dataesr/scientific-paragraphs-categorization huggingface dataset:
Microbial gene count table To create the gene count table, the METEOR software was used: first, reads were filtered for low-quality by AlienTrimmer (https://forgemia. inra. fr/metagenopolis/meteor). 8  Reads that aligned to the human genome (identity > 95%) were also discarded. Remaining reads were trimmed to 80 bases and mapped to the Integrated Gut Catalog 2 (IGC2),  53, 54  comprising 10. 4 million of genes, using Bowtie2. 55 First, the unique mapped reads (reads mapped to a unique gene in the catalog) were attributed to their corresponding genes. Second, the shared reads (reads that mapped with the same alignment score to multiple genes in the catalog) were attributed according to the ratio of their unique mapping counts of the captured genes. The resulting count table was further processed using the R package MetaOMineR v1.
Metagenomic Species (MSP) profiles The IGC2 catalog was organized into 1990 Metagenomic Species (MSP), clusters of minimum 100 genes, using MSPminer 54 MSP taxonomy was assigned with the Genome Taxonomy Database. 56 lative abundance of an MSP was computed as the mean abundance of its 100 'marker' genes (that is, the genes that correlate the most altogether). If less than 10% of 'marker' genes were seen in a sample, the abundance of the MSP was set to 0. Relative abundances at higher taxonomical ranks were computed as the sum of the MSP that belong to a given taxa. MSP count was assessed as the number of MSP present in a sample (that is, whose abundance is strictly positive).
Microbiome functional potential Genes from the IGC2 catalog were mapped with diamond  57  onto KEGG orthologs (KO) from the KEGG database (version 8. 9). 58 Each gene was assigned to the best-ranked KO among hits with e-value <10-5 and a bit score >60. For a given sample, we assessed the abundance of a KEGG module or a Gut-Metabolic Modules (GMMs)  59  with the following procedure: • We restricted the gene content of each MSP detected in the sample to its genes also detected in the sample, plus the core genes of the MSP; • We computed the fraction of the module present in the restricted set of genes for the pair sample/MSP; • We considered that an MSP carried a module in a given sample if the computed fraction was above 90%; • We summed the abundance of all MSPs that carried the module for a given sample.
The IGC2 was also annotated on the Carbohydrate-Active enZYmes Database (CAZy, November 2018) (http://www. cazy. org/ 60 ). 54 The abundance of each CAZYme group (glycoside hydrolases (GH), glycosyltransferases (GT), polysaccharide lyases (PL), and carbohydrate esterases (CE)) was computed using the same process as KEGG modules and GMM. The abundance of each CAZyme group (glycoside hydrolases (GH), glycosyl-transferases (GT), polysaccharide lyases (PL), and carbohydrate esterases (CE)) was computed using the same process as KEGG modules and GMM.
4 TS Level 1M6. 61 o assess if a given metagenomic feature was impacted by the multi-fiber bread, we used the R package nparLD  62  to perform non-parametric analysis of the longitudinal data. P-values from the interaction of the factors "bread" and "visit" were corrected for multiple test with the Benjamini-Hochberg procedure. A feature was considered significant when corrected p-values ≤0. 1. Finally, Wilcoxon signed-rank test were performed on each bread separately (i. e. , before/after multi-fiber bread, and before/after control bread) to assess which bread impacted the feature. Effect size was computed using the Cliff's Delta with the R package effsize. 63 This measure gives an information similar to log-fold change, but is comprised between -1 and 1. (0: no effect; +1 or -1: large effect).
The magnitude of the effect size d is assessed as negligible if |d| <0. 147, small if |d| <0. 33, medium if |d| <0. 474, and large otherwise. 64 or clinical variables, the effect of multi-fiber bread was evaluated by calculating the estimated group difference, i. e. , the difference between the changes induced by each of the two breads. A linear mixed model for repeated measures, with Toeplitz or autoregressive structure (AR) as covariance structure, was used to determine whether the difference between the changes induced by each of the two breads was statistically significant. Bread, time, period, and sequence were included as fixed variables. In order to account for variability between subjects and to adjust for any nonspecific differences, subjects were included as random effects. When the normality of the model residuals was not assumed, a logarithmic transformation of the data has been done.
Funding This study was funded by  Bridor  and in part by  Metagenopolis  grant  Agence Nationale de la Recherche   ANR-11-DPBS-0001. JD acknowledges funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (Grant agreement ERC-2017-AdG No. 788191 -Homo. symbiosus). We thank all the subjects who participated in this study. We thank  Bernard Henrissat  who annotated the IGC2 with CAZy.
| Anatomy We combined previously published data on TT cross-sectional areas (from HRSP-5 trees;  Oldham et al. , 2010)  with leaf length and area data for the same samples. Shoots used for this structural analysis were collected across the crown depth from five trees. Height-paired inner and outer crown samples were taken from 26 heights, along with five treetop samples, and fully hydrated before preservation or scanning (N = 57 samples, each of which is the mean value of two replicate leaves, see anatomical methods in  Oldham et al. , 2010). After verifying through additional longitudinal leaf sectioning (for this study) that TT as a tissue does not taper along leaf length and extends nearly to the tip, we estimated its volume by multiplying mean TT cross-sectional area (highlighted in Figure  1a ) by 95% of total leaf length.
| Calculation of transpiration sustained We estimated the seconds of transpiration sustained solely by water released during TT buckling (temporal reserves) in two steps. First, we computed the increased transpiration rate (ΔE, mol m -2 s -1 ) for a given combination of stomatal conductance (g s ) and ΔVPD via the following equation (based on  Nobel, 1983) : events occurring at locally estimated g smax (see Figure  3a ). As reported above, equations for estimating buckling fraction and g smax from height were based on small datasets due to sampling constraints and limited access to microCT beamtime. They are thus limited in predictive power. To avoid over-reliance on these trends, we assessed the implications of minimum, maximum, and predicted buckling fraction across the full range of operational conditions under which tracheids might buckle.
ACKNOWLEDGMENTS We are thankful to  B. Chin  for assistance with the experiment,  R. Van Pelt  for providing the tree illustration in Figure 2,  Z. Moore  for preparing the cross-section in Figure 1a, and  W. Azuma  and the anonymous reviewers for useful comments on the manuscript. J. Heimer  of the statistical consulting service at ETH-Zurich was very helpful in planning the analysis of our experimental data. We also thank  D. Parkinson  and  H. Barnard  at the  LBNL Advanced Light Source , beamline 8. 3. 2, for their assistance with the tomography. Alana R. O. Chin  is very grateful to  A. M. F. Tomescu  for many years of discussion of tracheid buckling, she was supported by a  Graduate Research Fellowship  from  NSF ,  Paula Guzmán-Delgado  was supported by a  Katherine Esau Fellowship  from  UC Davis.
In these types of graphical models, variables are represented as "nodes" that are connected by "arcs" (or edges). Arcs specify the conditional dependencies among the variables, with the arrow of the arc pointing in the direction of a possible causal association. Dynamic Bayesian networks (DBNs) are one class of probabilistic graphical model that is increasingly used to identify potential directional associations in longitudinal data. For example, in a follow-up study of the 2000 British National Psychiatric Morbidity study, DBNs were used to identify plausible causal associations and feedback loops between depressive, anxious, and psychotic-like symptoms, as well as problems related to sleep and alcohol and substance use  [18]. One advantage of DBNs is that they can be interpreted as possible causal networks linking variables across time, and have been used in time-series and longitudinal datasets to study cancer prognosis  [19] , gut microbiota interactions  [20] , gene regulatory networks  [21] , and functional brain connectivity  [22] , among other dynamic systems.
Assessments Individual and sociodemographic characteristics Data about age, sex, zygosity (monozygotic, MZ; dizygotic, DZ), marital status, occupation, and highest level of education were collected using questionnaires. Self-rated mental health symptoms and subthreshold syndromes Three self-report scales were used to assess 23 psychological symptoms (see Supplementary Table  S1 ). The 12-item Somatic and Psychological Health Report (SPHERE-12) measured the presence/absence of somatic (SOMA-6 subscale, e. g. , hypersomnia, anergia) and anxious-depressive symptoms (PSYCH-6 subscale, e. g. , feeling overwhelmed, hopelessness) over recent weeks  [27]. Five hypo-manic symptoms (persistence >2 days of, e. g. , decreased need for sleep, feeling elated) were examined using an investigator-devised self-rating scale (used in several of our studies)  [23, 24, 28].
e. , complete data for study variables at 19Up and 25Up and no full-threshold CIDI diagnosis at 19Up) (N = 664). To examine progression to a subthreshold syndrome, the second DBN included only participants who did not have a subthreshold syndrome at 19Up (N = 538). Given the purpose of the study (i. e. , exploring temporal relationships across 19Up-25Up), we disallowed the hillclimbing algorithm from inferring: (a) any arcs between 19Up items; (b) any arcs between 25Up items; and (c) any arcs moving backward from 25Up to 19Up. Therefore, these constraints only allowed arcs directed from 19Up items to 25Up items. For this study's purposes, all eligible participants are treated as singletons (i. e. , separate DBNs were not modeled within MZ/DZ pairs); however, a twin/nontwin item was included in the DBNs.
Sample characteristics As of March 2020, data were available for 2,773 individuals who participated in 19Up and 2,627 individuals who participated in 25Up. Of these, 2,292 participated in both waves, and a total of 664 participants met all eligibility criteria for this study (i. e. , participants with no missing data for our variables of interest and no evidence of a CIDI diagnosis at 19Up). As shown in Table  1 , ~60% of the study sample were female (N = 402); the median age at the 19Up wave was 26 years (IQR = 23-29) and at the 25Up wave was 30 years (IQR = 27-34). Over 99% of participants were in the 18-35 age-range at 19Up and 88% also were at 25Up. There were 224 MZ individuals (56 complete MZ pairs), 230 DZ individuals (29 complete DZ pairs), and 210 nontwin siblings. While the age and sex distributions of our sample are very similar to the total 19Up and 25Up samples, our distribution of complete twin pairs is comparatively lower, probably due to our requirement for complete data and differential completion of waves within twin pairs.
Third, the interval between waves was ~5-6 years, and it is probable that meaningful relationships occurring at shorter timescales were missed. Fourth, the repeated cross-sectional design means that we cannot confirm persistence of symptoms over time. Fifth, we selected social/lifestyle factors measured using simple assessments, sometimes employing single items to represent complex phenomena (e. g. , social support/ having a confidante), and we did not include biological data (e. g. , genetic risk). Sixth, while we included a twin status item in the DBNs (which was not influential over any other nodes), we did not stratify the analyses to better leverage the familial structure of the data. Finally, there are two important points regarding generalizability. Sample attrition was significant, with ~25% of the total sample eligible for this study; ≈1,000 participants were deemed ineligible because they were not administered the SPHERE at 19Up, and of the remaining participants, 346 were excluded because they already met criteria for a full-threshold mood and/or psychotic disorder by 19Up.
G. M. , N. A. G. , I. B. H. ;  Enda M. Byrne : Data duration; Investigation; Project administration; Writing-review and editing. Financial Support. The  Brisbane Longitudinal Twin Studies (BLTS)  have been supported by  NHMRC  project grants  1031119  and  1049911  (CI: N. G. M. ). The "19Up" follow-up was supported by  NHMRC  ( APP10499110 ) and the  NIH  grants ( K99R00  and  R00DA023549 ). The "25Up" follow-up was supported by  NHMRC  ( APP1069141 ). J. J. C. is supported by a  Breakthrough Mental Health Research Foundation  Fellowship and an  NHMRC   Emerging Leadership Fellowship. B. C. -D. is supported by a  C. J. Martin Fellowship  ( APP1161356 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
I. INTRODUCTION Along with the relentless growth of current Internet-of-Things (IoT) technologies, ever more diversified and specialized IoT use cases are expected in the near future. Faced by the exponential surge of IoT mobile data traffic, Beyond 5G (B5G) and 6G networks have the daunting task of providing seamless and ubiquitous coverage to billions of users and devices, yet in a sustainable manner. Long Range (LoRa) has been regarded as one of the most prominent technologies to cope with these issues. LoRa is a physical layer for Low Power Wide Area Networks (LPWANs), and is most suited for IoT applications that only need sporadic, limited data rate and delay-tolerant transmissions, but require very wide coverage, up to tens of kilometers in rural scenarios. LoRa is envisioned to become a key enabler of some essential IoT services for supporting future society, among which smart environments and energies, smart cities, smart homes, etc.
, as depicted in Fig. 1. Despite the ability of LoRa to trade-off data rate and coverage by means of its modulation based on Chirp Spread Spectrum (CSS) with various Spreading Factors (SFs), LoRa as such is unable to cope with the B5G joint demands of massive connectivity, long range and higher data rates. Additionally, current LoRa systems are based on LoRaWAN, a simplistic MAC layer that barely handles packet collisions. Current LoRa systems are thus unable to face the aforementioned scalability issues. One essential aspect is hence the design of LoRa-specific collision decoding algorithms. Various approaches for LoRa collision decoding at gateways have been proposed so far  [1] , an example of which is illustrated in Fig. 1. These techniques may improve LoRa performance whenever the node density increases, or when each node generates higher traffic.
Despite their improved performance, current methods are limited to the decoding of only few concurrent packets, and are still insufficient to cope with the imminent surge of IoT data traffic, as envisioned in future IoT applications such as massive 3D sensing. More performant yet cost-efficient LoRaspecific collision decoding methods are crucially needed. Another essential aspect is the design of suitable MAC protocols which can perform well with various densities of devices and traffic patterns. While some MAC protocols aim at avoiding collisions, dense scenarios will always produce them. It becomes primordial to integrate collision decoding algorithms into MAC protocols. Yet, very few works in the literature have addressed this issue so far. May 4, 2022  DRAFT Therefore, the purpose of this article is to clarify how LoRa collision algorithms can be integrated with MAC protocols.
Signals sent on different SFs are quasi-orthogonal. Nevertheless, this imperfect SF quasiorthogonality may significantly deteriorate the overall network performance as experimentally verified in various works and analyzed in  [2]. LoRa also exhibits the capture effect for transmissions using the same SF, which allows a strong signal to be received even if a weaker interference signal is present, provided that the difference of power is at least 6 dB. May 4, 2022 DRAFT However, the level of orthogonality (orthogonality of channels, quasi-orthogonality for different SFs, capture effect for same SFs) that can be reached with LoRa is not sufficient to accommodate the upcoming surge of IoT data traffic. In dense networks, collisions are likely to occur on all channels and for all SFs, and conditions for capture effect might not hold.
Such collisions reduce network throughput, increase frame delays, and increase overall energy consumption. In order to lessen all these negative impacts, collision decoding approaches will be primordial. LoRaWAN (Long Range Wide Area Network) is the most common MAC protocol based on LoRa. In the LoRaWAN topology, end-devices send their frames to gateways using LoRa, and gateways communicate to a network server (and eventually, to an application server) via IP. LoRaWAN is based on the ALOHA medium access mechanism: an end-device can transmit its data as soon as it is available. After each transmission, an end-device opens two short receive windows in order to receive potential data from the network server through gateways. Reception power tracking focuses on identifying the amplitude of all FFT peaks across several symbols, and mapping the peaks of similar amplitude to the same transmitter.
This is realized by optimizing LoRa resource allocation based on the proposed mathematical modeling of the target system, where the impairments of co-SF and inter-SF interferences are fully taken into account. 2) Distributed access protocols: LoRaWAN is based on the ALOHA access method, which is suitable when the traffic load is very low. In ALOHA-based protocols, devices can transmit frames as soon as data is available. ALOHA access methods are very simple to implement, but do not scale with the number of nodes. Among them, LoRaWAN is the fundamental protocol for May 4, 2022 DRAFT LoRa. However, as the demand for capacity increases, the ALOHA method quickly saturates. CSMA-CA-based protocols aim to avoid signal collisions. To do so, the devices sense the channel before sending a frame: if the channel is detected busy, the transmission is postponed.
These features render this modulation particularly amenable to support long-range, large-scale and high-capacity satellite IoT networks. Even though this modulation enables much lower collision probabilities as compared to LoRaWAN, its network capacity is still severely limited given the millions of concurrent transmissions that would occur, owing to the vastness of satellite coverage zones. Furthermore, the scalability improvements of LR-FHSS over LoRa come at the cost of much-reduced data rate per device. Hence, one of the major challenges is to enhance the device-wise QoS satisfaction levels required by each IoT application, while achieving the tremendous coverage and scalability increase offered by LR-FHSS. Towards this end, the LoRa collision decoding and access protocol methods discussed so far should be extended by including collisions among LR-FHSS packets, as well as between LoRa and LR-FHSS packets.
Background: Novel biomarkers are needed to progress toward individualized patient care in sepsis. The immune profiling panel (IPP) prototype has been designed as a fullyautomated multiplex tool measuring expression levels of 26 genes in sepsis patients to explore immune functions, determine sepsis endotypes and guide personalized clinical management. The performance of the IPP gene set to predict 30-day mortality has not been extensively characterized in heterogeneous cohorts of sepsis patients. Methods: Publicly available microarray data of sepsis patients with widely variable demographics, clinical characteristics and ethnical background were co-normalized, and the performance of the IPP gene set to predict 30-day mortality was assessed using a combination of machine learning algorithms.
Results: We collected data from 1,801 arrays sampled on sepsis patients and 598 sampled on controls in 17 studies. When gene expression was assayed at day 1 following admission (1,437 arrays sampled on sepsis patients, of whom 1,161 were alive and 276 (19. 2%) were dead at day 30), the IPP gene set showed good performance to predict 30-day mortality, with an area under the receiving operating characteristics curve (AUROC) of 0. 710 (CI 0. 652-0. 768). Importantly, there was no statistically significant improvement in predictive performance when training the same models with all genes common to the 17 microarray studies (n = 7,122 genes), with an AUROC = 0. 755 (CI 0. 697-0. 813, p = 0. 286). In patients with gene expression data sampled at day 3 following admission or later, the IPP gene set had higher performance, with an AUROC = 0.
To this end, we decided to: 1) collect publicly available microarray data sets of sepsis patients with patient-level information on mortality; (2) co-normalize data sets using COCONUT; (3) optimize ML models using the expression of the IPP gene set on day 1 following admission as input and 30-day mortality as the outcome of interest to evaluate the predictive performance of the IPP signature. Additional objectives were to evaluate if better predictive performances could be attained either by using another gene signature, or by using GE data sampled more than 2 days after hospital admission.
Data Collection and Pre-processing We searched NCBI GEO and EMBL-EBI ArrayExpress databases for studies with the following inclusion criteria: (1) publicly available GE data from micro-array experiments collected by whole blood sampling, with at least one sample collected at day 1 following hospital or intensive care unit (ICU) admission; (2) adult or pediatric patients with sepsis, according to Sepsis-1 (30), Sepsis-2  (31)  or Sepsis-3 (32) definitions;  (3)  individual patient data on mortality (assessed between 28 and 30 days after blood sampling); (4) at least 5 control patients (healthy volunteers or patients with non-septic inflammation), which was mandatory for co-normalization across studies. Data sets using endotoxin or lipopolysaccharide infusion as a model for inflammation or sepsis, as well as datasets derived from sorted cells and RNAseq experiments were excluded.
We collected normalized GE data from selected studies when it was available, and inspected normalization visually by plotting individual patient data for each study. In case normalized data was not available, raw GE data was downloaded and normalized using the gcRMA method (R package affy) for Affymetrix chips, and normal-exponential background corrected and quantile normalized (R package limma) for Agilent and Illumina chips. When several microarray probe sets pointed toward one common gene under the HUGO gene nomenclature data base, we used the collapseRows function in the R package WGCNA to select the probe set with the highest mean value (MaxMean method)  (33). Individual patient data related to demographics and clinical characteristics were also extracted when available, including data on age, gender, ethnicity, clinical severity scores, and bacterial vs.
Co-normalization Using COCONUT Comparison of GE data from different microarray studies is limited by different background measurements for each gene between microarrays, and potential batch effects among studies using the same types of microarrays. To analyze pooled data from different studies, co-normalization methods must be applied in such a way that: (1) no bias is introduced that could influence final classification; (2) there should be no change in the distribution of a gene within a study; and (3) a gene should show the same range of distributions between studies after normalization  (34). To this end, we used the R package COCONUT  (35) , which implements a modified version of the ComBat empirical Bayes normalization method (36), using the assumption that all healthy/control patients from different studies come from the same distribution.