dataesr/scientific-paragraphs-categorization huggingface dataset:
R E S E A R C H A R T I C L E JCI Insight 2021;6(11):e147617 https://doi. org/10. 1172/jci. insight. 147617 decrease to 0. 75 was observed in both sequenced samples. Thus, changes in the frequency of PIK3CA-mutant cells could not explain the observed differences in response to HER2-targeted treatments. To investigate whether the residual tumors acquired new mutations after treatment, we compared single nucleotide variants (SNVs) of treated tumors to the untreated controls. We set out to identify mutations that would be present in both replicates (2 independent tumors) within a treatment group compared with the untreated samples. In PDX1, we identified 10 nonsynonymous mutations that were present in both sequenced tumors from each treatment group (Supplemental Table  6 ). Only 3 out of these 10 mutations were pathogenic according to the Catalogue Of Somatic Mutations In Cancer (COSMIC) database, namely ankyrin repeat domain 17 (ANKRD17) p.
V600A. ARID4B is a subunit of the histone deacetylase-dependent SIN3 repressor complex and promotes mammary tumorigenesis and metastasis  (17). This observation again points to epigenetic remodeling as a potential mechanism for acquired resistance in PDX2. Interestingly, trastuzumab-and lapatinib-treated PDX2 tumors that did not show differences in tumor growth did acquire novel mutations, many of which have not previously been reported in the COSMIC database (Supplemental Table  7 ). These findings suggest that different HER2-targeting therapies exert distinct evolutionary pressures on the tumors. Intrinsic resistance to HER2-targeted therapy is linked to activation of signaling pathways. Since the differential mutational patterns and acquisition of or selection for new mutations did not fully explain the observed treatment resistance, we next performed transcriptional profiling of the PDX tumors to address the intrinsic resistance to paclitaxel and HER2-targeting agents in our 2 PDX models.
Thus, the development of resistance to paclitaxel and T-DM1 in this xenograft model might be regulated by a small population of alveolar epithelial cells potentially via the induction of antitumor immune responses. Stromal cells within PDX2 largely classified into similar cell types identified in PDX1 (Figure  6G  and Supplemental Table  11 ), with several additional clusters emerging. The distribution of cells within the clusters was more variable with treatments compared with PDX1 (Figure  6H ), and 3 distinct macrophage clusters were differentially affected by treatments in PDX2. Macrophages in cluster 0 were most similar to the adipose tissue macrophages profile in ImmGen database (Supplemental Figure  12  and Supplemental Table  11 ), and macrophages in clusters 0, 1, and 4 demonstrated distinct pathway activation (Figure  6I ).
Methods Human tissue samples. The Norwegian cohort comprised 30 cases of HER2 + breast cancer, with matched untreated biopsy and post-neoadjuvant treatment sample. The Response Evaluation Criteria In Solid Tumors  (33)  were used to score the effect of the neoadjuvant treatment, with pCR defined as no invasive tumor cells in the primary tumor region or lymph nodes after neoadjuvant treatment. No-pCR was defined as the presence of residual invasive tumor cells in primary tumor region or lymph nodes. In this cohort, 11 patients had a complete response, 13 had a partial response 1, 2 had partial response 2, and 2 had stable disease after neoadjuvant combination of chemotherapy and trastuzumab. The details of adjuvant treatment are shown in Supplemental Figure  1. The T-DM1 cohort comprised untreated FFPE tumor sections from 20 patients with HER2 + breast cancer from the ClinicalTrials.
gov NCT02326974 clinical trial. For each case 2 different blocks with spatially distinct diagnostic biopsies were used. Pathological response to neoadjuvant treatment was reported using the Residual Cancer Burden calculator  (34). Since this trial is ongoing, longterm responses are not yet available. PDXs. PDX1 and PDX2 were derived from human HER2 + breast cancer at Institut Curie, Paris. PDXs were in accordance with published protocols. JCI Insight 2021;6(11):e147617 https://doi. org/10. 1172/jci. insight. 147617 PDX1 (HBCx-91) was derived from a 55-year-old woman with HER2 + , ER -, and progesterone receptor-negative (PR -) breast cancer undergoing mastectomy. The patient received adjuvant FEC100 (3 cycles), taxol/trastuzumab (3 cycles), and trastuzumab (1 year). No relapse was present in 2020.
5. 1 (https://www. R-project. org). For data analysis details see Supplemental Methods. Xenograft experiment. Female NOG (NOD. Cg-Prkdc scid Il2rg tm1Sug /JicTac) mice (Taconic) were used at 4-6 weeks of age. PDXs were expanded in NOG mice. A total of 2 million cells in 50% Matrigel (BD Biosciences) in DMEM (Gibco, Thermo Fisher Scientific), total volume 50 μL, were injected per mammary fat pad in a total volume 50 μL. In our experiments, we injected 30 animals with either PDX1 or PDX2, 2 contralateral fat pads per animal. Tumor diameter was monitored weekly. When all tumors reached over 0. 5 cm diameter, the mice were randomized into 6 treatment groups. Paclitaxel (i. p. ), T-DM1, trastuzumab, and pertuzumab (i. v) were delivered once weekly, while lapatinib was dosed by gavage 5 days a week (Supplemental Figure  4 ).
R v3. 5. 1 (https://www. R-project. org) and Bioconductor v3. 8  (41)  were used for all downstream analyses. The list of CNVs was filtered to only include those with at least 100 reads. IRanges v2. 16. 0  (42)  was used to identify CNVs with overlapping regions between the 2 replicates from each treatment group. CNVs with no overlaps in the other replicate were filtered out. RNA-Seq and analysis. For bulk RNA-Seq, RNA was extracted from a snap-frozen tissue with QIAGEN Blood and Tissue Kit. Libraries were prepared using Illumina kit and sequenced according to standard protocol. Demultiplexing and alignment of data were performed as previously described  (35). To remove mouse contamination, reads that mapped uniquely to the human genome were kept for further analysis. A subset of these also mapped uniquely to the mouse genome, for which a threshold was determined for the minimum difference in alignment scores between human and mouse.
The thresholds were determined, for each sample separately, by repeatedly taking random samples of the reads that mapped uniquely to both genomes, specified by varying alignment score differences, and statistically comparing sample alignment scores between human and mouse genomes using a 2-tailed t test. The minimum difference by which samples confidently displayed P ≤ 0. 01 defined the threshold, beyond which reads were kept. Read counts for individual genes were generated using the htseq-count script of the HTSeq framework (version 0. 6. 1p1) (43) using modified parameters (--stranded no) and the hg19/mm10 refGene annotation file available at the UCSC Genome. The DESeq2 (44) R package version 1. 16. 1 was used to generate differential expression gene lists, with a fold change value ≥ 2 and P adj ≤ 0.
05. Pathway enrichment analysis was performed using MetaCore from Clarivate Analytics. Alternative splicing analysis was performed using LeafCutter v0. 2. 9  (45). GENCODE v19 (46) (GRCh37) was used to annotate exons and splice junctions. scRNA-Seq and data analysis. Single-cell suspensions from 2 independent tumors were used from each xenograft and treatment group. Cell and library preparation for scRNA-Seq was performed according to 10x Genomics Chromium v2 protocol, targeting 2000 cells per sample. Cell Ranger v3. 0. 2 (47) was used to preprocess the scRNA-Seq output. cellranger mkfastq was used to demultiplex the base call files into FASTQ files; cellranger count was used to align and filter the FASTQ files individually, aligning to both the human (hg19) and mouse (mm10) genomes, and to generate feature-barcode matrices; cellranger aggr was used to normalize the separate runs to the same sequencing depth and aggregate the samples into 1 combined feature-barcode matrix.
All together this resulted in separate feature-barcode matrices for stroma and tumor cells for each PDX model and replicate. R v3. 5. 1 (https://www. R-project. org) and Seurat v3  (48)  were used for all downstream analysis. The data were filtered to include cells with at least 500 expressed genes and with a maximum of 25% of expressed genes coming from mitochondrial genes. The numbers of cells before and after filtering are shown in Supplemental Table  9. Cell cycle scores were assigned to each cell based on expression of G2/M and S phase markers using CellCycleScoring, using the marker gene list "cc. genes. updated. 2019" as provided by Seurat. Data were normalized using SCTransform. The percentage of mitochondrial RNA per cell was regressed out during normalization, and default values were used for all other parameters.
Acknowledgments We thank members of our laboratories for their critical reading of this manuscript and useful discussions. We thank  Zach Herbert  from the  DFCI Molecular Biology Core Facilities  for his dedication and technical expertise. This work was supported by the  NIH U54CA143798 (FM and KP) ,  U01CA195469  (JWG, PTS, FM, and KP),  R35 CA197623  (KP),  K99/R00CA201606  (MJ), and  F31CA239565  (SS);  NIH/National Institute of General Medical Sciences   T32GM074897  (SS); the  Center for Cancer Evolution (FM and KP) ; the  Norwegian Cancer Association ; and Norwegian South-East Regional Health Authorities and Radiumhospitalets legater (HGR and IHR). Address correspondence to:  Michalina Janiszewska ,  The Scripps Research Institute, 130 Scripps Way, Jupiter, Florida 33458, USA. Phone: 561.
All participants who were asked to complete the survey did so. A total of 155 surveys were completed and recorded in a common relational database. The resulting sample of respondents is therefore a convenience sample, and we do not seek empirical generalization to urban agriculture in all contexts or even all urban agriculture in Europe and the US. Instead, like many case study research efforts, we seek theoretical generalizability  (Tsang, 2014; Yin, 2009). In other words, we do not seek to argue that certain characteristics of our sample are "typical of a population," but rather we are concerned with our ability to draw conclusions about relationships between variables that are relevant to populations aside from our cases  (Tsang, 2013) , such as the relationship between certain farm and garden characteristics, participant motivations, and social impacts of urban agriculture.
Data analysis 2. 4. 1. Descriptive statistics We used descriptive statistics to summarize the demographics of the respondents, their self-assessed motivations for growing food, and the self-assessed impacts of urban agriculture on their lives. We calculated the mean of respondents' active hours working in the farms/gardens per week, and years of experience farming or gardening. Monthly income data was collected in binned categories based on the currency used in each country, with the following bin options: <500, 500-1000,  1001-1500, 1501-2000, 2001-3000, 3001-4000, and > 4000. For rescaling, we used the midpoint for bins with a range of incomes (e. g. , for 500-1000, a value of 750), a value of 500 for the lowest option, and a value of 4000 for the top option. To compare income levels across countries, respondents' reported income was divided by the country's average monthly household income for the 2017 year as reported by the Organisation for Economic Cooperation and Development (OECD 2020) to demonstrate the relative income of respondents compared to the country average.
Exploratory factor analysis We conducted exploratory factor analysis to create valid and reliable scales from the survey data on participants' motivations and impacts. After removing surveys with missing responses on items used for factor analysis we were left with 142 valid surveys for factor analysis. Kaiser-Meyer-Olkin values and the significance of Bartlett's test of sphericity were calculated to ensure the data were appropriate for factor analysis  (Bandalos & Finney, 2018). Factors were extracted using the fa function in the psych package in R, with weighted least squares regression to account for the ordinal nature of the data, and oblimin rotation (Revelle, 2020). The number of factors extracted was determined through eigenvalues greater than one and scree plot analysis  (Bandalos & Finney, 2018).
Multilevel model To determine characteristics of individual participants and farms that maximize wellbeing for participants, we analyzed a linear multilevel model using maximum likelihood estimation with the lme4 package in R  (Bates et al. , 2015). We used individual farm or garden sites as our cluster variables, and individual respondents as our individual variables. We used the general wellbeing impacts scale as the outcome variable (see our factor analysis results for details). For the multilevel model, we imputed data for all demographic variables: education level, years of experience gardening, income, age, weekly working hours on the farm or garden, social motivations, and economics scale score using the Amelia package  (Honaker et al. , 2011). We did not impute data for the outcome variable of general wellbeing impacts; thus, we report on a resulting sample size of 137 respondents across 62 clusters, for an average cluster size of 2.
Funding This paper is based on FEW-meter project, funded by  ESRC, UK , grant number  ES/S002170/2 ; by  BMBF :  Germany , grant number  01LF1801A ; by  ANR: France , grant number  ANR-17-SUGI-0001-01 ; by  NSF :  USA ,  Belmont Forum   18929627 ; by  NCN :  Poland , grant no  2017/25/Z/HS4/ 03048 ; and by  European Union 's  Horizon 2020 research and innovation programme  ( GA No 730254 ) under the  JPI Urban Europe's call "SUGI -FWE Nexus". The German-American Fulbright Commission  also provided support for this project.
Dams, reservoirs, and other water management infrastructure provide benefits, but can also have negative impacts. Dam construction and removal affects progress toward the UN sustainable development goals at local to global scales. Yet, globally-consistent information on the location and characteristics of these structures are lacking, with information often highly localised, fragmented, or inaccessible. A freely available, curated, consistent, and regularly updated global database of existing dams and other instream infrastructure is needed along with open access tools to support research, decision-making and management needs. Here we introduce the Global Dam Watch (GDW) initiative (www. globaldamwatch. org) whose objectives are: (a) advancing recent efforts to develop a single, globally consistent dam and instream barrier data product for global-scale analyses (the GDW database); (b) bringing together the increasingly numerous global, regional and local dam and instream barrier datasets in a directory of databases (the GDW directory); (c) building tools for the visualisation of dam and instream barrier data and for analyses in support of policy and decision making (the GDW knowledge-base) and (d) advancing earth observation and geographical information system techniques to map a wider range of instream structures and their properties.
Our focus is on all types of anthropogenic instream barriers, though we have started by prioritizing major reservoir dams and run-of-river barriers, for which more information is available. Our goal is to facilitate national-scale, basin-scale and global-scale mapping, analyses and understanding of all instream barriers, their impacts and their role in sustainable development through the provision of publicly accessible information and tools. We invite input and partnerships across sectors to strengthen GDW's utility and relevance for all, help define database content and knowledge-base tools, and generally expand the reach of GDW as a global hub of impartial academic expertise and policy information regarding dams and other instream barriers.
There is a need for a freely available, curated (i. e. selected, organized, and verified using expert knowledge), consistent, and regularly updated global database of existing dams and other instream infrastructure and their attributes, to facilitate more transparent decision-making and management  (Januchowski-Hartley et al 2013 , Nazemi and Wheater 2015 , Pelayo-Villamil et al 2015 , Thieme and Tickner 2015 , Winemiller et al 2016 , Grill et al 2019 , Belletti et al 2020 , Maavara et al 2020). Existing dam inventories vary widely in scope (types of dams and other barriers included), quality (degree of curation), geographical coverage (local to global), consistency of mapping effort between basins and countries, and accessibility (public domain, pay-walled or restricted). Nevertheless, an impressive range of analyses (figure  1 ) and tools have been produced over the last decade, which could be further accelerated by a concerted effort focused on creating and providing collaborative, open-access dam data and tools.
Examples of current open-access tools include the reservoir assessment tool  (Biswas et al 2021) , which provides information on the operation of current and planned large dams, and the Global Reservoirs and Lakes Monitor [G-REALM]  (Birkett et al 2009) , which provides water level information for lakes and reservoirs based on satellite altimetry. Though there have been concerted efforts to develop comprehensive local and regional dam datasets, these cannot easily be aggregated into a global repository. In particular, data consistency is necessary for global analyses and includes: (a) consistently applied definitions and classifications of dams and instream barriers; (b) a consistent methodology and effort in mapping between countries and basins; and (c) consistently recorded or calculated dam attributes.
The few globally consistent datasets that currently exist are known to be highly incomplete, as they miss many smaller dams or important attributes. Despite the current limitations, the utility of consistent global dam inventories and their potential to advance a broad range of water resource assessments has been showcased in a variety of recent cutting edge studies. For example,  Veldkamp et al (2017)  used the global reservoir and dam database (GRanD;  Lehner et al 2011)  to ascertain that reservoirs alleviated water scarcity for 8. 3% of the global population but exacerbated water scarcity for 8. 8%, most of whom lived downstream of reservoirs. Frederikse et al (2020)  used GRanD to gauge how reservoir filling in the 1970s and 1980s slowed sea level rise associated with global warming. Maavara et al (2020)  combined GRanD and the future hydropower reservoir and dam database  (FHReD; Zarfl et al 2015)  to assess how reservoirs affect the ratio of key nutrients that are transported through rivers to the oceans at the global scale, and how future hydropower development will change those ratios over the coming decades.
Gonzalez  Sanchez et al (2020)  used GRanD and the global surface water dataset (GSW; Pekel et al 2016) to analyse the gross water lost through evaporation in African hydropower reservoirs, and  Grill et al (2019)  combined GRanD and the global georeferenced database of dams (GOODD;  Mulligan et al 2009 Mulligan et al , 2020b) )  to present a high-resolution river connectivity assessment and identify the world's remaining freeflowing rivers. Barbarossa et al (2020)  combined GOODD, GranD and FHReD data to examine impacts on geographic range connectivity of freshwater fish globally and Zarfl et al (2019) evaluated how future large hydropower dams might impact global freshwater megafauna. Finally,  Cooley et al (2021)  used GOODD to understand human alteration of global surface water storage variability.
Here, we advocate for developing a more comprehensive, freely available and curated global dam database alongside a suite of tools to help grow, manage and convert these data into open-access, actionable national, basin and global intelligence on dams to accelerate sustainable solutions at the water, energy, food and environment nexus. As stewards and custodians for this effort, we hereby introduce the Global Dam Watch (GDW) initiative (www. globaldamwatch. org) with the following goals: (a) Establishing cross-sector collaborations to expand on previous efforts to develop a single, globally consistent dam and instream barrier database (the GDW database). This could be used as a community standard to further investigate pressing global-scale questions (examples are given in figure  1 ). This database is currently being compiled, and while the intent is to include all types of dam and instream barriers and to clearly separate the different types, the compilation has started from the largest and thus most widely documented structures.
The initial databases to be included in this harmonization effort are available at www. globaldamwatch. org (GOODD, GranD and FHReD) and currently contain 38 660, 7320 and 3700 dams, respectively, which represent medium to large (GOODD), large only (GRanD), and planned dams (FHReD). (b) Bringing together the increasingly numerous global, regional and local dam and instream barrier datasets in a directory of databases (the GDW directory). The GDW directory currently links to 14 regional databases and 7 global databases. (c) Building tools for the presentation and assessment of dam and instream barrier data and for analyses in support of policy and decision making (the GDW knowledge-base). The GDW knowledge-base currently includes 121 396 dams under curation and a total of more than 8 million attribute values.
The challenge of multiple inconsistent and static databases Existing dam inventories provide a valuable foundation for any researcher who seeks to investigate the effects of dams. The challenge is that the data exist, but vary widely in scope, quality, spatial coverage, temporal scale, and accessibility, and there is little guidance as to which data might be most appropriate for a given research or policy question. This slows research progress and hinders access by decision-makers. Across regions and within countries, there have been some concerted efforts to develop comprehensive local dam datasets. For example, in Europe,  Belletti et al (2020)   Speckhann et al (2021)  for Germany. Built for a particular place and purpose, the criteria for dam inclusion and the associated attributes within regional and country-level databases vary, making them difficult to integrate.
Existing global databases have the benefit of more consistent information across regions but tend to be skewed towards larger dams (Garcia de Leaniz et al 2019), are only updated sporadically, and often place an emphasis on certain types of instream infrastructure, such as hydropower dams. The focus on large dams for global scale databases makes sense given that these are easiest to detect. At the global scale, the GRanD database includes georeferenced locations and a variety of attributes for over 7000 dams but focuses on dams with large reservoirs  (Lehner et al 2011). The GOODD database contains more than 38 000 georeferenced dam locations, including medium-sized infrastructure, but lacks associated attributes (such as year of construction, physical attributes of the dam and reservoir, drainage area, or purpose) because the data were manually mapped from satellite imagery  (Mulligan et al 2009 , Mulligan et al 2020b).