dataesr/scientific-paragraphs-categorization huggingface dataset:
Multiprocess parallelization has been implemented, when possible and Cython has been used to speed up inner loops. The resulting code has a speed comparable to that of a compiled one. The user interface has been designed to be intuitive and straightforward, in the spirit of the python language, and allows to perform the fundamental analyses using just a couple of scripting lines. This task is also facilitated by providing meaningful default values for all options. However, the user has complete control over the details of the algorithms and can quickly perform complex tasks by combining the basic building blocks of Pytim. In particular, the structure of the interface is meant to encourage the user to experiment with the possibilities offered by combining Pytim with other libraries and create their own analysis tools.
The present implementation of GITIM can calculate the Delaunay triangulation using the scipy library, which uses, in turn, a wrapper to the Quickhull  [24]  implementation Qhull. In addition, it can use the faster pytetgen python wrapper to the tetgen software. [25] oth algorithms scale on average as O N logN ð Þand in the worst case as O N 2 ð Þ, setting, therefore, the scaling of GITIM. Also in this case the only free parameter is the probe sphere radius α. Figure  2  reports an example of how to use the GITIM algorithm in Pytim. Willard and Chandler's instantaneous liquid interface. This algorithm  [8]  defines a continuous interface as the isodensity surface of a Gaussian kernel density estimate. [26, 27] A continuous Gaussian density function, with given height h G and width w G , is associated with each atom.
In this case, it is more effective to look at the local density rather than at the connectivity. The DBSCAN density-based cluster algorithm  [31]  is well-suited for this task, and has been already used to study supercritical fluids  [32]  and a two phase system of highly miscible fluids. [15] The main idea of the algorithm is to include in a cluster all atoms with overlapping neighborhoods where the density is higher than a given threshold. In Pytim, we implemented an approach for the automatic determination of the density threshold, based on a kmean analysis  [33, 34]  of the density distribution. [15] gorithms to compute interfacial properties Once the surface atoms are determined, these are available through the python interface for further analysis. In the Pytim package, we provide the user with an extendable set of classes for the computation of observables, suited both for the calculation of bulk properties, and interfacial ones.
Installation instructions The latest development code can be retrieved from the address https://github. com/Marcello-Sega/pytim as a git repository, or as a zipped archive. From the source code, the command python setup. py install --user will install the package in the user's directories. Requirements for the installation are setuptools and numpy python package and Cython. The setup. py script will automatically download and install other required dependencies. On the other hand, the user can install the latest stable version from the Python package index (https://pypi. python. org) by invoking pip install --user pytim, or from Anaconda (https://anaconda. org/conda-forge/) with conda install -c conda-forge pytim.
Usage examples The simplest script that can be used to compute the interfacial atoms is the following: import MDAnalysis as mda import pytim from pytim. datafiles import WATER_GRO u = mda. Universe(WATER_GRO) inter = pytim. ITIM(u) The script above starts with importing the MDAnalysis and Pytim modules, and one of the example files present in the distribution. Next, the configuration is loaded, and the interfacial atoms are computed using ITIM with default values. The interfacial atoms are accessible in a number of ways, one of which being the array inter. atoms that lists all of them, irrespective of the layer/side in which they are located. In addition to the usual properties that are available for the Atom and AtomGroup classes of MDAnalysis, Pytim introduces new ones, namely, layers, sides, and clusters.
These properties can be accessed from each atom in the system, for example, as u. atoms. layers, and give access to which layer the atoms have been assigned to (layers), to which side of a planar interface the atoms are (sides, ITIM only) and whether the atoms are in the main cluster or in one of the smaller ones (clusters). These properties can be used to quickly select the atoms of interest. For example, one can access the atoms in the first layer as u. atoms[u. atoms. layers==1]. Some usage examples involving the different methods have been already presented in Figures  1 2 3. Here, we describe two typical scripts that can be used to compute intrinsic and nonintrinsic profiles. Figure  7  shows an example of how one can compute an intrinsic density profile in Pytim. Note that the Pytim distribution provides several sample configurations and trajectories (in this case, the configuration of the Lennard-Jones system is pointed at by the label LJ_GRO).
Some larger trajectories, which would be too big for the distribution, can be retrieved from the online repository using the pytim_data. fetch() function, as, in this case, for the LJ_BIG_XTC binary trajectory. This script initializes first the interface calculation using ITIM, then instantiates a Profile object. The interface option signals that the profile has to be computed relative to the interface itself, that is, it will be an intrinsic profile. Because no observable is passed to the Profile constructor, this defaults to the number density profile calculation. In the successive lines, the code iterates over the trajectory frames, computing the surface atoms automatically every time a new frame is loaded, and sampling the intrinsic profile. The profile is stored internally with 0. 01 Å resolution, and can be accessed through the get_values() function with the desired binning, resulting in the plot shown in Figure  7 , with the liquid part located on the left (negative position values) of the interface (a delta function centered at zero, not shown) and the vapor part on its 5.
Another example, on how to compute the distribution of the surface layers along the interface normal is shown in Figure  8. In this case, one instantiates five different profiles (without specifying an interface option, the code will compute the non-intrinsic profile), and passes to the sample() function a selection of atoms corresponding to those in the different layers. As a final example, in Figure  9  we show the analysis of the intrinsic curvature at the surface of the DPC micelle, together with the calculation of the local normal vector, using the method of Yesylevskyy and Ramseyer. [11] ther examples are provided on the github page of the project, in the online manual, and in the example files in the Pytim distribution, including details on how to compute time correlation functions or how to use the pytim tools on top of MDTraj or in an OpenMM simulation run.
List of functionalities The code, thanks to the use of the MDAnalysis, allows to perform several surface-related analyses of many popular trajectories and configuration file formats. The full list is available on the website of MDAnalysis (http://www. mdanalysis. org). Both Python 2. 7 and Python 3 are supported. The functionalities of the code can be grouped in the following classes: Interface/surface identification. are suitable to be used in the calculation of the following correlations/distributions: 1. Intrinsic and non-intrinsic profiles 2. Three-and two-dimensional radial distribution functions 3. Time correlation functions, also for variable sets of atoms (both in the continuous and intermittent variants) Test systems. Several configurations and small trajectory files are provided for testing purposes: the present set of systems includes so far as follows:.
API documentation and tests Pytim code has been written using docstrings for all public classes/functions, and for the most important private ones. The code reference manual, generated automatically from the code, is available at https://marcello-sega. github. io/pytim/. Along with the reference manual come several tutorials, which cover several topics ranging from the installation to troubleshooting, and include common usage of the main modules. In the source distribution, there is a directory that includes several python example files that show how to use the main modules and the analysis tools. In addition, jupyter notebooks are also present, and can be visualized (statically) directly from a web browser, without needing to run the code, from https:// github. com/Marcello-Sega/pytim. Code snippets within the documentation of the code are used to provide a testing framework using the doctest module in combination with Sphinx (http://www.
Conclusions We presented Pytim, a new package for the interfacial analysis of molecular systems. Pytim, is based on the python scripting language and provides the user with a number of analysis tools dedicated to the properties of fluid interfaces. Being built on top of the MDAnalysis library, Pytim allows to analyze trajectory files in the native formats of the most common simulation packages, freeing the user from the burden of conversion. The package implements three main surface analysis algorithms, namely, ITIM, GITIM, and the Willard-Chandler instantaneous liquid interface algorithm, as well as two different clustering algorithms to be used for identifying different phases. All algorithms have been carefully optimized using either fast, vectorized functions from the numpy and scipy libraries, or by writing, when necessary, Cython code, as well as parallel, multiprocess code.
Acknowledgments P. J. acknowledges financial support of the  Hungarian NKFH Foundation  under project no. 119732. B. F. is supported by the  UNKP-17-3-I New National Excellence Program  of the  Ministry of Human Capacities. This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreement no. 708175.
Android IoT devices We modify the Android OS code to implement our protocol. We instrument the Java class UsbSettings in the package "com. android. settings. deviceinfo". When the device is connected, Android blocks the access to media and picture files until the user chooses the type of transfer protocol. After having selected the type of transfer protocol (MTP or PTP), we verify the application installed in the personal computer identity by asking the Authentication Server. If the installed in the personal computer is authenticated and has a valid token, we authorize the access to the device data exclusively to the given application. Since we are preventing the mounting process of the file system, awaiting for the application authentication, we create a RAM disk with the following characteristics: We consider the application malicious only when the token has been modified.
Computer The device delegates the authentication and authorization to the Authentication Server. The application on the computer will not have access to data unless the AS responds favorably to the Android device authentication and authorization request. To be able to perform the authentication, the application should be registered to the AS and then communicate its id to the Android system device. When initiating data request on the device, in order to receive a fresh token, the application will have to send a token null with its id. The implementation of this step is possible with the MTP library (libmtp) and the PTP library (libptp). It consists of writing on the unique file shown by the device, the application id and token. Before generating a new token, the application will have to authenticate itself.
The detection system flags alerts based on temporally-related anomalous behavior detected in multiple monitored modules. GoodUSB  [27]  enforces permissions of devices by encoding user expectations into USB driver loading. GoodUSB includes a security image component and a honeypot mechanism for observing suspicious USB activities. All solutions cited above assume that maliciousness comes from devices connected to the computer. In our approach, the malicious process is installed in the personnel computer and not in the device. Many dynamic taint analysis approaches defined in smartphones like TaintDroid  [7] , AppFence  [15]  and Graa et al. [11] ,  [9]  allow detection of sensitive data leakage by third party applications running in the Android device using the data tainting. Wang et al. [28]  enforce security policies on data flows for Android applications to prevent unauthorized usb hardware flows.
Memory Architecture The contribution of this paper targets temporal graphs that can be efficiently shared and used concurrently among different processes. Unlike other approaches that follow a fork-and-join paradigm, our workers can arbitrarily read and update any part of the temporal graph. Nonetheless, we ensure global consistency at the momentum changes are saved (i. e. , committed) using our proposed merge operator (cf. , Section 4. 2 and 4. 3 for more details). In our implementation, every worker hosts an instance of the TGraph as depicted in Figure  1. This includes a graph API to create, update, delete and modify nodes of the graph and their associated attributes. Our graph API is currently available for Java and JavaScript. Temporal graphs are then mapped to an underlying memory structure, which decomposes the temporal graphs into a sequence of chunks (e.
Immortalized and transformed HMECs form 3 distinct clusters defined by the activated oncogene Next, we determined whether the different immortalized and transformed HMEC sublines presented distinct profiles of genetic and epigenetic anomalies. Copy Number Alterations (CNA), DNA Methylation, miRNA (miR) and mRNA expression levels were determined at different steps of cell immortalization and transformation. To integrate these data corresponding to diverse molecular features and different technological platforms and increase the possibility of discovering coordinated changes, we used moCluster (moGSA package), a multivariate single sample gene-set analysis method, developed to produce integrative clustering on multiple omics data. In brief, this approach is based on multivariate latent variable decomposition to discover correlated global variance structure across datasets  [19].
Two independent biological replicates of each immortalization or transformation step, (R2, R2 shp53 , R2 shp53-WNT1 , R2 shp53-CCNE1 , R2 shp53-RAS and R2 shp53-WNT1. SA , R2 shp53-CCNE1. SA , R2 shp53-RAS. SA HMECs) were included in this analysis. These analyses pointed at similarities and differences between distinct sublines and stages of transformation. These were determined by means of Principal Component Analysis (PCA). Changes defining the most variant PCA vector were subsequently analyzed by Ward clustering. The combined analysis of the four datasets (CNA, DNA methylation, mRNA and miRNA expression) defined 3 clusters. The first one, at the trunk, organized around R2 and R2 shp53 early and late passages. The second corresponded to the four R2 shp53-RAS sublines. The third cluster included R2 shp53-WNT1 and R2 shp53-CCNE1 sublines (Fig  2A ) Hence, the MoGSA analysis revealed that the HMEC sublines transduced with different genetic elements showed clear differences in their genetic and epigenetic patterns.
HMECs result in distinct profiles of copy number alterations Genomic regions involved in CNAs were identified with the Nexus 7. 5 Software (Biodiscovery, CA. , USA) and only CNAs covering at least 2 Mb were used in the moGSA analysis. The PCA classification of the different HMEC sublines singled out three clusters. Cluster 1 positioned at the trunk encompassed R2 and R2 shp53 replicates, cluster 2 formed by the R2 shp53-RAS and R2 shp53-RAS. SA sublines and a more dispersed cluster 3 comprising R2 shp53-WNT1 , R2 shp53-CCNE1 , R2 shp53-WNT1. SA and R2 shp53- CCNE1. SA (Fig  3A ). The distance along the PC1 vector in the principal component analysis, separating cluster 2 from cluster 3, illustrated the strong differences at the CNA level between the RAS and the WNT1 or CCNE1 transformed sublines (Fig 3A ).
Using a methylation difference of 0. 2 as inclusion criterion, we next performed a SAMR analysis of these data to identify differentially methylated regions (DMRs) that varied most significantly between sublines. Identified DMRs were analyzed in moGSA and used to classify cell lines by Principal Component Analysis (PCA). The PCA classification based on DNA methylation was remarkably similar to that generated with CNAs. Here again R2 shp53-RAS and R2 shp53-RAS. SA defined a clearly distinct cluster of the R2 shp53-CCNE1 , R2 shp53-WNT1 , R2 shp53- CCNE1. SA and R2 shp53-WNT1. SA cluster ( Altogether, both CNA and DNA methylation profiles indicated that the ectopic expression of an oncogenic form of RAS in R2 shp53 HMECs induced specific genomic and epigenetic alterations, that markedly differed from those observed in HMECs transduced with WNT1 or CCNE1.
Chronology of the genetic and epigenetic changes occurring during transformation of HMECs. Monitoring the genetic and epigenetic changes occurring at each step of the cell transformation process allowed us to reconstruct their chronology. This could be inferred from the PC2 vectors in the PCA classifications. Starting from normal R2 primary cells, moving to early and late immortalized cells and then to anchorage independent cells growing in soft agar, we noted that the different sublines bore different positions in these classifications according to the dataset considered. In the miR classification, the immortalized R2 shp53 (Early and Late passages) were separated from primary R2 and co-clustered with R2 shp53-CCNE1 and R2.
, Sunnyvale, CA). Data were extracted from scanned images using NimbleScan Normalized files were used as input for the Nexus 6. 1 Software (Biodiscovery, El Segundo, CA, USA). Analysis settings for data segmentation and calling were the following: significant threshold for FASTST2 Segmentation algorithm: 1. 0E-7, Max Continuous Probe Spacing: 1000, Min number of probes per segment: 10, high level gain: 0. 485, gain: 0. 17, loss: 0. 2, homozygous copy loss: 0. 485. Hierarchical clustering was done using Nexus 6. 1 using average linkage setting. Interval files from each of the 18 samples were exported and converted to a BedGraph format and merged (using the Merge BedGraph files tool (v 0. 1. 1, http://galaxy. sb-roscoff. fr) in order to define common interval between samples. Intervals smaller than 2 MB were removed resulting in a set of 268 intervals with CNA changes in at least one sample.
miR expression profiling Biotinylated cRNA were prepared according to the Affymetrix IVT Express protocol from 100 or 200 ng total RNA and hybridization was done as follows. CRNA were fragmented, 12 mg hybridized for 16 h at 45 C, washed and stained in the Affymetrix Fluidics Station 450 with Hybridization Wash & Stain kit. GeneChips were scanned using the Affymetrix GeneChip Scanner 3000 7G. Raw feature data were normalized using Robust Multi-array Average (RMA) method (R package affy). All subsequent analyses were performed on normalized datasets. To determine genes differentially expressed between sublines we used sam_multiclass command with one class for two biological duplicates (samr R package median). Lines containing identical GeneSymbol were collapsed with a max function. This file containing expression values for 18 samples and 2063 genes was used as input in MoCluster.
MoGSA To integrate the omics data of different origins (CNA, mIR and Mrna and DNA Methylation), we used the MoGSA package  (Meng, 2017a,b)  to identify Joint Patterns Across Multiple Omics Data Sets. Mogsa have proved to be particularly efficient in term of computational time and compared favorably to icluster  (Meng et al. , 2016). We used consensus PCA from the MoGSA R package and displayed the results from the first and second principal component either for each omic dataset (CGH, DNA, mRNA or miR) or all together. The features with highest coefficient in the definition of the first axis of the PCA were selected and submitted to unsupervised Ward clustering and presented as heatmaps. Features varied according to the analysis and were as follows; Coefficient higher or equal to 0. 07 in.
High throughput data analysis Detailed methods are presented in the Supplementary Methods. Raw array and RRBS data can be accessed at GSE114849 (see S Appendix). CCNE1 /R2 shp53-WNT1 HMECs. Expression difference levels (Exp diff column) were calculated relative to expression levels in the R2 and R2 shp53 cluster and expressed in log2 scale. Genes are listed for each cluster (RAS for R2 shp53-RAS , C/W for R2 shp53- CCNE1 /R2 shp53-WNT1 ). A. Genes modified as a consequence of CNA have been selected from a broader list on the basis of their assignment either as cancer or tumor suppressor genes (assembled under the cancer gene category) or as members of the RAS pathway. Highlighted in red, genes overexpressed in regions of gain (Gain/OE), in blue, genes underexpressed in regions of loss (Loss/UE).
profiles clearly distinguished the RAS and CCNE1/WNT1 transformed cells in two separate clusters (Fig 5A and Fig 5C). We observed that many genes presented an inverted pattern of expression in the RAS compared to the CCNE1/WNT1 sublines (Fig 5B and Fig 5D) in the Ward clustering analysis of expression changes defined by the PC1 vector in the miR and mRNA datasets. GeneGo Metacore (Thompson Reuter) network analyses of the miR and mRNA gene lists that discriminated cluster 2 and cluster 3 identified several differentially activated functional gene networks. Based on the miR dataset, this analysis clearly pointed at strong differences in EMT-associated pathways (S5 Fig), illustrated by the level of miR200c, a well characterized regulator of the expression of the EMT transcription factor ZEB1.
shp53-WNT1 (Fig 5A). In contrast, in the mRNA-based and DMR classifications, primary R2 co-clustered with early passage R2 shp53 cells. Late passage R2 shp53 had undergone changes that brought them closer to R2 shp53-CCNE1 and R2 shp53-WNT1 (Fig 4B and Fig 5C). Finally, the classification based on CNAs, revealed that early and late passages R2 shp53 coclustered with R2 (Fig 2B). These co-clustering analyses suggested a temporal hierarchy of the genetic and epigenetic events that primed transformation in our models. The clear clustering differences observed between early and late passage R2 shp53 cells according to either the miR, mRNA, DMR or CNA datasets, strongly suggested that upon p53 inactivation in HMECs, miR expression was the first to be modified, whilst the modification of mRNA expression profiles and massive modifications of the methylation landscape occurred later.
Fig). Using a restricted Claudin-low classifier [29], R2 shp53-RAS were classified as Claudin-low, R2 shp53-CCNE1 and R2 shp53-WNT1 as epithelial, while R2 and R2 shp53 formed an intermediate group, combining characteristics of both the Claudin-low and the epithelial group (Fig 7A). Next, we sought to compare the characteristics of our HMEC models with that of the Claudin-low and the Basal-like breast cancer subgroups in the human primary breast cancer METABRIC dataset. Restricting our analysis to TP53 mutated and Basal-like tumors (defined with the PAM50 classifier), we selected 259 tumors, which we stratified in Claudin-low/Basal-like (83) and Basal-like/Non-Claudinlow (176) subsets. We determined that tumors in this subgroup presented a significantly lower incidence of CNAs than the Basal-like/Non-Claudin-low (Fig 7B-C) and that the KRAS pathway was among the top activated pathways in the Claudinlow/Basal-like tumors (S9A-B Fig).