dataesr/scientific-paragraphs-categorization huggingface dataset:
Here, the shRNA sequence that integrate into the genome serves both as perturbation and as DNA barcode. Further multiplexing can be achieved by indexing the second perturbation in a single sequencing run. For example, a MPS run yielding 10 8 aligned reads on average allows 100 cell lines to be screened against 100 drugs at a reasonable average sequencing depth of 10,000 reads per DNA barcode. In both cases the abundance of DNA barcodes amplified from the genomic DNA is measured after a period of exponential cell growth and used as a score for cellular fitness. To deconvolute, analyze and visualize this type of data in a modular and intuitive manner we established TOPS.
Implementation Programming Language, external software and system requirements TOPS was developed as a graphical user interface for an analysis pipeline that we originally developed in R. In order to make these R scripts accessible to non-expert users, in particular scientists with little background in scripting languages, we took advantage of the R package rJava (http://www. rforge. net/rJava/) to run the scripts from a Java based interface. TOPS can therefore run its R scripts on all platforms for which rJava is available (currently MacOS, Linux and Windows). The hardware requirements to run TOPS depend mostly on the volume of data to be analyzed and the algorithm to be used. We have developed and tested TOPS on two independent datasets derived from different experimental approaches, and on a standard office computer with a 2.
4 GHz dual core processor and 2. 5 GB of RAM. We note that for larger datasets the linear model (linearModel. R) algorithm provided with this release requires more memory than is usually available on an office computer. Therefore, we have provided an alternative that splits the analysis in smaller parts (line-arModel_split. R) and later assembles the results. This modification necessitated an adjustment in the data normalization [Figure  2 ]. Output from the R interface, including and error messages, is stored in text files for troubleshooting. The overall progress of a session can be monitored and is logged separately.
User skills and installation Installation of TOPS consists of unpacking the zipped file into a directory. For TOPS to run, Java, R and the package rJava are must also be installed. Upon first launch, definition of the paths in which R and rJava are located may be required. The most common locations for MacOS, Linux and Windows are checked automatically and the user will be prompted only if the libraries cannot be found. For convenience, alternative locations to be checked in this manner can be added in libPaths. txt in the TOPS directory. TOPS uses a single semicolon separated values (CSV) data input file. This file has to be generated by the user and can for instance be exported from a Microsoft Office Excel spreadsheet. Alternatively the example datasets can be used for testing. To use TOPS no scripting skills are necessary.
Example dataset 1: a gene-drug interaction screen in breast cancer To simulate oncogenic events in breast cancer we created a panel of 70 different isogenic cell lines ectopically expressing oncogenes or knocking down tumor suppressor genes commonly found in breast cancer. Findings based on this dataset have been published previously  [7]. For multiplexing purposes, each cell line was infected with a vector introducing a genetic barcode (xMAP tag) into the genomic DNA. The cell lines were then pooled, distributed among multi-well plates and treated with 87 individual drugs. After a period of exponential growth, genomic DNA was harvested and barcodes were quantified using Luminex xMAP beads. Raw bead data was converted to median measurements per interaction by calculating the truncated mean.
Example dataset 2: a gene-gene interactions screen in breast cancer We screened 27 isogenic cell lines expressing oncogenes against a library consisting of approximately 400 shRNA vectors covering 80 deubiquitinating enzymes (DUBs). DUBs represent an emerging class of cancer targets involved in many different cellular processes with therapeutic potential. After a period of exponential growth cells were harvested and genomic DNA was extracted in order to PCR-amplify the shRNA barcode cassettes. During the amplification process primers with 60 unique barcode sequences corresponding to the 27 cell lines in 4 replicates were used as described in  [9]. The tagged PCR samples were then pooled, a sequencing library was generated and the samples were sequenced on two lanes (50 basepairs) of a Hiseq 2000 instrument (Illumina).
The MPS raw data were converted to FASTQ format and split according to the indexing PCR primers with a custom Python script. Each sample's NGS reads were aligned to the shRNA sequences using shALIGN  [10]  allowing for zero, one, and two mismatches. The output CSV file is in the format: 'Cell line (name)', 'shRNA (name)', 'number of reads'. Number of reads corresponds to shRNA abundance and this is the readout for relative fitness.
Importing data A typical input file consists of five columns with the following information: perturbation A; perturbation B; fitness score; replicate 1; replicate 2 In the provided example dataset 1, perturbation A corresponds to the gene (i. e. overexpression or knockdown) and perturbation B is a drug treatment. For dataset 2, perturbation B is an shRNA vector targeting a specific DUB and perturbation A is the gene. Fitness score represents the barcode abundance in these examples. Replicates 1 and 2 designate if measurements are replicates of each other. Replicate 1 identifies multiple perturbations towards the same outcome (i. e. multiple different hairpins for one gene, multiple vectors encoding the same cDNA) while replicate 2 identifies the replicate number for a given A-B combination.
Pre-analysis filtering We implemented a filtering method to allow the user to detect and remove bad replicates or samples before running the analysis as this can result in false positive hits. This filtering is based on the assumption that the frequency of gene-gene or gene-drug interactions is low among all possible combinations  [11]. Therefore noisy perturbations can be identified by how well they correlate with others. For each perturbation a pairwise Spearman correlation is calculated against all other perturbations in the dataset. A poor correlation then indicates a technically noisy sample that the user may wish to exclude from further analysis. We perform this correlation for both A and B perturbations and correlation cutoffs can be chosen for each individually [Figure  3 ].
Statistical analysis: linear model Linear models have been used previously to normalize  [12, 13]  and analyze  [14, 15]  high-throughput measurements. Accordingly, our analysis is composed of two consecutive linear models: the first one is used for data normalization; the second one identifies outliers that represent "hits" i. e. potentially biologically meaningful interactions that warrant further study. Data normalization relies on the assumption that relative fitness values intensity i as determined by barcode quantification (via MPS or Luminex) are constituted of four components: 1) the systematic effect of perturbation A on fitness 2) the systematic effect of perturbation B on fitness 3) the signal that is due to the (synergistic) combination of perturbation A and B consistent across biological replicates, which is the signal of biological interest 4) noise caused by biological and technical variability between the replicates.
This function also offers the possibility to calculate the statistical significance of each coefficient by performing a one-sided t-test (β A|B ≠ 0 or β B|A ≠ 0). For small sample sizes this test can suffer from false positives due to underestimating variance as described in Axelsson et al. [16]. As both linear models are applied after log transformation of the input data these additive coefficients reflect modeling a multiplicative effect of the fitness response of the pairwise fitness effects β A and β B  [17, 18]. In the case of the sLM split linear model aimed at reducing computation time and memory usage, the dataset is subdivided into groups of 50 perturbations (A and B), resulting in less data points available for the t-test, hence the reduced statistical power.
Importing analyzed data into the "results browser" and generating plots Once data has been analyzed by either method, the results of the analysis are stored in a text file in the following format: perturbation A; perturbation B; relative magnitude A; relative magnitude B; absolute magnitude A; absolute magnitude B; p-value A; p-value B; combined p-value by Brown's Method. Where perturbation A and B are the identifiers for the tested perturbations, relative magnitude A and B gives an indication of the ratio of treatment versus the median of all treatments (control), absolute magnitude A and B are the raw signals from the assay and the p-values are calculated as discussed previously. Given this format, other datasets not generated with our analysis pipeline can also be imported, filtered and plotted using TOPS.
Post-analysis filtering After the analysis is complete, the results are displayed in a table in the results browser [Figure  5 ] and can be sorted ascending or descending by clicking on the column headers. Here the user can sort and filter the obtained results before visualization by entering a string of filtering criteria. These criteria can be connected with the AND (&) or OR (|) operator and also allows for partial matches using the tilde (~) sign or exclusion using a logical NOT (!). Details on filtering strings can be found in TOPS's help files. The optimal filtering parameters vary with the technology used to obtain the data. In our experience, the mean Luminex signal (xMAP) and mean number of reads (MPS) are reasonable parameters to use for filtering out false positives. Filtering strings can be entered in the text field below the results table and data can be sorted by clicking on a column name.
Comparison of performance of the analysis models In order to compare the performance of all three models (linear, split-linear, and Mann-Whitney U-test) we generated a randomized dataset with a distribution based on dataset 1. In this set we generated true positive hits with an average signal of 0. 5, 0. 7 (reduced fitness) 1. 3 and 1. 5 fold (increased fitness) compared to the average of true negatives. To estimate false positive and false negatives, we used this dataset to determine receiver operating characteristics (ROC) for all three algorithms [Figure  7 ]. Overall, all algorithms show comparable characteristics with the U-Test performing slightly better at lower-fitness hits, and the linear models being more sensitive for increased fitness hits. In general, decreased fitness hits were more readily detected by our algorithms due to the non-normal distribution of the data.
In terms of run-time performance the U-Test and Split Linear Model algorithm are practically identical while the Linear Model algorithm is slightly more computationally costly [Figure  8 ]. For dataset 1, interactions have also been validated experimentally. Comparing the three algorithms in terms of the verified true positives they can identify from a ranked list of the top 50 hits after filtering as described in  [7]  shows comparable performance [Table  1 ] and an acceptable true/false positive rate overall compared to other high-throughput screens  [20]. In [Figure  9A -C] we show a scatter plot of P-values for perturbations A and B with the successful and failed validation indicated. We can observe an increasingly better spatial separation comparing the U-test with sLM and LM models, which could be for instance be captured by principal component analysis.
However, since the localization of successful versus failed validation data points does not follow a simple pattern according to the original coordinates provided by the two P-values and is likely to be different for distinct datasets, this would necessitate to have available such a validation dataset to train a classifier performing better than simple P-valuebased thresholds. As a generic tool TOPS does not offer this functionality but users might consider implementing it since we release the code of the system as open source. Combining the P-values for perturbations A and B does not have a negative effect on discriminative power but allows to provide a single interaction score [Figure  9D-F].
The direction of the interaction Figure  6  Basic layout of the analysis process in TOPS. TOPS has been written in order to be flexible with regard to user modules. User made importers can be written in Java and integrated with the GUI while custom analysis algorithms can be supplied as R scripts. (increased or decreased fitness as compared to control) is encoded by different colors [Figure  10 ]. The identifiers of one set of perturbations is plotted at the circumference of the circleplot. The second perturbation can be identified by hovering the mouse over a datapoint in the plot. TOPS' plots can be exported as bitmaps in PNG format or as vector graphics as a PDF file. The top 50 interactions from a ranked hit list are categorized by experimentally validated as true or false positive. Hits that were not experimentally tested are indicated as "unverified".
Conclusions We present a user-friendly software package to analyze and visualize interaction data from functional genomic dual perturbation screens. Although multiple tools for analyzing cell viability screens are available  [16, [21] [22] [23] [24]  these have their limitations by either being based on commercial software (mostly MATLAB) or requiring command line skills. TOPS incorporates statistical models designed for the analysis of pairwise interactions of larger gene/drug sets. Furthermore it is fully based on free software and provides a graphical user interface. The software is easily accessible and offers a powerful analysis tool for the benchtop scientist while being expandable enough to be attractive to users who would like to run their own analysis methods. Importantly, not only Luminex xMAP and Sequencing data can be analyzed with the presented methods but in principle any data from other technologies can be imported as long as the data can be reduced to a "perturbation A", "perturbation B", "score" format and true positives are relatively rare in comparison to true negatives which is a necessity due to the nature of normalization of the in-built analysis.
Availability and requirements Project name: TOPS. Project home page: https://sourceforge. net/p/topscemm/ wiki/Home/. Operating system(s): Win32/OSX/Linux. Programming language: Java and R. Other requirements: Java 6 or newer. R 2. 14 or newer. License: Creative Commons Attribution ShareAlike License V3. 0. Any restrictions to use by non-academics: Only those imposed by the license.
A green roof is an option for improving a building thermal comfort. The investigation is here performed within the specific climate context of Reunion Island in south hemisphere. This type of roof system involves choice difficulties for plant species that are more favorable to establish that comfort. The objective of this work is to simulate the dynamic behavior of this system towards external requests in wet tropical zones and vis-à-vis influences of certain number of physical parameters related to this system. As long as possible, authors used the electrical analogy method to establish a mathematical model associated to the studied system. Based on this model, a Matlab computing code was finalized. Weather data of Reunion Island were used for simulations; the green roof potential and benefit were highlighted by surveying the temperature gain and the heat flux crossing the roof as well as the energy saving performance.
The crystal lattice of single-crystal silicon gives rise to anisotropic elasticity. The stiffness and compliance coefficient matrix depend on crystal orientation and, consequently, Young's modulus, the shear modulus and Poisson's ratio as well. Computer codes (in Matlab and Python) have been developed to calculate these anisotropic elasticity parameters for a silicon crystal in any orientation. These codes facilitate the evaluation of these anisotropy effects in silicon for applications such as microelectronics, microelectromechanical systems and X-ray optics. For mechanically bent X-ray optics, it is shown that the silicon crystal orientation is an important factor which may significantly influence the optics design and manufacturing phase. Choosing the appropriate crystal orientation can both lead to improved performance whilst lowering mechanical bending stresses.
Zhang (2010)  presented matrixbased Matlab code for the calculation of the anisotropic elastic properties of silicon, and application to bendable mirror width profile optimization. This study showed the influence of the crystal orientation on the bending force and stress in the mirror. The anisotropic mechanical properties of silicon were also considered in thermal deformation analysis of liquid-nitrogen-cooled silicon crystals under high heat load  (Zhang et al. , 2013). Many existing synchrotron light sources (ESRF, APS, SPring-8,. ) are planning and implementing significant facility upgrades, and some low-emittance synchrotron light sources (NSLS II, MAX IV,. ) are under construction. The improved source characteristics of these light sources can only be fully utilized if the beamline performance and consequent specifications of optical components are pushed to higher levels than the current stage.
1. It is very convenient to calculate the stiffness matrix C, compliance matrix S, Young's modulus E, shear modulus G and Poisson's ratio by using computer code. We wrote matrix-based Matlab code  (Zhang, 2010)  and also implemented in Python using NumPy; these codes are summarized in the supporting information. 1 Using these codes we have performed calculations of Young's modulus E, the shear modulus G and Poisson's ratio for some commonly used silicon crystal orientations Si(100), (  110 ), (  111 ), (311). The new coordinate system (e 0 1 , e 0 2 , e 0 3 ) is defined as explained previously. As an example for Si(100): the vector e 0 1 = e 1 is in the surface normal direction [1 0 0], and the vectors e 0 2 and e 0 3 are in the crystal plane obtained by rotating an angle of the initial crystal axes e 2 and e 3 as shown in Fig.
Summary The anisotropic elasticity of single-crystal silicon has been fully reviewed for arbitrary orientation of the crystal. A matrix-based computer algorithm is proposed for the calculation of the stiffness coefficient matrix, compliant coefficient matrix, Young's modulus, shear modulus and Poisson's ratio. It can be easily implemented in any numerical computing environment and programming language that include matrix analysis (Matlab and NumPy-Python examples are given in the supporting information). Analytical formulae to calculate Young's modulus, the shear modulus and Poisson's ratio are also summarized in this paper. Numerical values of Young's modulus, the shear modulus and Poisson's ratio have been compared with those in the literature, and have revealed discrepancies in some papers.
Background The relationship between physical activity, disease severity, health status and prognosis in patients with COPD has not been systematically assessed. Our aim was to identify and summarise studies assessing associations between physical activity and its determinants and/or outcomes in patients with COPD and to develop a conceptual model for physical activity in COPD. Methods We conducted a systematic search of four databases (Medline, Embase, CINAHL and Psychinfo) prior to November 2012. Teams of two reviewers independently selected articles, extracted data and used the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) to assess quality of evidence. Results 86 studies were included: 59 were focused on determinants, 23 on outcomes and 4 on both. Hyperinflation, exercise capacity, dyspnoea, previous exacerbations, gas exchange, systemic inflammation, quality of life and self-efficacy were consistently related to physical activity, but often based on cross-sectional studies and low-quality evidence.
Data sources and searches This study was part of the European Commission funded PROactive project (http://www. proactivecopd. com), which aims to develop and validate patient-reported outcome (PRO) instruments that capture the dimensions of physical activity in daily life relevant to patients with COPD. We utilised standard systematic review methodology following the handbooks of the Centre for Reviews and Dissemination  7  and the Cochrane Collaboration. 8 The manuscript follows the PRISMA 9 statement for reporting of systematic reviews and meta-analyses. All methods were specified in advance, documented in a protocol (see online supplement 1), and approved by the PROactive consortium. Details of the performed searches are provided in online supplement 2. The bibliographic details of all retrieved articles were stored in a RefWorks-COS file.
Study selection Two reviewers independently assessed the title and abstract of every citation retrieved by the database searches (form available in online supplement 3). The decisions of the reviewers (order full text or reject citation) were recorded in the RefWorks-COS file and compared. We ordered all articles that were deemed potentially eligible by at least one member of the consortium. Two independent reviewers evaluated the retrieved full texts and made a decision on inclusion or exclusion according to the predefined selection criteria (form available in online supplement 4). Any disagreements in any phase were resolved by consensus, with close attention paid to the previously defined inclusion/ exclusion criteria. In the case of persistent disagreement, a third member adjudicated. All studies that did not fulfil the predefined criteria were excluded and their bibliographic details were listed with the specific reason for exclusion.
Future research direction The results of the present review should be taken into account for further research. Specifically, physical activity should be included as an outcome in randomised controlled trials to provide knowledge on the potential effects of drug and  non-drug treatments on this relevant outcome. Also, this review supports the need, prior to start any study, of a careful review of the existing literature in order not to miss (in data collection or statistical analyses) relevant potential confounders. We acknowledge that cross-sectional studies are easier to perform than longitudinal studies but the quality assessment here shows that they can just yield low-quality evidence about the association of determinants and outcomes of physical activity. The research community should make an effort to conduct prospectively planned cohort studies and randomised trials to provide a stronger evidence base for determinants and outcomes of physical activity and for developing recommendations for or against treatments in clinical guidelines.
Acknowledgements The authors thank  Mr Jordi Figuerola  ( Centre for Research in Environmental Epidemiology ) for creating the Microsoft Office Access data extraction form and  Dr Marla K Beauchamp ,  Dr Michael Berry ,  Dr Dina Brooks ,  Dr Francisco Garcia-Rio ,  Dr Yoko Goto ,  Dr Fabio Pitta ,  Dr Vanessa S Probst  and  Dr Benjamin Waschki  for sending the data requested from their manuscripts. Contributors MAP and JG-A led the systematic review. EG-S, AF, RAR,  NSH ,  MIP, HvR, TT, KK, NK ,  MAP  and  JG-A developed the study protocol. EG-S and AF  conducted the electronic database searches; EG-S conducted the additional searches. EG-S and AF coordinated the references in RefWorks. EG-S, AF, YR,  NSH ,  MIP , HvR, KK, MAP and JG-A screened titles and abstracts. EG-S, AF, CS-S, YR,  NSH ,  MIP ,  HvR ,  MAP  and JG-A assessed full texts.