dataesr/scientific-paragraphs-categorization huggingface dataset:
Flow Cytometry Twenty-four hours post transfection, cells were analyzed on Guava EasyCyte8 Flow Cytometer (Millipore, Burlington, MA, USA) to determine the transfection efficiency. Briefly, cells were washed with 1× PBS; fluorescence intensity was adjusted upon loading the untransfected control (CTL) sample. Green Fluorescence (GRN-HLog) versus Side Scatter (FSC-HLin) was measured, and 10,000 events were collected. The percentages of transfected cells were quantitated by the software Guava Soft 2. 7.
In Silico Predicted and Experimentally Validated Target Databases Two predicted target databases microT-CDS (Diana Tools, http://diana. imis. athenainnovation. gr/DianaTools/index. php (accessed on 6 January 2022)) and TargetScanHuman 7. 2 (https://www. targetscan. org/vert_72/ (accessed on 6 January 2022)), and an experimentally validated database Tarbase 7. 0 (Diana Tools) were utilized to search for predicted mRNA targets of hsa-miR-126-3p. A PubMed search was performed to check for validated miRNA-mRNA interaction in BC and other types of cancer, in addition, to checking the role of the resulting mRNA targets in BC.
4. 13. In Silico Kaplan-Meier Analysis miRpower web-tool was utilized to determine the survival of BC patients with dysregulated miR-126 or SLC7A5 expression. Metadata for Kaplan-Meier survival analysis were obtained using https://kmplot. com (accessed on 6 January 2022)  [47]. The KM-plotter tool utilizes the METABRIC dataset which is deposited at the European Genome-Phenome Archive (http://www. ebi. ac. uk/ega/ (accessed on 31 January 2022)) and hosted by the European Bioinformatics Institute, under accession number EGAS00000000083. It contains RNA sequencing and whole genome sequencing for 2433 samples of primary breast tumors, of which 1262 samples had miRNA profiling data. Patients with ER+ status, with different nodal status and histological grade, were selected from the METABRIC dataset.
Currently, Thailand's HIV/AIDS estimates for children and youth living with HIV (CYHIV) rely mainly on two published models: the UNAIDS Spectrum software package and the Asia Epidemic Model (AEM)  [18, 19]. Spectrum estimates MTCT rates and HIV prevalence among children aged 0-14. While it also includes youth aged 15-25, it did not until recently distinguish between perinatal and non-perinatal acquisition for people older than 15  [19] [20] [21]. AEM is a full-process dynamic transmission model that simulates people older than 15 years  [18]. Both models require a complex set of inputs to estimate the numbers of PLHIV and of new infections. We developed a focused, multi-state model that could be used easily by program planners, with three objectives: 1) to estimate the magnitude of the pediatric and adolescent HIV epidemic among 0-25-year-olds in Thailand between 2005 and 2020, 2) to project the likely trends in CYHIV through 2025, and 3) to inform research priorities by identifying key gaps in available data and models that markedly impact estimates of the pediatric and adolescent HIV epidemic in Thailand.
Using the population size in each of these provinces, we derived a weighted high-risk:low-risk ratio among MSM in Thailand. The uncertainty around this ratio was addressed in sensitivity analyses. The Thai Pediatric and Adolescent HIV Modeling Working Group was established to review available data, provide recommendations for key input data for this model, and support development of local capacity on the use of the model. The Working Group is comprised of national AIDS program planners, U. S. CDC Thailand office members, clinicians, epidemiologists, and modelers. Model inputs, including all transition probabilities from one state to another were derived for each calendar year, age, sex, and population. Data were derived from national surveys and national projections available at https://hivhub.
Discussion We developed a focused, multi-state transition model to estimate the magnitude of the pediatric and adolescent HIV epidemic among 0-25-year-olds in Thailand between 2005 and 2025 and to identify key gaps in input data to inform research and intervention priorities. Our analysis had several main findings. First, we estimated a total of 30,760 children, adolescents, and youth aged 0-25 years are living with HIV in Thailand in 2020, among whom one-third are living with perinatally-acquired HIV. Second, in the 2018-2025 period, we estimated high numbers of young MSM living with HIV who comprise a large proportion of the adolescent and youth HIV epidemic in Thailand; this highlights urgent needs to identify young MSM at risk for HIV and to increase their access to HIV testing and treatment.
These populations are not mutually exclusive, and they are not consistently defined in currently available data. Additionally, the burden of drug use is high among young MSM and FSW populations and data available to date do not specifically address potential overlap between groups identifying as PWID, MSM, and FSW  [47] [48] [49]. Consequently, our model may overestimate the adolescent and youth epidemic when high-risk populations overlap. Furthermore, inputs related to access to care were estimated from the National AIDS Programme (NAP) database. Although the care provider can categorize key population (MSM, PWID, FSW. ) in the database, this variable is often missing. In our analyses, we made several assumptions based on data available from IBBS to stratify access to care by population. Improving the completeness of populationcategorized data entry in NAP would be helpful for monitoring each population's HIV cascade, providing HIV targeted services for specific populations as needed, and improving data report that can be used for modeling and projections in the future.
Acknowledgments We thank the  CEPAC research team at the Medical Practice Evaluation Center at Massachusetts General Hospital  and  Dr. Annette Sohn  of  TREAT Asia  for facilitating this analysis. We are particularly grateful to  Giulia Park ,  Christopher Alba , and  Alyssa Amick  for assistance with manuscript preparation. Finally, we would like to acknowledge with gratitude, valuable discussion and feedback- Dr. Eileen Dunne ,  Ms. Thananda Naiwatanakul ,  Dr. Suchunya Aungkulanon ,  Dr. Sarika Pattanasin ,  Dr. Marta Ackers ,  Dr. Michael Martin ,  Ms. Suvimon Tanpradech  of  Thailand MOPH-U. S. CDC Collaboration (TUC) ;  Dr. Thanyawee Puthanakit ,  Dr. Wipaporn Songtaweesin  of  Chulalongkron University ;  Dr. Walairat Chaifoo  of  Bureau of AIDS TB and STIs (BATS) ;  Dr. Wadchara Pumpradit  of  Bumrungrad International Hospital ;  Dr.
This work was supported by  LIFE+, Vienna ,  Austria  and  TREAT Asia-amfAR , the  Foundation of Research. AC is funded by the  Eunice Kennedy Shriver National Institute of Child Health & Human Development (NICHD)  under Award Number  R01HD079214. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding entities.
It follows that the amount of storage needed by the unstructured type is normally larger. The need for dedicated tools to assist the user with the preparation of input data appeared to be evident since the earliest release of APOLLO2 in the 1980s  [2, 3]. APOLLO2 was the first deterministic neutron transport computer code developed at SERMA offering CPM and MOC capabilities by the solver TDT (Two-and Three-Dimensional Transport). The graphical user interface (GUI) Silène was specifically developed for TDT, which used unstructured geometries. Silène also integrated the post-processing of results. Later, Silène extended the support of geometry and mesh generation to the Monte Carlo code TRIPOLI-4 ®  [4]. The tool allowed sharing work applications efficiently among users with easy backup, by unifying the data model and by checking its consistency.
Silène was an original, intuitive and successful product distributed with all major releases of APOLLO2. TDT was later integrated and upgraded in APOLLO3 ®  [5] , still taking advantage of the mesh generation by Silène. Now APOLLO3 ® also offers the P N DG-FEM solver NYMO (PN stands for spherical harmonics and DG-FEM for discontinuous Galerkin Finite Element Method), which can input the same mesh used by TDT. ALAMOS was expressly developed for APOLLO3 ® as a new GUI, relying on the extensive experience gained with Silène in over thirty years, and fully operating in the framework of the SALOME platform. ("SALOME: The Open Source Integration Platform for Numerical Simulation", https: //salome-platform. org/. accessed on 30 December 2021) In addition to Silène, ALAMOS offers a more user-friendly and comprehensive programming interface that allows users to build their own portable and extensible applications in Python, creating simple scripts, advanced modules, and specialized packages.
The controller receives and validates input data from the user, calls actions and passes data to the model. Silène was developed in Java, the programming language supported by Oracle, and has been running since Java standard edition 8 (a release with long-term support)  [2, 6, 7]. Along with the compiler, Java is distributed with all libraries for serialization, graphics, documentation, internationalization, database management and more, without needing particular customization for the specific operative system used or for the computer architecture. A single precompiled executable is generated running on the Java virtual machine. Silène programming is object oriented with all entities defined as objects. There are two working modes. The component mode operates with structured geometries, such as cells, grids, boxes and variables, while the drawing mode is meant for unstructured geometries, which use nodes, equations, meshes and regions.
Silène also handles boundary conditions. Objects are stored and handled (created, modified and deleted) by linked lists, where they are identified by pointers. At any time, the user can find them by their order number in the lists. ALAMOS makes extensive use of the Python libraries provided in the framework of the SALOME platform. SALOME provides a large set of tools, called modules, for general pre and post processing of numerical simulations. Indeed, ALAMOS has been developed as a module of SALOME to take advantage of its module MEDCoupling, which is a library specialized in operations with fields defined upon unstructured geometries, representing scalar, vector and tensor quantities (among other rich functionalities). ALAMOS is almost entirely programmed with a Python API (Application Programming Interface) accessing SALOME's C++ functions.
ALAMOS defines the model, the view and the controller by three different Python classes. The view is composed of three types of widget: a tab widget displaying the geometry under construction; an info widget displaying information about the sizes, the identifiers of the different elements and other properties; and a tree widget gathering all geometry objects, distinguished by their names. The model is based on a standard Python structure, using a timestamp mechanism to detect out-of-date data, and hence trigger a view refresh. Finally, the controller has been implemented using an "action queue" where each user request in the interface, such as drawing a circle, assigning a value to a field, or others, is encoded as an action that is stored in a dedicated incremental list. This mechanism serves two purposes.
Data Models The data models of the two GUIs are presented separately in this section. Objects are generally drawn or described by components in Silène, and by layers in ALAMOS. Both store geometry, material and other data, such as information about the relationship with the other objects, but using different programming languages, and mostly, a different level of abstraction. In particular, Silène makes use of many specialized classes that must have been defined beforehand by the developers, whereas ALAMOS exploits the object-oriented approach more effectively, letting users define new high-level classes more easily.
The Model of Silène The data model of Silène relies on collections that are coherent sets of all geometrical components used for a particular study. Complex geometries are always decomposed into the most basic geometries. Silène adopts different operating modes for the two types of geometries by creating regular or structured components or by drawing unstructured shapes. Although the modes are distinct, the user can work simultaneously on both, until achieving the final result by combining the two modes. A structured model can be converted into an unstructured one at any time, which allows using mesh refinement methods from the unstructured mode on structured geometry, and conversely, the simplification of unstructured geometry down to a structured one. In the component mode, the regular geometries can be sketched visually by selecting values for the different object properties through window panels, or described by the symbolic interpreted language of Silène using a text editor.
This approach is flexible because the geometry is described locally. The code does not know the shape of the geometry, nor the meshes. Many functionalities are available to assist the user during the drawing work. Some are elementary, allowing the creation and removal of nodes and equations, with automated renumbering and ordering of the elements in the model. New nodes can be added on existing lines by positioning relative to existing nodes (about 20 different ways). Media modifications, starting from simple changes in names, apply to all cells of the mesh after assignment. Additionally, Silène is capable of defining output zones and of applying boundary conditions. Silène integrates continuous checking of geometry consistency to prevent openness and concavity in the geometry model. It is possible to combine geometries by functionalities taking on global scope: import methods for the insertion of a mesh into another, side junction, mesh superposition or overwrite, up to the splitting of a geometry into its constituents.
The Model of ALAMOS The ALAMOS data model is based on the object layer that is constituted of a geometry made of points and lines, upon which fields of properties and references are associated (see below). In the Python implementation depicted briefly in Figure  1 , all layers derive from the abstract Python class "AbstractLayer" that contains all the information of the property fields defined on the geometry. Only the class "LayerModel" contains the methods necessary for visualization and geometry export to file, being also the only one exploited by View. All layers need a conversion to a LayerModel instance to be sketched on the tab widget. Two main types of layers can be instantiated, namely structured and unstructured layers, with class names "StructuredLayer" and "Layer", respectively, still drawing twodimensional geometries.
Conclusions SERMA has a long tradition in the R&D of mathematical methods and computer codes for lattice transport and reactor calculations. Two GUIs have been developed in the last thirty years to assist reactor engineers and physicists to build the geometries and the spatial meshes, and to assign the material property maps on the same geometries. Back in the 1990s, APOLLO2 was released with Silène, the first GUI entirely developed in Java by Zarko Stankovski, capable of generating all geometry models needed by MOC and CPM solvers. Silène has now achieved its final state of maturity and will not undergo any major upgrade. With the advent of APOLLO3 ® , a new GUI working on the SALOME platform and offering a Python (v3) API was developed, taking advantage of the fast-growing Python programming language and of the FEM library MEDCoupling to manage unstructured geometry models.
This leads to a massive InSAR dataset with time. This is also the same context for the next Tandem-L  [18]  and NISAR missions  [2]. The challenge is how to exploit such Big InSAR Data for long-term monitoring  [5]. Recently, there have been two initiative processing schemes to tackle DS targets (e. g. , P-SBAS (  [19] ) and Sequential Estimator [20]). Particularly, in  [20] , this can be extended to work on PSDS analysis. The main objective of this work is to tackle this task by (i) developing a Compressed PSDS InSAR (so-called ComSAR) algorithm to handle the massive volume data and (ii) demonstrating it to map land subsidence of the salt mine of Vauvert area, France. The ComSAR concept had been presented at the 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS  [21].
We show that the use of ComSAR to deal with SAR Sentinel-1 data offers two main advantages: (1) ComSAR can be adopted to work on massive time-series data for long-term high-precision monitoring; and (2) it yields consistently better performance when compared with the state-of-the-art PSDS InSAR and PSI techniques. Finally, we implement the PSDS and proposed ComSAR algorithms as an open-source TomoSAR package (https://github. com/DinhHoTongMinh/TomoSAR (accessed on 4 January 2022)). To our knowledge, TomoSAR is the first public domain tool available to jointly handle PS and DS targets.
ComSAR: Compressed PSDS InSAR Algorithm In this section, we aim to provide an approach that can reduce the data volume (and hence computational effort) and can maintain the performance. To reduce the data volume, it is natural to use only a subset of the dataset. This is the principle of SBAS approaches. However, using the SBAS technique can lead to the risk of a phase drift from stochastic and from systematic effects  ([20,34] ). Consequently, this can yield large errors where the standard deviation increases with time. To compress data volume, in the literature, principal component analysis (PCA) is a well-known technique to apply  [35, 36]. However, PCA fails to correctly incorporate the statistical properties of the complex covariance matrix data. This is because PCA is a geometrical rather than a probabilistic approach  [36].
On the other hand, the MLE phase linking is a purely probabilistic approach, and it is well-known for estimation precise phase estimation  [20, 26, 37]. In this way, the simple idea is to use the most coherent interferograms from their linked phases. This is mainly because the linked phases are optimum from all possible interferometric phases. Let us assume that the N SAR dataset (ordered temporally) can be divided into small batches or mini-stacks with M images. The compressed version Ŝ of M SAR images for the kth sequence (see Figure  1 ) can be determined by a coherent summation as follows [20]: Ŝ(r, x) = M ∑ m=1 S m (r, x) ξ m (2) where ξ = Λ Λ = [ξ 1 , ξ 2 ,. , ξ M ]; Λ = exp(j λk ) is the linked phase from M SAR images; and S(r, x) is the scene complex-valued SLC at the slant range-azimuth position.
The model parameters for the C-band Sentinel-1 dataset are reported in  [25]. For example, γ 1 = 0. 18, γ 2 = 0. 25; ω 1 = 0. 03 rad/day, ω 2 = 0. 002 rad/day; τ 1 = 11 days, τ 2 = 50 days; and γ ∞ = 0. 13. Based on this, a coherence matrix is simulated for a three year time series (e. g. , 6 days interval and 180 temporally ordered measurements). Each measurement contains an ensemble of 300 statistically homogeneous samples. The simulation is carried out with 1000 realizations. The root mean square error (RMSE) (e. g. , between the estimated and the simulated phases) is considered for the performance comparison. To compare different scenarios, we consider the phase linking performance for three estimators. A Small temporal Baseline Subset (StBAS) with bandwidth 10 is equivalent to using up to lag-10 measurements at each processing level.
Processing SAR Data For demonstration, to provide recent displacement, we processed 89 images (January 2018-December 2020) of Sentinel-1 SAR satellites (ascending track 59) using no compression (i. e. , PSI and PSDS InSAR) and ComSAR techniques. They are imaged in interferometric mode (i. e. , TOPS-Terrain Observation by Progressive Scans  [17] ). The coregistration is accomplished using two bursts of the second swath. It is worth noting that the coregistration was carefully handled to avoid a phase jump in the azimuthal direction  [40]. In detail, in the azimuth direction, the accuracy of a few per-milles of a pixel is required. To meet this accuracy, the process is carried out by exploiting spectral diversity information, which considers the interferometric phase of the burst overlap regions  [41].
We applied the processing over a 377 × 1612 pixel portion of the second swath of the images. We implemented the PSDS InSAR and proposed ComSAR algorithms as an opensource TomoSAR package (https://github. com/DinhHoTongMinh/TomoSAR (accessed on 4 January 2022)) (see Appendix A). To make it more practical, we exploited the SNAP as an InSAR (  [42] ) processor and StaMPS ([15]) as an InSAR time series tool. For more automated processing, a SNAP-StaMPS tool can be used  [43]. These tools are open source so that people can apply our PSDS and ComSAR methods for an end-to-end processing chain. We set the small-batch as 5, resulting in 17 isolated mini-stacks. The phase linking estimation is performed on full spatial resolution in each mini-stack (see Figure  4a ). The pointwise complex covariance matrices are estimated based on an ensemble of pixels in the homogenous region surrounding each pixel.
For a two-sample test in DS selections, we used the Baumgartner-weiB-Schindler algorithm (  [44, 45] ). In this way, a brotherhood of DS can be created, resulting in a family of SHPs (see Figure  4b ). The DS candidate can be identified as having a number of SHP greater than a certain threshold. We set a 9 × 35 (azimuth-range direction) window to identify the number of SHP. To improve the spatial stationarity in the homogeneous region, the topographic induced phase is simulated using the 30-m resolution Shuttle Radar Topography Mission digital elevation model and compensated (i. e. , subtracting the topographic phase from the original interferometric ones) prior to the coherence estimation. At each mini-stack, the data are compressed into one component. This leads to 17 images rather than 89, resulting in compression by 80 percent.
Performance Evaluation We consider three estimators: PSI, PSDS, and ComSAR for comparison. The PSDS and ComSAR are carried out (see Sections 2 and 3) after SNAP processing. The DS candidates are initially identified as a 20 SHP threshold and the phase linking coherence 0. 25. After PSDS and ComSAR processing, the time series InSAR analysis is the same for three estimators by using StaMPS. We then identify the candidates with a threshold of 0. 4 for the amplitude dispersion index criterion for three datasets. Finally, for each pixel, the phase noise standard deviation for all pixel pairs including the pixel is calculated. To drop pixels low stability, we set the minimum standard deviation as a 0. 8 threshold. Figure  5  shows the average velocity maps at PS pixels solely (Figure  5a ) and PSDS points using either the PSDS InSAR (Figure  5b ), or the ComSAR (Figure  5c ) techniques.
Discussion This work introduces a novel ComSAR algorithm based on a compression technique for reducing computational efforts. We obtained good results and maintained the performance robustly while working on compressed versions. First, the ComSAR performance is validated via simulation, showing that the compressed estimator is close to the theoretical Cramer-Rao lower bound under a realistic C-band Sentinel-1 decorrelation scenario. Both persistent and distributed scatterers are exploited in the ComSAR algorithm. We demonstrated its application to Sentinel-1 data to map land subsidence of the salt mine Vauvert area, France. We provided a very density spatial coverage in the Vauvert area, identifying some deformation features that are difficult to detect with PS pixels. The proposed Com-SAR yields consistently better performance when compared with the state-of-the-art PSDS and PSI techniques.