dataesr/scientific-paragraphs-categorization huggingface dataset:
The root reason for which developers look for this non-intrusiveness property is that no additional (non-functional) statement has to be inserted in their program. This is exploited by developers, e. g. , to avoid code versioning noise  [1]  (because code has been modified with instrumentation), to separate debugging behavior and debugged programs' behavior  [11, 13] , to intercept and profile meta-events from the Pharo IDE without modifying its source code  [35] , or to cope with constraints on code modifications  [65]. To visualize and control instrumentation, which is invisible to users and to system tools, developers often built and used dedicated tools suiting their own usecase  [1, 11, 12, 14, 64, 65].
g. , object-centric behavioral instrumentation or breakpoints). New gen. debuggers [21]  Instrumentation of methods to record metadata after the execution of an expression and its sub-expressions. Recorded data is used in the debugger to show the last values of executed (sub)expressions. Moldable debugger [9, 10]  Evaluation of reflective code conditioning (de)activation of breakpoints before specific message calls/sends within the body of methods. Conditions use metadata reified from the execution context. On-demand announcements [65]  On-demand instrumentation of methods to add boilerplate code for an announcement mechanism, without affecting the original base code. Post-mortem exec. analysis  [64]  Snapshot of objects' internal properties by instrumenting code that changes objects' structure (e.
Another proposition is to control the presence of metalinks on specific AST nodes like variable accesses  [64]. That solution provides means to install metalinks on all accesses to specified named variable. After each recompilation, the AST is checked and for each variable access the metalink is reinstalled. It inspired an API, which has been integrated in Reflectivity. This API is already in use in some projects  [12, 14]. Performance overhead. The impact of Reflectivity on performance is two-fold: there is an overhead due to the metalink mechanism, and an overhead due to the metalink installation. Some benchmarking were reported  [9, 10, 11, 39, 50] , but the measured overhead also involved performance loss due to the project using Reflectivity as a backend. Some experiments also reported that the overhead is significantly higher when data is reified from the execution context, and that the more reifications are requested, the higher is the overhead.
5:19 To better characterize the impact of using the current release of Reflectivity, we summarize in the following a basic evaluation of the performance overhead due to the use of Reflectivity, and the execution cost of installing metalinks on all methods in a set of classes. Detailed results are available in appendix C. Instrumentation Cost. We measured the instrumentation cost with metalinks of message sends and instance variable read/write. We compared these measurements with their counterpart instrumentation performed from the base level with code insertion. Worst case scenario is for message sends, when calling a meta-object and reifying all data needed to execute the send, which led to a ≈75. 1 % slowdown. With less reifications, this overhead diminished to ≈16. 3 %, which confirms that requesting data from the execution context increases the overhead.
Limitations inherited from Pharo We inherit two limitations from Pharo: thread safety of reflective change and hot code update of methods on the stack. Limited thread safety. Reflective operations that modify the structure, like adding or removing methods or classes, are not thread safe in Pharo (or Smalltalk). For Reflectivity, we inherit this property: installation of metalinks is not thread safe, but once they are installed, the resulting code can be executed safely. The client has to make sure that metalink installation does not interfere, by e. g. using a monitor to coordinate changes from multiple threads. Non-applicability to methods on the stack. Methods already on the execution stack cannot be modified. So if metalinks are installed on such methods, they will only take effect for next calls to those methods.
In combination with getting reifications from the VM, an improved AST compilation model with transformations would provide reifications in a static way instead of dynamically manipulating the stack to retrieve them. Finally, we plan to experiment with non-destructive AST transformations for text to AST compilation: we do not need to re-create the whole AST of a method (and loose all metalinks). Instead the editor could just update the existing AST for every keystroke. Thread safety and hot code update We currently inherit two problems from the implementation platform: thread safety and hot code update of methods that are on the stack. We decided to not solve these problems just for Reflectivity, but instead solve them for Pharo in general. All reflective operations (e. g. changing classes) need to be thread safe.
All kinds of code update should provide a way to update methods on the stack. Some research has been done in these directions for Pharo  [42, 63] , which will be used as the starting point for future research. Metalinks on all structural elements. The Reflectivity metalinks are annotations on the structure of methods in the form of the AST. But there is nothing inherent in the metalink idea that binds it to the AST. In the future we want to extend the concept of 5:21 metalink to any structural reflective entity: classes, packages. In first experiments, we have added support to annotate first class variable definitions, which is now available in Pharo 8.
B. 1 Building reflective libraries The API and abstractions offered by Reflectivity have been used to build reflective libraries or language extensions. Unified API to control reflective operations. Controlling reflection is a challenging task  [39]. The paper presents five dimensions of meta-level control from related literature and proposes a unified API. Contribution Papoulias, Denker, Ducasse, and Fabresse proposed a meta-object protocol to control reflective operations of Pharo, through a generalized and unified API  [39]  using a new entity, the reflectogram. When a reflective operation is triggered, a jump to the meta-level is performed and reified. This reification holds contextual information about the meta-level jump and provides fine control over the execution and the result of the reflective operation.
Reflectivity usage All the facilities proposed by this meta-object protocol are implemented on top of Reflectivity. Metalinks are used as means to provide reifications of the execution context, to delegate control and behavior, and dynamically control the (de)activation of the meta-behavior. Implementing Aspect-Oriented Programming. AOP  [28]  is a programming paradigm, with which developers can inject cross-cutting concerns in the program. Cross-cutting behavior is encapsulated in language constructs named advices, which are injected at key points in the base code named pointcuts. The code injection is called weaving, and produces programs with an instrumented control flow. Contribution Strauss introduced AOP for Squeak on top of partial behavioral reflection  [56]. Reflectivity has been extended to build an implementation of AOP named Dynamic Aspects, integrated with existing concepts of Smalltalk.
Contribution Uhnák and Pergl proposed an approach and a tool to visualize and ease the post-mortem analysis of run-time object structure  [64]. The tool selectively captures the execution of a program and exposes its structural properties through live visualizations, in which users can navigate. Reflectivity usage Metalinks are used as the underlying instrumentation mechanism to trigger snapshots of objects and connect them to their domain model. A toolkit 4  based on Reflectivity has been developed to experiment with pervasive instrumentation of the program, for example to target all assignments of a variable and to install persistent metalinks on the corresponding AST nodes. Incremental and Interactive Analysis of Features. Many maintenance tasks are usually expressed in terms of user features.
In this paper, using the OpenSky Impala data interface, we first sample over one month of raw BDS 1,7 messages from around the world. Around 40 million messages are obtained. We then decode and analyze the GICB capability messages. The resulting data contain Comm-B capabilities for all aircraft available to OpenSky during this month. The analyses in this paper focus on exploring statistics of GICB capabilities among all aircraft and within each aircraft type. The resulting GICB capability database is shared as an open dataset.
Data Extraction from OpenSky Using the Impala Interface First of all, we need to obtain GICB Mode S replies from the OpenSky historical database. In this task, we used the pyopensky library (formally known as pyModeS-opensky  [5] ) to perform the Impala querying and Comm-B messages types inference. OpenSky Impala interface provides access to historical raw Mode S messages that have been collected by receivers in the OpenSky network. Raw data stored in the rollcall_replies_data4 table are extracted for the analysis completed in this paper. We have selected September 2019 as the month for the demonstration. Since the total amount of data are too large to be downloaded through the internet within a reasonable time, we have extracted the data selectively. For every 15 min, we download the first one-minute segments of all raw messages around the world.
Extracting Aircraft GICB Information Once a GICB message is successfully identified, corresponding Comm-B capabilities are extracted as shown in Table  2. Based on the transponder address code (ICAO24 address), we can relate each message to a specific aircraft where the registration, typecode, and model are known (OpenSky aircraft database, https://opensky-network. org/aircraft-database) is used to find out specific aircraft information). Since the same aircraft can transmit different GICB messages depending on the availability of source data, we consider all capabilities that are enabled by an aircraft during the entire period of the data as the final GICB capabilities of the transponder. After combining duplicated messages for each aircraft, around 200,000 unique messages are found. Once GICB information from all aircraft is gathered, we can perform different analyses using this aggregated dataset.
Database The resulting data from this paper is shared publicly (The dataset can be found at: https://github. com/junzis/gicb-db). It contains approximately 50,000 aircraft that were seen by the OpenSky network during the month of September 2019. The structure of this dataset is illustrated with the following fragment (with commas removed): 48406f PH-LAB C550 1 1 1 1 1 1 1 0 1 0 0 0 0 0 0 1 0 0 1 0 0 0 1 1 4843de PH-LAG P28A 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4850cf PH-LAU F9EX 1 1 1 1 1 1 1 0 1 0 0 0 0 0 0 1 0 0 1 0 0 0 1 1 4845e4 PH-LAV BALL 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4843e5 PH-LAW C310 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0. The first three columns are transponder address, aircraft registration, and aircraft typecode. The remaining columns are the Comm-B Data Selector (BDS) codes, which correspond to specific GICB capabilities.
Conclusions In this paper, we studied commonly supported Mode S Comm-B capabilities in aircraft around the world, using the Mode S common usage GICB reply messages, which are designed for air traffic controllers to acquire Mode S capabilities of aircraft. The inference and decoding of this type of messages are made possible by the open-source library pyModeS. We made use of one month of global raw Mode S data from the OpenSky network to conduct this research. Combined with the OpenSky open aircraft database, we are able to produce statistics for approximately 50,000 aircraft flown during this month globally, including passenger, cargo, general aviation, helicopters, and other aircraft categories. Among all available Mode S capabilities, the statistics showed that around 67% of the aircraft are compliant with the requirements of ADS-B and Mode S enhanced surveillance.
It is also found that, among all the aircraft, only around 3. 5% of them support downlinking meteorological reports. It is worth noting that the BDS identification process may introduce errors. Sometimes, other types of messages can be identified as BDS 1,7 messages. In rare cases, it is also possible that errors in one message would produce a valid BDS 1,7 message that seems to come from a different aircraft. This is the limitation of inferring and decoding Mode S message as a third-party observer. To mitigate this problem, in the future, we could make use of larger segments of continuous data instead of the one-minute sample as in this paper. This would allow us to better filter out BDS 1,7 messages. The resulting database of this paper is shared publicly. However, only the sampling of an entire month of data are analyzed and produced in this paper.
1j-m ). Cheng et al. have recently published a single-cell RNA sequencing atlas of epithelial cells originating from three healthy human scalp samples, enabling us to study cells from the pilosebaceous unit  29. We used the Seurat R package to reanalyse the raw data from this study, performed unbiased clustering, and ran uniform manifold approximation and projection (UMAP) nonlinear dimensional reduction. This approach identified 24 distinct clusters (Fig. 1f ). Five hundred and eighty six GATA6+ cells were identified, while 41,355 cells were negative for GATA6. Furthermore, GATA6+ cells were significantly enriched in cluster 22 (Fig. 1f, g ). Fifty-one percent of the cells present in this cluster expressed GATA6 (Fig. 1g  and Supplementary Data 1). To further elucidate the identity of cluster 22 cells, we measured differential gene expression.
2a  and Supplementary Fig. 2 ). The reduction in GATA6 expression in acne was further supported by reanalysing published microarray datasets comparing acne skin with matched healthy skin from the same patients or from control patients  30, 31. In both datasets, GATA6 expression was decreased in acne skin, although the difference was only significant in the larger dataset (Fig. 2b ). We conclude that GATA6 expression is decreased or lost in acne lesions, regardless of their severity. GATA6 reduces keratinocyte and sebocyte proliferation. The observed downregulation of GATA6 in acne samples led us to hypothesise that GATA6 could be involved in the physiological maintenance of the upper pilosebaceous unit in humans. To study this, we inducibly overexpressed GATA6 in primary human IFE and outer root sheath (ORS) keratinocytes, and in the immortalised human sebocyte line SebE6E7.
3 ). INF keratinocytes are hyperproliferative in acne lesions  33. There was a significant increase in Ki67 expression in acne samples in published microarray datasets (Fig. 3b ), and a higher abundance of basal and suprabasal Ki67+ cells in acne lesions (Fig. 2a  and Supplementary Fig. 2 ). Consistent with these observations, the growth rate of GATA6-expressing ORS and IFE keratinocytes was significantly decreased as compared to controls (Fig. 3c  and Supplementary Fig. 4a ), and there was a reduction in the number and size of keratinocyte colonies (Fig. 3d  and Supplementary Fig. 4b ). GATA6 induction also reduced SebE6E7 sebocyte proliferation (Fig. 3e ) and decreased colony size in clonogenicity assays (Fig. 3f ). We ruled out an effect of GATA6 on apoptosis by performing a TUNEL assay on sebocytes (Fig.
7i ) where GATA6 is expressed. By analysing single-cell RNA sequencing data from human scalp epithelial cells  29  , we obtained DEG for GATA6+ cells as compared to all other epidermal and adnexal cells present in the dataset (Supplementary Data 2). We used the IPA Upstream Regulator Analysis tool to interrogate the transcriptomics data from GATA6+ cells. This analysis revealed that several components of the TGFβ pathway were able to resolve the gene expression changes in GATA6+ cells compared to GATA6-cells (Fig. 7j  and Supplementary Fig. 8c ). This analysis also identified FoxO signalling (Fig. 7j ), consistent with the pivotal role of FoxO1 in acne  12. In addition, analysis of microarray data revealed that TGFβ signalling was downregulated in acne (Supplementary Fig. 8d, e ). In summary, our work identifies GATA6 as a regulator of the human upper pilosebaceous unit in humans.
Sections were incubated for 30 min with primary antibodies, rinsed, and then incubated with a biotinylated secondary antibody. Sections were rinsed and the reaction was developed according to the manufacturer's guidelines (streptavidin-peroxidase with an automated BOND, Leica Microsystems). Primary antibodies were used at the indicated dilutions: GATA6 (1:750, D61E4 clone, Cell Signalling 5851), Ki67 (1:100, Clone MIB-1, Dako M7240) and KRT5/6 (1:100, Clone D5/16 B4, Dako M7237). Biotinylated secondary antibodies (Vector Laboratories) were used at 1:400 dilution. Images were acquired with a Pannoramic 250 Flash slide scanner (3DHistech) and visualised with QuPath version 0. 1. 2 (https://qupath. github. io). Immunofluorescence microscopy of sebaceous organoids. Sebaceous organoids were washed twice with ice-cold PBS.
The following primary antibodies were incubated with the cells for 20 min at 4 °C in FACS buffer: CD86-PE (1:50, IT2. For all flow cytometry experiments, data analysis was performed using FlowJo software version 10. 4 (FlowJo). Cell fractionation and western blotting. For nuclear/cytoplasmic fractionation, cells grown in a 10 cm-dish were washed twice with PBS on ice and scraped into Subcellular Buffer (250 mM Sucrose, 10 mM HEPES pH 7. 4, 5 mM KCl, 1. 5 mM MgCl 2 , 1 mM EDTA). Lysates were passed ten times through a 26 Gauge needle, incubated for 15 min on ice, and centrifuged at 700×g for 5 min at 4 °C. The supernatant was kept as the cytosolic fraction, while the pellet was washed twice with buffer F1 (20 mM Tris pH 7. 4, 0. 1 mM EDTA, 2 mM MgCl 2 ) and three times with buffer F1 supplemented with 0.
Protein bands and cytokine arrays were detected using a ChemiDoc Touch Imaging System (Bio-Rad Laboratories). Processing of western blot images was performed with Image Lab software (Bio-Rad Laboratories). Image analysis. Digital images from fluorescence microscopy experiments were processed using NIS elements Advanced Research version 5. 11. 01 (Nikon), NIS elements Viewer version 4. 11. 0 (Nikon), ImageJ version 2. 0. 0-rc-69/1. 52p (https:// imagej. nih. gov/ij/) or the Harmony high content analysis version 4. 1 software package (Perkin-Elmer). When performed, image processing was restricted to changes in brightness and/or contrast and was applied equally across the entire image and different images of the same experiment, including controls. For image analysis, at least three fields per biological replicate were randomly chosen.
To measure positive cells, NIS elements Advanced Research was used. Maximal intensity projections were obtained. Spot detection was applied to detect bright spots with a typical diameter of 10 μm and above a certain intensity threshold for the DAPI channel. Object counts of DAPI spots gave the total number of cells. A similar process was applied to detect objects in the other channels (typical diameter between 10 and 20 μm depending on nuclear or cytoplasmic staining). Thresholds were determined per staining and kept similar between conditions within the same experiment. The percentage of positive cells was determined by the ratio of positive cells over the total number of cells. To measure staining intensity, the Harmony high content analysis software package was used with custom algorithms.
The DAPI channel was used to detect nuclei. Cytoplasmic area was determined as a ring of constant size around each nucleus and was used to measure the mean fluorescence intensity of the staining of interest. For clonogenicity assays, colony detection was performed using a home-made Python script that automatically detects colonies from 6-well plates. Briefly, areas of interest were selected, a threshold was applied to detect colonies and noise was removed. Individual colonies were then detected (even from merged colonies), and their size and number were measured. Full script is available in GitHub (https:// github. com/MATBEO/clonogenicity_assay_script). For IncuCyte Zoom analysis, one phase-contrast image was acquired every hour per field (at least two fields were acquired per well). Confluence was measured as a percentage of the surface occupied by cells detected by a mask in the IncuCyte Zoom software version 2018A (Essen Bioscience).
For analysis of cytokine arrays, the intensity of each spot was measured using Image Lab software version 4. 1 (Bio-Rad Laboratories). Background was removed from all values, and they were normalised to the positive control spots. Quantitative data are presented only for the targets reaching a level of expression above background for all conditions. Computational analysis. We performed unsupervised clustering and differential gene expression analyses of the previously published single cell RNA sequencing dataset EGAS00001002927  29  in the Seurat R package v2. 3. 0 72 using R version 3. 6. 1 (https://www. r-project. org). We reanalysed the previously published microarrays GEO GSE53795 31 and GSE6475 30 using NCBI GEO2R tool (https://www. ncbi. nlm. nih. gov/geo/geo2r/). Upstream regulator analysis was performed using Ingenuity Pathway Analysis (IPA) software version 01-13 (Qiagen).
KEGG pathway and Gene Ontology analyses were performed using DAVID software version 6. 8 (https://david. ncifcrf. gov). Network visualisation was performed using String software versions 10. 5 and 11 (https://string-db. org) and microarray gene expression overlay of signalling pathways using IPA and R. Statistics and reproducibility. Statistical analyses were performed using Prism 7 and 8 software (GraphPad). No statistical method was used to predetermine sample size. The number of independent experiments and of replicates (n) is indicated in the figure legends. Unless stated otherwise, at least three biological replicates were performed for each panel and came from at least two independent experiments. When appropriate, normalisation of the data was performed within each independent experiment.
Acknowledgements We are grateful to all members of the  Watt laboratory  for helpful discussions. We thank  Simon Broad  and  Ajay Mishra  for constant help and advice;  Rocio Sancho  for helping with organoid protocols;  Arsham Ghahramani  for helping with computational analysis; and  Mark Soldin  for providing skin samples. We thank  Anne Couvelard ,  Karim Tabbech  and  Amina El Hilali  from the  Pathology Department of Hôpital Bichat  ( AP-HP, Hôpitaux Universitaires Paris Nord Val de Seine, France ) for histopathological analyses. We are grateful for funding from the  Department of Health  via the  National Institute for Health Research comprehensive Biomedical Research Centre (BRC)  award to Guy's & St Thomas' National Health Service Foundation Trust in partnership with  King's College London and King's College Hospital NHS Foundation Trust.
| Hippocampal subfield segmentation in the different FreeSurfer versions The original van Leemput et al. method implemented in FreeSurfer 5. 3 was evaluated against manual segmentation in high-resolution MRI scans (0. 38 × 0. 38 × 0. 38 mm 3 )  (Van Leemput et al. , 2009) , but only in a few slices of the hippocampal body and not against manual segmentations of the same protocol in lower-resolution 1 mm 3 isotropic data, to which it is commonly applied. Although FreeSurfer 5. 3 has been deprecated, as stated on the software website, (https:// surfer. nmr. mgh. harvard. edu/fswiki/HippocampalSubfieldSegmentation) and subfield segmentation performed with this version has been criticized for low construct validity (Box 1) of the segmentation protocol (de Flores et al. , 2015; Wisse, Biessels, & Geerlings, 2014), FreeSurfer 5.