dataesr/scientific-paragraphs-categorization huggingface dataset:
Gene ontology analysis Ensembl gene identifiers were translated into ENTREZ gene identifiers prior to the enrichment analysis. ENTREZ genes linked to several Ensembl IDs were associated with the one having the highest expression level. Functional gene-set enrichment analysis was performed using the Fisher statistical test for the over-representation of differentially expressed genes (gene-level adjusted P-value lower than 5% For the of analysis epigenetic regulatory factors, the differential expression values, expressed as log2 fold changes (log2 FC), were computed for each gene, and were visually depicted as a network using the Cytoscape software package (version 3. 10. 0). Each node within this network projects the log2FC information. Protein-protein interactions were extracted from the STRING Database (version 5.
Proteomics data processing For identification, the data were searched against the Mus musculus (UP000000589_10090) and the Leishmania amazonensis MHOMBR71973M2269 in the TriTrypDB (release 60) databases using Sequest HT through proteome discoverer (version 2. 4). Enzyme specificity was set to trypsin and a maximum of two miss cleavages sites were allowed. Oxidized methionine, Met-loss, Met-loss-Acetyl and N-terminal acetylation were set as variable modifications. Maximum allowed mass deviation was set to 10 ppm for monoisotopic precursor ions and 0. 6 Da for MS/MS peaks. The resulting files were further processed using myProMS v3. 10. 0 (https://github. com/bioinfo-pf-curie/myproms)  (Poullet et al. , 2007). FDR calculation used Percolator  (The et al. , 2016)  and was set to 1% at the peptide level for the whole study.
The label free quantification was performed by peptide Extracted Ion Chromatograms (XICs), reextracted across all conditions and computed with MassChroQ version 2. 2. 21  (Valot et al. , 2011). For protein quantification, XICs from proteotypic and non-proteotypic peptides were used by assigning to the best protein, peptides shared by multiple match groups, and missed cleavages, charge states and sources were allowed. Label-free quantification (LFQ) was performed following the algorithm as described  (Cox et al. , 2014). The final LFQ intensities were used as protein abundance and further analyzed using Prostar v1. 32. 1 following the guidelines outlined in the software manual  (Tardif et al. , 2023). Briefly, we addressed missing data in all conditions by first removing Partially Observed Values (POV) and Missing in Entire Condition (MEC).
From 787 EpiRFs obtained from manual curation of publicly available databases including EpiFactors (http://epifactors. autosome. org. , Table  S1 ), 315 showed significant changes in transcript abundance during infection, with increased expression observed for 154 (e. g. Dnmt, FC = +1. 39; Dot1l, FC = +2. 78), and decreased expression affecting 161 (e. g. Sirt3, FC = -1. 46; Ehmt,: FC = -1. 38) (Figure  1B ). Overall, 39. 7 % of EpiRFs were modulated by the parasite, revealing epigenetic regulation as a key target for parasite subversion of host cell immune-metabolomic functions. Pharmacological interference with macrophage epigenetic regulation reduces burden of intracellular L. amazonensis. We next used a pharmacological approach to assess the relevance of this epigenetic subversion strategy for intracellular L.
With financial support from  "la Région Île-de-France " ( N°EX061034 ) and  ITMO Cancer of Aviesan and INCa  on funds administered by  Inserm  ( N°21CQ016-00 ) for MS analysis, and from  A-PARADDISE/602080 FP7  Project,  FISR2019_00374   MeDyCa , and  Progetto di Ateneo Sapienza 2021  (no. RM12117A61C811CE ) for the preparation of LSD1 inhibitors.
Search strategy We performed our electronic search in 13 databases from their inception to September 2022, these databases are: Web of Science, PubMed, Sport Discus, Scopus, Cochrane, Psych Net, Springer, Scien-ceDirect, Pascal & Francis, Sage journals, Pedro, Google Scholar, and Sage. We used the following MeSH terms, corresponding alternatives, and Boolean operators: initial search. Fig. 1  shows the flowchart of the study selection process.
This meta-analysis also remained general and inconclusive regarding the type of exercise, duration, and frequency. To date, studies tend to specify exercise modalities to counteract neuroinflammation and cognitive decline in AD patients. Our systematic review is among the few systematic reviews that searched 13 electronic databases, whereas most systematic reviews and meta-analyzes examine 5 or 6 databases. Interestingly, our systematic review is in line with the expectations of some authors. The latter have worked on animals and recommended reproducing the same work in humans to investigate the effects of exercise in human patients with AD, comparing different exercise modalities according to the limitations of the subjects  (Vasconcelos-Filho et al. , 2021). However, only eleven studies were found and examined the human model.
[13] Thus, revealing the differences between bench-scale synthesis and in situ TEM data is essential to better use LCTEM results in nanomaterial and SC fabrication. 10b,c,14] In this work, we synthesized and assembled gold triangles (AuNTs) and silver nanorods (AgNRs) by depletion-induced selfassembly. We analyzed the structure of the SCs with a multiscale approach from the bulk solution (SAXS and ex situ scanning electron microscopy (SEM)) to the single NP level (in situ LCTEM). Thus, we revealed how the confinement within the liquid-cell affects the structural properties of SCs and we quantitatively studied their evolution under electron irradiation to evaluate the possible driving forces of these structural reconfigurations.
The particles were centrifuged at an appropriate speed and washed with CTAC 1 mm. SAXS Experiments: The colloidal crystals structures were probed by SAXS at the SWING beamline of the SOLEIL synchrotron (Saint-Aubin, France), using a beam energy of 16 keV and a sample-to-detector distance of 6. 481 m. Data treatment (angular averaging and normalization) was done using the Foxtrot software developed at the beamline. LCTEM: Liquid STEM imaging were simultaneously performed in bright field and dark field modes using an aberration corrected JEOL ARM 200F microscope operating at 200 kV and a liquid-cell TEM holder (Poseidon select, Protochips inc. ). A solution of SCs (i. e. , colloidal solution with 115 mm of CTAC for the AuNTs and 40 mm CTAC for the AgNRs) was encapsulated in the liquid-cell by using conventional loading process.
Acknowledgements The CNRS is acknowledged for funding and support. This work had benefited from an  Investissements d'Avenir grant  from  Labex PALM  ( ANR-10-LABX-0039-PALM ) and the  ANR  Project  ARTEMIA. The present work had also benefited from the electronic microscopy facility of  Imagerie-Gif , (http://www. i2bc. parissaclay. fr),member of IBiSA (http://www. ibisa. net),supported by " France-BioImaging " ( ANR10-INBS-04-01 ), and the  Labex "Saclay Plant Science"  ( ANR-11-IDEX-0003-02 ). The authors acknowledge SOLEIL for the provision of synchrotron radiation facilities (experiment 20210815) and  Thomas Bizien  for help using the SWING beamline. The authors acknowledge the French national project  METSA  for supporting the microscope platform of  MPQ laboratory.
Non-active agricultural land (NAAL) mapping in West Africa is essential to accurately assess agricultural systems and its contribution to food security and agro-ecological sustainability of current practices, and yet the available mapping methodologies are not adapted to the environmental and cropping conditions encountered when addressing tropical smallholder agriculture. In this study we present a strategy that makes use of Sentinel-2 image time series, CHIRPS monthly rainfall data and multiple years of in-situ data obtained from the JECAM database to map NAAL in a Soudanian site in Burkina Faso (Koumbia) between the years 2016 and 2021. In a first step we generated annual land use maps in four broad classes (managed, unmanaged, evergreen and non-vegetated) to detect fields being actively cultivated in a given year, and in a second step, we used these annual land use maps to differentiate non-active agricultural land by identifying shifts from one year to another.
Reference data set As reference data, we used the Burkina Faso subset of the recently published JECAM (Joint Experiment for Crop Assessment and Monitoring) harmonized in-situ data set  (Jolivot et al. , 2021). This ground data set includes land cover and agricultural land use in-situ observations collected annually over the Koumbia site during the 2013-2021 period (on average, c. a. 840 observations per year). More precisely, each object (a polygon) is annotated at both a land cover level, using a general nomenclature including the agricultural land in a single class (cropland, which includes also ''fallow'' land), and a set of classes related to natural areas (discriminating herbaceous savannah and natural pastures from shrubby and woody savannah and forests), water bodies and non-vegetated areas (built-up surfaces and mineral soils).
In the JECAM data set, information on the crop type is also available for each polygon within the cropland class. We used this information to build an intermediate-level nomenclature named ''Crop group'', to keep fallow fields and active agricultural land (cash crop, cereals, leguminous, oilseed) separated. Details on the final nomenclature along with the surface covered by each class are given in Table  1. Information for year 2019, originally missing from the published data set, was collected in 2020 by a local agent who carried out a delayed field survey, during which details on fallow land could not be collected.
A multi-year land use approach for NAAL mapping The main constraint of our approach is the availability of a proper reference data set covering multiple consecutive years, which provides information, for each year and with no ambiguities, on whether a labeled surface belongs to the active cropland, and hence to a managed agricultural land class, or to an unmanaged class, which can in turn include both non active cropland and natural herbaceous vegetation. Information might also be provided for land cover classes which are more resilient in terms of seasonal variations in vegetation dynamics, information which can be useful to detect and mask out such areas which are less related to the purpose of NAAL mapping. Needing no specific details on these classes, we could complete our annual land cover model by grouping them all under two classes, namely evergreen and non-vegetated, to address respectively the constant presence and absence of green vegetation.
As already mentioned in the data description section, the JECAM data set provides annual information on both land cover and crop group classes, and it can easily be used to comply to the 4-class model  (managed, unmanaged, evergreen, non-vegetated)  and allow the production of the annual base maps used for multi-annual NAAL identification. On these premises, the method we intend to propose here is quite straightforward, composed of three stages tackling respectively : (a) the production of annual base maps inferring the 4-class land cover model to the entire area, (b) the application of transition rules for the analysis of land cover trajectories allowing the identification of NAAL, and (c) a suitable processing of the original reference data set to provide a reliable and fair validation of NAAL detection.
Sentinel-2 based annual land use mapping Our first step consists in training a set of annual supervised classification models using the corresponding time series of Sentinel-2 images as a main input. A first straightforward possibility could have been to simply perform a supervised classification directly using the JECAM data set as-is to train the classifier. However, it is clear from Table  1  that a strong class imbalance is present in terms of annotated surface, which may lead to poor performances on less represented classes. This is particularly true for the fallow class which, as arguably deductible observing the entangled NDVI profiles shown in Fig. 3  (for year 2017, without loss of generality), is likely to be confused with some active crop class and hence erroneously detected as managed, in contradiction to the provided definition of NAAL.
Hence, we here decide to pre-process the JECAM data set prior to the supervised classification process, by relabeling samples of the active crop classes into a single managed class (namely the cash crops, cereals, leguminous and oilseed classes), and grouping fallows with other deciduous vegetation classes (namely herbaceous, shrubby savannah and pastures) into the unmanaged class. Woody savannah and forest fall into the evergreen class, while the non-vegetated class includes all constantly non-vegetated areas (bare soil, built-up, water bodies). We report in Fig. 4  the final number of pixels per class after the proposed class grouping and the rasterization of the source vector layer using the Sentinel-2 10 m grid. As expected, the final result shows a much more balanced configuration of class samples, especially between the managed and unmanaged classes whose transitions through subsequent years are susceptible to trigger the detection of NAAL areas.
NAAL validation and assessment In the proposed NAAL classification workflow, a major issue is represented by the necessity to work out a test reference data set to validate the methodology and assess the accuracy of the NAAL maps output by the multi-year trajectory analysis. Matter of facts, we do not dispose of a multi-annual data base of properly annotated NAAL fields. In principle, we could limit our validation to the original JECAM data set and consider the fallow class for NAAL assessment. Yet, only relying on this would be unfair, since much more of the arable land could be de facto included into the wider NAAL class, including a part of the fallow land which has been annotated as belonging to another deciduous vegetation class because of missing explicit information on cropping practices.
However, it is possible to derive some NAAL-specific validation set from the original JECAM data set by applying the same rules that we use for trajectory analysis on land cover maps, using objects from the data base instead of map pixels, and the intersection of objects over multiple years to track transitions between the managed and unmanaged classes. This is made possible by the fact that the field survey protocol for the Koumbia site was enforced by taking annotations over the same fields year-by-year to the extent possible. Still, this process is more subtle than it may appear, since a sufficiently large quantity of surface which is overlapping over time and may be labeled as NAAL (which appears to be a small percentage of the area according to the JECAM data set) is needed. Moreover, field boundaries are not static across years, which may lead to a dramatic reduction of usable surface if a simple intersection rule is applied, and in particular a single missing annotation for a given field will make all the data from the relative field unusable.
Thus, we decided to provide several versions of the validation data set, each obtained with a different set of rules for the analysis of the overlaps over time. This goes from a data set obtained by a rigorous intersection of polygons, which is very precise in term of NAAL identification but with a possibly insufficient surface for a reliable assessment, to progressively admitting extrapolation of missing data in space and time, in order to extend the total validation surface, and hence the statistical sufficiency, at the price of some ''noise'' in the resulting data set. The different processing approaches over the reference data set, rendered so as to comply to our 4-class model for trajectory analysis, are described below and summarized on Fig. 5. All these processes start with a rasterization of the reference polygons, available in vector format, using the Sentinel-2 grid as geometric reference.
As already mentioned, all proposed strategies aim at the relabeling of the whole set of reference pixels from the 4-class nomenclature used for annual mapping to a binary (NAAL vs. non-NAAL) nomenclature based on the same transition rules applied for trajectory analysis. This means that all reference pixels that, for a given year, are not concerned by a relabeling to NAAL are grouped in the unique antagonist non-NAAL class. Obviously, the resulting data sets are expected to be strongly unbalanced (NAAL accounts only for a small percentage of the whole area), but in this case the unbiased assessment of the detection of NAAL areas will eventually be possible using per-class figures (precision, recall and F1-score). Data set A : Original JECAM data set. This validation set is simply made up of the original JECAM Koumbia data set in which the objects are relabeled to fit the 4-class trajectory model.
No spatial or temporal modification is applied on the source data. This represents the original data set where only ''fallow'' fields are considered as NAAL data. There is no data alteration on this case. Data set B : Strictly reliable surfaces. This is the case when the rigorous multi-annual intersection of reference objects is performed, basically retaining only the most reliable surfaces. In other words, only those pixels where data is available in all consecutive years are considered for trajectory analysis and NAAL relabeling. Any point with missing data for a given year is dropped out of the data set. Data set C : Locally reliable surfaces. In this scenario, we make the implicit assumption that the validity of a surface for a given year only depends on its past validity. This means that, for a given year, the intersection is only performed with the surface of the overlapping object in the previous year.
In this way, all pairs of pixels from two consecutive years that are eligible to be relabeled to NAAL are taken into consideration, regardless of what happened before or after, while the portion of object which is non comparable is discarded. Compared to data set B, this data set do not guarantee that a comparable set of surfaces are used for validation year-by-year, leading to possible discrepancies in the multi-annual assessment strategy. Data set D : Sub-field NAAL relabeling. Here, we basically propose the same logic than for data set C at pixel level, with the exception that the portions of objects which are left out from an intersection between two consecutive years are not discarded, and can still be used for NAAL relabeling in subsequent years. Alternatively said, relabeling can occur at sub-field level.
Data set E : field-level NAAL extrapolation. Based on the observation that field boundaries can move through time, and considering how densely cultivated the area is, for this data set we make the underlying hypothesis that, if a certain field is known to have been actively cropped on year 𝑌 𝑖 , a newly annotated unmanaged field on 𝑌 𝑖 + 1 whose surface is only partly overlapping with the past year's field is probably a whole NAAL field. Hence, similarly to data set D, in this case we still consider pairwise overlaps between consecutive years and no data is lost from the source objects after processing. However, relabeling to NAAL can be propagated to an entire object if the intersection of the given object with the one from the previous year is larger than a given threshold in terms of area percentage, here heuristically fixed to 30%.
A resume of the total number of NAAL pixels for each validation data set and per-year is reported in Fig. 6 , along with the percentage it represents with respect to the full data set (including non-NAAL pixels). Note that, by only retaining the strictly reliable surfaces, data set B systematically provide a severe selection of NAAL pixels w. r. t. the total samples, but the other strategies which try to extrapolate NAAL areas at the plot scale progressively tend to first restore the original amount and ratio observable in the data set A (actual Fallow fields w. r. t. the total annotated surface), then add some potential NAAL surface in reasonable measure (within a factor of 2). The slight drop in the amount of NAAL pixels in 2020-21 is due to the absence of Fallow annotated polygons in the original JECAM data base in 2019.
Annual LULC maps For the production of annual 4-class land use maps reliable enough to enable trajectory analysis for NAAL detection, we carried out a strategy based on multiple per-year Random Forest (RF) classifications. Such solution deemed necessary to cope with the relatively low quantity of available annotated surface, especially if compared to the complexity of the landscape and the dimensionality of the input variables (593 variables per pixel in total), as well as to ensure comparability among the different maps. More precisely, for each available year (2016-2021, no full Sentinel-2 time series was available before 2016), we opted for a 5-fold cross validation approach by dividing the 4-class reference data set derived by the original JECAM Koumbia subset (see Section 3. 1) into 5 randomly selected subsets of equal size.
1. 3) are reported in Fig. 13  First of all, non surprisingly, data set A is the one providing the poorest accuracy figures, with a F1-score over NAAL averaged over the period of analysis around 0. 53, with the exception of 2017 when a satisfying score of 0. 76 is reached. One may think that this may be due to the missed detection (omission error) of older fallows, which would in principle require a ''bootstrap'' period of five years (according to the agreed-upon definition) to be properly covered by our trajectory model. However, looking at Table  3  it is clear how the low F1-score is mainly due to a systematically low user's accuracy, hence to a high commission error. Indeed, in the JECAM database a field is annotated as fallow only if a history of active cropping is explicitly known, otherwise non-active cropland fields fall back into the herbaceous vegetation class.
This again seems to confirm the hypothesis that not all of the NAAL are correctly represented in the original reference database as fallow land, making it necessary to validate NAAL mapping with a different strategy. Accuracies improve significantly when validating using data set B (strictly reliable surfaces), with an average score of more than 0. 8 over the period. However, looking at the number of available reference data points in Fig. 6 , we must face the evidence that this value might not be statistically sufficient, as testified by the larger uncertainty. Moreover, this validation potentially suffers from a positive bias due to the fact that, although the two data sets have a different nomenclature (managed/unmanaged vs. NAAL/non-NAAL) and data distribution among classes, the validation data points belong to the training surface used for annual land use classification.
As expected, accuracy values progressively drop when validating over the augmented validation data sets C/D/E, attaining average F1-scores ranging from 0. 68 to 0. 59, but since they rely on a greater number of validation samples (Fig. 6 ) the uncertainty is reduced as well. For each sample, the image on the left is a very high resolution acquisition (SPOT6/7) which has also been used for field delineation in the JECAM data set  (Jolivot et al. , 2021) ; Dotted blue lines have been added to highlight photo-interpreted fields for which NAAL reference data was available for that given year. Dotted green lines represents fields for which no reference data was available that given year. All the fields shown here for which a reference polygon existed was correctly labeled as ''NAAL''. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.