dataesr/scientific-paragraphs-categorization huggingface dataset:
Key messages. Several openly available data sets are available that contain PPG signals. However, most are only suitable for assessing techniques which use a single PPG signal to estimate BP, or for investigating associations between PPG-derived parameters and chronological age. This review identified six openly available data sets that have been used to assess PPG-derived parameters of vascular age, as summarized in Table  A4. All the data sets contain a PPG signal and reference BP values, so are suitable for assessing techniques to estimate BP from a single PPG signal. Most data sets include subjects' chronological ages. The MIMIC and University of Queensland datasets also contain ECG signals, so may be suitable for assessing techniques that use both PPG and ECG signals (although the time delay between ECG and PPG signals in the MIMIC database is not necessarily constant, even within a particular subject)  (231).
The MIMIC data set also contains reference ABI measurements. The Pulse Wave Database contains simulated PPG pulse waves (4), so does not permit in vivo assessments, but can be used to assess how simulated pulse waves vary under a range of cardiovascular conditions (including changes in arterial stiffness) for subjects aged from 25 to 75. Consequently, it may be helpful for initial assessment of techniques, and also to aid the design of in vivo studies  (4). Additional openly available data sets would be highly valuable for future research, ideally containing simultaneous ECG signals and PPG signals at different sites, and reference indicators of vascular age (such as PWV, BP, and PAD diagnoses). There is relatively little code available to estimate parameters of vascular age from the PPG. Only one publication was identified for which the related code is publicly available  (41).
The "PulseAnalyse" tool  (4, 232)  may also be of use to researchers, as it 1) identifies individual pulse waves in PPG signals, 2) identifies fiducial points on pulse waves, and 3) derives feature measurements suitable for assessing vascular age. In addition, "TTalgorithm" is a helpful script for extracting PTT measurements from simultaneous pulse wave signals  (233). In the future, the field would benefit greatly from researchers making their analysis code publicly available,  In the future, data sets containing annotations of fiducial points on PPG pulse waves could be used to assess pulse wave analysis algorithms. We are not aware of any data sets containing manual annotations of fiducial points, although the Pulse Wave Database does contain machine-generated annotations (4).
Collating search results. Results from each search engine were downloaded as text files. Files were obtained in Bibtex format for all engines apart from PubMed, for which the "Abstract (text)" format was used. The results were then collated using the ppg_vascage_review_collate_search_data. m MATLAB script, available in Supplemental material at https://doi. org/ 10. 5281/zenodo. 5039640.
Key aspects of the experimental methodologies used to assess PPG-derived parameters of vascular age are summaries in Table A3, and discussed in How Has the Performance of PPG-Derived Parameters of Vascular Age Been Assessed? Methods Used to Assess the Performance of PPG-Derived Parameters of Vascular Age The openly available data sets that have been used to assess PPG-derived parameters of vascular age are summarized in Table A4, and discussed in What Resources are Available to Researchers? GRANTS This article is based upon work from COST ACTION "Network for Research in Vascular Ageing" CA18216 supported by COST (European Cooperation in Science and Technology): www. cost. eu. The work was supported in part by British Heart Foundation Grants PG/15/104/31913 and FS/20/20/34626 (to P. H. Charlton); in part by the European Regional Development Fund Project No.
01. 2. 2-LMT-K-718-01-0030 (to V. Marozas) under grant agreement with the Research Council of Lithuania; in part by the Estonian Ministry of Education and Research under personal post-doctoral research funding PUTJD815 (to K. Pilt); and in part by the Serbian Ministry of Education, Science and Technological Development Grants 32040 and 41022 (to D. Žikić). DISCLOSURES S. Zanelli collaborates with Axelife, a company that designs and develops PPG-based medical devices. D. Kulin is shareholder and employee in E-Med4All Europe Ltd. , a Hungarian med-tech startup developing various PPG-based telemedicine solutions. M. Hallab is CEO of Axelife and has authored patents used by Axelife. E. Bianchini is co-founder of QUIPU s. r. l. , Pisa, Italy, a spin-off company of the Italian National Research Council and the University of Pisa developing medical software for ultrasound image processing.
H. Charlton); in part by the European Regional Development Fund Project No. 01. 2. 2-LMT-K-718-01-0030 (to V. Marozas) under grant agreement with the Research Council of Lithuania; in part by the Estonian Ministry of Education and Research under personal post-doctoral research funding PUTJD815 (to K. Pilt); and in part by the Serbian Ministry of Education, Science and Technological Development Grants 32040 and 41022 (to D. Žikić). DISCLOSURES S. Zanelli collaborates with Axelife, a company that designs and develops PPG-based medical devices. D. Kulin is shareholder and employee in E-Med4All Europe Ltd. , a Hungarian med-tech startup developing various PPG-based telemedicine solutions. M. Hallab is CEO of Axelife and has authored patents used by Axelife. E. Bianchini is co-founder of QUIPU s.
6) Classification statistics, e. g. , sens, spec 20 (12. 3) Number of datasets used 1 141 (87. 0) 2 15 (9. 3) ! 3 6 (3. 7) AIx, augmentation index. V. M. interpreted results of experiments; P. H. C. , K. P. , M. B. D. Ž. , and V. M. prepared figures; P. H. C. , B. P. , K. P. , M. B. , S. Z. , D. K. , J. A. , V. D. , D. V. , D. Ž. , and V. M. , drafted manuscript; P. H. C. , B. P. , K. P. , M. B. , S. Z. , D. K. , J. A. , E. B. , C. C. M. , D. T. -P. , B. H. , D. V. , D. Ž. , and V. M. edited and revised manuscript; P. H. C. , B. P. , K. P. , M. B. , S. Z. , D. K. , J. A. , M. H. , E. B. , C. C. M. , D. T. -P. , V. D. , B. H. , D. V. , D. Ž. , and V. M. , approved final version of manuscript. sure in the radial artery using strain-based pulse wave and photoplethysmography sensors. Estimation system of blood pressure variation with photoplethysmography signals using multiple regression analysis and neural.
University of (229) PPG, ECG, BP BP 32 Recordings from patients during Queensland Vital anesthesia, ranging from Signs Dataset minutes to hours in duration. Pulse Wave (4, 230) PPG, BP BP, pulse wave ve- 4,374(simulated) Single simulated PPG pulse waves Database locity, chronologi- representative of healthy adults cal age, others aged 25-75. BP, blood pressure; PPG, photoplethysmogram.
Parallel iterative solver Massively parallel architectures divide computational tasks into threads which, in the case of graphics processing units, are grouped into blocks (indexed by b) following a common sequence of instructions, and able to take advantage of shared data, see Remark 2. 1. Following [WDB + 08, JW08, GHZ18], the main loop of our iterative eikonal equation solver is designed to take advantage of this computational architecture, see Algorithm 1. It is written in the Python programming language, which is also used for the pre-and post-processing tasks, and launches Algorithm 2 as a CUDA kernel via the cupy 2  library. The discretization domain X h , which is a three dimensional Cartesian grid (7), is split into rectangular tiles X b h , indexed by b ∈ B h , see Figure  (3, left).
It is important to point out that the P. berghei ANKA strain used throughout this study produced gametocytes with expected ookinete conversion rates (data not shown). For proteomic experiments, a schematic illustration of the methodology used is shown in Figure  1. Schizonts and gametocytes were purified, and IP experiments were performed with proteins extracted from five independent biological replicates using beads coupled to anti-RFP antibody (see Section 3 for details). Prior to MS proteomic analysis, the presence of tagged baits in each IP was checked by immunoblot (illustrated in Figure  1 ). As control experiments, we performed IP on proteins extracted from the same parasite stages of the parental wild-type strain. In all experiments, the eluted PbPP1c and PbI2-interacting complexes were analyzed by LC-MS/MS.
Identification of PbPP1c-Interacting Proteins in Schizonts and Gametocytes The digested samples were directly analyzed by LC-MS/MS using five biological replicates to identify PbPP1c-associated proteins. After statistical analysis, the data revealed a total of 489 proteins including the mCherry-PbPP1c bait protein. Among them, 157 proteins were present in both schizont and gametocyte extracts and considered as common interacting proteins (CIPs) (Supplementary Materials Table  S1 , sheets 2-4 in pink; Figure  2 ). In addition, 68 proteins were detected as specific interacting proteins in schizonts (SIPS) (Supplementary Materials Table  S1 , sheet 2 in green), and 263 proteins were found as specific interacting proteins in gametocytes (SIPGs) (Supplementary Materials Table  S1 , sheet 3 in blue).
Identification of PbPP1c-Interacting Proteins in Schizonts and Gametocytes The digested samples were directly analyzed by LC-MS/MS using five biological replicates to identify PbPP1c-associated proteins. After statistical analysis, the data revealed a total of 489 proteins including the mCherry-PbPP1c bait protein. Among them, 157 proteins were present in both schizont and gametocyte extracts and considered as common interacting proteins (CIPs) (Supplementary Materials Table  S1 , sheets 2-4 in pink; Figure  2 ). In addition, 68 proteins were detected as specific interacting proteins in schizonts (SIPS) (Supplementary Materials Table  S1 , sheet 2 in green), and 263 proteins were found as specific interacting proteins in gametocytes (SIPGs) (Supplementary Materials Table   1. Workflow and study overview.
Gene Ontology Enrichment Analyses of PbPP1c-Interacting Proteins In order to gain some insight into potential pathways in which PP1c could be involved and to avoid bias in their identification, GO enrichment pathways including all detected proteins in each parasite stage were explored using PlasmoDB software (release 51, March 2021). We analyzed enriched GO terms/pathways of computed and curated biological processes. In the case of schizonts, 37 pathways were found to be enriched, including 5 with a strong fold enrichment (>5) involved in spliceosomal SnRNP assembly, formation of cytoplasmic translation initiation complex, cytoplasmic translation, ribonucleoprotein complex assembly, and subunit organization (Supplementary Materials Table  S2 ). Closer examination of GO-enriched terms revealed that a large list of genes had multiple annotations and, in some cases, GO pathways included shared or the same set of genes, leading to overlap and duplication.
Consequently, and in the search for the most informative data, full GO terms were reduced using the GO slimmer tool (PlasmoDB release 51) to obtain Slim annotations that were further inspected to reassign, when appropriate, to GO parent terms (Table  1 ). However, general GO terms were excluded (such as biosynthetic process and metabolic process) while branch information was retained to ensure that the enriched family of each function was present in the data. The results showed that, in schizont stages, PbPP1c was predominantly associated with "cellular component assembly" (20 proteins), encompassing "protein-containing complex assembly" (18 proteins), "ribonucleoprotein assembly" (13 proteins), and in "translation" complex (57 proteins) (Table  1 ). Next, the proteins involved in each main GO Slim pathways underwent the retrieval of networks for interacting proteins using STRING software (v.
Proteins from both of these KEGG pathways are colored in purple. Assignation to coat protein complexes, clathrin-associated adaptor protein complex 1 and to Sec machinery was conducted manually based on protein description in the PlasmoDB and literature. Black stars indicate proteins with RVxF motifs based on the Wakula consensus sequence  [19]. Proteins of each network are listed in adjacent tables showing PlasmoDB geneID, short name, and protein description. STRING analysis was performed using the 11. 0b version of the database. Figure  5. STRING network visualization of PbPP1c-interacting proteins belonging to "protein transport" GO Slim in gametocyte stages. Using the Cytoscape STRING application, a network was retrieved for the 24 proteins from the protein transport GO Slim of the PbPP1c interactome in gametocyte stages.
The combined PbI2-interacting proteins were first analyzed for GO enrichment annotations. This analysis did not show significant enrichment of any specific pathway in terms of biological processes and molecular processes (data not shown). However, the analysis revealed significant enrichment of cellular components in schizonts (GO term "cytoplasmic microtubule") and gametocytes (GO term "cytosol"), including two and six proteins respectively (data not shown). Next, in order to set-up possible functional associations, all PbI2interacting proteins detected in each parasite stage were further searched using the STRING database for interaction mapping. These analyses did not lead to any protein-protein interaction enrichment (p-value = 0. 237 for schizonts and 0. 604 for gametocytes). However, significant functional enrichment in axonemal dynein light chains kinesin motor, tubulin, and actin clusters were observed in the PbI2 interactome in schizonts (data not shown).
At 121 min, the gradient stayed at 80% for 5 min, and at 126 min, it returned to 5% to re-equilibrate the column for 20 min before the next injection. One blank was run between each replicate to prevent sample carryover. Peptides eluting from the column were analyzed by data dependent MS/MS, using the top-10 acquisition method and fragmented using higher-energy collisional dissociation (HCD). Briefly, the instrument settings were as follows: the resolution was set to 70,000 for MS scans and 17,500 for the data dependent MS/MS scans in order to increase speed. The MS AGC target was set to 3 × 10 6 counts with a maximum injection time set to 60 ms, while the MS/MS AGC target was set to 1 × 10 5 with maximum injection time set to 120 ms. The MS scan range was from 400 to 2000 m/z. Dynamic exclusion was set to a 30 s duration.
The MS files were searched with the MaxQuant software version 1. 6. 6. 0 and searched with the Andromeda search engine against the database of Mus musculus from Swiss-Prot 07/2020 and Plasmodium berghei ANKA from PlasmoDB (v. 37). To search parent mass and fragment ions, we set a mass deviation of 3 and 20 ppm, respectively. The minimum peptide length was set to 7 amino acids and strict specificity for trypsin cleavage was required, allowing up to two missed cleavage sites. Carbamidomethylation (Cys) was set as fixed modification, whereas oxidation (Met) and N-term acetylation were set as variable modifications. The false discovery rates (FDRs) at the protein and peptide levels were set to 1%. Scores were calculated in MaxQuant as described previously  [36]. The reverse and common contaminants hits were removed from MaxQuant output.
Proteins were quantified according to the MaxQuant label-free algorithm using LFQ intensities; protein quantification was obtained using at least 2 peptides per protein. Matches between runs were allowed. Statistical and bioinformatic analyses, were performed with Perseus software (version 1. 6. 12. 0) freely available at www. perseus-framework. org [37]. For statistical comparison, we set six groups: PbWT schizonts and gametocytes as the negative control (S_WT, G_WT), immunoprecipitates from schizonts and gametocytes expressing PbPP1c-mCherry (i. e. , S_PP1 and G-PP1), and immunoprecipitates from schizonts and gametocytes expressing PbI2-mCherry (i. e. , S_I2 and G_I2). Each group contained five biological replicates. We then filtered the data to keep only proteins with at least 5 valid values in at least one group.
Assignation to Eukaryotic translation initiation 3 subunits and to microtubule-associated proteins was conducted manually, based on protein description in PlasmoDB and literature. Black stars indicate proteins with RVxF motifs based on the Wakula consensus sequence [19]. Proteins of each network are listed in adjacent tables showing PlasmoDB geneID, short name, and protein description. STRING analysis was performed using the 11. 0b version of the software.
Background: Many studies link G protein-coupled receptors (GPCRs) to cancer. Some endocrine tumors are unresponsive to standard treatment and/or require long-term and poorly tolerated treatment. This study explored, by bioinformatics analysis, the tumoral profiling of the GPCR transcriptome to identify potential targets in these tumors aiming at drug repurposing. Methods: We explored the GPCR differentially expressed genes (DEGs) from public datasets (Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA)). The GEO datasets were available for two medullary thyroid cancers (MTCs), eighty-seven pheochromocytomas (PHEOs), sixty-one paragangliomas (PGLs), forty-seven pituitary adenomas and one-hundred-fifty adrenocortical cancers (ACCs). The TCGA dataset covered 92 ACCs. We identified GPCRs targeted by approved drugs from pharmacological databases (ChEMBL and DrugBank).
Results: The profiling of dysregulated GPCRs was tumor specific. In MTC, we found 14 GPCR DEGs, including an upregulation of the dopamine receptor (DRD2) and adenosine receptor (ADORA2B), which were the target of many drugs. In PGL, seven GPCR genes were downregulated, including vasopressin receptor (AVPR1A) and PTH receptor (PTH1R), which were targeted by approved drugs. In ACC, PTH1R was also downregulated in both the GEO and TCGA datasets and was the target of osteoporosis drugs. Conclusions: We highlight specific GPCR signatures across the major endocrine tumors. These data could help to identify new opportunities for drug repurposing.
Recent evidence supports the involvement of many GPCRs and their ligands in controlling the initiation and progression of tumors (cell proliferation, metastasis, adhesion or angiogenesis)  [5] [6] [7]. Recently, many studies revealed that GPCRs are mutated and/or their expression dysregulated in multiple cancers  [5, 8]. Therefore, GPCRs can be considered as attractive targets for novel therapeutic treatments of tumors or for the repurposing of approved drugs with a target-based approach. Bioinformatics approaches have allowed for the identification of potential therapeutic and/or prognostic targets in cancer. The Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases are the most frequently used to identify differentially expressed genes (DEGs). GEO is a database repository of microarrays gene expression data.
The Cancer Genome Atlas (TCGA) is a project that has generated publicly available genomic and clinical data for various types of cancer  [9]. GPCRomic studies have been conducted for the most frequent cancers, such as prostate or breast cancer  [7, 10, 11]. In addition, the team of P. Insel focused more specifically on pancreatic adenocarcinoma  [10, 12, 13]. Moreover, we recently established an atlas of GPCRs in radioactive iodine-resistant thyroid cancer by compiling data from the GEO database, TCGA cohort and our transcriptomic analysis  [14]. However, the dysregulation of GPCR expression has not been investigated in depth in other endocrine tumors. Endocrine tumors include tumors in glands that produce hormones (thyroid, parathyroid, pituitary, adrenal gland) and those originated from neuroendocrine cells.
Collection of GEO Datasets and TCGA Data To comprehensively evaluate GPCRs expression between endocrine tumors and nontumoral tissues, we incorporated the expression data of GPCRs from GEO. We selected four types of endocrine tumors: medullary thyroid cancer (MTC), pituitary neuroendocrine tumors (PitNETs), pheochromocytoma (PHEO) and paraganglioma (PGL) and adrenocortical carcinoma (ACC). An electronic search of the GEO databases was carried out with the following key terms: "medullary thyroid", "adrenocortical", "pituitary" "pheochromocytoma", "paraganglioma", AND "cancer OR carcinoma OR tumor". The GEO datasets published before 1 April 2020 were included. We filtered data by 'Expression profiling by array' in 'homo sapiens'. The reference lists were manually reviewed for further identification of relevant studies.
We selected the studies with the following inclusion criteria: studies contained mRNA expression data in tumoral and non-tumoral tissues in previous cited endocrine tumor. The exclusion criteria were: cell lines, xenograft studies, miRNA analyses, no comparison with normal tissue, duplicate. The microarray datasets were downloaded from the GEO database for each endocrine tumor and are summarized in Table  1. To validate transcriptomic analysis, we also obtained the GPCRs expression profile of human ACC from The Cancer Genome Atlas (TCGA, https://cancergenome. nih. gov/, accessed on 17 December 2021) and adrenal normal tissues from The Genotype-Tissue Expression Database (GTEX, https://gtexportal. org/home/, accessed on 17 December 2021) with online analysis tool (https://insellab. github. io/, accessed on 17 December 2021).
The DEGs with an adjusted p < 0. 05 were considered as the cut-off criteria. Then, to strengthen the significance of the data, we chose to select DEGs obtained from all independent datasets of tumor tissue samples rather than combine the GSE data, as in previous publications  [11, 37]. We applied for Venn software online (http://www. interactivenn. net/, accessed on 26 June 2020) to obtain the common DEGs in all independent cohorts for each endocrine tumor  [38].
Identification of GPCRs as Targets for Approved Drugs We evaluated drug targets among GPCRs differentially expressed between normal and tumoral samples and the list of approved drugs for each of these receptors. This was conducted using two pharmacological databases: ChEMBL and DrugBank. DrugBank is a clinically oriented drug database that provides information about drug and drug action for more than 500,000 drugs  [39]. ChEMBL is an open database containing information extracted from the medicinal chemistry literature regarding the compounds tested and their primary target  [40]. Approved drugs were verified with those listed by the Food and Drug Administration (FDA) (https://www. fda. gov, accessed on 3 January 2022) and by the European Medicines Agency (EMA) (https://www. ema. europa. eu, accessed on 3 January 2022).
Survival Analysis The GEPIA database (http://gepia. cancer-pku. cn, accessed on 14 February 2022) was used for survival analysis of the dysregulated GPCR genes. Data from TCGA-ACC project (88 patients with ACC) were analyzed. To analyze the prognostic value of GPCR genes, the patient samples were split into 2 groups according to the median expression of the biomarker. The two patient cohorts were compared by a Kaplan-Meier survival plot, and the logrank P value was calculated.
Medullary Thyroid Cancer For medullary thyroid cancer (MTC), only one study was available from the GEO database (GSE27155). The samples consisted of four normal thyroid tissues and two medullary carcinomas. The analysis revealed 14 DEGs for GPCRs (seven downregulated and seven upregulated) (Table  2 ). The TSH receptor (TSHR) was the main receptor downregulated in MTC compared to normal thyroid tissue (log 2 FC: -2. 22) as well as GPRC5A; an orphan receptor was also found to be downregulated (log 2 FC: -1. 93). For the upregulated GPCRs, the majority were weakly upregulated, such as dopamine receptor (DRD2, log 2 FC: 0. 62) and cholecystokinin receptor (CCKBR, log 2 FC: 0. 59). The two most upregulated GCPRs compared to normal thyroid tissue were the adhesion receptor ADGRG2 (log 2 FC: 1. 42) and the receptor LGR5 (Leucine Rich Repeat Containing G Protein-Coupled Receptor 5, log 2 FC: 1.