dataesr/scientific-paragraphs-categorization huggingface dataset:
Photo-z have been derived from high-quality ultraviolet-to-infrared data (up to 32 bands;  Laigle et al. 2016). Hardness ratios (HR = H-S H+S , where H and S are the net counts of the sources in the hard and soft band, respectively) were estimated for all X-ray sources using the Bayesian estimation of hardness ratios method (BEHR;  Park et al. 2006). The intrinsic column density, N H , for each source was then calculated using its redshift and assuming an X-ray spectral power law with slope Γ = 1. 8. This information is available in the catalogue presented in  Marchesi et al. (2016). In this work, we used sources within the UltraVISTA region (1. 38 deg 2  McCracken et al. 2012)  of the COSMOS field. There are 1718 X-ray sources within this region with log [L X,2-10 keV (erg s -1 )] > 42. In our analysis, we used the X-ray dataset used in  Mountrichas et al.
The galaxy control sample The main goal of this work is to compare the SFRs and SFHs of AGN host galaxies in different cosmic environments with the SFRs of non-AGN systems in similar density fields. To this end, we used the galaxy control sample presented in  Mountrichas et al. (2022c; see their Sect. 2. 2). We applied the same selection criteria used for the X-ray dataset and the same mass completeness limits, which resulted in 89 375 sources A137, page 2 of 10 (Table  2  in  Mountrichas et al. 2022c). We note that X-ray sources have been excluded from the galaxy control sample, as well as sources with a strong AGN component, calculated by the SED fitting (see next section and Sect. 3. 3 in  Mountrichas et al. 2022c). To include information about the cosmic environment of the sources in the galaxy control sample, we crossmatched the control sample with the catalogue presented in  Yang et al.
Calculation of SFR and M * The (host) galaxy properties of both the X-ray AGNs and the galaxies in the control sample were calculated via SED fitting using the CIGALE code  (Boquien et al. 2019; Yang et al. 2020 Yang et al. , 2022)). The SED fitting analysis is described in detail in Sect. 3. 1 in  Mountrichas et al. (2022c). In brief, the galaxy component was modelled using a delayed SFH model with a function form SFR ∝ t × exp(-t/τ). A star formation burst was included  (Ciesla et al. 2017; Małek et al. 2018; Buat et al. 2019)  as a constant ongoing period of star formation of 50 Myr. Stellar emission was modelled using the single stellar population templates of  Bruzual & Charlot (2003)  and was attenuated following the  Charlot & Fall (2000)  attenuation law. To model the nebular emission, CIGALE adopts the nebular templates based on  Inoue (2011).
Specifically, a flat SFR norm -L X relation was found for the least and most massive systems (log [M * (M )] < 10. 5 and log [M * (M )] > 11. 5), with SFR norm ∼ 1. Albeit, for intermediate stellar masses (10. 5 < log [M * (M )] < 11. 5), SFR norm was found to be SFR norm ≤ 1 at low-tomoderate L X (log [L X,2-10 keV (erg s -1 )] < 44), whereas at higher L X , SFR norm > 1 (e. g. , see Fig. 5  in  Mountrichas et al. 2022a). Amongst the 1005 X-ray AGNs in our dataset, ∼80% (809 sources) have 10. 5 < log [M * (M )] < 11. 5. Therefore, there are not enough sources to probe lower or higher M * in a statistically robust manner. Thus, we restricted our analysis to AGNs (and galaxies) with 10. 5 < log [M * (M )] < 11. 5. Furthermore, sources in our datasets (AGNs and non-AGNs) associated with filaments or the field span redshifts up to 2.
5. Nevertheless, in our samples, sources in clusters lie at z < 1. 2. To make sure that our results and conclusions are not affected by the different redshift ranges probed by sources in different cosmic environments, we also restricted both our AGN and non-AGN datasets  0:3 0:4 0:5 0:6 0:7 0:8 0:9 1:0 1:1 1:  to z < 1. 2. There are 551 (430 of which are with spec-z) X-ray AGNs and 16 917 (6653 of which are with spec-z) non-AGN galaxies that met these two criteria (M * , redshift). Among them, 31 (  1048 ) AGNs (non-AGNs) are associated with clusters, 328 (9622) AGNs (non-AGNs) are associated with filaments, and 192 (  6247 ) AGNs (non-AGNs) are in the field. The distribution of AGNs in the L X -redshift plane is presented in Fig. 1. These sources were used in the analysis presented in Sect.
Star formation history In  Mountrichas et al. (2022b) , we examined the SFHs of AGN and non-AGN galaxies, and we found that AGNs of low-tomoderate L X (log [L X,2-10 keV (erg s -1 )] < 44) tend to have older stellar populations and are less likely to have experienced a recent star formation burst compared to non-AGN systems. In this work, we not only study the SFHs of the two populations but also take into account their cosmic environment (Sect. 4. 2). For that purpose, we crossmatched our AGNs and galaxies in the control sample with the LEGA-C catalogue (van der  Wel et al. 2016 Wel et al. , 2021;; Straatman et al. 2018). This catalogue includes spectra for 4081 galaxies (3741 unique objects) within 0. 6 < z < 1. 3. A137, page 3 of 10 We made use of two tracers that are sensitive to stellar age and are available in the LEGA-C dataset.
These tracers are the equivalent width (EW) of H δ absorption and the D n 4000 index (e. g. ,  Kauffmann et al. 2003; Wu et al. 2018). The D n 4000 is small for young stellar populations and large for old metalrich galaxies. On the other hand, the EW of H δ rises rapidly in the first few hundred million years after a burst of star formation, when O-and B-type stars dominate the spectrum, and then decreases when A-type stars fade (e. g. ,  Kauffmann et al. 2003; Wu et al. 2018). We applied the same quality selection criteria described in Sect. 2. 2. 3 in  Mountrichas et al. (2022b) , which reduced the number of sources in the LEGA-C catalogue to 2834. Then, we crossmatched our X-ray and galaxy control samples with the LEGA-C dataset. This resulted in 52 AGN and 1172 non-AGN galaxies. The average uncertainties on H δ and D n 4000 are 17% and 3%, respectively.
We note that the control sample was reduced to those sources with log [M * (M )] > 10. 6 to better match the M * range of the X-ray dataset. The redshift and M * distributions of the two populations are shown in Fig. 2. Both distributions are similar between the two populations. A Kolmogorov-Smirnov (KS) test gave a p-value of 0. 57 and 0. 12 for the redshift and M * distributions of AGN and non-AGN galaxies, respectively (the two distributions differ with a statistical significance of ∼2σ for a p-value of 0. 05). Similar values were found when applying a Mann-Whitney test (p-value of 0. 90 and 0. 29, respectively, for the redshift and M * distributions). Therefore, we considered that the M * and redshift distributions of AGN and non-AGN galaxies are not significantly different to affect the results presented in Sect.
Morphology To examine the role of the morphology of AGN and non-AGN galaxies in different cosmic environments (see Sect. 4. 3), we used the catalogue presented in  Ni et al. (2021). The morphological classification was done using a deep learning-based method to separate sources into BD and non-BD galaxies. The term BD refers to galaxies that only display a significant spheroidal component without obvious disc-like or irregular components (for more information, see Appendix C and Sect. 2. 3 in  Ni et al. 2021; Yang et al. 2019, respectively). We crossmatched the datasets described in Sect. 3. 2 with the catalogue of  Ni et al. (2021) , which resulted in 37 AGN and 1086 non-AGN galaxies. Type 1 AGNs were removed from the  Ni et al. (2021)  sample since they can potentially affect host galaxy morphological measurements (see their Sect.
Selecting smaller boxes, though, has an effect on the errors of the calculations  (Mountrichas et al. 2021 ). The top panel of Fig. 3  presents the SFR norm as a function of L X for different cosmic environments. Measurements are the median values of SFR norm grouped in bins of L X of size 0. 5 dex. The errors presented are 1σ, calculated using bootstrap resampling (e. g. ,  Loh 2008). Only bins that have more than ten sources are presented. We note that the SFR norm -L X relation is flat for sources found in the field. However, an increase of SFR norm was observed, in particular at log [L X,2-10 keV (erg s -1 )] > 43, for sources associated with filaments and clusters. This implies that for X-ray luminosities probed by our dataset, AGNs in the field have lower SFRs compared to the SFRs of non-AGN systems, regardless of the L X.
Specifically, the SFR norm in denser fields is higher compared to fields of low density, with a ∼2σ significance level, and there is an increase of SFR norm with L X for sources in dense environments, whereas in fields of low density, the SFR norm -L X relation is flat, and the SFR norm is consistently less than one (i. e. , the SFRs of AGNs are lower compared to the SFRs of non-AGN galaxies). To examine if our results are affected by possible evolution with cosmic time within the redshift range that our datasets span (0. 3 < z < 1. 2), we restricted our samples to a narrow redshift interval, that is, 0. 8 ≤ z ≤ 1. 2. There are 238 AGNs and 8019 sources in the control sample within this redshift range. The results using these subsets are presented in the bottom panel of Fig. 3. The statistical uncertainties are larger due to the smaller size of the subsets used, but the same trends were observed with those using the sources within the wider redshift interval.
In the case of AGNs, the fraction of Q systems is similar in all environments (∼30%). Next, we repeated the SFR norm -L X measurements but excluded Q systems. The results are presented in Fig. 4. We note that due to the smaller size of these samples, there is only one bin that satisfied our requirement for the minimum number of sources (>10) in the case of AGNs in clusters. Overall, when Q systems are not included in the analysis, the amplitude of the SFR norm is lower (by ∼0. 2 dex) for all cosmic environments and for the L X spanned by our datasets compared to the SFR norm values derived using the total samples (Fig. 3 ). However, the trends mentioned earlier remain unchanged. Specifically, the SFR norm is higher for denser environments, and an increase of SFR norm with L X was observed for sources associated with filaments or clusters.
The star formation histories of AGN and non-AGN galaxies as a function of the cosmic environment In this section, we study the SFHs of AGN and non-AGN galaxies. Our goal is to check whether their stellar populations agree with the picture drawn in Sect. 4. 1 (i. e. , systems with lower SFRs are expected to have older stars compared to systems with higher SFRs). Moreover, the study of the SFHs could allow us to uncover what drives the trends we observed in the previous section. We note that the selection of sources with available D n 4000 and H δ measurements does not bias our samples against Q systems. In other words, the AGN and non-AGN datasets used in this part of our analysis include the same fraction of Q systems with the datasets used in the previous section. Therefore, a direct comparison can be made regarding the results of these two parts of our analysis.
Specifically, AGNs with log [L X,2-10 keV (erg s -1 )] > 43 tend to have lower D n 4000 (= 1. 32 vs. = 1. 44) and higher H δ values (= 4. 20 vs. = 2. 56) compared to their lower L X counterparts (p-value of ∼0. 3 for both spectral indices). These differences, although not statistically significant based on KS tests, are in agreement with the results we found in the previous section, where higher L X AGNs have increased SFR norm compared to lower L X sources. Regarding the non-AGN galaxies, they appear to have flatter D n 4000 and H δ distributions compared to AGNs (median D n 4000 = 1. 46, H δ = 3. 46). Finally, the bottom panel of Fig. 5  presents the D n 4000 and H δ distributions for AGN and non-AGN systems that are associated with the most dense cosmic environments. Since there are only four AGNs that live in clusters in our dataset, we followed the same approach we used in the previous section.
On the other hand, AGNs, and in particular X-ray sources with log [L X,2-10 keV (erg s -1 )] < 43, have consistent H δ and, especially, D n 4000 values in all cosmic environments (Table  1 ). Their D n 4000 and H δ distributions have less prominent tails compared to non-AGN galaxies (Fig. 5 ). More luminous AGNs show a tendency of lower D n 4000 and higher H δ values in the most dense environments, although the sub-sample is small and larger datasets are required to confirm these findings. These results can provide an explanation as to what drives the trends we observed in the previous section. Specifically, the increase of the SFR norm as we moved from the field to filaments and to clusters (Fig. 3 ) is mainly driven by the lower SFRs of galaxies in higher density fields and, to a lesser degree, is due to the increased SFRs of the more luminous AGNs in denser cosmic environments.
Environment versus morphology In this section, we examine the morphology of AGN and non-AGN galaxies in different cosmic environments. This allows us to study the SFHs of AGN and non-AGN systems in different environments but with a fixed morphological type. It also enables us to examine the link between morphology (e. g. , BD) and galaxy phase (e. g. , Q) for the two populations. There are nine (24%) AGNs in our dataset that live in BD systems. A similar fraction (21%) of BD galaxies was also found in the non-AGNs sample. We also note that the fraction of Q systems is similar (∼40%) for AGN and non-AGN galaxies. We found similar fractions between the AGNs and the galaxies in the control sample to be associated with fields, filaments, and clusters (see Table  3 ). A noteworthy difference, though, is that the percentage of BD systems shows an increase (albeit mild) towards higher density environments amongst the non-AGN galaxies, similar to the increase of the fraction of Q systems in denser fields.
In contrast, the percentage of BD systems that also host AGNs is significantly reduced in AGNs associated with dense cosmic environments, while the fraction of Q AGN hosts remains roughly the same, independent of the density field. When we associated sources with high-density regions based on the overdensity values of the sources, the fractions of BD systems in both the AGN and the non-AGN datasets (numbers in square brackets in Table  3 ) is the same as those we found using their cosmic environment, and therefore the same conclusions were reached. Thus, based on our analysis, BD systems are nearly equally likely to be found in non-AGN galaxies regardless of their cosmic environment. In the case of galaxies that host (type 2) AGNs, BD systems are preferentially found in AGNs associated with less dense fields.
Moreover, the link between morphology (BD) and galaxy phase (Q) is weaker for AGNs compared to non-AGN galaxies. This was confirmed by the study of the stellar populations, presented in Table  3. The stellar population of non-AGN galaxies is different for BD and non-BD systems as compared to galaxies that host AGNs, in which case smaller differences are observed in the stellar populations for different morphologies (see also  Mountrichas et al. 2022b ). We note, however, that in the case of AGNs, the size of the available dataset is small, and larger samples are required to confirm our findings. A137, page 7 of 10 Notes. The median D n 4000 and H δ values of each subset are also presented. The numbers in the square brackets show the number of AGNs when we classified sources using their overdensity values (see text for more details).
Conclusions We studied 551 X-ray AGNs and 16 917 non-AGN galaxies at 0. 3 < z < 1. 2 in the COSMOS field, comparing their SFRs (SFR norm ) as a function of L X in different cosmic environments. We restricted the M * of both populations to the range of 10. 5 < log [M * (M )] < 11. 5. The classification of the sources in three environments (i. e. , field, filaments, clusters) is available in the catalogue of  Yang et al. (2018). The categorisation is based on their field density, using the method described in  Darvish et al. (2015). The (host) galaxy properties were calculated via SED fitting, using the CIGALE code and the measurements presented in  Mountrichas et al. (2022c). We crossmatched our datasets with the LEGA-C catalogue, which allowed us to study the SFHs of the two populations for different field densities.
This information is available for 52 AGN and 1172 non-AGN galaxies. Finally, we compared the role of the environment versus morphology based on the morphological information in the catalogue of  Ni et al. (2021) , using 37 AGN and 1086 non-AGN galaxies that are common between the datasets. Our main results can be summarised as follows: -The SFRs of galaxies that host AGNs is lower compared to the SFRs of non-AGN systems (SFR norm < 1) for isolated sources (field) at all L X spanned by our dataset (42 < log [L X,2-10 keV (erg s -1 )] < 44). However, in denser environments, an increase of SFR norm (SFR norm ∼ 1) is observed at intermediate L X (log [L X,2-10 keV (erg s -1 )] > 43). -The SFR norm appears higher in fields of higher density compared to lower density fields. -Non-AGN galaxies tend to have older stellar populations (higher D n 4000 values) and are less likely to have undergone a recent star formation burst (lower H δ values) in denser environments compared to isolated sources (field).
In this work, we studied the SFRs and stellar populations of AGN and non-AGN galaxies in different cosmic environments (and morphologies). Larger samples that include sources observed in wider fields, such as the eFEDS field, would be extremely useful to confirm our findings and expand our investigations to larger M * , L X , and redshift baselines. Previous studies have showcased the importance of M * when comparing the star formation and stellar populations of AGN and non-AGN systems. Therefore, larger samples that span, in sufficient numbers, lower (log [M * (M )] < 10. 5) and higher (log [M * (M )] > 11. 5) M * are important to examining whether our results change at different M * regimes. Datasets that cover a wider redshift baseline will also allow for examination of a possible dependence with cosmic time.
Acknowledgements. This project has received funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement no. 101004168 , the  XMM2ATHENA  project. The project has received funding from  Excellence Initiative of Aix-Marseille University -AMIDEX , a  French 'Investissements d'Avenir' programme. This work was partially funded by the  ANID   BASAL  project  FB210003. M. B. acknowledges support from  FONDE-CYT regular grant   1211000. This research has made use of TOPCAT version 4. 8 (Taylor 2005).
| Tea production economic efficiency Primary data concerning economic aspects of tea production were conducted using a household survey over 3 years from 2019 to 2021, which consisted of 35 households that adopted agroecological tea production and 31 conventional tea-producing households from the 4 communes listed above. To ensure the credibility of this study, we have closely collaborated with local agricultural agencies and tea cooperatives to select the most representative tea-growing households in the 4 communes, where about 70% of the total tea production areas of the city are produced. Criteria for selecting the representative households for interview included the production areas (at least 1000 m 2 for one selective plot), identity of tea variety and tea ages being cultivated (LDP1 variety, 6 years old as of 2019), household investment capacity and labor availability (number of working adults), tea farming experience and having equal access to extension services and technological support.
| Statistical analyses Data used in this study were analysed using Microsoft® Excel, XLSTAT  (Addinsoft, 2016)  and R software. Comparison data of economic efficiency between conventional and agroecological cultivation methods were subjected to one-way analyses of variance (ANOVA), while the different effects of cultivation management and land conversion practices on tea root AMF colonization, tea soil fauna compositional communities, tea yield and yield components, as well as sensory indicators, were determined using two-way ANOVA. Soil physicochemical data were ln(x) transformed and the normal distribution verification was performed before two-way ANOVA. To examine the differences between levels within each factor, Tukey-HSD tests were performed for post-hoc comparisons. In addition, a principal component analysis (PCA) was employed to assess the correlations between the soil characteristics and the mycorrhization indicators.
The principal component analyses (PCA) of the soil physicochemical indicators and tea root mycorrhization parameters are presented in the Figure  2a, b. The first two axes together explained nearly 52% of the cumulative variability. The first axis (F1), which accounted for approximately 32% of the accumulated variability, was closely related to soil chemical indicators including OM, N total and soil pH (0. 610; -0. 619 and 0. 45, respectively). By contrast, soil texture (silt and clay) was strongly linked to the third axis (F3), which represented around 16% of the variation in the dataset. Root mycorrhizal F and M were strongly linked to the first axis (0. 688 and 0. 806, respectively) and significantly correlated with soil OM, soil pH and soil total N. The PCA observation charts clearly show the positioning of the agroecological and conventional tea farms but were unable to distinguish between the converted and nonconverted plantations.
ACKNOWLEDGEMENT We would like to sincerely thank  Mr. Nguyen Thanh Duong , Chairman of the  Trung Du tea Cooperative in Tan Cuong commune, the Thai Nguyen city department  for agricultural service for their great support in selecting the research sites and sampling, and the tea farmers for their collaboration. This work was supported by  Deakin University and Alliance of Bioversity International and International Center for Tropical Agriculture (CIAT) ,  Asia Hub ,  Common Microbial Biotechnology Platform (CMBP), Hanoi, Vietnam , and The  Northern Mountainous Agriculture and Forestry Science Institute (NOMAFSI) ,  Phu Tho , Vietnam.
Similarly, it has been shown that a common latent dimension can be identified across psychiatric diagnoses  (37)  and it is unknown if a similar dimension is observed for genetic risk. Our overall aim was to systematically compare effect sizes and patterns of subcortical alterations associated with rare genetic risk for NPDs. Specifically, we aimed to: i) characterize volumetric and shape subcortical brain alterations in 11 CNVs, ii) relate effect sizes of CNVs on subcortical metrics with previously reported effects of CNVs on risk for NPDs, and iii) identify latent subcortical brain morphometry dimensions across CNVs, and NPDs. To this end, we assembled the largest T1-weighted brain MRI dataset across all recurrent CNVs (n=11) previously associated with varying levels of risk for psychiatric illness (Table  1 ), and characterized volume, 3D surface, and thickness maps of subcortical structures.
METHODS Participants: Recurrent deletions and duplications were included in the study if 1) the level association between the CNV and psychiatric conditions (or lack thereof) as well as the effect size on cognitive ability was previously established  (15, 17, 20, 21, (38) (39) (40) (41) (42)  and 2) MRI data were available for at least 20 carriers of the same CNV. This minimum sample size of 20 was established based on the power to detect large effect sizes as previously published (  14 )). Clinically ascertained groups: CNV carriers were recruited after either being referred for genetic testing due to the diagnosis of a neurodevelopmental disorder or as the relative (e. g. , parent) of a CNV carrier. Controls within clinically ascertained groups were either noncarriers within the same families.