dataesr/scientific-paragraphs-categorization huggingface dataset:
The number of cells in the mapped dataset labeled with each label are shown between brackets after cell type names. Cell type labels not present in the HLCA are boxed. AT: alveolar type. DC: dendritic cells. EC: endothelial cells. MT: metallothionein. Mph: macrophages. NK: natural killer. SM: smooth muscle. SMG: submucosal gland. TB: terminal bronchiole. Cluster 0 is split into control ('Healthy') and IPF, further subdivided by study. d, Composition of monocyte-derived macrophage (MDM) clusters by study. e, As d, but by tissue sampling method. f, Expression of MDM cluster marker genes shown per cluster, with clusters split into studies. Studies with fewer than 200 were grouped into 'Other' for each cluster. g, Composition of MDM clusters by study, subsetted to only cells from donors with COVID-19.
Resourcehttps://doi. org/10. 1038/s41591-023-02327-Expression of CCR7 among cells annotated as migratory DCs by label transfer. Expression of CCR7 is shown for all cells that were annotated as migratory DCs with low uncertainty (<0. 2) and all other cells annotated as DC (bottom) by label transfer from the HLCA core to the extended HLCA. Cells are grouped based on study of origin (some studies contain multiple datasets). X-tick labels show study, number of cells annotated as migratory DCs, and number of total cells (in thousands) per study. CCR7 counts shown are counts that were normalized based on the total count among 2000 genes used for mapping to the HLCA core, and then log-transformed. DCs: dendritic cells.
Resourcehttps://doi. org/10. 1038/s41591-023-02327-Disease-specific cellular states and states shared across diseases in the extended HLCA. a, Label transfer uncertainty shown per cell type, comparing cells from control samples ('healthy', blue) to cells from IPF samples (orange). Results are shown per dataset, only showing datasets that include both control and IPF mapped samples. Alveolar fibroblasts, the cell type chosen for downstream analysis, are boxed in red. AT: alveolar type. DC: dendritic cell. TB: terminal bronchiole. EC: endothelial cell. Mph: macrophage. MT: metallothionein. NK: natural killer. SM: smooth muscle. b, Composition of alveolar fibroblast clusters by study. c, Expression of several genes highly expressed in IPF-enriched alveolar fibroblast cluster 0, shown per cluster.
Acknowledgements This publication is part of the  Human Cell Atlas  (www. humancellatlas. org/publications/). This work was supported by the  Chan Zuckerberg Initiative (CZI; LLC Seed Network  grant  CZF2019-002438  (Lung Cell Atlas 1. 0) to P. B. , M. D. L. , A. V. M. , M. C. N. , D. P. S. , J. R. , P. R. T. , K. B. M. , F. J. T. and H. B. S. );  National Institutes of Health (NIH ;  R01HL145372 ) and  Department of Defense  ( W81XWH-19-1-0416 ) (to J. A. K. and N. E. B. );  Fondation pour la Recherche Médicale  ( DEQ20180339158 ),  Conseil Départemental des Alpes Maritimes  ( 2016-294DGADSH-CV ),  Inserm Cross-cutting Scientific Program HuDeCA 2018 ,  Agence Nationale de la Recherche SAHARRA  ( ANR-19-CE14-0027 ),  ANR-19-P3IA-0002-3IA ,  National Infrastructure France Génomique  ( ANR-10-INBS-09-03 ) and  PPIA 4D-OMICS  ( 21-ESRE-0052 ) (to P.
B. );  H2020-SC1-BHC-2018-2020  Discovair (grant agreement  874656 ) (to P. B. , K. B. M. , S. A. T. , M. C. N. , F. J. T. , M. P. , H. B. S. and J. L. );  NIH   1U54HL145608-01  (to M. D. L. , K. Z. , X. S. , J. S. H. and G. P. );  Wellcome  ( WT211276/Z/18/Z ) and Sanger core grant  WT206194  (to K. B. M. and S. A. T. );  ESPOD  fellowship of the  European Molecular Biology Laboratory European Bioinformatics Institute and Sanger Institute  (to E. M. );  R01 HL153312 ,  U19 AI135964 ,  P01 AG049665 ,  R01 HL158139 ,  R01 ES034350  and  U54 AG079754  (to A. V. M. );  Lung Foundation Netherlands  project numbers 5. 1. 14. 020 and 4. 1. 18. 226 (to M. C. N. );  NIH  grants  R01HL146557  and  R01HL153375  (to P. R. T. );  German Center for Lung Research and Helmholtz Association  (to H. B. S.
Data availability The HLCA (raw and normalized counts, integrated embedding, cell type annotations and clinical and technical metadata) is publicly available and can be downloaded via cellxgene (https://cellxgene. cziscience. com/collections/6f6d381a-7701-4781-935c-db10d30de293). The HLCA core reference model and embedding for the mapping of new data to the HLCA can moreover be found on Zenodo (https:// doi. org/10. 5281/zenodo. 7599104). The original, published datasets that were included in the HLCA can also be accessed under GEO accession numbers GSE135893, GSE143868, GSE128033, GSE121611, GSE134174, GSE150674, GSE151928, GSE136831, GSE128169, GSE171668, GSE132771, GSE126030, GSE161382, GSE155249, GSE135851, GSE145926 and GSE178360, GSE227136, GSE158127, European Genome-phenome Archive study IDs EGAS00001004082, EGAS00001004344, EGAD00001005064 and EGAD00001005065 and URLs https:// www.
synapse. org/#!Synapse:syn21041850, https://data. humancellatlas. org/explore/projects/c4077b3c-5c98-4d26-a614-246d12c2e5d7, https://www. ncbi. nlm. nih. gov/projects/gap/cgi-bin/study. cgi?study_ id=phs001750. v1. p1, https://www. nupulmonary. org/covid-19-ms2/? ds=full&meta=SampleName, https://figshare. com/articles/dataset/ Single-cell_RNA-Seq_of_human_primary_lung_and_bronchial_epithe-lium_cells/11981034/1, https://covid19. lambrechtslab. org/downloads/.
Code availability The HLCA pipeline for processing the sequencing data for counting matrices, used for a subset of HLCA datasets (Methods), is available at https://github. com/LungCellAtlas/scRNAseq_pipelines. All further code used for the HLCA project can be found in the HLCA reproducibility GitHub repository (https://github. com/LungCellAtlas/HLCA_reproducibility). The landing page of the HLCA, including up-to-date links, can be found at https://github. com/LungCellAtlas/HLCA. Automated mapping to the HLCA and label transfer can be done with scArches  71  at FASTGenomics (https://beta. fastgenomics. org/p/hlca) or using the code and tutorial in the HLCA mapping GitHub repository (https:// github. com/LungCellAtlas/mapping_data_to_the_HLCA). Links to additional and updated platforms will be published on the HLCA landing page (see above).
Ethics approval and consent Ethics approval information per study was as follows. For the pooled data from refs. 21,27, approval was given by the Vanderbilt Institutional Review Board (IRB) (numbers 060165 and 171657) and Western IRB (number 20181836). All samples were collected from declined organ donors who were also consented for research. For ref. 6, the study was approved by the Comité de Protection des Personnes Sud Est IV (approval number 17/081). Informed written consent was obtained from all participants involved. For Jain_Misharin_2021 (A. V. M. , M. J. and N. S. M. , newly generated dataset), the protocol was approved by the Northwestern University IRB (STU00214826). Written informed consent was obtained from all study participants. For ref. 5, patient tissues were obtained under a protocol approved by Stanford University's Human Subjects Research Compliance Office (IRB 15166).
2 Extended Data Fig. 8 | 28 Resourcehttps://doi. org/10. 1038/s41591-023-02327-Expression of CCR7 among cells annotated as migratory DCs by label transfer. Expression of CCR7 is shown for all cells that were annotated as migratory DCs with low uncertainty (<0. 2) and all other cells annotated as DC (bottom) by label transfer from the HLCA core to the extended HLCA. Cells are grouped based on study of origin (some studies contain multiple datasets). X-tick labels show study, number of cells annotated as migratory DCs, and number of total cells (in thousands) per study. CCR7 counts shown are counts that were normalized based on the total count among 2000 genes used for mapping to the HLCA core, and then log-transformed. DCs: dendritic cells.
2 Extended Data Fig. 10 | 210 Resourcehttps://doi. org/10. 1038/s41591-023-02327-Disease-specific cellular states and states shared across diseases in the extended HLCA. a, Label transfer uncertainty shown per cell type, comparing cells from control samples ('healthy', blue) to cells from IPF samples (orange). Results are shown per dataset, only showing datasets that include both control and IPF mapped samples. Alveolar fibroblasts, the cell type chosen for downstream analysis, are boxed in red. AT: alveolar type. DC: dendritic cell. TB: terminal bronchiole. EC: endothelial cell. Mph: macrophage. MT: metallothionein. NK: natural killer. SM: smooth muscle. b, Composition of alveolar fibroblast clusters by study. c, Expression of several genes highly expressed in IPF-enriched alveolar fibroblast cluster 0, shown per cluster.
In this article, we study snow avalanche activity during the snow seasons of 2017-2020 using four automatic time-lapse cameras strategically positioned along the southwestern slope of Tasiapik Valley, near the village of Umiujaq, in Nunavik (northern Québec, Canada). Over the three snow seasons, cameras helped to detect evidence of 130 avalanche events, scattered over seventy-eight distinct avalanche days. The evolution of weather conditions prior to each avalanche release was detailed according to data from a nearby weather station. Moreover, the time of release, the release type, the surface texture, and whether rocky material was present in the deposits were documented from the photographs. To explore relationships between weather data and avalanche releases, conditional inference tree (CIT) analysis was conducted.
Data from the VDTSILA weather station are available for download via the Nordicana D platform of the Center d'études nordiques  (CEN 2020). Snowfall data were extracted from snow cover depth data measured directly at the VDTSILA weather station by Québec's Ministère de l'Environnement et de la Lutte contre les Changements Climatiques (MELCC) since 2012 (MELCC 2019). Rainfall has also been measured hourly in the area since 2012 at the VDTSILA station by an automatic cumulative rain gauge belonging to the MELCC. These installations are the closest to the study area, with Kuujjuarapik's weather stations (160 km south) and Inukjuaq's weather station (230 km north) being the next closest for weather data collection in the region.
Statistical analysis In the present study, because our aim was to explore relationships between weather data and avalanche releases, a conditional inference tree analysis was conducted using the ctree function found in the partykit package that can be implemented in the statistical software R  (Hothorn and Zeileis 2015). Our data set was composed of twenty-three independent variables, which are listed in Table  1. The dependent variable was binary, and it expressed whether avalanche deposits had been observed that day (avalanche day). The conditional inference tree was prioritized over other types of statistical analyses because of the limited number of avalanche days contained in our data set. Furthermore, it produces a simple representation that explains complex relationships between the predictors and the dependdnt variable  (Horton, Towell, and Haegeli 2020).
Winter and spring seasons summary Over the three winter seasons documented in this study, the four automatic time-lapse cameras helped us to detect forty-six winter avalanche events scattered over forty-two distinct avalanche days in a total of 606 winter days (Figure  2 ). The duration of the winter season varied from 213 days in 2017-2018 down to 181 days in 2019-2020 (Table  2 ). The percentage of winter days with efficient wind conditions for cornice formation (and growth) varied from 18 percent in 2017-2018 to 17 percent in 2018-2019 and 12 percent in 2019-2020. The mean air temperature during the winter seasons remained constant, measuring approximately -11. 5°C each season. The greatest amount of snowfall occurred in 2017-2018 according to data from the VDTSILA weather station. Over the three spring seasons documented in this study, the four automatic time-lapse cameras helped to detect eight-four avalanche deposits over a total of 130 days (Figure  2 ).
General weather conditions According to the mean air temperature data registered by VDTSILA, the shift from autumn conditions to winter conditions occurred on 26 October 2017. Winter conditions then lasted until 27 May, for a total of 213 days. Over the winter, snowfall totaled 629 cm in   the upper valley area, and snow cover depth reached a maximum of 100 cm at the VDTSILA location on 17 April. The winter season started with frequent blizzard-like conditions as snowfall totaled 118 cm between 1 November and 20 November inclusive, as reported by  Veilleux (2021). For the whole winter season, the mean air temperature was -11. 5°C and thirty-eight days (18 percent) had efficient wind conditions suitable for snow transportation along the gently inclined SW slope toward the ridgeline (Figure  3 ).
General weather conditions In 2018-2019, the shift from autumn conditions to winter conditions occurred on 10 October, which was 21 days earlier than in the year before. Winter conditions remained until 10 May, totaling 212 days. According to data from the VDTSILA weather station, snowfall totaled 323 cm during the winter of 2018-2019, and a maximum snow cover depth of 70 cm on the valley floor was measured on 24 March 2019 (Figure  4 ). Between 1 November 2018 and 10 May 2019, mean air temperature was again -11. 5°C. Within that same time frame, 36 days were reported to have efficient wind conditions suitable for snow transportation according to the mean wind speed and wind orientation data recorded by the VDTSILA weather station. A total of eleven avalanche deposits were recorded by the cameras: six by TAS 2 and five by TAS 3.
General weather conditions In 2019-2020, the shift from autumn to winter conditions occurred on 30 October according to data from VDTSILA. Thereafter, daily mean air temperature remained below 0°C until 28 April 2020 (181 days). Over the winter, snowfall totaled 304 cm, reaching a maximum snow cover of 117 cm on 27 April (Figure  5 ). A total of twenty avalanche deposits were reported. These were scattered over nineteen different avalanche days (thirteen recorded by TAS 2, four by TAS 3, and three by the newly placed TAS 4). The first avalanche deposits were observed on 13 November 2019 in photographs recorded by TAS 2. Evidence of eleven cornice failures was also recorded primarily in photographs from TAS 1 but also in some from TAS 3 and TAS 4. Five cornice failures are likely to have led to the release of avalanches on the slopes below, according to our observations of avalanche deposits in corresponding photographs from the TAS 2, TAS 3, and TAS 4 cameras.
Limitations The three years of slope monitoring in Tasiapik Valley have given us a database totaling 130 avalanche deposit observations scattered over seventy-eight distinct avalanche days (forty-two in winter, thirty-six in spring). They have also allowed us to detect forty-six days of cornice failures. This remote sensing method, although not particularly expensive, enables continuous data collection at regular time intervals and provides a safe observer position that does not alter the physical properties of the study site  (Eckerstorfer et al. 2016 ). However, it also comes with nonnegligible limitations. These limitations concern the spatially limited character of the observations contained in the resulting data set as well as issues with visibility due to harsh weather conditions and poor lighting.
In addition, the cameras and the weather station used in the study are not at the same altitude, which may lead to some divergence in the findings. For instance, the VDTSILA weather station is situated at an altitude of 126 m. a. s. l. in the valley and is protected from winds by the high SW slope, whereas the TAS 1 camera is located at 300 m. a. s. l. and is completely exposed to strong westerly winds, as we experienced on the field during summer 2021. Effectively, the use of automatic time-lapse cameras also means that the database is spatially limited to the avalanche tracks we choose to monitor. As mentioned by  Veilleux, Decaulne, and Bhiry (2021) , the conditions may be locally favorable for avalanche releases. For instance, during the first year of monitoring, many events were observed using TAS 2 (thirty-six), whereas TAS 3 only detected three events.
This also restricts the kinds of analyses that may be performed with the data set and diminishes its predictive power. The authors would also like to reiterate that many of the photographs taken by the cameras could not be used to gather any useful information on the avalanche dynamic due to poor visibility conditions. We think this issue affected the study the most during the first snow season of the study, which is when the VDTSILA weather station data showed the most total snowfall (~629 cm). Accordingly, during a fieldwork campaign in summer 2019,  Veilleux, Decaulne, and Bhiry (2021)  were told through informal discussions with locals that the snow season 2017-2018 was particularly long and harsh. Such conditions could most certainly have triggered more avalanches than usual. However, the severe winter conditions also contributed to a very high number of unusable pictures due to fog, frost, blowing snow, and/or snow accumulation on the camera lenses, as observed during photo interpretation from winter season 2017-2018.
5°C. This study of weather conditions favoring avalanche releases over the monitored paths of Tasiapik Valley represents a first step toward avalanche forecasting based on weather data in Nunavik. Time-lapse monitoring will continue over the years to come. Weather data and photographs are now being registered by two weather stations and five time-lapse cameras from the snow season 2020-2021 and onwards. The data are currently being processed to enlarge our observation data set and add another layer to avalanche hazard knowledge in northern Québec.
Acknowledgments We gratefully acknowledge the  Centre d'études Nordiques (CEN) , a strategic cluster funded by the  Fonds québécois de recherche Nature et Technologie , for access to its research infrastructure for professional and logistical support. Special thanks are extended to the community of  Umiujaq  for their generosity and for agreeing to host our research. The authors also thank  Samuel Veilleux ,  Denis Sarrazin ,  Carl Barrette ,  Félix Faucher ,  Julien Lebrun , and  Þorsteinn Saemundsson  for their valuable assistance in the field. Finally, we thank one anonymous reviewer as well as  Dr. Pascal Haegeli  for their valuable and constructive feedback on previous versions of the article.
Funding Funding for this project was provided by the  Natural Sciences and Engineering Research Council of Canada (NSERC) ,  IPEV DeSiGN , LabEx # 1148 ,  DRIIHM  ( French program  " Investissements d'Avenir ,"  ANR-11-LABX-0010 , managed by the  French National Research Agency, ANR) , and  OHMi NUNAVIK.
Attacking RS+FD: Uncovering the Sampled Attribute (→ SMP) Because the objective of the RS+FD solution is to hide the LDP value among fake data  [4] , discovering the sampled attribute of each user would convert RS+FD into the SMP solution again. Even more, unlike SMP (and SPL), RS+FD utilizes an amplified  ′ > , which decreases the "plausible deniability" of the user's report (cf. Section 3. 2. 1) and could thus be leveraged for re-identification attacks (cf. Section 3. 2. 4) under multiple data collections. For instance, consider the scenario in which a given user  ∈ U whose sampled attribute is  ∈ [] produces an RS+FD's output tuple as y = [ 1 ,  2 ,. ,   ]. In this situation, the baseline classification model is just a random guess t =    ([]). In addition, we propose a classifier learning setting in which an attacker aims to train a classifier over a learning dataset D  = {(y  ,   )|  ∈ [ ]} of  rows and  =  + 1 columns.
That is, for each user   , y  is the output tuple of the RS+FD solution (LDP/fake values, i. e. , a full profile of  attributes) and   is the sampled attribute (target is a class within []). Because the sampled attribute   of users should be unknown to the attacker, in this work, we propose three settings to build a learning dataset D  , which depends on the attack model. In all these settings, we assume that the attacker has the knowledge of the privacy budget  and the LDP protocol used by users with the RS+FD solution. Finally, the attacker's attribute inference accuracy (AIF-ACC) is measured by the number of correct predictions  = t over the number of users in the testing dataset   : 𝐴𝐼 𝐹 -𝐴𝐶𝐶 (%) = 100 • 𝑛 𝑡 𝑖=1 𝑓 (𝑡𝑖, t𝑖 ) 𝑛 𝑡 , in which  , t = 1 if  = t and 0 otherwise. Profiles. This second setting considers the scenario in which the attacker has knowledge about the sampled attribute of   <  real users, i.
Experimental Setup Environment. All algorithms were implemented in Python 3. In all experiments, we report the results averaged over 20 runs. Datasets. For ease of reproducibility, we conduct our experiments on two census-based multidimensional and open datasets. • ACSEmployement. This dataset is generated from the Folktables Python package  [16]  that provides access to datasets derived from the US Census. We have selected the "Montana" state only, which results in  = 10, 336 samples with  = 18 discrete attributes (target included) and domain size k = [92, 25, 5, 2, 2, 9, 4, 5, 5, 4, 2, 18, 2, 2, 3, 9, 3, 6]. • Adult. This is a classical dataset from the UCI ML repository  [18]  with  = 45, 222 samples after cleaning. We selected  = 10 attributes ("age", "workclass", "education", "marital-status", "occupation", "relationship", "race", "sex", "native-country" and "salary") with domain size k = [74, 7, 16, 7, 14, 6, 5, 2, 41, 2], respectively.
Experimental evaluation. We set #surveys = 5, in which each survey  ∈ [#surveys], has a different number of attributes   =      2 ,. ,  (i. e. , with at least  2 attributes). The attributes are also selected at random per survey. Due to space constraints, we only present here the experiments with the FK-RI model (cf. Section 3. 2. 4), considering the -dimensional dataset as background knowledge D  , and with the uniform privacy metric setting from Section 3. 2. 2. Finally, we measure the attacker's RID-ACC after #surveys ≥ 2, which results in the inferred profile of each user having respectively 2, 3, 4 or 5 attributes, to be used for the re-identification attack. Results. Fig. 2  illustrates the attacker's RID-ACC metric on the Adult dataset for top- re-identification using the SMP solution, the FK-RI model with uniform -LDP privacy metric across users, by varying the LDP protocol and the number of surveys.
Additional results with all LDP protocols, Adult and ACSEmployement datasets, FK-RI and PK-RI models, uniform and non-uniform privacy metric settings as well as with the relaxed LDP metric of  [36]  are presented in Appendix C of  [6]. Analysis. In general, the experimental results of Fig. 2  match the numerical results of the expected values from Fig. 1. From Fig. 2 , one can observe that our re-identification attacks present significant improvement over a random baseline model that has  - ≪ 1% (i. e. , top-/). For instance, with a single shot (i. e. , top-1), the attacker's RID-ACC is already significant for GRR (and -SS) and SUE after about #surveys ≥ 4, with at most ∼ 10% of RID-ACC. In comparison, both OUE and OLH protocols have about 10x less RID-ACC, (i. e. , at most ∼ 1% of RID-ACC for top-1).
On the other hand, when there is a set of top-10 profiles, the adversary achieves - ≥ 2. 5% for GRR (and -SS) after only 2 surveys with an upper bound of about 33% of RID-ACC after 5 surveys. Though with slightly smaller RID-ACC, the SUE protocol also achieves about 28% of RID-ACC after 5 surveys, and both OUE and OLH are upper bounded by about 5% of RID-ACC. Although the user is not uniquely re-identified, this still represents a threat due to the possibility of performing, e. g. , homogeneity attacks  [12, 31, 34]. Overall, these "high" re-identification rates may be explained by many factors. First, the combination of multiple attributes within the Adult dataset leads to several unique people or small groups of people (this is also the case for the ACSEmployement dataset in Fig. 9  of Appendix C of  [6] ).