Automatic C Compiler Generation from Architecture Description Language ISAC

This paper deals with retargetable compiler generation. After an introduction to application-specific instruction set processor design and a review of code generation in compiler backends, ISAC architecture description language is introduced. Automatic approach to instruction semantics extraction from ISAC models which result is usable for backend generation is presented. This approach was successfully tested on three models of MIPS, ARM and TI MSP430 architectures. Further backend generation process that uses extracted instruction is semantics presented. This process was currently tested on the MIPS architecture and some preliminary results are shown

Instructor Selector Generation from Architecture Description

We describe an automated way to generate data for a practical LLVM instruction selector based on machine-generated description of the target architecture at register transfer level. The generated instruction selector can handle arbitrarily complex machine instructions with no internal control flow, and can automatically find and take advantage of arithmetic properties of an instructions, specialized pseudo-registers and special cases of immediate operands

Notes on complexity of packing coloring

A packing k-coloring for some integer k of a graph G = (V, E) is a mapping ϕ : V → {1, . . . , k} such that any two vertices u, v of color ϕ(u) = ϕ(v) are in distance at least ϕ(u) + 1. This concept is motivated by frequency assignment problems. The packing chromatic number of G is the smallest k such that there exists a packing k-coloring of G. Fiala and Golovach showed that determining the packing chromatic number for chordal graphs is NP-complete for diameter exactly 5. While the problem is easy to solve for diameter 2, we show NP-completeness for any diameter at least 3. Our reduction also shows that the packing chromatic number is hard to approximate within n1/2−ε for any ε > 0. In addition, we design an FPT algorithm for interval graphs of bounded diameter. This leads us to exploring the problem of finding a partial coloring that maximizes the number of colored vertices.This is a manuscript of an article published as Kim, Minki, Bernard Lidický, Tomáš Masařík, and Florian Pfender. "Notes on complexity of packing coloring." Information Processing Letters 137 (2018): 6-10. doi: 10.1016/j.ipl.2018.04.012. Posted wih permission.</p

Quantitative phase microscopy timelapse dataset of PNT1A, DU-145 and LNCaP cells with annotated caspase 3,7-dependent and independent cell death

Time-lapse dataset of prostatic cell lines (DU-145, PNT1A, LNCaP) exposed to cell death-inducing compounds (staurosporine, doxorubicin) and black phosphorus. The time-lapse dataset is annotated as follows: (1) cell masks and cell numbers, (2) by cell death type and timepoint of death in the attached xlsx file. This dataset is supplementary to the article: Tomas Vicar, Martina Raudenska, Jaromir Gumulec, Michal Masarik, Jan Balvan. Detection and characterization of apoptotic and necrotic cell death by time-lapse quantitative phase image analysis. bioRxiv, 589697; DOI: https://doi.org/10.1101/589697 Code is available at https://github.com/tomasvicar/CellDeathDetect Methods Cell culture and cultured cell conditions LNCaP cell line was established from a lymph node metastase of the hormone-refractory patient and contains a mutation in the AR gene. This mutation creates a promiscuous AR that can bind to different types of steroids. LNCaP cells are AR-positive, PSA-positive, PTEN-negative and harbor wild-type p53 {Skjoth, 2006 #150; Mitchell, 2000 #149}. PNT1A is immortalized non-tumorigenic epithelial cell line. PNT1A cells harbour wild-type p53. However, SV40 induced T-antigen expression inhibits the activity of p53. This cell line had lost the expression of androgen receptor (AR) and prostate-specific antigen (PSA) (Raudenska, 2019). DU-145 cell line is derived from the metastatic site in the brain and contains P223L and V274F mutations in p53. This cell line is PSA and AR-negative and androgen independent (Chappell, 2012). All cell lines used in this study were purchased from HPA Culture Collections (Salisbury, UK). and were cultured in RPMI-1640 medium with 10 % FBS. The medium was supplemented with antibiotics (penicillin 100 U/ml and streptomycin 0.1 mg/ml). Cells were maintained at 37°C in a humidified (60%) incubator with 5% CO2 (Sanyo, Japan). Correlative time-lapse quantitative phase-fluorescence imaging QPI and fluorescence imaging were performed by using multimodal holographic microscope Q-PHASE (TESCAN, Brno, Czech Republic). To determine the amount of caspase-3/7 product accumulation, cells were loaded with 2 µM CellEventTM Caspase-3/7 Green Detection Reagent (Life Technologies, Carlsbad, CA, USA) according to the manufacturer’s protocol and visualized using FITC 488 nm filter. To detect the cells with a loss of plasma membrane integrity, cells were stained with 1 ug/ml propidium iodide (Sigma Aldrich Co., St. Louis, MO, USA) and visualized using TRITC 542 nm filter. Nuclear morphology and chromatin condensation were analyzed using Hoechst 33342 nuclear staining (ENZO, Lausen, Switzerland) and visualized using DAPI 461 nm filter. Cells were cultivated in Flow chambers μ-Slide I Lauer Family (Ibidi, Martinsried, Germany). To maintain standard cultivation conditions (37°C, humidified air (60%) with 5% CO2) during time-lapse experiments, cells were placed in the gas chamber H201 - for Mad City Labs Z100/Z500 piezo Z-stages (Okolab, Ottaviano NA, Italy). To image enough cells in one field of view, lens Nikon Plan 10/0.30 were chosen. For each cell line and each treatment, seven fields of view were observed with the frame rate 3 mins/frame for 24 or 48 h respectively. Holograms were captured by CCD camera (XIMEA MR4021 MC-VELETA), fluorescence images were captured using ANDOR Zyla 5.5 sCMOS camera. Complete quantitative phase image reconstruction and image processing were performed in Q-PHASE control software. Cell dry mass values were derived according to {Prescher, 2005 #177} and {Park, 2018 #178} from the phase (eq. (1)), where m is cell dry mass density (in pg/μm2), φ is detected phase (in rad), λ is wavelength in μm (0.65 μm in Q-PHASE), and α is specific refraction increment (≈0.18 μm3/pg). All values in the formula except the Phi are constant. Phi (Phase) is the value measured directly by the microscope. Integrated phase shift through a cell is proportional to its dry mass, which enables studying changes in cell mass distribution (Park et al., 2018). File description There are three archives included for particular cell lines: QPI_annotated_timelapse_DU145.zip for DU-145 cells QPI_annotated_timelapse_PNT1A.zip for PNT1A cells QPI_annotated_timelapse_LNCaP.zip for LNCaP cells The archive includes of following files: Tiff with time-lapse quantitative phase image (32-bit files 600x600px with values in pg/um2 with framerate 1 frame/3minutes with 1.59 px/um), named QPI_cellline_treatment_FOV.tiff Tiff file with segmentation mask for particular cells named mask_cellline_treatment_FOV.tiff xlsx table with cell death type (1 for apoptosis, 2 for necrosis, 3 for ambiguous/surviving) and time of death for representative cell number from mask, named labels_cellline_treatment_FOV.xlsx file naming has following conventions: cell names: DU145, PNT1A, LNCaP for particular cell line treatments: st, bp, do for staurosporine, black phosphorus and doxorubicin fields of view: 1 to 7 e.g. QPI_DU145_st_4.tif, mask_DU145_st_4.tif, labels_DU145_st_4.xls

Cisplatin enhances cell stiffness and decreases invasiveness rate in prostate cancer cells by actin accumulation: dataset of confocal and atomic force microscopy

Summary Dataset of imaging data of the experiment "Cisplatin enhances cell stiffness: Biomechanical profiling of prostate cancer cells". This dataset includes image data of atomic force microcopy (Young modulus) and confocal microscopy(staining of F-actin and β-tubulin) of prostate cell lines PNT1A, 22Rv1, and PC-3. Materials and Methods Cells, cell culture conditions Cells confluent up to 50–60% were washed with a FBS-free medium and treated with a fresh medium with FBS and required antineoplastic drug concentration (IC50 concentration for the particular cell line). The cells were treated with 93 µM (PC-3), 38 µM (PNT1A), and 24 µM (22Rv1) of cisplatin (Sigma-Aldrich, St. Louis, Missouri), respectively. IC50 concentrations used for treatment with docetaxel (Sigma-Aldrich, St. Louis, Missouri) were 200nM for PC-3, 70nM for PNT1A, and 150nM for 22Rv1. Long-term zinc (II) treatment of cell cultures Cells were cultivated in the constant presence of zinc(II) ions. Concentrations of zinc(II) sulphate in the medium were increased gradually by small changes of 25 or 50 µM. The cells were cultivated at each concentration no less than one week before harvesting and their viability was checked before adding more zinc. This process was used to select zinc resistant cells naturally and to ensure better accumulation of zinc within the cells (accumulation of zinc is usually poor during the short-term treatment of prostate cancer cells). Total time of the cultivation of cell lines in the zinc(II)-containing media exceeded one year. Resulting concentrations of zinc(II) in the media (IC50 for the particular cell line) were 50 µM for the PC-3 cell line, 150 µM for the PNT1A cell line, and 400 µM for the 22Rv1 cell line. The concentrations of zinc(II) in the media and FBS were taken into account. Actin and tubulin staining β-tubulin was labeled with anti- β tubulin antibody [EPR1330] (ab108342) at a working dilution of 1/300. The secondary antibody used was Alexa Fluor® 555 donkey anti-rabbit (ab150074) at a dilution of 1/1000. Actin was labeled with Alexa Fluor™ 488 Phalloidin (A12379, Invitrogen); 1 unit per slide. For mounting Duolink® In Situ Mounting Medium with DAPI (DUO82040) was used. The cells were fixed in 3.7% paraformaldehyde and permeabilized using 0.1% Triton X-100. Confocal microscopy The microscopy of samples was performed at the Institute of Biophysics, Czech Academy of Sciences, Brno, Czech Republic. Leica DM RXA microscope (equipped with DMSTC motorized stage, Piezzo z-movement, MicroMax CCD camera, CSU-10 confocal unit and 488, 562, and 714 nm laser diodes with AOTF) was used for acquiring detailed cell images (100× oil immersion Plan Fluotar lens, NA 1.3). Total 50 Z slices was captured with Z step size 0.3 μm. Atomic force microscopy We used the bioAFM microscope JPK NanoWizard 3 (JPK, Berlin, Germany) placed on the inverted optical microscope Olympus IX‑81 (Olympus, Tokyo, Japan) equipped with the fluorescence and confocal module, thus allowing a combined experiment (AFM‑optical combined images). The maximal scanning range of the AFM microscope in X‑Y‑Z range was 100‑100‑15 µm. The typical approach/retract settings were identical with a 15 μm extend/retract length, Setpoint value of 1 nN, a pixel rate of 2048 Hz and a speed of 30 µm/s. The system operated under closed-loop control. After reaching the selected contact force, the cantilever was retracted. The retraction length of 15 μm was sufficient to overcome any adhesion between the tip and the sample and to make sure that the cantilever had been completely retracted from the sample surface. Force‑distance (FD) curve was recorded at each point of the cantilever approach/retract movement. AFM measurements were obtained at 37°C (Petri dish heater, JPK) with force measurements recorded at a pulling speed of 30 µm/s (extension time 0.5 sec). The Young's modulus (E) was calculated by fitting the Hertzian‑Sneddon model on the FD curves measured as force maps (64x64 points) of the region containing either a single cell or multiple cells. JPK data evaluation software was used for the batch processing of measured data. The adjustment of the cantilever position above the sample was carried out under the microscope by controlling the position of the AFM‑head by motorized stage equipped with Petri dish heater (JPK) allowing precise positioning of the sample together with a constant elevated temperature of the sample for the whole period of the experiment. Soft uncoated AFM probes HYDRA-2R-100N (Applied NanoStructures, Mountain View, CA, USA), i.e. silicon nitride cantilevers with silicon tips are used for stiffness studies because they are maximally gentle to living cells (not causing mechanical stimulation). Moreover, as compared with coated cantilevers, these probes are very stable under elevated temperatures in liquids – thus allowing long-time measurements without nonspecific changes in the measured signal. Identification of files Files are separated into individual zip files. The dataset of confocal microscopy is separated based on treatments: untreated control, docetaxel-treated cells, cisplatin-treated cells, zinc-treated cells. Filenames actin_tubulin_Zstack_cisplatin.zip, actin_tubulin_Zstack_untreated_control.zip, actin_tubulin_Zstack_zinc.zip, actin_tubulin_Zstack_docetaxel.zip. Files included in these ZIP archives are named as follows: "cellline_treatment_FOV". Files are 3-layer 16bit tiff files with layer sequence as follows: F-Actin (Phalloidin)/b-tubulin/Hoechst 33342. The dataset contains 242 FOVs of three cell line types/three treatments + one control, files are Z-stacks made of 50 slices. The dataset of atomic force microscopy (AFM) is included in one ZIP archive "AFM_YoungModulus_SetpointHeight.zip", which includes data on Young modulus and Setpoint Height of cell lines 22Rv1, PNT1A and PC-3 and treatments zinc, docetaxel, cisplatin (+control), i.e. identical like for confocal microscopy. The file naming is as follows: "AFM_cellline_treatment_FOV_Youngmodulus.tif" for Young modulus and "AFM_cellline_treatment_FOV_setpointheight.tif" for setpoint height. The data are filtered 32-bit tiff images, where the pixel value correspond to cell stiffness (young modulus) in Pa or setpoint height in m

Comparison and practical review of segmentation approaches for label-free microscopy

<p>This dataset contains microscopic images of PNT1A cell line captured by multiple microcopic without use of any labeling and a manually annotated ground truth for subsequent use in segmentation algorithms. Dataset also includes images reconstructed according to the methods described below in order to ease further segmentation. </p> <p><strong>Materials and methods </strong></p> <p>Cells were cultured in RPMI-1640 medium supplemented with antibiotics (penicillin 100 U/ml and streptomycin 0.1 mg/ml) with 10% fetal bovine serum. Prior microscopy acquisition, cells were maintained at 37 cenigrade in a humidified incubator with 5% CO2. Intentionally, high passage number of cells was used (>30) in order to describe distinct morphological heterogeneity of cells (rounded and spindle-shaped, relatively small to large polyploid cells). For acquisition purposes, cells were cultivated in Flow chambers µ-Slide I Luer Family (Ibidi, Martinsried, Germany).</p> <p>Quantitative phase imaging (QPI) microscopy was performed on Tescan Q-PHASE (Tescan, Brno, Czech republic), with objective Nikon CFI Plan Fluor 10x/0.30 captured by Ximea MR4021MC (Ximea, Münster, Germany). Imaging is based on the original concept of coherence-controlled holographic microscope \cite{Kolman:10,Slaby:13}, images are shown in grayscale with units of pg/µm2.</p> <p>DIC microscopy was performed on microscope Nikon A1R (Nikon, Tokyo, Japan), with objective Nikon CFI Plan Apo VC 20x/0.75 captured by CCD camera Jenoptik ProgRes MF (Jenoptik, Jena, Germany). </p> <p>HMC microscopy was performed on microscope Olympus IX71 (Olympus, Tokyo, Japan), with objective Olympus CplanFL N 10x/0.3 RC1 captured by CCD camera Hamamatsu Photonics ORCA-R2 (Hamamatsu Photonics K.K., Hamamatsu, Japan).</p> <p>PC microscopy was performed on a Nikon Eclipse TS100-F microscope, with a Nikon CFI Achro ADL 10x/0.25 objective captured by CCD camera Jenoptik ProgRes MF.</p> <p><strong>Folder structure and file and filename description</strong><br> <br> <em>folder "source data+groundtruth"</em><br> - includes raw microscopic data <br>   (uncompressed 16-bit for DIC, HMC and PC, 32-bit for QPI)<br> - includes manualy annotated groundtruth (zip file - imageJ ROI file, 1bit png mask)</p> <p>e.g. <br> DIC_01_raw.tif<br> DIC_01_groundtruth_imagejROI.zip<br> DIC_01_groundtruth_mask.png</p> <p><br> <em>folder "reconstructions"</em></p> <p>includes reconstructed images using reconstructions with highest dice coefficient achieved. </p> <p>for DIC and HMC: rDIC-Koos, rDIC-Yin, and rWeka<br> for PC: rPC-Top-Hat, rDIC-Yin, and rWeka<br> for QPI: rWeka</p> <p>note that for rWeka images numbered 01 for DIC, HMC and PC and 01-03 for QPI were used for learning.</p> <p><strong>Abbreviations</strong><br> DIC, differential image contrast<br> HMC, Hoffman modulation contrast<br> PC, phase contrast<br> QPI, quantitative phase imaging<br> rDIC-Koos, DIC/HMC image reconstruction according to Koos et al, Sci Rep. 2016;6:30420<br> rDIC-Yin, DIC/HMC image reconstruction according to Yin et al, Inf Process Med Imaging. 2011;22:384-97.<br> rPC-Yin, PC image reconstruction according to Yin et al,  Med Im Anal. 2012; 16(5):1047<br> rPC-Top-Hat, Top-Hat filter according to Dewan et al, IEEE Transactions on Biomedical Circuits and<br> Systems.2014;8(5):716-728<br> rWeka, probability map using Trainable Weka segmentation according to Arganda-Carreras et al. Bioinformatics. 2017</p

Level of zinc in sera and tissues by tumor type.

<p>Summary of individual meta-analyses. For model used and heterogeneity, see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0099790#pone-0099790-t001" target="_blank">Table 1</a>.</p

Serum metallothionein in newly diagnosed patients with childhood solid tumours

Tumour markers are substances produced by malignant cells or by the organism as a response to cancer development. Determination of their levels can, therefore, be used to monitor the risk, presence and prognosis of a cancer disease or to monitor the therapeutic response or early detection of residual disease. Time-consuming imaging methods, examination of cerebrospinal fluid or tumour tissue and assays for hormones and tumour markers have been used for cancer diagnosis. However, no specific marker for diagnosis of childhood solid tumours has been discovered yet. In this study, metallothionein (MT) was evaluated as a prospective marker for such diseases. Serum metallothionein levels of patients with childhood solid tumours were determined using differential pulse voltammetry - Brdicka reaction. A more than 5-fold increase in the amount of metallothionein was found in sera of patients suffering from cancer disease, compared with those in sera of healthy donors. The average metallothionein level in the sera of healthy volunteers was 0.5 ± 0.2 μmol · dm-3 and was significantly different (p<0.05, determined using the Schefe test) from the average MT level found in serum samples of patients suffering from childhood solid tumours (3.4 ± 0.8 μmol · dm-3). Results found in this work indicate that the MT level in blood serum can be considered as a promising marker for diagnostics, prognosis and estimation of therapy efficiency of childhood tumours

Flow diagram for identification of relevant studies.

<p>Flow diagram for identification of relevant studies.</p