dataesr/scientific-paragraphs-categorization huggingface dataset:
In situ assessment of necrosis, cell proliferation, angiogenesis and apoptosis When present, necrosis was delineated on virtual slides created on a Nanozoomer2. 0H scanner (Hamamatsu/ Japan), and quanti ed using DotSlide2 software. Results were expressed as the sum of necrotic areas for each section, and the mean ± SEM. For micro-vessel density, proliferation and apoptotic counts, an indirect immunoperoxidase method was performed on 5 µm-thick tissue sections, using monoclonal mouse anti-human Ki67 antibody (M724001-2, 1:100, Agilent) as primary antibody for proliferation, polyclonal rabbit anti-human cleaved-caspase-3 antibody (Asp175, 1:200, Cell signalling Technology) as primary antibody for apoptosis, and rabbit polyclonal anti-mouse CD31 antibody (SZ31, 1:30, Dianova) as primary antibody for micro-vessel density.
For CD31 staining, the secondary antibody was a rabbit anti-rat IgG H&L (ab6703,1:200, Abcam), for Ki67 staining, a rabbit anti-mouse IgG1 H&L (clone M1gG51-4, 1:200, Abcam) was used. The secondary antibody was coupled with an anti-rabbit OmniMap detection kit (Roche diagnostic, Meylan, France). For each tumor section analyzed, proliferation and apoptotic cell counts were performed on ve different elds at x400 magni cation, using a ProvisAX70 microscope (Olympus, Tokyo) with a wide-eld eyepiece number 26. 5 providing a eld size of 0. 344mm2 at X400 magni cation. Microscope images were captured using a ColorView-III digital camera, and analyzed using Olympus-SIS Cell F software. The percentage of positive cells in 100 cancer cells was determined, and results were expressed as means ± SEM. For micro-vessel density, CD31-positive micro-vessels were counted on ten different elds, at X400 magni cation.
Statistical analysis Statistical analyses were performed using R Studio 1. 3. 1073 statistical software. For analysis of the copy number variation derived from the result from Oncoscan® technology and our meta-analysis data, we used the copynumber package in R. For counts of NOTCH1-expressing tumor cells, the mean ± SEM was calculated for each tumor sample (primary RCC, metastasis or tumor-xenograft), and represented in bar graphs. Quantitative values were compared using Student's t-test (two-tailed), while proportions were compared using the Z-test. P values under 0. 05 were considered signi cant.
In contrast to LY411575, using CB-103 designed to block GSI-insensitive dominant active forms of NOTCH1-ICD, we obtained a strong anti-tumor effect, particularly when this drug was combined with sunitinib. In vitro, CB-103 was able to inhibit the growth of LAL-T expressing NOTCH1-ICD, and of triple negative breast cancer cell lines, while GSIs were inactive  [50]. CB-103 is currently being evaluated in a phase-I/II clinical trial for advanced solid tumors and haematological malignancies (https://clinicaltrials. gov/ct2/show/NCT03422679), but our study opens the way to further development in metastatic RCC. In our study, using two opposed RCC patient-derived xenografts, we showed that the pL1575P_c4724T_C NOTCH1 mutation could be used as a predictive biomarker for response to CB-103 inhibitor: the higher the NOTCH1 allelic frequency, the greater the anti-tumor effect.
Acknowledgments We would like to thank  Angela Swaine  and  Sarah Leyshon  for the revision of the  English language. Funding Bourse d'Excellence de l'Ambassade de France au Viet Nam  (Grant number: 971522C). INSERM. Authors' Contributions Conception and design:  A. G. Bousquet Development  of the methodology:  T. O. Bui ,  M. Bouchtaoui , G. Bousquet Data acquisition (provided animals, acquired managed patients, provided facilities, etc. ):  T. O. Bui ,  G. Gapihan ,  J. Paris ,  M. Soussan ,  M. Ziol ,  V. Glabeke ,  M. El Bouchtaoui ,  A. Janin ,  G. Bousquet.
Introduction: Multiple groups have reported on the usefulness of ablating in atrial regions exhibiting abnormal electrograms during atrial fibrillation (AF). Still, previous studies have suggested that ablation outcomes are highly operator-and centerdependent. This study sought to evaluate a novel machine learning software algorithm named VX1 (Volta Medical), trained to adjudicate multipolar electrogram dispersion. Methods: This study was a prospective, multicentric, nonrandomized study conducted to assess the feasibility of generating VX1 dispersion maps. In 85 patients, 8 centers, and 17 operators, we compared the acute and long-term outcomes after ablation in regions exhibiting dispersion between primary and satellite centers. We also compared outcomes to a control group in which dispersion-guided ablation was performed visually by trained operators.
4] [5] [6] [12] [13] [14]  Here, we aimed to evaluate an artificial intelligencebased, expert-trained, real-time dispersion adjudication tool named VX1 (Volta Medical). The preliminary evaluation of the AIFib Software Trial (Ev-AIFib) was conducted to determine the feasibility and relevance of constructing VX1 dispersion maps for the ablation of persistent AF. We tested the hypothesis that the use of VX1 allows for a robust center-tocenter standardization of ablation outcomes.
| VX1-enabled algorithmic dispersion adjudications, construction of dispersion maps The VX1 device consists of a computer connected to cardiac electrophysiology recording systems through a customized data cable. Analog data received by VX1 is then digitized by an integrated analog-to-digital converter. VX1 relies on the off-line pretraining of multiparametric machine learning algorithms on a database of annotated AF intracardiac electrograms (Figure  1A ,B-see Supporting Information for more details). As shown in Figure  1A ,B, VX1's interface presents operators with a simple color coding of multipolar catheters' dipole numbers: blue for no dispersion; orange for the high likelihood of dispersion; red for a very high likelihood of dispersion. Then, stable adjudications are sound-coded and color-coded on an upper hemicycle schematic of the dipoles under consideration (see Supporting Information: Video).
I G U R E 1 (A) The VX1 software is based on machine learning classification algorithms including a deep learning algorithm. VX1 is trained offline on a large proprietary database. Electrograms are digitized and processed in real-time by VX1 which in turn provides operators with visual and audio cues representing areas of interest. A direct connection from the acquisition system to a computer installed with the VX1 software allows for data transmission. (B) Flowchart diagram explaining the main steps carried out when the trained algorithm is used on new data. Electrogram information provided by the electrophysiology recording system is processed by a feature extraction module extracting 65 features per single track (Step 1), these features are analyzed by a trained machine learning algorithm to produce an array of dispersion likelihood A (Step 2).
F I G U R E 1 (A) The VX1 software is based on machine learning classification algorithms including a deep learning algorithm. VX1 is trained offline on a large proprietary database. Electrograms are digitized and processed in real-time by VX1 which in turn provides operators with visual and audio cues representing areas of interest. A direct connection from the acquisition system to a computer installed with the VX1 software allows for data transmission. (B) Flowchart diagram explaining the main steps carried out when the trained algorithm is used on new data. Electrogram information provided by the electrophysiology recording system is processed by a feature extraction module extracting 65 features per single track (Step 1), these features are analyzed by a trained machine learning algorithm to produce an array of dispersion likelihood A (Step 2).
Lipases are versatile enzymes widely used in the pharmaceutical, cosmetic, and food industries. They are green biocatalysts with a high potential for industrial use compared to traditional chemical methods. In recent years, lipases have been used to synthesize a wide variety of molecules of industrial interest, and extraordinary results have been reported. In this sense, this review describes the important role of lipases in the synthesis of phytosterol esters, which have attracted the scientific community's attention due to their beneficial effects on health. A systematic search for articles and patents published in the last 20 years with the terms "phytosterol AND esters AND lipase" was carried out using the Scopus, Web of Science, Scielo, and Google Scholar databases, and the results showed that Candida rugosa lipases are the most relevant biocatalysts for the production of phytosterol esters, being used in more than 50% of the studies.
General Aspects Several stearic enzymes have been successfully tested for the synthesis of phytosterol esters, with lipases as the most  [71]. Esters produced with biocatalysts can be considered "green" products and satisfy the recent demand for sustainability  [72]. Thus, to produce phytosterol esters, biocatalysis provides a good way to obtain the mandatory quality for use in food. Biocatalysis of phytosterol esters can be performed by direct esterification or by transesterification (Figure  4 ). A systematic search for the terms "Phytosterol AND esters AND lipase" in articles published in the last 20 years (2001-2021) was performed using the Scopus, Web of Science, Scielo, and Google Scholar databases. The papers retrieved were critically evaluated, and those related to the enzymatic synthesis of these molecules were selected.
Reviews and original papers related to chemical synthesis, nutritional and medical effects, physicochemical properties, analytical procedures, and economic aspects were excluded. The final set of articles contained 66 documents, which were the object of our analysis (Table  1 ). More than 85% of these studies used direct esterification as the main reaction strategy. The process conditions and results reported in these studies are summarized in Table  1. Table  1  shows that Candida rugosa lipases are the most relevant biocatalysts to produce phytosterol esters, being used in more than 50% of the studies found in Scopus, Web of Science, Scielo, and Google Scholar databases. C. rugosa lipases are commercially available, usually as a mixture of isoenzymes, and are among of the most frequently used enzymes in biotransformations  [138].
Chemoenzymatic methods have also been reported in some studies. Tan and Shahidi  [97]  studied the chemoenzymatic synthesis of phytosteryl caffeates by means of an intermediate caffeate vinyl, which was initially produced chemically and later esterified with phytosterols by lipase-assisted alcoholysis. In another study, Tan and Shahidi  [139]  chemoenzymatically synthesized two new groups of phytosteryl phenolates: phytosteryl sinapates and vanillates. Some patents related to the biocatalysis of phytosterol esters have also been granted in the past 20 years. The search for "Phytosterol AND esters" in the title and abstract of these texts in the Espacenet database led to 85 results, four of which dealt with biocatalysis (Table  2 ). Table  2  shows that C. rugosa lipase was also identified in those patents, indicating that, indeed, this is a good biocatalyst for industrial production of phytosterol esters.
Phytosterol esters were separated by a non-aqueous reversed-phase chromatography technique using acetone (A) and acetonitrile (B) as mobile phase with a flow rate of 1. 0 mL/min in gradient condition: A 100% at 0 min, A 30% at 10 min, A 20% at 30 min, A 0% at 40 min, and A 0% (B 100%) until 55 min. The analytes were ionized in an atmospheric pressure chemical ionization ion source, and more than 20 compounds were identified. The complete separation of phytosterols and phytosterol esters was performed, including the higher fatty acids such as palmitic acid (C16), stearic acid (C18), oleic acid (C18:1), linoleic acid (C18:2), and linolenic acid (C18:3) without preliminary separation and quantified based on authentic standards. Free and conjugated phytosterols' profile in rice bran and its oil were determined by high-temperature GC-MS (HTGC-MS), combined with the method of a single standard to determine multi-components to provide qualitative and quantitative data on free sterols and steryl glycosides  [183].
To achieve a more comprehensive chemical profile, steryl ferulates were determined by high-performance liquid chromatography with a photondiode array detector (HPLC-PDA). The authors claimed to have reduced laborious sample pre-treatment and native analytes' losses, improving overall data quality and quantification accuracy. Analysis of phytosterol esters is a subject of continuous interest, and, therefore, effective methods of separation and quantification are continually being developed and published. Recently, an innovative method was developed for phytosterol derivatization to improve HPLC-PDA analysis and ESI-MS detection for analysis of plant sterols and stanols  [184]. This derivatization method aims at improving the detection of these esters in the LC chromatographic techniques by reducing lipophilicity and attaching a chromophore group.
Lipases are carboxylic ester hydrolases that act selectively on triacylglycerols and, as such, are classified by the European Commission as EC 3. 1. 1. 3: EC 3, Hydrolases; EC 3. 1, Acting on ester bonds; EC 3. 1. 1, Carboxylic ester hydrolases; and EC 3. 1. 1. 3, Triacylglycerol lipase (IntEnz; Integrated relational Enzyme database, available at www. ebi. ac. uk/intenz/, accessed on 29 December 2021). Several hundreds of lipase sequences are listed in databases, and the Protein Data Base (available at www. rcsb. org, accessed on 28 December 2021) has over 200 three-dimensional lipase structures. However, as the same lipase can have several structures in varying conformations or with different substrates, the 221 structures registered in December 2021 represent lipases from 51 known organisms, in addition to 10 structures from metagenomes or unknown organisms.
Study population and design This study was conducted as part of the European Union-funded project LifeCycle (Fig. 1 ). The LifeCycle project aims to study early-life stressors that influence health throughout the life cycle by establishing the EU Child Cohort Network, a long-term and open Europe-wide network of birth cohort studies with harmonised data of more than 250,000 children and parents (more information on the project can be found elsewhere)  (Jaddoe et al. , 2020; Pinot de Moira et al. , 2021). Data from the LifeCycle project allows researchers to use standardised methodologies and increased sample sizes. The large sample sizes achieved through this consortium promote higher statistical power, which is needed for increased accuracy of estimates and obtaining more robust findings. The LifeCycle geographic coverage spans much of Northern, Western, Central, and Southern Europe.
, 2016) ,  INMA (Infancia y Medio Ambiente, Spain, 2003 -2008)   (Guxens et al. , 2012) ,  KANC (Kaunas Birth Cohort, Lithuania, 2007 -2009)   (Grazuleviciene et al. , 2009 ), MoBA (the Norwegian Mother, Father and Child Cohort Study, Norway, 1999 -2008)   (Magnus et al. , 2016) , NINFEA (Nascita e INFanzia: gli Effetti dell'Ambiente,  Italy, 2005 Italy, -2016) )   (Richiardi et al. , 2007 ), and RHEA (Rhea Mother & Child Cohort Study, Greece, 2007 -2008 )  (Chatzi et al. , 2017). Before study enrolment, a signed consent form was obtained from all the participants in accordance with each centre's ethics committee. The harmonised data are kept within each institution and analysed remotely through DataSHIELD (Data Aggregation Through Anonymous Summary-statistics from Harmonised Individual-level Databases), a privacy-preserving federated data analysis platform.
Exposure to natural environments The assessment of natural environments was conducted at the mother's residential address during pregnancy following a standardised protocol for harmonised urban environment stressor data across all cohorts developed in the Lifecycle project (available online: https://lifec ycle-project. eu/wp-content/uploads/2021/07/Protocol_v4_2021_06_ 25. pdf). We obtained five indicators of exposure to greenspace (i. e. , residential surrounding greenspace, residential distance to green space, and accessibility to green space) and two indicators of exposure to blue space (i. e. , residential distance to blue space and accessibility to blue space) during pregnancy for each participant.
Water was not removed but negative values in the images have been reclassified to null values previously. Residential distance to green space was calculated using the Urban Atlas (European Environment Agency. Data and maps 2022) generated by European Environmental Protection Agency (except for INMA-Gipuzkoa and MoBa, where maps from the European Nature Information System (EUNIS) (EUNIS, 2022) and the Norwegian Mapping Authority (The Norwegian Mapping Authority. Kartkatalogen. , 2022) were used, respectively) (Supplementary Materials, Table  S1 ). Residential distance to green space consisted of the Euclidean distance in metres from the residential address to the nearest green space ≥ 5,000m 2 (i. e. , major green space) (The WHO Regional Office for Europe, 2017). Accessibility to greenspace around residential address was defined as living within 300m of a public major green space (yes/no) (The WHO Regional Office for Europe, 2017).
Indicators of exposure to blue space Residential distance to blue space was calculated as the Euclidean distance in metres from the residential address to the nearest blue space ≥ 5,000m 2. This size was given by the area extension of each polygon in the Urban Atlas layer (European Environment Agency. Data and maps 2022). Accessibility to blue space around residential address was the presence (yes/no) of a blue space ≥ 5,000m 2 in 300m from home. Both indicators were calculated based on the Urban Atlas (European Environment Agency. Data and maps 2022) (except for INMA-Gipuzkoa and  MoBA, where maps from EUNIS (EUNIS, 2022) and the Norwegian Mapping Authority (The Norwegian Mapping Authority. Kartkatalogen. , 2022) were used).
Covariates We selected a set of covariates a priori based on previous literature: maternal age (continuous, year), parity (binary, nullipara/multipara), gestational age at birth (continuous, days), sex of the child (binary, boy/ girl), maternal education level (categorical, low/medium/high), residential area-level deprivation index (categorical, low deprivation/medium deprivation/high deprivation), a multidimensional evaluation of an area's socioeconomic conditions, and maternal smoking during pregnancy (binary, yes/no). Data on parity was obtained from birth medical registry data in all cohorts. The maternal level of education was based on the highest ongoing or completed education at the time of delivery. This categorization followed the International Standard Classification of Education 97/2011 (ISCED-97/2011); mapping tools for specific countries can be found here: https://uis.
Discussion We investigated the associations between seven metrics of exposure to natural environments during pregnancy and birth weight and SGA probability. Capitalising on data from 11 birth cohorts from across Europe enabled us to evaluate, for the first time, this association across different countries and shed light on the role of region on this association. We found that more residential surrounding greenspace and shorter residential distance to green space were associated with higher birth weight and lower odds of SGA after controlling for individual and neighbourhood covariates. There was some evidence that these associations were stronger for people from lower SES groups and those living in the northern parts of Europe. Our findings for accessibility to green space, residential distance to blue space, and accessibility to blue space were close to null.
, 2017; Jamelot & Reymond, 2015; Lalli et al. , 2019). This type of linear approximation rapidly provides the tsunami heights along the coastline, with an associated error on the order of a factor 2 at best  (Gailler et al. , 2017; Jamelot & Reymond, 2015; Reymond et al. , 2012). Such approaches are adapted to the near-field context, but not all local effects are effectively resolved and estimates of run-up and horizontal inundation are lacking. To provide a rapid estimate of the run-up,  Wronna et al. (2020)  proposed a tsunami runup predictor, relating the accelerating phase of the wave to the length of the beach slope over which the wave is traveling. Other recent studies develop methods involving the use of pre-calculated databases. Gusman et al. (2014)  compare the tsunami waveforms simulated at different virtual observation points with the waveforms contained in a database.
Each waveform in the database is associated with a high-resolution map of the inundation, pre-calculated with the non-linear model. This allows, once the best scenario of the database is found, to use the corresponding map to quickly estimate the flood. Such pre-calculated databases can be used in deep-learning approaches. Indeed,  Mulia et al. (2018)  improved the method of  Gusman et al. (2014)  by using low-resolution maps containing the maximum tsunami heights calculated by a linear model instead of tsunami waveforms. The dimensions of the matrices are reduced using principal component analysis, generally used in computer vision. Recently,  Fauzi and Mizutani (2020)  and  Mulia et al. (2020)  applied deep learning algorithms to a tsunami inundation database, and  Lee et al. (2020)  proposed a tsunami run-up response function including a method to reduce the number of simulations to build this function.
The tsunami run-up distribution is therefore decomposed into source run-up and topographic run-up. The source run-up can be modeled by earthquake fault parameters, and the normalized topographic run-up is associated with local topographic characteristics. Alternative approaches for capturing localized maximum wave heights are introduced here, with one approach also being capable of capturing the inundation. These automated approaches are based upon transfer functions and machine learning techniques. In practice, they are extremely quick to run  ((s)) and are computationally cheap once the local response to an incoming tsunami is captured ahead of time. In a warning context, all that is required are the regional (coarse grid) maximum wave heights, which could be sourced from pre-computed databases  (Gailler et al.
2. If h 1 < h ≤ 1 m: the predictor point is chosen to be the nearest deeper neighbor in geographical space. 3. If 1 m < h < 0 m: the wave heights are extrapolated to the shoreline using the values obtained at the nearest deeper neighbor with h < 1 m. The code which carries out this workflow is predominantly written in Python with calls to functions written in C to carry out the optimization algorithm (Section 2. 1. 2). Despite the code operating serially, due to reasons outlined above, the hybrid language (Python and C) implementation allows one to rapidly map the amplification parameters for a site (runtime  (mins) ). The Python and C code can be accessed at the following repository: Giles (2022).
The MLP is then trained on the mother/daughter grid pairs, with standard gradient descent and back-propagation techniques used to optimize for the weights and bias terms in the network. A python code which utilizes the Keras and TensorFlow packages  (Abadi et al. , 2015)  was developed to construct and train the MLP. The mother grid has dimensions 150 × 150, therefore the input to the MLP is a flattened array of dimensions 22,500 × 1. It is a fully connected network with the two hidden layers having 256 neurons each. The output dimension is 120,000 × 1, as the daughter grid has dimensions 300 × 400. The activation function in each layer is the popular Relu function. L2 regularization and neuron dropout are implemented in each of the hidden layers to avoid overfitting. Identical to the extended Green's law approach, the loss function is chosen to be the mean squared error.