dataesr/scientific-paragraphs-categorization huggingface dataset:
Bacterial Diversity The differences in the bacterial community evenness and diversity, according to the farm of origin and to the lactic starter dose, were evaluated. The bacterial community in the milk and in the cheese cores differed in richness depending on the farm they originated from, as indicated by the Observed, Chao1, ACE and Fisher indexes (Table  S4 ). Milk from F_15 showed a higher richness than that from the F_38 but, during the ripening, for all lactic starter doses combined, the difference was reversed, with the richness index higher with the F_38 than with the F_15. Considering the data of the two farms combined, the most significant differences in the bacterial community richness and evenness were linked to the lactic starter dose, especially in the cheese cores (p-value ≤ 0.
Acknowledgments: The authors thank  René Lavigne  ( INRAE UMR 0545 Fromage ) for the making of the cheese,  Romain Pigearias  for his technical help in DNA extractions,  Beatrice Desserre  ( INRAE UMR 0545 Fromage ) for laboratory management,  Isabelle Constant  ( INRAE UMR1213 Herbivores ) for her assistance in the rheological measures and  Céline Gres  ( INRAE UMR 0545 Fromage ) for administrative support. They also thank  Bruno Martin  ( INRAE UMR1213  Herbivores),  Hélène Falentin  ( INRAE UMR 1253 Science et Technologie du Lait et de l'OEuf ) and  Anne Saint-Eve  ( AgroParisTech UMR 782 Génie et microbiologie des procédés alimentaires ) for their helpful feedback on the experiment design,  Patricia Mabrut  for her very helpful English revisions and thank  Alain Kondjoyan  ( INRAE UR0370 Qualité des Produits Animaux ) for the management of the project  CPER Qualimont.
All patients have given their written informed consent. T h i s t r i a l w a s r e g i s t e re d a t C l i n i c a l T ri a l s. g o v (NCT01956552). The ESOPE study was approved by the Institut Curie review board and ethics committee (reference number: 2010-A00781-56). Conventional biopsy procedures, pathological analyses, tissue preparation process, and DNA extractions are detailed in the supplementary methods (Additional file 1). All pathological, immunohistochemical, and genomic analyses were centrally performed at Institut Curie.
Identification of somatic mutation by targeted and whole-exome sequencing After DNA extraction, 82 paired PT/M FFPE samples were available for sequencing analysis. Targeted sequencing was applied to a custom-made panel of 91 "breast cancer-specific" genes selected for their involvement in breast cancer (Additional file 2: Table  S1 ). This "Breast-Curie" panel included the most frequently mutated genes (mutation frequency more than 1%) in breast cancer from TCGA and genes with potential therapeutic targeted mutation (Additional file 1)  [16]. Briefly, after preparation of DNA libraries, targeted NGS was performed using Illumina Hiseq2500 technology according to the manufacturer's instructions (Illumina, San Diego, CA, USA). Sequence data were aligned to the human reference genome (hg19) and we executed variant calling to detect SNV and indels.
We retained variants observed at a frequency lower than 0. 1% in population (dbSNP or 1000Genome database) and with an allele frequency superior to 5% and covered by 100 reads minimum. Potential pathogenicity of identified variant and druggable genes were selected using multiple public databases. Specifically, ESR1 pathogenic mutations were confirmed by digital droplet PCR (Additional file 1). Next, wholeexome sequencing was performed on 30 pairs of primary tumors and metastases, and their match germline DNAs. Libraries were prepared using SureSelect Human Clinical Research Exome Regions (Agilent) and sequenced on an Illumina HiSeq2000 genome analyzer, generating 2 × 100 bp paired-end reads. For three of the de novo patients (#1, #2, and #11), we downloaded Fastq file from Sequence Read Archive (SRA, SRP055001)  [17].
Mutational signature analysis The mutational signatures of variants classified as either shared or private were generated separately. Both silent and non-silent SNVs were used as input to Deconstructsigs  [18]. This R package compares the profiles of 96 trinucleotides mutations count to the 30 signatures found in the COSMIC classification in order to determine the implication of known mutational processes on each tumor sample  [19]. We then merged signatures with the same biological meaning to enhance their weight classification.
Univariate Cox proportional hazard models was performed and hazard ratios and their associated 95% confidence intervals were calculated. Variables with a p value for the likelihood ratio test no greater than 0. 05 in univariate analysis were included in the multivariate model. Backward selection was used to establish the final multivariate models. All analyses were done using data obtained up to November 2018. These analyses were done with R 2. 13. 2 software. Regarding molecular analysis, statistics were performed using GraphPad Prism (version 7. 01) software. Comparison between PT and M mutations was performed using two-sided Fisher's exact test paired or unpaired as appropriate. Two-way ANOVA were performed to compare mutational profiles according to several subgroups as tumor molecular subgroup, met sites, met onset time, or phenotypical change.
Funding This work was supported by the 2009 " Programme Hospitalier de Recherche Clinique " (Grant # 02-067 ). High-throughput sequencing has been performed by the  ICGex NGS platform of the Institut Curie  supported by the grants  ANR-10-EQPX-03  ( Equipex ) and  ANR-10-INBS-09-08  ( France Génomique Consortium ) from the  Agence Nationale de la Recherche  (" Investissements d'Avenir" program ), by the  Canceropole Ile-de-France  and by the  SiRIC-Curie program -SiRIC Grant  " INCa-DGOS-4654. "FB, ZT, and RL are supported by a grant from the  Breast Cancer Research Foundation (BCRF, USA)  [grant number  BCRF-19-097 ].
Open conduit volcanoes are characterized by frequent, small scale explosive eruptions, which have a significant impact. Ash-forming explosions are impacting over larger areas with respect to effusive or poorly explosive events and, consequently, are more significant for hazard assessments. Quantifying the hazard associated with them requires understanding the processes and parameters controlling explosive style, and tephra dispersal and obtaining a comprehensive dataset to constrain syn-eruptive dynamics and particle transport in the volcanic plume. We present a study focused on Etna volcano (Italy), which, despite its continuous outgassing through the summit vents, has very frequent explosive eruptions dispersing ash along the southern Mediterranean area. The goal of this study is to obtain a statistically valid dataset on ash morphology and texture and investigate how various particle types distribute spatially in the tephra blanket.
, 2020) , such as column height, the size distribution and morphological properties of tephra, which are key parameters in tephra transport modelling, in parallel with meteorological conditions  (Mastin et al. , 2009; De Micheli Vitturi et al. , 2016). To achieve these two goals comprehensive studies coupling eruption monitoring, tephra and petrological studies are required. Because of its rapid quenching, ash records the characteristics of magma at syneruptive times, but it is also an important tool for reconstructing the processes of conduit ascent and explosive fragmentation, uncovered through morphological and textural analysis. Ash studies, usually performed over thousands of particles, have a pivotal role in the understanding of the mechanisms controlling explosive eruptions, because they are able to provide statistically representative datasets that can be related to magma properties and eruption dynamics.
, 2019). This strongly limits our ability to: i) interpret data in terms of fragmentation mechanisms, ii) compare the data from various eruptions, and iii) fully understand the actual impact of ash dispersal. For example, in contrast with grainsize or textural studies  (Gurioli et al. , 2015) , the community lacks a shared protocol for ash sampling for morphology studies as to obtain a uniform dataset with significant potential to give fundamental insights into ash formation processes. To address these issues, we investigated the particle shape and componentry relationship of ash erupted during a well monitored and studied eruption, the 21 May 2016 Etna paroxysm. For the first time, we coupled morphology, texture and componentry data on a statistically relevant number of particles collected along the dispersal axis, at variable distance from the vent.
The textural componentry, which distinguishes sideromelane, tachylite, crystal and lithic particles, and the shape componentry, which distinguishes globular, elongated, blocky and irregular particles, were performed on all the SEM images (more than 500 particles counted per sampling location). Dual textural and shape componentry (i. e. , both a textural and shape category was assigned to each particle) was performed using a subset of SEM images (about 100 particles per sampling location). Single particle vesicularity was assessed by image analysis using the software Fiji  (Schindelin et al. , 2012) , based on SEM binary images; it was measured on a total of 73 particles, 45 of 710-1,000 µm size and 27 of the 125-180 µm size.
The 2D projected area and shape of the particlesthe Apparent Projected Shape of Ash (APASH)were measured by the G3 microscope at 2. 5x magnification. Morphological parameters such as size and 2D shape parameters were automatically analysed from APASH measurements. Raw data were filtered from the G3 output to remove particle clusters from the final datasets. Shape analysis of SEM images was performed with Fiji and using the shape macro from  Liu et al. (2015b). Each image was thresholded to binary and particles smaller than 1,000 px 2 excluded remove fragments produced by the thin section preparation. An image of numbered particle outlines was then created for later referencing of particles. As the crop resulted in a square canvas, adjacent particles were often included on the edges. These were manually removed.
Clustering of particles was negligible due to effective grain separation. However, where this occurred such particle clusters were excluded from the dataset. Holes within particles were filled in order to calculate particle area. An equivalent image set was created for the convex area of each particle and used to calculate particle convexity. Particle measurements (area, perimeter, Feret diametermaximum and minimum, convex hull area, and convex hull perimeter) were measured directly in Fiji using the appropriate image set. These primary measurements were used to calculate CI, AR and FF. On all the SEM images, textural componentry was associated with the particle morphology data via the outlined reference images. Both textural and shape componentry were associated with the particle morphology data on a subset of SEM images.
The wind data are extracted from weather forecasts (ARPA-SIM, the HydroMeteorological Service of the Emilia-Romagna Regional Agency for Environmental Protection in Italy) at the location of Mount Etna on 21 May 2016, while the other parameters are calculated following the International Standard Atmosphere model: T T 0 -Lh (6) P P 0 T T 0 5. 256 (7) ρ a P RT (8) μ a 1. 458 × 10 -6 T 3/2 T + 110. 4 (9) where T 0 and P 0 are the temperature and pressure at sea level (288. 2 K and 101. 29 × 10 3 Pa, respectively), L is the lapse rate for the troposphere (6. 5 K/km), h is the altitude, and R is the gas constant for air (287 m 2 s -2 K -1 ). Re, Vt and Cd are inter-dependent and are thus calculated iteratively in each atmospheric layer. Given that Vt depends also on the air properties, it varies with the altitude.
Bulk Tephra Morphological properties of the bulk 125-180 μm and 710-1,000 μm fractions assessed by Morphologi G3, and of the bulk 125-180 μm fraction assessed by SEM, are compared in Figure  5 ; Table  2. The Morphologi G3 datasets show variations in CI and FF with grainsize (increase in CI and decrease in FF with the particles coarsening), suggesting a smoothening of the particle contours with decreasing grainsize, potentially due to the breakage of the grains below the size of the largest vesicle population found in these tephra. AR values increase only slightly with the grainsize, suggesting a faint trend of elongation of the particles towards finer grainsizes. CI, FF and AR assessed with the Morphologi G3 dataset show little changes with distance between 6 and 27 km from vent in both the size fractions investigated, highlighting the inability of the bulk tephra population to depict the shape-dependant fractionation processes occurring during transport in the plume.
This is confirmed by the SEM dataset in the 125-180 µm fraction, which also shows little change with distance for the three morphological parameters computed here. The Morphologi G3 and the SEM datasets in the 125-180 µm fraction show striking differences, with SEM data consistently displaying rougher (higher CI and lower FF values) and more elongated forms (lower AR and FF values), and a larger variability, than G3 data. Such differences, also highlighted in a previous work  (Buckland et al. , 2018) , are related to the smoothing effect of imaging projected shapes with Morphologi G3 vs. the roughening effect of imaging cross sections with the SEM. The ranges of AR and FF values measured here by Morphologi G3 are in good agreement with data acquired on other Etna ash (2002 eruption) by previous work using SEM imaging of grain silhouette (i.
Textural Components The shape parameters of each textural componentry group were calculated using the 125-180 µm SEM dataset (Figures  7, 8 , Table  2 ). Due to the paucity of lithic particles, shape parameters were not assessed for this component, while crystals were separated into mafic crystals (olivine and pyroxene) and plagioclases. In the AR vs. CI and FF vs. CI spaces (Figure  7 ), the different componentry groups show distinctive characteristics despite some overlapping. Sideromelane particles are characterised by the largest variability of the three parameters. They show the lowest and highest CI values (Figure  7 ), which correspond to particles with very smooth (CI close to 0) and highly rough contours (CI > 0. 7), respectively. The former are most certainly particles from the globular category, which is mostly composed of sideromelane (Figure  4 ) and have the lowest CI values of all the morphological components (Figure  6A ).
To conclude, while sideromelane grains are highly variable in terms of roughness and form, tachylite particles are more homogeneous and display equant regular shapes. Yet, sideromelane grains are relatively homogeneous texturally, while tachylite particles have highly heterogeneous textures (Figure  2  and Shape Componentry). Both mafic crystals and plagioclases show a narrow range of low CI values (Figure  8 ), indicating relatively smooth contours, and a wider range of AR values (Figure  8 ), suggesting a variety of crystal shapes from equant to elongated, in agreement with the habits of these crystals. FF for all the components mirror CI, as demonstrated by the negative correlation appearing in the FF vs. CI space where high FF values correspond to low CI values (Figure  7 ). This confirms that FF is mostly controlled by roughness in our dataset.
4b ), but less so in COL4A3 -/-mice on low Pi diet (Fig. 4b ). To identify if a low Pi diet affects inflammation or the acute phase response in Alport mice, we assessed gene expression of inflammatory cytokines and acute phase proteins. Compared to wild-type mice on normal diet, liver Il1b, Il6 and Saa1 transcript levels were significantly elevated in COL4A3 -/-mice but much less elevated on 0. 2% Pi (Fig. 4c, d ). These data support the notion that excess Pi in Alport mice aggravates inflammation. As our results show that a low Pi diet decreases inflammation, we next explored its impact on functional iron deficiency. Compared to wild-type mice on normal diet, liver Hamp transcript levels were significantly elevated in COL4A3 -/-mice with complete reversal by low Pi diet (Fig. 4e ). Assessing hematological responses, COL4A3 -/-mice on normal diet were anemic, with significant reductions in RBC count, MCV, hemoglobin and serum iron levels (Fig.
We next analyzed gene expression by qPCR of inflammatory cytokines and acute phase proteins in isolated hepatocytes treated with increasing concentrations of Pi or Na 2 SO 4 with LPS and IL6 treatments used as positive control. Elevations in Il1b, Il6 and Saa1 transcript levels were noted not only following LPS and IL6 but also Pi treatments (Fig. 6b, c ). Treatments with Na 2 SO 4 had no effect on gene expression. As inflammation is a known mediator of hepcidin synthesis, we analyzed Hamp mRNA levels. LPS, IL6 and Pi treatments all elevated Hamp transcript levels when compared to control (Fig. 6d ). These data indicate high extracellular Pi can act on hepatocytes to increase the synthesis of inflammatory cytokines and hepcidin. Given that inflammation and NFB signaling regulate PiT1 expression  (Koumakis et al.
All immunoblots were repeated with a minimum of three independent trials, with comparable results. Statistics. Data organization, scientific graphing and statistical significance of differences between experimental groups were performed by using GraphPad Prism (version 9. 0. 0). All results are expressed as mean ± SEM. Depending on number of experimental groups and factors analyzed, we performed a two-way ANOVA followed by a post-hoc Tukey test (for studies affected by 2 factors) or in the form of a one-way ANOVA (for studies measuring variance in 3 groups or more). Correlation and slope analyses were examined by simple linear regression. Statistical significance was set at a P value of less than or equal to 0. 05. Sample size was determined on the basis of sample availability, prior experimental studies performed in our laboratory and from prior literature.
Acknowledgments BC designed and performed experiments, analyzed data and wrote the manuscript. KH, IC, CY, DK, DW assisted with experiments. GJ measured liver and spleen iron concentrations in Alport mice. OG, JBL, IBS, MH assisted with data analysis and interpretation. CF supervised project, secured funding, assisted with data interpretation and edited manuscript. All authors discussed results, read and contributed edits to manuscript and approved final version. This study was supported by  NIH  grants  F31-DK-117550  (BC),  T90-DE-022736  (KH),  F31-DK-115074  (CY),  K24-DK-116180  (OG),  R01-DK-087727  (JLB),  U01-DK-119950  (IBS),  K08-DK-111980  (MH),  R01-HL-128714  and  R01-HL-145528  (CF); and by grants from the  Deutsche Forschungsgemeinschaft (DK)  and the  National Science Foundation (IC).
The combination of thermodynamics processes simulations with machine learning techniques offers promising results for determining a set of parameters empirically from monitored data  [5, 39, 40]. In  [39] , a thermal forecasting model is defined and used for predicting temperatures surrounding servers in data centers. Continuous streams of temperature and airflow measurements are collected for obtaining online predictions with real-time sensor measurements. In  [40] , a fast converging solution is proposed using both a feed-forward network and a dynamic recurrent artificial neural network. The neural networks learn incrementally, using the incoming stream of data samples. Adaptiveness is presented as an essential feature, as the model can learn the characteristics of a server room with minimal training, and then it may continuously adapt to new data fed without retraining  [41].
Machine Learning-Based Model The machine learning model aims to infer and correlate the heat demand that needs to be generated by the server executing the allocated workload out of monitored data (see Figure  3 ). We considered that the room temperature is collected using heat sensors placed at a certain distance from the server. The heatsink temperature is collected using the server's embedded temperature sensor, whereas the power consumption is obtained using a wattmeter. All data needs to be smoothed so that outliers are eliminated. We aimed to detect areas of interest, defined by a high correlation coefficient between the heatsink and ambient temperature, limited between two local peaks of the temperatures and power. Thus, we used the Pearson Correlation Coefficient, as defined in Equation (  35 ), to measure the linear correlation between two datasets.
Only relevant samples with values larger than a The machine learning model takes as inputs the actual room temperature T Room (0), the initial power consumption P server (0), the desired room temperature, T set-point , and provides as output the heat demand that the server should generate by executing workload, to reach the desired temperature, T Room (∇). All data needs to be smoothed so that outliers are eliminated. We aimed to detect areas of interest, defined by a high correlation coefficient between the heatsink and ambient temperature, limited between two local peaks of the temperatures and power. Thus, we used the Pearson Correlation Coefficient, as defined in Equation  (35) , to measure the linear correlation between two datasets. Only relevant samples with values larger than a threshold were considered for further processing, namely data where the room temperature, server temperature, and power consumed by the server present similar patterns.
A sensors-based monitoring infrastructure was used to acquire relevant data regarding the Ambiental temperature  , power consumption of the QRad ( ) and temperature of the server ( ). The data acquired spans over several months, being recorded at a granularity of 10 s. Both the characteristics of the QRad heaters and monitored data acquired by the installed infrastructure are the inputs of our study. However, the monitored data used to model the thermal behavior of the QRad heaters must be extracted for larger intervals that show suggestive temperature and power changes. Thus, a pipeline of data pre-processing operations was employed to extract the relevant data samples (see Figure  5 ). A sensors-based monitoring infrastructure was used to acquire relevant data regarding the Ambiental temperature T Room , power consumption of the QRad (P server ) and temperature of the server (T server ).
The data acquired spans over several months, being recorded at a granularity of 10 s. Both the characteristics of the QRad heaters and monitored data acquired by the installed infrastructure are the inputs of our study. However, the monitored data used to model the thermal behavior of the QRad heaters must be extracted for larger intervals that show suggestive temperature and power changes. Thus, a pipeline of data pre-processing operations was employed to extract the relevant data samples (see Figure  5 ). The acquired data were smoothed using an exponential weighted moving avera window with the span of 90 data points corresponding to a time window of 15 min mean filter with a window span of 60 data points, corresponding to a time window of The acquired data were smoothed using an exponential weighted moving average window with the span of 90 data points corresponding to a time window of 15 min.