Quadro P6000, 24Gb VRAM video card. The workstations were operated by Linux Ubuntu 20. 04LTS. ClearMap 2. 0 was used on Anaconda Python 3. 7 environment. Data analysis: ClearMap Fos+ cell counting Tiled acquisitions of Fos-immunolabeled iDISCO+ cleared brains scanned with the light sheet microscope were processed with ClearMap 2 (https://github. com/ChristophKirst/) (Kirst et al.
, 2020). For voxel maps, spheres of 375mm diameter were drawn on each filtered cell. P-Value maps of significant differences between groups were generated using Mann-Whitney U test (SciPy implementation). Aligned voxelized datasets from each group of animals were manually inspected to identify the regional overlaps of p-value clusters, and volcano-plots of regional counts where generated. ITK-Snap  (Yushkevich et al. , 2006)  was used to manually annotate the EWcp nucleus, based on a scan of iDISCO+-labeled brain with CART aligned onto the 25mm template. Data analysis: TrailMap and TubeMap axon segmentation Data for axon mapping were acquired the same way as for Fos mapping, and stitched with Wobbly Stitcher  (Kirst et al. , 2020). Stitched raw images were segmented with TrailMap  (Friedmann et al.
Automated nesting segmentation To automatically segment interaction with nestlets in mice, we designed, developed, and built an open field system inspired by the Live Mouse Tracker (LMT)  (Chaumont et al. , 2019). Briefly, using a structured light-based RGB-D camera (Kinect V2, Microsoft) placed at 45 cm from the cages bedding and the PyKinect2 Python library (https://github. com/Kinect/PyKinect2), we recorded both color (RGB) and registered (8bit) depth videos at $15 fps each. The videos were then processed offline using the OpenCV Python library (https://opencv. org) to segment nest-building behavior automatically. The code for behavioral segmentation is available here: https://github. com/Tom-top/TopMouseTracker. To extract bouts of nest-building behavior, we first transformed the RGB image's color space to HSV (easing the segmentation).
Elevated plus maze For the elevated plus maze experiments, animals were first habituated to the behavioral room for at least 15 minutes. The mice were placed in the center of the plus maze of dimensions 76 cm x 76 cm, raised 60cm above the ground. Behavioral recordings were performed for 10 minutes and automatically analyzed using the CleverSys software (http://cleversysinc. com/CleverSysInc/). https:// www. any-maze. com Chemogenetics Mice were habituated to the open field video tracking system for 24h before the experiment. The dark phase preceding the recording, nestlets were removed from the cage. At the onset of the light phase, CART-Cre mice expressing hM4D(Gi) were injected intraperitoneally (i. p. ) between E15-16 with either saline (0. 9% NaCl) or CNO (1 mg/kg) (Enzo Life Sciences, Inc.
48 N. A; Doric Lenses) by a quick release interconnect (ADAL3; Thorlabs). The fluorescence emission was then demodulated using lock-in amplifier detection. Signals were collected at 240 Hz using the Doric neuroscience studio software (Doric Lenses). Analysis was performed using a custom Python script inspired by a previously published method  (Vesuna et al. , 2020). Briefly, both the 410 and 465 nm channels were first smoothed using a convolution of a scaled window with the signals (width $400 ms). The smoothed 410 nm signal was regressed using non negative robust linear regression (least absolute shrinkage and selection operator (LASSO) algorithm) onto the 465 nm signal, and the regression coefficients were used to scale the 410 nm signal to the 465 nm one. The scaled 410 nm signal was then subtracted from the 465 nm signal.
3 AAVs expressing different gRNAs were obtained for each gene. Catalogue numbers are AA01-MCP001598-AD01-3-200 a, b and c (UCN1, both variants GenBank:NM_021290. 2 & GenBank:NM_001346010. 1), 217AA01-CCPCTR01-AD01-100 (scrambled), 217AA01-MCP291368-AD01-3-200 a, b and c (CCK, GenBank:NM_031161. 4) and 217AA01-MCP299483-AD01-3-200 (Cartpt, GenBank:NM_013732. 7). AAV1-flex-taCasp3-TEVp (Yang et al. , 2013) DNA was obtained from Addgene (ref. 45580) and produced at the Paris Brain Institute viral core facility. pAAV-EF1a-DIO-ChrimsonR-mRuby2-KV2. 1-WPRE-SV40 (Chettih and Harvey, 2019) AAV9 particules were obtained from Addgene (ref 124603-AAV9). pAAV-Ef1a-DIO-EYFP AAV2 particules were obtained from Addgene (ref 27056-.
ropean  Research Council ERC-Stg NeuroRemod 758817, Paris Emergence(s)  award,  Agence Nationale de la Recherche ANR PRC BaVar  awarded to N. R. , the  Agence Nationale de la Recherche Investissement  d'avenir  ANR-10-IAIHU-06 , and  ICM Foundation core funding. N. R. and P. G. are INSERM researchers. T. T. is the recipient of a Sorbonne UniversiteEd3C and a  Fondation pour la Recherche Medicale fellowships.