dataesr/scientific-paragraphs-categorization huggingface dataset:
Bumble bee and floral communities A total of 13 293 bumble-bee-flower interactions were included in our final data set, comprising 15 bumble bee species, 163 plant species (110 genera, 37 families) and 730 unique bumble-bee-plant interaction pairs. Most sites hosted 8 or more species per year (Supporting information), suggesting that interspecific competition was not the main driver of community assembly. Bombus pascuorum ('pasc'), B. pratorum ('prat'), B. soroeensis ('soro'), B. terrestris-lucorum ('telu') and B. wurflenii ('wurf ') were the most abundant species, and they occurred at all sites. Bombus hortorum ('hort'), B. jonellus ('jone'), B. monticola ('mont') and B. psithyrus ('psit') occurred at moderate abundance. The remaining species -B. gerstaeckeri ('gers'), B. hypnorum ('hypn'), B.
Acknowledgments -We thank the  National Park Berchtesgaden  for their support and the permission to work in the national park, and we thank the owners of the study sites outside the national park for access to their land. D. J. McNeil  provided for helpful conversations during data analysis. O. Mitesser kindly provided a high-performance computing environment to expedite null modeling. Funding -The field work of this study was performed within the framework of the joint research center  FORKAST as part of the 'Bavarian Climate Programme 2020 ', with funds awarded to ISD. A. Classen ,  A. F. Maihoff  and  I. Steffan-Dewenter  acknowledge further funding from the  Bavarian Climate Research Network  (bayklif ).
Data availability statement All data used in this study have been archived on Dryad digital repository (<https://datadryad. org/stash/dataset/doi:10. 5061/ dryad. 7d7wm37wp>). Related software is published at Zenodo: <https://doi. org/10. 5281/zenodo. 5724496>, and supplemental information is also published at Zenodo: <https://doi. org/10. 5281/zenodo. 5724498>.
Alcat1(usually upregulated in mouse models of NAFLD) and Nrf2 (that orchestrates an antioxidant response) were downregulated in shG8-Fruct mice (Figure  4I ). Both, oxidative stress and ER stress contribute to the process of liver inflammation. In fact, the data obtained indicate that fructose pIRE-induced pathological ER stress switches to an adaptative/recovery ER stress-mediated by ATF6 (Figure  5A ). Interestingly, the examination of the ER stress markers after liver GLUT8 silencing show that, unlike pIRE and CHOP, proteins such as ATF6 and BIP were markedly upregulated (Figures  5B  and 5C ). These results were supported by FunRich comparative analysis indicating the contribution of the main biological processes in Cont-Fruct and shG8-Fruct involved in ER stress (Figure  S10B ). Next, we evaluated the liver inflammatory response by mRNA expression of IL6, EGF-like module-containing mucin-like hormone receptor-like-1 (F4/80), Interleukin 15 receptor, tumor necrosis factor-alpha, and tumor growth factor-beta.
For Sirius Red staining, samples fixated in paraffin were dewaxed, hydrated and stained in PicroSirius staining red (SIGMA, 365548) for an hour. After two washes with acidified water, samples were dehydrated in three changes of 100% ethanol, cleared in xylene and mounted in a resinous medium. Sections were photographed using a BX51 microscope equipped with a DP70 digital camera (Olympus) and analysed using ImageJ software 1. 43r (NIH, Bethesda).
Proteomic studies Protein identification and quantification were performed as described in previously published studies  (Alvarez et al. , 2019; Del Pilar Chantada-Vazquez et al. , 2020; Hermida-Nogueira et al. , 2020)  according to following methods. In the qualitative protein Identification, previously digested peptides of each sample were separated by Reverse Phase Chromatography in a nanoLC400 (Eksigent Technologies, Sciex) coupled to a Triple TOF 6600 mass spectrometer (Sciex) using a microflow source. Datadependent workflow (DDA) method was used for data acquisition. In the SWATH quantitative analysis, first it necessary the spectral library creation created using Pooles samples from each group that was run in a data dependent workflow (DDA). Only peptides with a confidence score above 99% were selected to be included in the spectral library.
The SWATH acquisition method was created with 100 windows, using the Ion information from the spectral library. Then, the individual samples were analysed using a data-independent acquisition (DIA) method. The fragment ion chromatogram traces from the SWATH runs were extracted in PeakView (version 2. 2) software using the SWATH Acquisition MicroApp (version 2. 0) and the integrated peak areas (processed. mrkvw files from PeakView) were directly exported to the MarkerView software (Sciex) for relative quantitative analysis. An unsupervised multivariate statistical analysis (PCA) was performed to compare the data across the samples, using, in this case, a range scale method. The average MS peak area of each protein in each sample was derived from the data of the biological replicates that were analysing by Student's t-test analysis (MarkerView software).
This was made in order to perform a comparison among the samples based on the averaged total area of all the transitions derived for each protein. The t-test indicated us how well each variable distinguishes the two groups and it reported as a p-value. The cut off used to select the proteins were those with ap-value <0. 05 and a 1. 5-fold increase or decrease. Functional analysis was performed by FunRich (Functional Enrichment analysis tool) open access software for functional enrichment and interaction network analysis (http://funrich. org/index. html). RNA extraction, cDNA synthesis and quantitative real time PCR analysis RNA was extracted using RNeasy Mini Spin Columns (QIAGEN, USA) according to the manufacturer's instructions. RNA concentrations of 1 µg were used for cDNA synthesis using the SensiFAST™ cDNA Synthesis Kit (BIOLINE, UK) following product protocol.
Quantitative realtime PCR was performed using the QuantStudio 5 instrument (Applied Biosystems) with TaqMan™ Gene Expression Master Mix (ThermoFisher Scientific) or SYBR green reagent (Precision ®PLUS Master Mix, PRIMER DESIGN). Specificity of the PCR amplification was considered by using negative control reactions, containing all reagents except the sample. The oligonucleotide specific primers are shown in Supplemental Table  S1. Gene expression data were analysed using the ΔΔCt method, calculating the change in Ct between the gene of interest normalised against the HPRT gene and expressed as a fold change relative to the control group.
Western blotting Total protein lysates from the liver, LX2 and THLE2 cells were subjected to SDS-PAGE and electrotransferred on a polyvinylidene fluoride membrane (Millipore). Membranes were probed with the antibodies and specific conditions described in Supplemental Table  S2. Detection of proteins was performed using HRP-conjugated secondary antibodies and an enhanced chemiluminescence reagent (Thermo Scientific). Optical densities of the immunoreactive bands were measured using ImageJ software. Values were expressed relative to β-actin or α-tubulin levels. Data are expressed as a percentage of the control group.
ACKNOWLEDGMENTS We thank  Ana Senra  and  Jose Malago  ´n, at the  University of Santiago de Compostela , for their technical support. We thank  Prof Juan Zalvide  for providing Rab-7 antibody. MGN is recipient of '' Juan de la Cierva-Incorporacion '' fellowship ( IJCI-2017-32606 ) from  Ministerio de Ciencia, Innovacion y Universidades, Spain. This work has been supported by grants from  FEDER/Ministerio de Ciencia, Innovacion y Universidades-Agencia Estatal de Investigacion  (CD:  BFU2017-87721 ; RN:  RTI2018-099413-B-I00 ). Financial support from the  Xunta de Galicia (Centro singular de investigacion de Galicia accreditation   2019-2022-ED431G 2019/02 ) and the  European Union  ( European Regional Development Fund -ERDF)  is gratefully acknowledged. The research leading to these results has also received funding from  Xunta de Galicia   2015-CP080  and  2016-PG057  (RN).
By combining the individual satellite tracks with eight oceanographic variables (physical, surface, and subsurface predictors), our study aims at (a) investigating seasonal movements in the Indian Ocean, (b) predicting the potential distribution, and (c) assessing the diel pattern in sperm whale diving behavior. Since deep divers such as sperm and beaked whales might show a weak dependence on surface oceanographic characteristics  (Mannocci, Laran, et al. , 2014) , we also included relevant subsurface covariates describing the vertical characteristics of the water column, that is, the mixed layer depth and the bottom temperature. These covariates are rarely taken into account in the habitat modelling of cetaceans. By combining machine learning, state-of-the-art oceanographic variables, and the first tracking dataset around Mauritius, our results provide a first baseline needed to assess the spatiotemporal distribution of the vulnerable sperm whale in a poorly known region: the south-west Indian Ocean (SWIO).
The total length of the SPLASH10 tag was 215 mm, and the mass of the instrument with attachment spear was 250 g. The SPLASH10 tags collected summarized dive data in bins where dives to different depths and time spent at the same depths were binned into 100, 200, 300, 400, 500, 600, 700, 800, 900, 1,000, 1,100, 1,200, 1,300, >1,300 m bins. The duration of dives was summarized in these bins:  5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, and >65 min. In addition to that, the maximum depth of dives was recorded for each 24 hr. The tags were programmed to make a maximum of 250 transmissions per day between 04:00 and 16:00. The SPOT5 and SPOT6 tags were allowed to transmit every day in November through January and every other day the rest of the year. The SPLASH10 tags were allowed to collect dive data and transmit every day.
| Location data processing Location and dive data were obtained through the Argos Data Collection and Location System using the Kalman filter which greatly improves the location data  (Lopez et al. , 2014). Dive data were decoded in Wildlife Computers portal. All statistical analyses were performed using R software version 4. 0. 2 (R Core Team, 2020). We restricted our dataset to positions associated with a travel speed lower than 7 km/h  (Wahlberg, 2002). Locations on land were also discarded. Any individuals containing less than 10 locations (for each season) were also discarded from the analysis. In order to assess seasonal patterns and monsoon periods of the Indian Ocean, seasons were classified as follows: dry season from April to November and wet season from December to March.
0: pseudo-absence) as a response variable. The 14 models included the eight predictors mentioned above. All predictors were scaled between 0 and 1, and collinearity was checked using the variance inflation factor (below four). The dataset of each run was first randomly split between the training dataset (80% of the data) and the validation dataset (20% of the data). Each algorithm was run on the training dataset, while model evaluation was performed on the validation dataset. Model comparison was based on a tenfold cross-validation with three repetitions using the following performance metrics calculated for each run on the 20% validation dataset: accuracy, kappa, sensitivity, specificity, true skill statistics (TSS), and F1 score. The best selected model was then tuned by testing several values of the mtry argument (the number randomly selected predictors).
The "tuned model" was then used to generate ten prediction maps of the sperm whale's distribution (for each of the ten runs) and for each season separately. In parallel, the caretEnsemble package was used to generate ten predictions based on the combinations of the 14 algorithms previously tested, hereafter called the "stacking method. " The ten prediction maps of each approach were finally averaged to provide a final map of the potential distribution of sperm whales during the wet and dry season separately. The coefficients of variation were also calculated to provide a map of uncertainty (ratio of the standard deviation over the mean).
Rather than static and sometimes inadequate MPAs, here we recommend designing dynamic MPAs based on the seasonal prediction maps of the whales  (Maxwell et al. , 2015). In addition to filling a gap in our knowledge about the movements and habitats of sperm whales in the south-western Indian Ocean, our study will contribute to the implementation of concrete conservation measures in the waters of Mauritius and Reunion by clearly delineating the breeding and foraging grounds of this vulnerable species. Our findings are of particular importance in the Indian Ocean, where regional assessments are still lacking despite the presence of sperm whales  (Huijser et al. , 2020; Laran, Authier, et al. , 2017). Our results should therefore supplement existing sperm whale's records available from international databases like the Global Biodiversity Information Facility (GBIF) to support regional assessments.
We first formulate a Mixed Integer Linear Programming (MILP) model purposing to maximize total intra Data Center (DC)' servers and inter networking resources power efficiency as a function of users' request rates. Afterward, we proposed a new green location-aware, Parallel Global resource Topology Ranking (PGTR) method, prioritizing first the greenest server and network nodes. Depending on resulted ranking process classes, a Parallel and Proactive VNE (PPVNE) is therefore proposed to effectively maximize total DC's and networking resources power efficiency. After implementing the whole proposed algorithms namely (PGTR-PPVNE) under NSFNET network topology related data, extensive simulations results proved the improvement of the proposed (PGTR-PPVNE) approach over four other benchmark methods.
This later concept brings the implementation of network functions in software rather than physical devices  [29] , through a series of Virtual Network Functions (VNFs), making hence resources provisioning independent on specific hardware equipment. Moreover, even most existing works focus on single domain network, the large core backbone Internet has evolved from limited single domain network's resources to different network domains. Those multi-domain networks are managed by several operators and network infrastructure providers  [2]. Therefore, multi-domain networks' efficient resources virtualization under above emergent technologies, becomes the promising solution that surpasses internet ossification and reduces global Operational Expenditures (OPEX) and Capital Expenditures (CAPEX) in the future multi-cloud industry.
VI. PERFORMANCE EVALUATION We simulated the proposed Green and Distributed SD-ODCN architecture under NSFNET network topology as mentioned in (Figure  5 ), considering availabilities of different kinds of energy. We have implemented algorithms synthesized in (figure  4 ) on the latest version of CloudSim  [23]. We extended the following classes: the CloudCoordinator class, network topology classes, resources provisioning classes, cloudlet scheduler classes, as well as allocation policies classes. Experiments were run on a DELL G5-15-5590 Intel(R) (core (TM) i7-9750H, 12 CPUs (2. 6GHz), 16GB DDR4 RAM). For evaluation purpose, we compared our simulated results with four benchmark algorithms presented in  [11]. The following (Section VI. A) highlights the simulation setups with data collection sources and further simulation details.
Driven by this fact, we extracted only 1% of each collected energy availability. Therefore, we converted all obtained data in KW. From the same platform, we also collected carbon intensity in Kg/KWh associated to USA region as depicted in Table  5. As summarized in (Table  6 ), we tried to keep the same simulation setup  [11]  to remain compatible with benchmark algorithms. Therefore, we considered additionally hosts and VMs characteristics shown in (Table  7 ).
ACKNOWLEDGMENT This work was supported as part of Green IT projects launched by  L3I Laboratory and EIGSI school in La Rochelle (France) , to achieve digital sobriety transition. The  Moroccan Ministry of Higher Education (CNRST)  and  FRDISI  founded this work. Nomenclature SD-ODCN Software Defined Optical Data Center Networking VM/s Virtual Machine/s VL/s Virtual Links  VNR/s Virtual Network Requests Sub-VNR/s Sub-Virtual Network Requests VNO Virtual Networks Orchestrator VIP Virtual Infrastructure Provider MN/s Manager Node/s CN Central Node DC/s Data Center/s SDN Software Defined Network RE/s Renewable Energy/ies QoS Quality of Service GTR Global resources Topology Ranking CER Carbon Emission Rate OEO Optical Electrical Optical conversion.
MATERIAL AND METHOD 2. 1 Patients and study design TEXCAN (NCT02699073) was a multicenter, prospective, open-label, single-arm phase II trial of patients with mCRC refractory to standard therapy. The study aimed to evaluate the 2-month tumor response to treatment with regorafenib (160 mg once daily for 3 weeks, followed by 1 week off therapy) using different measurement methods. Inclusion/exclusion criteria, study design, study objectives/endpoints, and clinical results have been previously reported  [22]. Briefly, 55 patients (the intent-to-treat population) received at least one regorafenib tablet (1 tablet = 40 mg). According to Response Evaluation criteria in Solid Tumors (RECIST) 1. 1, no partial and complete responses were observed. The best overall response observed was stable disease in 20/35 patients evaluable for the primary endpoint (i.
IntPlex® analysis of cfDNA Analysis of cfDNA were done by IntPlex®, an allele-specific blocker quantitative PCR (ASB Q-PCR), which we previously described  [10, 21, 26] , according to the MIQE guidelines  [27, 28]. Q-PCR amplifications were carried out at least in two replicates in a total volume of 25 µL on a CFX96 instrument using the CFX manager software (Bio-Rad). Each PCR reaction was composed of 12. 5 µL of IQ Supermix Sybr Green (Bio-Rad), 2. 5 µL of DNase-free water (Qiagen) or specific oligoblocker, 2. 5 µL of forward and reverse primers (0. 3 pmol/mL), and 5 µL of template. Thermal cycling comprised three repeated steps: a hot-start activation step at 95°C for 3 minutes, followed by 40 cycles of denaturation-amplification at 95°C for 10 seconds, then at 60°C for 30 seconds. Melting curves were investigated by increasing the temperature from 60°C to 90°C with a plate reading every 0.
Proportional hazards assumptions were examined graphically by plotting the log-minus-log of survival. Restricted cubic spline methodology was used to estimate the association between continuous variables and OS in order to identify the best transformation to apply in Cox proportional hazard models and cut-off of interest. All analyses were performed using SAS version 9. 4 (SAS Institute, Cary NC) and R software version 2. 15. 2 (R Development Core Team, Vienna, Austria; http://www. r-project. org). P values of less than 0. 05 were considered statistically significant and all tests were two-sided. Due to the exploratory setting of the study, P values were not corrected for multiple tests.
Tumor genotyping data were obtained either from resections (n = 27) or biopsies (n = 13) of primary tumors (n = 18) or metastases (n = 7). Corresponding cfDNA genotypes are shown in Appendix Table  S1. Median treatment duration was 8. 29 weeks (IQR, 4. 14-15. 14). Median OS for all patients was 5. 3 months (3. 7-8. 6). The actual regorafenib median-dose of the prescribed dose was 82. 5%. The median cfDNA concentration at baseline (the cfDNA population) was 21. 18 ng/mL (IQR, 7. 81-62. 80).
Sons Ltd. This study is supported by unrestricted research grants from the Fondation ARCAD (Aide et Recherche en Cancérologie Digestive) and Bayer Healthcare Pharmaceutical. Funders have no role in study design, management, analysis, or interpretation of data, nor do they have any role in the writing of the final report. The authors acknowledge the editorial assistance of Magdalena Benetkiewicz (Sc. D; GERCOR).
SDS-PAGE A Mini-PROTEAN e Tetra system (Bio-Rad, Richmond, CA, USA) was used for SDS-PAGE analysis. First, samples (2 mg/mL protein content measured by BCA assay) were diluted 1:1 with Laemmli sample buffer containing 5% (v/v) β-mercaptoethanol and heated at 95 • C for 5 min on an Eppendorf ThermoMixer® C heating block (Eppendorf, Hamburg, Germany). Next, protein standard (10 μL) and sample solutions (20 μL) were loaded in separate wells in Mini-PROTEAN gels. The samples were separated at a constant voltage of 200 V at room temperature for approximately 30 min, after which the gels were stained with Coomassie G-250 for 1 h, and de-stained with water for 12 h. The gels were scanned using a GS-900 TM Calibrated densitometer (Bio-rad, USA) and analysed with Image Lab 5. 2. 1 software (Bio-Rad, USA).
Statistical analysis All measurements were done at least in triplicate on samples prepared at least in duplicate in independent experiments. The data were expressed as mean values ± standard deviation. Data were subjected to one-way analysis of variance (ANOVA) using the software package IBM SPSS statistics 23. 0. 0. 2 (SPSS Inc, Chicago, Illinois, USA). The Tukey HSD method was conducted post-hoc for mean comparisons, with P < 0. 05 being considered as significant. The lowercase letters in all figures are for comparison of different fractions within one system. The bold italic uppercase letters are for comparison between SPI and SPI-dextran systems, within the same fraction. The normal uppercase letters are for comparison between SPI and SPI-glucose systems within the same fraction.
Statistical analysis Unless otherwise indicated, numbers shown are mean ± standard error. Significance for stem cell number counts was determined using Mann-Whitney tests for pairwise comparison, or non-parametric ANOVA (Kruskal-Wallis test) followed by Dunn's multiple comparisons test to assess differences between multiple samples. For clone recovery rates, Fisher's exact test was carried out. All P values were determined using GraphPad Prism software. Plots show individual data points and the lines indicate the mean and 95% confidence interval.
Acknowledgments We are grateful to members of the  Amoyel, Fernandes, Poole and Barrios labs  for productive discussions, to  Vilaiwan Fernandes ,  Ben Simons ,  Claudio Stern ,  Richard Poole  and  Barbara Conradt  for critical comments on the manuscript. Many thanks to  Salvador Herrera  for suggesting the use of Gal80 flipout cassettes and to members of the fly community for stocks and antibodies.
| RESULTS 2. 1 | The neutralizing monoclonal antibody 93k bound to the varicella-zoster virus fusogen glycoprotein B (CASP: H1036 and T1036, PDB: 6VN1). Provided by Stefan L. Oliver, Wah Chiu, and Ann M. Arvin Members of the Herpesviridae are pathogens of humans and animals that cause a wide range of medically and economically important diseases. 8 The outer lipid membrane of herpesvirus virions is studded with glycoproteins that enable binding to cell membranes and fusion of the virus envelope to initiate entry and establish infection. [11] [12] [13] [14] [15]  The ectodomain architecture for gB orthologs consists of five structurally distinct domains (DI to V) that fold into a homotrimer with C3 symmetry. Varicella-zoster virus (VZV) is an alphaherpesvirus that causes chickenpox (varicella) upon primary infection.