dataesr/scientific-paragraphs-categorization huggingface dataset:
Comparative genomics and orthology analyses For comparative genomics analyses 45 species from diverse plant lineages were selected (Supplemental Table  2 ). The species used in this analysis were chosen based on the completeness of their genomes, phylogenetic distribution along the tree of viridiplantae and in the use of plantmodels. These plants belong to the monocotyledons and dicotyledons, encompassing 17 different clades. Five genomes belonging to Dioscoreales were added (Dioscorea alata, D. rotundata, D. dumetorum, D. zingiberensis, Trichopus zeylanicus). The completeness score of each plant proteome was verified by BUSCO  (Simão et al. , 2015)  using the Viridiplantae dataset of BUSCO. We chose a threshold of 70% of completeness to include public proteomes. For the inference of orthologous groups, OrthoFinder software v2.
Metabolic pathways selection and gene retrieval from D. alata We downloaded the complete list of enzyme codes (EC) from the pathway of pentose and glucuronate interconversions (map00040), starch and sucrose metabolism (map00500) and flavonoid biosynthesis (map00941) from KEGG database (https://www. genome. jp). These EC numbers were used to retrieve the genes annotated in the GFF file of D. alata (https://phytozomenext. jgi. doe. gov/info/Dalata_v2_1). The genes annotated with these EC numbers were further searched in the orthogroups produced by our comparative genomics study, and all the homologous genes from D. alata were retrieved. The VCF file was reduced to the middle coding sequences of the annotated genes of interest.
Candidate gene association analysis Three VCF files, one for each target metabolic pathway genes, containing non-synonymous SNPs were extracted from the whole VCF file and further filtered for minor allele frequency ≥ 0. 05, missing rate < 20% using TASSEL5. 0  (Bradbury et al. , 2007). The associations between the polymorphisms from each metabolic pathway candidate genes and the corresponding phenotypic traits were tested using TASSEL5. 0 based on the General Linear Model. Only significant variants with P ≤ 0. 001 were retained. The association analysis was conducted with phenotypic data from each location independently. The effect of alleles at significant SNPs was assessed by comparing phenotyping data for haplotype groups. A Student's t-test was used to compare the groups of haplotypes (P<0.
ACKNOWLEDGMENTS We are grateful to  Christian Mestres  for his valuable advice on yam quality and its attributes. We gratefully acknowledge  Nancy Terrier  for her critical reading of the manuscript. Our greatest debts go to CRP-RTB,  IITA  and PRC genebanks and  Juliane Kaoh ,  Mamy Tiana Rajaonah ,  Senanayake Ravinda Lakshan , and  Babil Pachakkil  for providing us with plant leaves material. We are also thankful to  Elie Nudol ,  Marie-Claire Gravillon ,  Christophe Perrot  and  Erick Maledon  for their invaluable help with field work and phenotyping, and  Sandrine Causse  and  Hélène Vignes  for laboratory assistance in preparing D. alata libraries. Finally, special thanks are due to  Sylvain Santoni  and  Muriel Latreille  for their assistance in transcriptome preparation.
Fluorescence measurements To measure the fluorescent signal of these strand exchange reactions, the ssDNA/RecA filament and dsDNA mixture was placed in a quartz cuvette. Initially, fluorescein emission was quenched by rhodamine because the outgoing and complementary strands were paired in the dsDNA. As strand exchange progresses, the outgoing and complementary strands separate and fluorescein emission increases. Using FluorEssence spectroscopy software and the automated FluoroMax spectrofluorometer (Horiba, Edison, NJ), the emission of the fluorescein label was read using 493 nm excitation wavelength and a 2 nm slit. The emission of the fluorescein fluorophore in counts per second (cps) was detected at 518 nm with a 2 nm slit. The reaction was run for 30 minutes, and emission measurements were collected every 1 second with an integration time of 0.
MP: Chu Family Foundation and  Harvard University ; No CP: ' Initiative d'Excellence' program  of the  French State  [ DYNAMO ,  ANR-11-LABX0011 ]; No VGC:  Northeastern University Skills for Capacity and Inclusion Program  by the  Howard Hughes Medical Institute  through the  Science Education Program ; No NK:  National Institute of Health ,  R35-GM-136322 ; No The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
The application of the ultrasound (US) pulse was conducted immediately after injection of oxygen microbubbles. Recently, we have shown by means of spectroscopic EPR measurements that in the group treated by intratumoral injection of NMBs there was no significant increase in tumor pO 2  [24]. Therefore, this group was not selected for imaging and radiotherapy experiments. The analysis of the obtained spectra was carried out using a script written in Matlab environment  [26]. A Shepp-Logan filter with a cutoff frequency of 0. 5, threshold 0. 55 and 4-fold interpolation was used. On the data prepared in this way, a two-stage reconstruction of the two-dimensional spectroscopic image was performed. The obtained spectral widths were converted into the values of the partial oxygen pressure by using the calibration curve.
The antibody was detected by a horseradish peroxidase conjugated secondary antibody (7074, 1:2000, Cell Signaling, USA) after an incubation of 1 h and developed with an enhanced chemiluminescence detection kit on ChemiDoc (Bio-Rad). The equal loading was confirmed by the detection of β-actin (D6A8, 1:1000, Cell Signaling, USA). An ImageJ software package was used for densitometric analysis.
Statistical analysis All results were presented as mean values and standard error of the mean (SEM). Statistical analysis was performed in STATISTICA 13. 1 software (Stat-Soft Inc. , USA). For each parameter, the Shapiro-Wilk normality test and the Levene test were used to determine a normal distribution and equality of variances, respectively. Depending on the data, one-way or two-way analysis of variance (ANOVA) followed by Tukey's post hoc HSD test was performed. P-values smaller than 0. 05 were considered statistically significant.
Acknowledgments We thank  Prof. Agata Exner  and  Prof. Wojciech Froncisz  for fruitful discussions and  Dr Michał Gonet  for providing EPR imaging reconstruction software tool. This work was supported by the grant from the  National Science Centre  OPUS No. 2015/17/B/NZ7/03005. The openaccess publication has been supported by the  Faculty of Biochemistry, Biophysics and Biotechnology  under the  Strategic Programme Excellence Initiative  at  Jagiellonian University in Krakow, Poland.
Acknowledgements The authors thank  Professor Albert Fournier , who initiated this clinical study. We also thank  Amiens University Medical Center and especially the Clinical Research and Innovation Directorate  (for logistical support), the  Clinical Research Center  (for study management), and the  Department of Nephrology, Internal Medicine, Dialysis, Transplantation , and  Intensive Care. We further thank the patients and physicians at the 18 dialysis centers for their participation in the NICOREN study. Lastly, the authors thank  Anaïs Okwieka  and  Marine Poulenard  for technical assistance with the  LC-MS/MS HCit  assay.
Reasons for exclusion from the study included death (n = 1), graft rejection (n = 1), graft loss (n = 2), unwillingness of the patient to continue periodic monitoring visits (n = 2), and missing data (n = 3). At each visit, the QF-CMV assay was performed and CMV-DNA load in the blood was quantified by real-time PCR. Clinical data were collected by means of Electronic Case Report Forms (eCRFs). Peripheral blood samples were stored at -80°C in the biological collection of the Limoges University Hospital (CRBioLim, AC-2021-4790, certified NF S96-900) authorized by the French Ministry of Health. TTV-DNA load in peripheral blood was retrospectively quantified at the Herpesviruses National Reference Center. This study was approved by the local institutional review board (approval number: I10 002) and the Ethical Committee (16/04/2012, CPP SOOM IV), and registered at ClinicalTrials.
Statistical analyses Statistical analyses were conducted by the biostatistics unit (CDCR, University Hospital of Limoges) using SAS Enterprise Guide software version 7. 1, Cary NC, USA. Significance level of p-value was 0. 05. For descriptive analysis, continuous data were described as means ± standard deviation (SD) or median with data range (minimum to maximum, interquartile range), depending on the data distribution. Categorical variables were reported as number of patients and percentage. Differences between continuous variables were compared using the Mann-Whitney U test. The performance of TTV viremia and QF-Ag or QF-Mg for detection of CMV reactivation was assessed by calculating sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Receiver operating characteristic (ROC) analyses Frontiers in Medicine 04 frontiersin.
An increase of at least 0. 75 log 10 copies/ml from D0 to M1 was identified as the optimal value. However, the AUC demonstrated a poor discriminatory power between patients with and without CMV reactivation. In addition, it provided comparable diagnostic performance of those estimated with a static measurement of TTV load at D0 and M1 (cut-off of 3. 45 log 10 copies/ml), and consequently conferred no additional benefit. Reconstitution of CMV-specific T-cell (QF-Ag) and overall T-cell (QF-Mg) responses was also analyzed according to antiviral prophylaxis administration in the 1-year study period. Our data demonstrated that patients receiving prophylaxis had significantly lower mean QF-Ag values between M1 and M6 compared to patients without prophylaxis. In addition, QF-Ag kinetics exhibited a first increase in mean values from M1 in patients without prophylaxis, whereas values only increased from M5 in the prophylaxis group.
Acknowledgments We thank the  Clinical Research department of the Centre Hospitalier Universitaire de Limoges  for help in research management, particularly  Robin Jeannet  for helpful advices in paper writing and reviewing of English language,  Eliza Munteanu  and  Delphine Chainier  ( French Reference Center for Herpesviruses and Bacteriology-Virology-Hygiene Clinical Research Unit ) for her help in collecting the clinical data, and  Sandrine Luce  ( Cebimer, CHU Limoges ) for her help as data manager. 10. 3389/fmed. 2023. 1180769 Frontiers in Medicine 15 frontiersin. org.
a b s t r a c t In recent years, the food industry has expended considerable effort to design novel products that replace animal proteins with legumes; however, the actual environmental benefits of such products are often not quantified. Here, we performed Life Cycle Assessments (LCA) to evaluate the environmental performance of four new fermented food products based on different mixtures of animal (cow milk) and plant (pea) protein sources (100% Pea, 75% Pea-25% Milk, 50% Pea-50% Milk, 25% Pea-75% Milk). The system perimeter encompassed all stages from agricultural production of the ingredients to the creation of the final ready-to-eat products. Impacts were calculated for all environmental indicators included in the EF 3. 0 Method in SimaPro software based on a functional unit of 1 kg of ready-to-eat product.
Life cycle inventories included all of the flows analyzed by the LCA (raw materials, energy, water, cleaning products, packaging, transport, waste). Foreground data were acquired directly on the manufacturing site; background data were taken from the Ecoinvent 3. 6 database. The dataset contains details on the products, processes, equipment, and infrastructure considered; mass and energy flows; Life Cycle Inventories (LCI); and Life Cycle Impact Assessment (LCIA). These data improve our understanding of the environmental impact of plant-based alternatives to dairy products, which is currently poorly documented. ©.
Value of the Data • This dataset presents LCIs (Life Cycle Inventories) and LCIAs (Life Cycle Impact Assessments) of four new fermented food products based on different mixtures of animal (milk) and plant (pea) protein sources. • The Life Cycle Inventory data and Life Cycle Impact data in this dataset ensure transparency in the LCA modeling described in the accompanying article  [1]. • The presented experimental setups and LCA methodology can serve as an orientation for further scientific work on this topic. • The Life Cycle Inventory data could be reused by scientists for future LCAs. • These data can be used to support assessments of sustainability in the food sector. • These data can be used to inform design recommendations for the production of more environmentally friendly foods.
Objective There is a lack of data on the environmental impacts of plant-based alternatives to cheese. We generated this dataset to fill this gap, by quantifying the environmental impacts associated to the production of new fermented products alternatives to cheese, created using different combinations of plant (pea) and animal (cow milk) protein sources. The new fermented products were all created using a cheese-making process (Camembert production) with pilot equipment that mimicked semi-artisanal production at the facility of a technology platform in Aurillac, France. These data were used in a research article in order to: (i) identify the environmental hotspots of the production process; (ii) compare the environmental performance of different mixes; (iii) compare the environmental performance of the studied products with that of Camembert (same manufacturing technology as the new products), hummus and tofu (two other plant-based products).
Data Description The dataset associated with this article ( 10. 57745/X4QWKZ ) contains the inventory data related to four fermented food products based on different mixtures of animal and plant protein sources (data on ingredients, processing, and transport) and the LCIA results presented in the associated article  [1]  : 1. LCI_fermented_products data: Life Cycle Inventory for the four fermented products. All the mass and energy flows are indicated, grouped by step and sub-step of the production process. The measured flows refer to the total mass of the final product. These data need to be combined with information from the LCI_parameters dataset to obtain the exact LCI flows for each food product. The names of all the data used for the inventories and the associated database are also given.
Some comments are added to help understand the meaning of the formulas describing the amounts of data. 2. LCI_parameters data: All complementary parameters needed to complete the LCI_fermented_products data (Pea_ratio, Milk_Ratio, Nb_molds, Losses_molding, Losses_demolding, Mass_product, Total_mass_final_product). 3. LCI_others: Life Cycle Inventories of frozen lactic acid bacteria, obtained from Ref. [2]  and adjusted based on the data used and the associated database. This file also contains the Life Cycle Inventory of household waste used in this study, together with the name of the data used and the associated database. protein sources. Tables  1  and 2  show the compositions of the pea and reconstituted milk suspensions, respectively. Table  3  displays the composition of the mixtures produced by combining pea suspension and reconstituted milk.
Equipment The equipment used is summarized in Table  4  together with the mass and nature of material that make up each item. These data were obtained from equipment data sheets or direct measurement when possible. It must be noted that, as a precaution, more jugs and mats were sterilized than were used for production. Also, the sterilization of all equipment for the four matrices was performed simultaneously in Autoclave 1. These materials were modeled in the LCI using temporal allocations ( Eq. (  1 ) ). The lifetime of all equipment was considered to be 30 years, except for the molds which were used only for this experiment (in this case, their lifetime was considered to be 45. 6 h).
Infrastructure We modeled the use of three different areas at the facility of the Aurillac technology platform: the preparation room, the resting room, and the ripening room. For each, we considered the resources consumed by the temperature control system: ethylene glycol, tap water, and electricity. These data were obtained from data sheets. For these rooms, a temporal allocation was applied as described by Eq. (  1 ). In addition, a volume allocation was used as follows: -Preparation room: 1/4 of the room per matrix (the entire room was used; the four matrices were prepared at the same time); -Resting room: 1/12 of the room per matrix (1/3 of the room was used in total; the four matrices rested at the same time); -Ripening room: 1/8 of the room per matrix (1/2 of the room was used in total; the four matrices were ripened at the same time).
Mass Flows Mass flows of ingredients, water, cleaning products, product losses, and waste were either measured manually or estimated during the experiment. The LCI of frozen lactic acid bacteria was obtained from a previous study  [2]. Loss of refrigerant of Cold room 1 was calculated as in Ref. [2]  ( Eq. (  2 ) ). Loss of re f rigerant = Equipment power * Re f r igerant char ge * Leakage rate  (2)  The equipment power and refrigerant charge were obtained from data sheets. The annual leakage rate of the refrigerant contained in the cold room was assumed to be 15%  [ 4 , 5 ]. Packaging and mold waste were disposed of either by incineration (53%) or in a landfill (47%).
Acknowledgments We thank our research colleagues from  UMRF (Céline Delbes ) and  UMR SayFood  ( David Forest, Anne-Sophie Sarthou ) for providing technical support during this study. We also thank  Lindsay Higgins  from  English Services  for  Scientists  for the English revision of the paper. We conducted this research as part of  the  project  DIETPLUS ANR-17-CE21-0 0 03 , which was funded by the  French National Research Agency (ANR). This data paper also benefited from the support of the  French National Research Agency  through the  DataSusFood  project (grant number  ANR-19-DATA-0016 ).
, Chassard, C. , Lavigne, R. , Irlinger, F. , Souchon, I. , Marette, S. , Saint-Eve, A. , Pénicaud, C. Environmental performance of mixed animal and plant protein sources for designing new fermented foods, Cleaner Environmental Systems, 9 (2023) 100115, 10. 1016/j. cesys. 2023. 100115. calculation, or were found in the technical and scientific literature. Background data mainly come from the database Ecoinvent 3. 6. Life Cycle Assessments were computed using SimaPro 9. 1. 0. 11software and the method "EF 3. 0 Method (adapted) V1. 00 / EF 3. 0 normalization and weighting set". Data format Raw Analyzed Description of data collectionThe dataset contains LCIs and LCIAs of four new fermented food products based on different mixtures (ratios) of animal (cow milk) and plant (pea) protein sources.
m -1. K -1 ) µ Dynamic viscosity (kg. m -1. s -1 ) ν kinematic viscosity (m 2. s -1 ) ρ Density of the mixture (kg. m -3 ) convection in a rectangular cavity filled with air. They applied higher temperature and concentration on the active part of the left-side wall and studied the effect of aspect ratio on the heat and mass transfer rate. They concluded that an increase in aspect ratio leads to an increase in the heat and mass transfer rate. Yu-Tuan Chou et al. [12]  used commercial software of SolidWorks flow simulation to analyze the thermal comfort and air quality in an office room environment. They estimated the PMV (Predicted Mean Vote) and PPD (Predicted Percentage of Dissatisfied) contours and found that thermal comfort in a completely insulated compartment is useful. Mladen et al. [13]  conducted a study on indoor air quality in educational institutions during the period of March to April, using computational fluid dynamic software to model different scenarios.
Long-Range Wide Area Networks (LoRaWAN) enable low-power data collection over long distances, and they are thus widely used for Internet of Things applications, despite their limitations to meet some traffic requirements (e. g. , reliability). To achieve the quality of service required by the applications, service differentiation is a promising approach which can be provided by network slicing. In this work, we show that related works are either incompatible with the LoRaWAN specifications, or do not isolate traffic, or assume an a priori known and stable traffic. In this paper, we propose a lightweight approach to achieve slicing in LoRaWAN, compatible with the LoRaWAN specifications. Our approach allows to isolate traffic, to protect confirmed traffic, and to deal with unsolicited traffic.
I. INTRODUCTION Long-Range Wide Area Network (LoRaWAN) is a wireless communications standard dedicated to data collection from low-power end-devices over long distances. It is widely used in Internet of Things (IoT) applications for three main reasons: (i) it can trade-off communication range with data rate, (ii) it enables the deployment of low-cost networks, and (iii) it is simple to implement. Applications using LoRaWAN typically include smart city monitoring, smart farming, and supply chain management and optimization. Because of the simple design of its medium access control (MAC) protocol, LoRaWAN is not able to support the Quality of Service (QoS) requirements of some IoT applications. For this reason, several proposals ( [1]-  [3] ) were made to use slicing in order to implement service differentiation over LoRaWAN, i.
Moreover, the current slicing approaches for LoRaWAN are unable to deal with unsolicited traffic, for instance coming from end-devices associated to an application server that has been terminated: such a situation is unfortunately likely to happen increasingly in the future, as some old application servers will eventually be switched off without removing the corresponding IoT nodes. In this paper, we propose a lightweight slicing approach for LoRaWAN that: (i) is compatible with the LoRaWAN specifications, (ii) isolates traffic, (iii) is dynamic, and (iv) is controlled by the network-server. Our approach is flexible, and it can handle slices based on radio channels, spreading factors or other radio/network parameters. We show that we can take benefit from the adaptive data rate (ADR) mechanism to create slices and to allocate end-devices to slices.
Note that these latter nodes produce unwanted traffic. 2) Frames management and slice allocation: Each LoRa frame is received by gateways, then forwarded to the network server (NS). The NS analyzes the header of each LoRa frame to determine the corresponding slice. For this, we use information already existing in the LoRaWAN messages. If the message type in the MAC header of the LoRa frame is set to 100, the traffic is confirmed. During a join procedure, which allows an end-device to be associated to a LoRaWAN, the application_id is set. We use this field to keep track of end-devices sending data to application servers that are not reachable, and thus to identify unsolicited traffic. The traffic generated by the other nodes is classified as normal. In each case, the NS uses our ADR-based slice mechanism to allocate the corresponding SF to the end-devices.
Results A total of 94 subjects (65 female) with an average age of 72. 7 ± 5 years (mean, SD), and mean education level of 9. 5 ± 5. 1 years were obtained from the ANDES cohort  (16, 17). Data from this cohort includes DPOAE measurements, audiogram thresholds, assessed by Pure-Tone Average (PTA), a battery of neuropsychological tests for cognitive decline, and structural brain MRI at 3-Tesla. The mean PTA of the 94 individuals was 24. 3 ± 8 dB HL, including 32 subjects with normal hearing thresholds (<25 dB HL) and 62 with mild hearing loss (between 25 and 40 dB HL). None of the individuals used hearing aids at the moment of evaluations. Regarding DPOAE, we calculated the total number of detected DPOAE in eight different frequencies in both ears (range between 0 and 16, bigger is better, see methods section), yielding an average number of 8.