dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments: We are grateful to the staff of the  Installation Expérimentale de Nutrition (INRAE of Saint Genès Champanelle-Theix)  for providing everyday care to the animals;  Elodie Pitois  for her expert technical assistance to the study. We thank  Claire Naylies  and  Yannick Lippi  for their contribution to microarray fingerprints acquisition and microarray data analysis carried out at  GeT-TRiX Genopole Toulouse Midi-Pyrénées facility (Toxalim (Research Centre in Food Toxicology ),  Université de Toulouse ,  INRA, ENVT, INP-Purpan, UPS, Toulouse, France ). The authors would also like to thank  Shauna O'donovan  and  Sander Kersten  from the  Division of Human Nutrition and Health (Nutrition, Metabolism and Genomics Group) of the Wageningen University (The Netherlands)  for their advices in the use of Cibersort.
All sections were processed in parallel. Staining was visualized using DAPI (Santa Cruz Biotechnology, Heidelberg, Germany). Images were obtained with a 20× microscope objective and the software NIS-Elements AR3-2 (Nikon Eclipse 400, Nikon Corporation, Champigny-sur-Marne, France). The number of Iba1-and Clec7a-positive cells in the hippocampus was counted using Image J software (Image J, open source).
Data Analysis Hierarchical cluster analysis was performed using R free software (79), version 4. 0. 3. Forty variables were used (Table  2 ). Then, unsupervised hierarchical analysis was performed with hclust function (80) using Ward's linkage method  (81). The resulting cluster dendrogram was then generated with the plot function. Correlation matrices were calculated and drawn in R with heatmap. plus, gplots, psy, RcolorBrewer, corrplot, ggplot2, Hmisc and ggcorrplot packages (cran. r-project. org). All aforementioned packages can be found on the CRAN repository (https://cran. rproject. org/). Statistical analyses were conducted with GraphPad Prism 7 (GraphPadSotfware, San Diego, USA). Graphs are represented as mean ± standard error of the mean (SEM). A 2-way ANOVA with repeated measures was used to analyze body weight (factors: diet and time).
FRAP Assay The antioxidant capacity of the WMF crude extracts was measured by means of a FRAP assay that evaluates the reduction of Fe 3+ -TPTZ to Fe 2+ -TPTZ, a blue-colored compound. A minor modified described method was applied  [27]. Briefly, each sample (25 µL) was added to FRAP reagent (175 µL) and further incubated for 30 min in the dark. The absorbance was assessed at 593 nm and the data were calculated as Trolox equivalents (TE) per gram of dw plant.
However, the other two antioxidant assays, FRAP and TEAC, showed the best values at longer extraction times and a higher percentage of water in acetone, at 50 min and 60%, respectively. The magnitude and direction of influence for each factor are illustrated in Figure  6. Once more, it can be noted that the variables with the highest impact vary between the responses. For DPPH and FRAP, the highest influence was detected for the solvent (acetone) followed by the percentage of water in acetone, whereas the order for the most influential factors is reversed for TEAC. The non-linear effects of the examined factors on the antioxidant activities can be very clearly observed in the curvature modeling (Figure  7 ). After the investigation of all the evaluated responses and how each factor influenced the extraction yield for the evaluated bioactive compounds and the antioxidant activity, the optimal extraction conditions for each evaluated response were generated using Modde software.
After the investigation of all the evaluated responses and how each factor influenced the extraction yield for the evaluated bioactive compounds and the antioxidant activity, the optimal extraction conditions for each evaluated response were generated using Modde software. Acetone was the solvent with the highest results. The best working conditions with the maximum extraction power for the bioactive compounds and for the DPPH assay are a 30-min extraction time and 40% water in acetone, whereas for FRAP and TEAC analyses, the optimum extraction time and solvent mixture was 50 min and 60% water in acetone, respectively (Table  5 ). TPC-total phenolic content (mg GAE/g); TFC-total flavonoid content (mg QE/g); CTC-condensed tannin content (mg CE/g); DPPH-antioxidant activity (mg TE/g); FRAP-antioxidant activity (mg TE/g); TEAC-antioxidant activity (mg TE/g).
The results obtained are comparable with the current data on Juglans sp. , with the reported IC50 values ranging from 20 to more than 1500 µg/mL, depending on the extraction method and solvent, as well as on the vegetal parts used for the extraction. The opportunity of natural products derived from Juglans regia in the fight against cancer was recently thoroughly reviewed by Catanzaro et al. [41]. A difference in susceptibility was also observed between the cancerous cell lines used, with the A549 cells being the most susceptible to the cytotoxic effects, whereas MCF-7 displayed a higher resilience (Table  8 ). In line with previous research from our group in which the anticancerous effects of a septum extract were evaluated, and also in line with the literature data, the MCF-7 cell line was the most resilient cell type to the cytotoxic effects of Juglans sp.
All fluorescence measurements were performed using ImageJ software. Images obtained from experiments in fixed cells were captured on a Leica SP8 laser scanning confocal microscope. To quantify the spine/shaft ratio of PLCPH-GFP fluorescence intensity in fixed neurons, line profiles were traced along the dendritic spine heads, the plasma membrane of the dendritic shaft, and the cytosol of the dendritic shaft, and the mean fluorescence intensity of PLCPH-GFP from the spine and respective dendritic shaft was determined by using the plot profile tool from ImageJ software. An average of 5 to 10 spine/shaft ratios were used per neuron.
Quantitative imaging analysis Imaging was performed on a Zeiss Axio Observer Z1 microscope using a Plan Apochromat 63×/1. 4 numerical aperture (NA) oil objective and an AxioCam HRm charge-coupled device camera. Images were quantified using image analysis software ImageJ. For quantification of total fluorescence intensity of GluA1 cell surface puncta and GluA1 synaptic clusters (VGluT1/PSD95-colocalized or VGluT1-colocalized), sets of cells were cultured and stained simultaneously and imaged using identical settings. The ROI was randomly selected avoiding primary dendrites, and dendritic length was measured using MAP2 staining. Measurements were performed in two to five independent preparations, and at least seven cells per condition were analyzed for each preparation. Quantitative imaging analysis was performed as previously described  (66).
The arena and the objects were carefully cleaned before running each animal and in between phases. The test was conducted at a room temperature of 23°C and 15 lux at the center of the arena (homogenously distributed light). Videos of the test were acquired using Noldus EthoVision software, and scoring was performed blinded to the treatment of the animals, using Noldus Observer. The elevated plus maze was performed according to  (71) , using a maze made in-house according to previously described specifications  (72). Animals were weighted and injected with the corresponding dose of inverse agonist or vehicle 10 min before starting the test and stayed in an empty transport cage. The test started by putting the animals in the central part of the maze with the nose aligned with the closed arms and run for 10 min.
Acknowledgments: We thank  AstraZeneca  for sharing AZ12861903 through the  AstraZeneca Open Innovation program. We thank  H. Wise  ( The Chinese University of Hong Kong ) and  H. Kessels  ( Netherlands Institute for Neuroscience ) for the GFP-tagged  GHS-R1a  and  SEP-GluA1  constructs, respectively. We thank  A. Irving  ( University College Dublin ) for the N terminus of GluA1 antibody and  T. Balla  for the PLCPH-GFP construct (Addgene plasmid # 51407 ). Funding: J. P. is supported by the  FCT IF Programme  ( IF/00812/2012 ). This work was further supported by a  NARSAD   Independent Investigator Grant  from the  Brain and Behavior Research Foundation , by national funds through the  Portuguese Science and Technology Foundation  (FCT;  POCI-01-0145-FEDER-007440 ,  POCI-01-0145-FEDER-PTDC/SAU-NMC/4888/2014 ,  POCI-01-0145-FEDER-28541 ,  POCI-01-0145-FEDER-022122 , and  UIDB/04539/2020 ), and by the  European Regional Development Fund (ERDF) , through the  Centro 2020 Regional Operational Programme  under project  CENTRO-01-0145-FEDER-000008 :BrainHealth 2020.
When weaned (postnatal day 29), piglets were moved to individual pens in a common barn. Piglets from a given sow were separated from the offspring of the other sows. From this point forward, DR piglets received 40% less feed than the amount consumed by those born to FF sows, an amount we calculated would result in a ∼50% reduction in growth rate  (19). Based on the typical growth phenotype of swine, where poorest performing animals grow at ∼80% of the rate of normal  (20) , we reasoned that a 50% rate reduction would represent moderately severe malnutrition. To achieve this level of consistent diet restriction, during the first 10 postnatal weeks, the amounts of nursery 1 and a second nursery diet (nursery 2) given to the DR group were adjusted weekly based on the amount of diet consumed by the FF cohort, while during postnatal weeks 11 to 22 the amounts of "grower" and "finisher" feed given were based on biweekly measurements of feed consumption by the FF group (see Dataset S1 for diet composition and nutritional analysis as well as the amount of each diet consumed by pigs in each treatment group, and Dataset S2 for their weights).
Neither the maternal diet nor the diets fed to their offspring contained antibiotics. Piglets born to the two DR sows had significantly lower birth and weaning weights compared to FF sow-derived piglets (P < 0. 001 and P < 0. 01, respectively; Mann-Whitney U test) (Fig. 1  B and C and Dataset S2). This result is consistent with the fetal growth impairments of DR reported previously  (21). There were statistically significant differences in weight gain between DR and FF animals throughout the 18-wk period when the nursery, grower, and finisher diets were consumed (P < 0. 001; linear mixed-effects model; treatment and time set as fixed effects and animals as random effect) (Fig. 1D ). By postnatal day 77, the targeted 45 to 50% difference in weight between the two groups had been achieved and was maintained for the remainder of the study (i.
e. , through postnatal day 154) (Fig. 1  D and E and Dataset S2). Pigs were raised under conditions designed to minimize exposure to enteropathogens; none of the animals in any of the treatment groups exhibited evidence of diarrheal disease. The impaired weight gain of DR animals was accompanied by statistically significant lower plasma IGF1 levels at the end of each of the five dietary phases compared to FF animals (Fig. 1F ). Distinguishing Microbiome Features of FF and DR Pigs. Corn and soy, the two principal ingredients in finisher diets, are rich in polysaccharides. The ability of the microbiome to ferment these polysaccharides is a determinant of feed conversion efficiency  (22) ; butyrate generated from fermentation is a major contributor to the energy intake of pigs  (23, 24). Gas chromatography-mass spectrometric (GC-MS) quantification of short-chain fatty acids (acetate, propionate, butyrate) in fecal samples disclosed no differences in their levels between the two groups at the end of nursery phase 2 treatment (postnatal day 70), but statistically significantly lower butyrate levels in DR pigs at the end of the finisher diet phase (postnatal day 154; P < 0.
05; Mann-Whitney U test) (Fig. 2 A  and B ). This finding raised two questions: What features distinguish the configurations of the gut microbiomes of DR and FF pigs, specifically those components involved in the catabolism of polysaccharides? What are the metabolic adaptations of the host to diet restriction and to what extent are they causally related to the gut microbiome? Microbial community datasets typically contain disproportionately high numbers of features (species/strains, genes, and so forth) relative to the number of biospecimens being characterized. Moreover, these features can be redundant, noisy, or not relevant to the biological question being considered. Consequently, feature selection algorithms are often employed to identify highly informative subsets of characteristics that can be used for analyses or to generate testable hypotheses for follow-up experiments.
Algorithms, such as Random Forests and indicator species analysis, have been used to select microbial community features that differentiate treatment groups or time points; they are generally not suitable when the microbiome configurations of different groups or across time points are highly similar (SI Appendix, Fig. S1 ). We developed a flexible feature selection algorithm (EMMER) that can accommodate a range of microbiome-related datasets, including those that consist of a single treatment group, or multiple groups with highly similar characteristics, or multiple groups with highly dissimilar characteristics. EMMER quantifies "information content" using von Neumann entropy (vNE), a generalization of Shannon entropy  (17, 18). For each dataset, the algorithm first identifies a set of "information-rich features" whose removal has a significant impact on overall vNE (SI Appendix, Fig.
Fig. 2C  illustrates how EMMER was applied to genes that encode carbohydrate active enzymes (CAZymes) in the gut microbiomes of DR and FF pigs. CAZymes include enzymes in the recognition and metabolism of various complex carbohydrates, including oligosaccharides, polysaccharides, and glycoconjugates  (25). DNA prepared from fecal samples, collected at the beginning and end of each of the dietary phases from animals in each of the two treatment groups, was subjected to shotgun sequencing (n = 181 fecal DNA samples) (Dataset S2). We identified 987 genes among the 15,885,872 genes in the combined DR and FF dataset that had CAZyme assignments and that were present in ≥33% of samples obtained at a given time point in a given treatment group. These CAZymes were subjected to feature selection using EM-MER.
A total of 55 information-rich CAZymes were selected from the DR dataset and 65 in the FF dataset; 22 were present in both datasets, yielding 98 unique CAZyme features (see their annotations in Dataset S2). An analysis of CAZyme-based microbiome configurations revealed a temporal pattern of separation between the FF and DR groups; this separation was statistically significant on postnatal day 49 (7 d after initiation of the nursery 2 diet) and, with the exception of a single measured timepoint (day 77), remained statistically significant (P < 0. 05; PERMANOVA) during subsequent exposure to the grower and finisher diets (Fig. 2C  and Dataset S2). A similar temporal pattern was observed when information-rich bacterial taxa (amplicon sequence variants identified from PCR of 16S rRNA genes) were used to characterize microbiota configuration (SI Appendix, Fig.
S2H  and Dataset S2). The nursery 2, grower and finisher diets are all rich in corn and soy, with the representation of corn increasing with each diet change (Dataset S1). Dry corn kernels contain ∼70% starch, 2% cellulose, and 3% oligosaccharides by weight (26-28) while soybean meal contains 19% polysaccharide, 3% sucrose, 2% stachyose, and 2% raffinose  (29). The representation of an information-rich CAZyme gene identified by EMMER that encodes a predicted endo-β-1,4glucanase belonging to a glycoside hydrolase (GH) family involved in cellulose degradation (GH8), exhibits a distinct temporal pattern in its abundance, rising from being undetectable during consumption of the grower phase diet (postnatal days 77 to 118), to prominence during the finisher phase (postnatal days 132 to 154) in both DR and FF microbiomes (Fig.
higher plasma triglyceride levels (P < 0. 05; Mann-Whitney U test) (Fig. 4A ); this difference was not accompanied by differences in levels of ketones, nonesterified fatty acids, 3-hydroxybutyrate, or lactate compared to their FF counterparts (P > 0. 05; Mann-Whitney U test) (Dataset S2). [Note that pigs are a hypo-ketonemic species with diminished capacity to generate ketones from fatty acids  (30). ] Undernourished children exhibit reductions in fatty acid oxidation when fasting  (31). Reduced fatty acid metabolism prompts use of amino acids for gluconeogenesis and to generate energy via their catabolism to TCA cycle intermediates; replenishment of these amino acids in the nonfasted state comes at the expense of protein synthesis. Interestingly, the glucogenic amino acids alanine and glycine, which accounted for >40% of free amino acids in plasma at postnatal day 154, were significantly elevated in nonfasting DR compared to FF animals (Fig.
4  B and C and Dataset S2). This increase in glucogenic amino acids was accompanied by significant increases in the urea cycle metabolites citrulline, ornithine, and arginine (Fig. 4  D-F and Dataset S2). Significantly higher levels of alanine, citrulline, ornithine, and arginine in DR pigs were first evident at the end of nursery 2 diet phase (postnatal day 70) (Fig. 4  B-F and Dataset S2), coinciding with the time when we observed a marked difference in weight gain (Fig. 1 D  and E ) and significant differences in microbiome CAZyme configuration (Fig. 2C ). These findings suggest that nonfasting DR pigs have a higher rate of catabolism of amino acids in their livers to generate glucose and cellular energy, with increased urea cycle activity to convert the resulting byproduct, ammonia, to urea  (32).
Feeding practices (FF versus DR) and microbial community composition are two factors that need to be considered when interpreting the results of our pig experiment. For example, previous studies showed that reductions in food intake can result in higher digestibility of crude fibers  (33). To disentangle the effects of diet restriction and microbiome configuration, we conducted functional assays of DR and FF microbiomes, sampled from pigs on postnatal day 154, using gnotobiotic mice fed the pig finisher diet. [Postnatal day 154 fecal samples were used rather than those from earlier time points when discordance in the abundances of information rich CAZyme first became evident because differences in butyrate levels were not evident until the later time point (Fig. 2  A and B and Dataset S2).
] In the first experiment, the diet was provided ad libitum while in the second recipients of both types of donor microbiome were given the same amount of diet each day. In the first experiment, just weaned 4. 5-wk-old germ-free male C57BL/6J mice were placed on pig finisher diet that was administered ad libitum for the duration of the experiment. Two days later, animals were colonized with intact uncultured fecal communities obtained from a representative 154-d-old DR or FF pig (n = 6 mice/pig donor microbiome; all recipients of a given pig microbiome were singly housed in cages placed in the same gnotobiotic isolator) (Fig. 5A ). Microbial community donors (pig 18 from the DR group and pig 11 from the FF group in Dataset S3) were selected based on two criteria: 1) the information-rich CAZyme gene content of their microbiomes, and 2) two metabolic parameters-fecal butyrate and plasma triglyceride levels (Dataset S2).
On experimental day 21, mice were killed after a 4-h fast and their cecal contents and feces were collected. Of the 18 informationrich CAZyme genes we had identified in postnatal day 154 pigs (Dataset S2),  16   (Dataset S4). Epididymal fat weights were not significantly different between mice harboring the FF compared to DR microbiome (0. 7 ± 0. 1 mg/g [mean ± SD] body weight versus 0. 6 ± 0. 1 mg/g [DR], respectively; P = 0. 2, Mann-Whitney U test). However, GC-MS analysis of cecal contents and fecal samples revealed that butyrate levels were significantly higher in those with the FF donor microbiome (P < 0. 005, Mann-Whitney U test) (Fig. 5  B and C and Dataset S4). Follow-up targeted liquid chromatography-triple quadrupole mass spectrometry (LC-QQQ-MS) of cecal contents also disclosed significantly lower levels of glucose in ad libitum fed mice containing the transplanted FF pig microbiome (P < 0.
05, Mann-Whitney U test) (Fig. 5D  and Dataset S4). To isolate the impact of microbiome composition on host physiology, we performed a follow-up, controlled feeding experiment where each mouse in each treatment group was given the same amount of the finisher diet per day (Fig. 5E ). Two days after introduction of the same DR and FF microbiomes used for the ad libitum feeding experiment, 4. 5-wk-old germ-free C57BL/6J male recipients (n = 6 per group) were singly housed. Each mouse initially received 3 g of the finisher diet per day for 7 d, followed by 3. 3 g/d for 10 additional days. To investigate the effect of microbiome composition on host fatty acid utilization and energy metabolism, we killed mice in both groups on experiment day 21 after a 4-h fast and collected their cecal contents, liver, gastrocnemius muscle, and serum.
Body weights, measured every 2 d, were not significantly different between the two groups at any time point during the 17-d controlled feeding period (weight gain, 4. 8 ± 5. 6% [mean ± SD; FF] and 7. 8 ± 2. 7% [DR]; normalized to the first time point where weights were measured after initiation of controlled feeding [experimental day 7]; P > 0. 05; linear mixed-effects model [donor microbiome and time set as fixed effects and animals as random effects]) (Dataset S5). As with the ad libitum feeding experiment, we found the same 16 postnatal day 154 informationrich CAZyme genes represented in the cecal communities of recipient mice (Dataset S5). [Based on assignments of taxonomy to the 16S rDNA dataset (34), 77% and 90% of species represented in the DR and FF communities were transferred to recipient mice, respectively].
Gnotobiotic mice colonized with the DR and FF microbiomes and fed a controlled amount of pig finisher diet recapitulated findings from the first pig-to-mouse microbial community transplant experiment, namely: 1) animals containing the DR pig microbiome exhibited significant reductions in cecal and fecal butyrate (P < 0. 05 and P < 0. 01, respectively; Mann-Whitney U test) (Fig. 5  F and G); and 2) cecal glucose levels were higher in DR mice, although the increase did not achieve statistical significance (P = 0. 065; Mann-Whitney U test) (Fig. 5H  and Dataset S5). A B C D E F G H I J K L M N To extend these findings, we examined the functional metabolic potential of the transplanted fecal microbiomes using the microbial community SEED (mcSEED) database  (35, 36). In silico mcSEED-based metabolic reconstructions combine homologyand genome context-based evidence with known sets of enzymatic reactions and nutrient transporters in order to group genes into "microbial community subsystems" that capture and project variations in particular metabolic pathways/modules across thousands of microbial genomes  (35, 36).
We assigned microbiome-encoded proteins to a collection of 104 mcSEED subsystems that encompass the metabolism of 19 amino acids, 10 vitamins, 40 sugars, and 6 fermentation products projected over 2,600 annotated reference bacterial genomes using procedures described in ref. 5. The results revealed the subsystems/pathways annotated as "sucrose, fructose, and fructooligosaccharides" and "glycolysis" had a significantly lower representation in the transplanted DR compared to FF microbiomes (P < 0. 05; Mann-Whitney U test) (Dataset S5). The lower abundances of genes corresponding to the glycolysis pathway is consistent with the higher glucose levels that we had documented in the cecums of mice harboring the DR community. The "butyrate fermentation" pathway was also less prominent in the microbiomes of mice colonized with the pig DR community compared those harboring the pig FF community.