dataesr/scientific-paragraphs-categorization huggingface dataset:
J Heatmap of inflammatory response-associated genes. K, L HUVEC were transduced with adenovirus containing ACLY-WT (WT) or ACLY-S455D (S455D) constructs and analyzed for the expression levels of VCAM-1 and E-selectin using Western blot analysis. LPS stimulus. The upregulation of VCAM-1 and E-selectin as well as MCP-1 was effectively abolished by BMS-303141 without affecting cell viability (Fig. 3C, D , Supplementary Fig. 1B-E ). The knockdown of ACLY with siRNA showed similar effects on EC inflammation (Fig. 3E-G , Supplementary Fig. 1F-I ). RNA sequencing data showed that ACLY inhibition significantly reversed the expression of genes in response to LPS (gene set 1 and 2) (Fig. 3H ). Among others, 262 genes was significantly regulated by both LPS stimuli and ACLY inhibition (Supplementary Fig.
C, D Western blot analysis showing the expression levels of glycolysis-related proteins in ACLY-WT-overexpressing (WT) or ACLY-S455D-overexpressing (S455D) HUVEC. E, F HUVEC were pretreated with BMS-303141 before LPS stimulation for 4 h. Expression levels of glycolysis-related proteins were analyzed using immunoblot. G HUVEC were pretreated with BMS-303141 before LPS stimuli, followed by the measurement of glycolysis rate (proton efflux rates, PER) for 80 min. Rot&AA: Rotenone & antimycin A; Oligo: Oligomycin. H Metabolomic data of ATP production in HUVEC pretreated with BMS-303141 before LPS stimuli (n = 5). I, J Metabolomic data of metabolites of de novo lipogenesis in HUVEC pretreated with BMS-303141 before LPS stimuli (n = 5). K, L HUVEC with ACLY-WT (WT) or ACLY-S455D (S455D) overexpression were analyzed for the expression levels of DNL-related proteins using immunoblots.
The upregulations of VCAM-1 and E-selectin induced by the ACLY-WT and ACLY-S455D overexpression were diminished upon MYC inhibition (Fig. 6M, N ). These data revealed   E-H ) HUVEC were pretreated with BMS-303141 or transfected with siRNA specific to ACLY before stimulatedn with LPS for 1 h. Levels of phosphorylated and total ACLY, acetylated and total FoxO1, as well as MYC were tested using Western blot assays. I Immunofluorescence staining showing FoxO1 localization in HUVEC stimulated with LPS in the presence of BMS-303141 (10 µM) or C646 (5 µM). J, K Western blot analysis showing the levels of acetylated FoxO1 and acetylated H3, and the expression of MYC in HUVEC with ACLY-WT (WT) or ACLY-S455D (S455D) overexpression, respectively. L Immunofluorescence staining representing FoxO1 nuclear export in ACLY-WT-and ACLY-S455D-overexpressing HUVEC (white arrow).
Additionally, ACLY has been identified among the O-GlcNAcylated proteins in septic mice. In NButGTtreated septic mice which showed globally increased protein O-GlcNAcylation, ACLY O-GlcNAcylation level was decreased, indicating the key role of ACLY O-GlcNAcylation in response to metabolic change and stress  [38]. In a pilot clinical study, an increased level of serum ACLY was identified in pediatic patients with sepsis. However, the level of ACLY was lower in non-survivors  [39]. It should be noticed that these data were observed in children which cannot be transposed to adult patients and further studies are needed to confirm the diagnostic value of ACLY in sepsis. All these data pinpoint the involvement of ACLY in sepsis and open a new therapeutic avenue for treating inflammationrelated diseases.
RNA sequencing analysis Total RNA was extracted from HUVEC stimulated with LPS in the absence and presence of BMS-303141. The concentration and purity of isolated RNA were measured using a ND-800 spectrophotometer (Thermo Fisher Scientific, DE, USA). The RNA libraries were constructed using a Truseq RNA Library Prep Kit. Sequencing was carried out using a 2 × 150 bp PE configuration. The clean data (reads) were mapped using Hisat2 (version 2. 1. 0). Then, we performed gene expression analysis using RSEM (version 1. 3. 1). Differential expression analysis among different groups was conducted using DESeq2, and then |FC| > 2 and FDR < 0. 05 were determined as thresholds for differentially expressed genes (DEGs). Gene Ontology (GO) analysis was used to determine the significant biological processes of a particular gene set (q < 0.
Statistics All data were expressed as mean ± standard deviation (SD) of at least three independent experiments. Statistical significance was determined using the two-tailed unpaired-sample t test between two groups or the one-way analysis of variance (ANOVA) followed with Bonferroni multiple comparison test. *P < 0. 05, **P < 0. 01, **P < 0. 001, ****P < 0. 0001. The images were prepared using GraphPad Prism version 9. 0 (GraphPad Prism Software, San Diego, USA).
ACKNOWLEDGEMENTS We thank  Shanghai Institute  of  Digestive Surgery in Ruijin Hospital  for supporting our research. This work was supported by  National Natural Science Foundation of China  ( 81701876 ,  82272234 ,  82241033 ,  82270087 ,  81772040 ),  Shanghai Natural Science Foundation  Project ( 22ZR1439400 ), and  Medical Innovation Research Project of Shanghai Municipal Science and Technology Commission  ( 20Y11901700 ).
The primary outcome measure is de ned as a minimum 20% improvement in the ALS Functional Rating Scale-Revised (ALSFRS-R) slope during the randomisation period (18 months) compared to the lead-in period (3 months), Randomization will be strati ed by country. Primary analysis will be conducted based on the intention-to-treat principle. Data recruitment commenced on February 22, 2019, and was closed on 23 rd December 2021. The database will be locked in September 2023. Discussion: This paper provides a comprehensive description of the statistical analysis plan in order to ensure reproducibility of the analysis and avoid selective reporting of outcomes and data-driven analysis. Sensitivity analyses have been included in the protocol to assess the impact of intercurrent events related to the Coronavirus disease 2019.
By focusing on clinically meaningful and robust outcomes, this trial aims to determine whether TUDCA can be effective in slowing the disease progression in patients with ALS. Trial registration: ClinicalTrials. gov NCT03800524. Registered on January 11, 2019, https://clinicaltrials. gov/study/NCT03800524 However, due to the impact of the COVID-19 outbreak on recruitment, a COVID-19 amendment dated 23 rd March 2020 [9] was implemented, and the recruitment was planned to be closed at 320 randomized patients. This sample size of 320 subjects allows to detect an absolute difference of 11% in the proportion of responders between TUDCA and placebo arm. It also provides a power of 80% to detect a 14% absolute difference in survival, corresponding to a hazard ratio of 0. 58.
Methods The analysis outlined in this document is in full compliance with International Conference on Harmonisation ICH E9 guidance  [7]  and follows the guidelines for the content of statistical analysis plans in clinical trials  [8]. The SAP has been nalized prior to the completion of the data collection. The study protocol has been registered at www. clinicaltrials. gov (NCT03800524). An updated version of the protocol, providing detailed information on background, design and rationale, has been published elsewhere  [9]. The trial is being conducted in accordance with the Helsinki declaration and the local laws and regulations of the respective countries, prioritizing the highest level of patient protection. The protocol was approved by all the involved ethics committees and by national regulatory authorities.
Randomization Patients were randomized 1:1 to receive TUDCA or placebo. After the lead-in period was completed at Month 0, eligible patients were randomized by means of a computerized central randomisation system, and coupled to a unique treatment code through an electronic platform. The randomization list has been generated using STATA software with the "ralloc" command. A permuted block randomization procedure, strati ed by country, has been adopted to ensure balance across treatment groups.
Sample size The study was powered to assess a potential bene t of TUDCA compared with placebo on the rate of progression, as measured by the ALS-FRS-R total score. The target number of participants was determined based on the expected proportion of responders. In the original planning, based on data of the phase II study  [5] , a sample size of 199 subjects per arm was calculated to detect a 10% difference between arms assuming a response rate of 10% in placebo group. This sample size provides at least 80% power with a two-sided alpha of 5%. Considering a dropout rate of 10% at 18 months, a total sample size of 440 patients was planned. Such sample size provides 83. 3% power to detect a 13% absolute difference in survival (de ned as death or respiratory insu ciency) between the two treatment groups, corresponding to a hazard ratio of 0.
Withdrawal/follow-up Patients may withdraw from this study at any time. In case of withdrawal, research samples and data will continue to be stored and analysed for future research, unless the patient speci cally requests the withdrawal of samples. Timing and reasons of withdrawal and/or lost to follow-up (LTF) will be summarized, whenever possible, as time to event data and will be presented using Kaplan-Meier curves, both overall and by treatment group.
Baseline patient characteristics Patients will be described with respect to all variables collected at baseline (Month 0, Figure  1 ), separately for the two randomized groups. These variables include demographic information, medical and disease history, physical examination, vital signs, electrocardiogram results, FVC, ALSFRS-R, MRC sum-score, EQ-5D-5L, ALSAQ-40, haematological and biochemical parameters, concomitant treatments. Categorical data will be summarised by numbers and percentages. Continuous data will be summarised by mean, standard deviation, range, median and interquartile range. Statistical signi cance tests will not be conducted for baseline characteristics; instead, any imbalances will be noted based on their clinical importance.
Imputation rules and key sensitivity analyses For each patient, all available ALSFRS-R scores will be included in the linear regression model to estimate the monthly decline in the primary analysis. The following rules for handling missing data will be applied: i. for patients dead, as death is clearly a poor outcome and there is no consensus on an alternative score, the lowest score of zero will be assigned at the time of death; ii. for withdrawals or LTF patients without a post-randomization assessment of the ALSFRS-R score, the monthly decline of ALRSFRS-R will be imputed using the "nearest neighbour" procedure  [15]. For each of these subjects, we will identify the ve other participants within the same treatment group whose baseline ALSFRS-R scores were closest to the baseline score of the patient.
Multiple imputation-based sensitivity analyses We intend to calculate the post-randomization slope only after imputing missing ALSFRS-R data in the 18 months of treatment period, then categorize participants in responder/non responder and nally conduct logistic regression analysis to estimate the effect of TUDCA. This approach is supported by simulation studies conducted by  Floden and Bell [16]. A rst sensitivity analysis will be carried out using the Multiple Imputation (MI) technique under Missing At Random (MAR) assumption to deal with missing ALSFRS-R data. The imputation model will include the treatment group, prognostic baseline characteristics, and ALSFRS-R at baseline. For patients deceased or withdrawn without a post-randomization assessment, the same rules speci ed for primary analysis will be applied.
Moreover, imputation of missing data under the Missing Not At Random (MNAR) assumption considering the reason for missingness will be carried out using the delta-based MI method. A range of penalties by reason of missingness (study termination due to adverse events AE and death of patients) will be considered. All other missing data will be imputed under MAR assumption, applying a null penalty. For withdrawals without a post-randomization assessment, the same rules speci ed for primary analysis will be used. A total of 50 imputations for each analysis will be performed, which should provide adequate precision since the amount of missing data is not expected to be large  [17]. Results from different imputed datasets will be combined using Rubin's rule.
Assessing the Impact of COVID-19 pandemic trial conduction To describe the impact of COVID-19 pandemic on trial conduction, with the aim to exclude treatment speci c data patterns by reason due to COVID-19, the following analysis will be performed by treatment group. Baseline characteristics will be summarised by enrolment period categorized in pre-pandemic, pandemic with restrictive measures, and post-pandemic. The number of visits conducted via telemedicine (including audio-visual connections and telephone contacts), missing data, protocol deviations, end of study by reason, COVID-19 infections, concomitant medications for COVID-19, and death due to COVID-19, will be reported overall and by treatment group. A time to occurrence analysis will be conducted for COVID-19related protocol deviations using Kaplan-Maier curves and the log-rank test.
Acknowledgements Members of TUDCA-ALS study group:  Paolo Tornese ,  Antoniangela Cocco ,  IRCCS Humanitas Research Hospital, Rozzano (Italy );  Michela Matteoli ,  Eliana Lauranzano ,  Maria Luisa Malosio ,  Chiara Adriana Elia ,  IRCCS Humanitas Research Hospital, Rozzano (Italy ) and  Institute of Neuroscience, National Research Council, Rozzano (Italy );  Adriano Chiò ,  Umberto Manera ,  Cristina Moglia ,  Andrea Calvo ,  Paolina Salamone ,  Giuseppe Fuda "Rita Levi Montalcini" ,  University of Turin (Italy );  Carlo Colosimo ,  Cristina Spera ,  Prabha Cristina Ranchicchio ,  Azienda Ospedaliera Santa Maria ,  Terni (Italy ). Giuseppe Stipa ,  Domenico Frondizi ,  Azienda Ospedaliera Santa Maria ,  Terni (Italy );  Christian Lunetta ,  Valeria Sansone ,  Claudia Tarlarini ,  Francesca Gerardi ,  Centro Clinico NEMO Fondazione Serena ONLUS, Milan (Italy );  Vincenzo Silani ,  Alberto Doretti ,  Eleonora Colombo ,  Gianluca Demirtzidis ,  IRCCS Istituto Auxologico Italiano, Milan (Italy ) and " Dino Ferrari "  Center, Universitá degli Studi di Milano, Milan (Italy );  Gioacchino.
Methods Study Population and CF/CG Sampling This retrospective study conducted at the Lyon University Hospital (France) included patients who had at least one sample for CF and/or CG detection sent to the central immunology laboratory between September 2013 and April 2021. This study concerned the biological characteristics of CF and CG, and will not address the clinical data of the patients. This study was approved by the Ethics Committee of the Hospitals of Lyon for the protection of people participating in biomedical research under the number AC-2010-1164 and AC-2016-2729. CF must be detected in plasma and not in serum samples, from tubes containing an anticoagulant, such as citrate or ethylenediaminetetraacetic acid (EDTA). In our laboratory, blood samples for CF detection were collected in 2 Â 4.
Statistical Analysis For continuous variables, the results were expressed as mean AE standard error of the mean or median [range] when the distribution was not normal. Distribution normality was tested using the D'Agostino-Pearson omnibus normality test. The Kruskal-Wallis test was used for the variance analysis and the Mann-Whitney test for the comparison of quantitative variables. The Spearman correlation test was used to analyze the association between quantitative variables. The Student's t-test and Wilcoxon test were used for the comparison of quantitative parameters. The chi-squared test and Fisher's exact test were used to analyze qualitative differences. p <0. 05 was considered statistically significant. Calculations were performed using the Graph-Pad Prism version 5. 01 software (La Jolla, California, United States).
Thus, when Ehoh needs to perform a KBO or LPO comparison of an instantiated term, it needs only follow the binding pointers. In full higher-order logic, however, instantiating a variable can trigger a chain of βη-reductions, changing the shape of the term dramatically. To prevent this, λE computes the βη-reduced instances of the terms before comparing them using KBO or LPO. which case the unifier can be read via the iterator's subst field. Otherwise, no more unifiers can be found, and the iterator is no longer valid. The function's pseudocode is given below, including two auxiliary functions: ForwardIter begins by backtracking if the previous attempt was successful (i. e. , all constraints were solved). If it finds a state from which it can continue, it takes term pairs from constraints until there are no more constraints or it is determined that no unifier exists.
6 ]. An oracle can return three results: (1) there is an MGU for the pair (Unifiable), which is recorded in subst, and the next pair in constraints is tried; (2) no MGU exists for the pair (NotUnifiable), which causes the iterator to backtrack; (3) if the pairs do not belong to the subclass that oracle can solve (NotInFragment), we generate possible variable bindings-that is, we guess the approximate form of the solution. λE has a dedicated module that generates bindings (NextBinding). This module is given the current constraint and the values of branch_iter and steps, and it either returns the next binding and the new values of branch_iter and steps or reports that all different variable bindings are exhausted. The bindings that λE's unification procedure creates are imitation, Huet-style projection, identification, and elimination (one argument at a time)  [41, Sect.
3]. A limit on the total number of applied binding rules can be set, as well as a limit on the number of individual rule applications. The binding module checks whether limits are reached using the iterator's steps field. Computing bindings is the only point in the procedure where the search tree branches and different possibilities are explored. Thus, when λE follows the branch indicated by the binding module, it records the state to which it needs to return should the followed branch be backtracked. The state consists of the values of constraints, steps, and subst before the branch is followed and the value of branch_iter that points past the followed branch. The values of branch_iter are either BindBegin, which denotes that no binding was created, intermediate values that NextBinding uses to remember how far through bindings it is, and BindEnd, which indicates that all bindings are exhausted.
Preprocessing, Calculus, and Extensions Ehoh's simple λ-free higher-order calculus performed well on Sledgehammer problems and formed a promising stepping stone to full higher-order logic  [42]. When implementing support for full higher-order logic, we were guided by efficiency and gracefulness with respect to Ehoh's calculus rather than completeness. Whereas Zipperposition provides both complete and incomplete modes, λE only offers incomplete modes. Preprocessing. Our experience with Zipperposition showed the importance of flexibility in preprocessing the higher-order problems  [40]. Therefore, we implemented a flexible preprocessing module in λE. To maintain compatibility with Ehoh, λE can optionally transform all λabstractions into named functions. This process is called λ-lifting  [19].
Evaluation We now try to answer two questions about λE: How does λE compare against other higher-order provers (including Ehoh)? Does λE introduce any overhead compared with Ehoh? To answer these questions, we ran provers on problems from the TPTP library  [38]  and on benchmarks generated by Sledgehammer (SH)  [28]. The experiments were carried out on StarExec Miami  [36]  nodes equipped with Intel Xeon E5-2620 v4 CPU clocked at 2. 10 GHz. For the TPTP part, we used the CASC 2021 2  time limits: 120 s wall-clock and 960 s CPU. For SH benchmarks and to answer the other question, we used Sledgehammer's default time limit: 30 s wall-clock and CPU. The raw evaluation data is available online. 3 Comparison with Other Provers. To answer the first question, we let λE compete with the top contenders in the higher-order division of CASC 2021: cvc5 0.
Compared with Ehoh, λE features a redesigned module for automatic scheduling, it can exploit multiple CPU cores, and its heuristics have been more extensively trained on higher-order problems. The results are shown in Figure  2. λE dramatically improves E's higher-order reasoning capabilities compared with Ehoh. It solves 20% more TPTP benchmarks and 7% more SH benchmarks. The reason for the higher performance increase for TPTP is likely that TPTP benchmarks tend to require more higher-order reasoning than SH benchmarks, which often have a large first-order component and for which Ehoh was already very successful. λE was envisioned as an efficient backend to proof assistants. As such, it excels on SH benchmarks, outperforming the competition. On TPTP, it outperforms all higher-order provers other than Zipperposition-coop.
Acknowledgment. Ahmed Bhayat  and  Martin Suda  provided Vampire configurations optimized for Sledgehammer. Andrew Reynolds  did the same for cvc5. Jannis Limperg  helped us debug the submission artifact. Simon Cruanes ,  Wan Fokkink ,  Mark Summerfield , and the anonymous reviewers suggested several textual improvements. We thank them all. This research has received funding from the  European Research Council (ERC)  under the  European Union 's  Horizon 2020 research and innovation program  (grant agreement No. 713999 ,  Matryoshka ). Vukmirović and  Blanchette  have received funding from the  Netherlands Organization for Scientific Research (NWO)  under the  Vidi program  (project No. 016. Vidi. 189. 037 ,  Lean Forward ).
Molecular phylogenetics We sampled 321 extant species out of 370 recognized species, including all genera and a species sampling across all genera as follows: Bowenia Hook. ex Hook. f. 2/2, Ceratozamia Brongn. 33/38, Cycas L. 104/118, Dioon 13/18, Encephalartos 65/65, Lepidozamia 2/2, Macrozamia Miq. 27/41, Microcycas 1/1, Stangeria 1/1, and Zamia 74/84. This results in 86. 8% (321/370) of the total species richness. Ginkgo biloba L. was added as an outgroup, which is recognized as the sister lineage of cycads  (Liu et al. , 2022; Yang et al. , 2022). The molecular matrix was assembled from sequences available on GenBank. We included 18 loci including plastidial (matK, rbcL), mitochondrial (26S), and nuclear genome sequences (AC3, F3H, GroES, RPB1, SAMS, CyAG, HTS, WRKY4, 40S, 5. 8S, LiSH, GTP, PHYP, HZP, and PEX4).
Sampling fossils and morphological data We generated a matrix of 31 morphological characters covering leaf morphology and cuticular anatomy, based on literature and direct observations (Supporting Information Methods S1; Table  S1 ; Notes S1). We then scored characters for 60 species of fossil cycad leaves, including macrofossils and dispersed cuticles, as well as for the extant taxa of the molecular matrix. Coding was based on observations of the specimens or descriptions and photographs of the specimens available in the literature  (Greguss, 1968; Mickle et al. , 2011; Barone Lumaga et al. , 2015; Coiro & Pott, 2017; Vovides et al. , 2018; Erdei et al. , 2019; Coiro et al. , 2020a Coiro et al. , , 2021;; Glos et al. , 2022).
Estimating ancestral latitudes The latitudinal span of the Cycadales throughout their evolutionary history was reconstructed using the directional Brownian-Motion method of  Silvestro et al. (2018b)  with the variable trend implemented in  Zhang et al. (2022). Mean latitude for the extant genera was obtained using occurrence data obtained from the Global Biodiversity Information Facility (GBIF). These occurrences were obtained by querying the different genera using the function occ_search from the R package RGBIF  (Chamberlain et al. , 2019) , and then cleaned using the function clean_coordinates from the R package COORDINATECLEANER  (Zizka et al. , 2019). Further cleaning of occurrences was done manually. This resulted in mean latitude estimates for 207 extant taxa. Paleolatitudes for the fossil taxa were obtained using the paleolatitude calculator  (van Hinsbergen et al.