1611154 research outputs found


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    The realm of 3D printing has been a valuable aspect of manufacturing and mechanical engineering to which complex geometries have been made that might be otherwise highly costly or not feasible by other manufacturing methods. This is where volumetric 3D printing has been advantageous by generating complex geometry with no defects on the surface. The problem with the research done so far is the material used uses photoinitiators that photo-synthesize using ultra-violet (UV) light. The problem with this material is that it is attached to the destructive issues brought on by interacting with UV light, making some additives useless. To solve part of this problem, a solution to the material problem must be shown that a resin can be cured using visible light, and a volume must be prepared. This study has resulted in successfully finding resin that can be fixed using visible light in the 470 & 530 nm range, a volume that has resulted in the experiments. By curing simple geometry using visible light, one can formulate a resin that can sustain just about any additives that can meet any goal, whether it is organic or not


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    Data for spatial transcriptomics on triple negative breast cancers The repository contains a number of (tarballed) directories. byArray - data by array/subarray classification - data relative to the classification / regression at the spot level, as well as the per-annotation gene expressions Clinical - clinical data and link array / samples clustering - data relative to the clusterings - by sample, the megaclusterings and the ecotypes deconvolution - gene expression in each annotation category, for each sample, obtained by deconvolution external datasets - external datasets used to confirm the results Images - original images (jpg, png...) misc - various annotation and other files patches - info about the patch size distribution rawCountsMatrices - raw count matrices Robjects - formatted R objects used for the analyses byArray Data by array/subarray. Mapping between those arrays and samples is in ids.RDS (Clinical directory). In each directory, there are the following files - A Cy3 jpg file giving the location of the spots An HE jpg file which is the H&E image For both image, there can be a .rot version if it was necessary to rotate them for the analysis. spot_data... give the localization of all or selected spots small.jpg is a subsampled version of the image. selection.RData counts of selected spots all.RData counts of all spots allSpots.RData position of all spots annotBySpot.RDS if available, the annotation (in pixel) for each spot artefacts.RDS if present, gives the number of pixels annotated as an artefact in each spot classification Data relative to the classification / regression at the spot level. LOOstart.RData - Data with all the samples together used for the Xvalidation baseClassif.RDS - Rotation / scaling used on the gene-level data loosReg.RData - Result of the Xvalidation classifAll.RDS - Classification of each spot projectedSamples Data at the spot level projected on the PC from baseClassif.RDS. regressors The regressors obtained. Must be loaded with xgboost. baseClassif.RDS is used to rotate the genes prior to classification. See code for details, or use directly the function classifySpots in the STstuff package. Clinical Clinical data. Clinical.xlsx - clinical data as an Excel table, identical to the one of paper Clinical.RDS - clinical data in R format (survival objects already created and slide-level annotation fractions included) ids.RDS - link between arrays and sample IDs clustering Data relative to the clusterings - by sample, the megaclusterings and the ecotypes. intraPatientClust Clusterings in each patient. Each RDS contains the following objects - clusts - the prototypes for the best clustering in 2 to 10 clusters km - the best kmeans for 2 to 10 clusters (cluster id for each spot) qv - quality of the clusterings N - number of clusters for each item / column in clusts / km. scg - score giving how spatial a gene is clustPrototypes Deconvolution of the best prototypes from intraPatientClust. List with the following items - proto - deconvoluted prototypes fit - the fitted model kOrig - the original kmeans k - the kmeans after removing some clusters (which we did not do) Nreads - total number of reads for the spot of each cluster Kmeans MC Kmeans on the deconvoluted prototypes (megaclustering), in 5 to 20 clusters. looMC Leave-one-out cross validation of the recovery of the MC in the bulk dataset. MC deconv Deconvolution of the MC at the spot level. Each file contains the following objects - m - the fraction of each MC in each spot idSpot - the id of each spot (slide and position) fm2 - the fitted model deconvolution Per-annotation gene expressions used for TLS / tumor /stroma etc analyses. Each file is an RData, containing - prot - the expression in each annotation, as pseudo-counts fm - the fitted models Ntot - the pseudo-number of spots corresponding to each annotation, obtained by summing the fractional presence in each spot (e.g. 2 spots with 50% of annotation in each equal 1 spot). external datasets External datasets used to confirm the results. otherTNBC SCAN-B and METABRIC iSpy2 Data from Ispy2 Immunotherapies - directory with data for immunotherapies with expression data Images The original images in png/jpg/ndpi format. imageAnnotation Original annotation images. Coded as - colAnn2 = c(Nothing="#FFFFFF", Tumor="#017801", Necrosis="#000000", `Fat tissue`="#000080", `Low TIL stroma`="#ff904f", Vessels="#dc0000", Artefacts="#6e2400", `Lactiferous duct`="#9980e6", `High TIL stroma`="#e9e900", `in situ`="#ccffcc", `Lymphoid nodule`="#80801a", `Hole (whitespace)`="#40e5f6", Lymphocyte="#c4417f", `Stroma cell`="#ff9980", Nerve="#4d8080", `Heterologous elements`="#808080", `Acellular stroma`="#e9d1bb", `Tumor region`="#258a15") imagesLarge Original H&E images in high def, not rotated/translated. The annotated image is not rotated either so those can be directly compared. imagesHD Original H&E images in highest def. Similar to imagesLarge but bigger. artefacts Contours from Qpath used to flag artefacts in non-annotated images. Correspond to imagesLarge. Images IHC CD3/CD20 IHC images that were used to help slide annotation. Note that those images are on a subsequent slide, and so do not correspond exactly to any of the subarrays. misc Various files that are used for image registration, deciding what to plot, annotations, etc. The most useful ones are described here. For the others check the code (in particular ST TNBC figs.R for plotting). registration.xlsx Transformations applied to superpose the images. Columns: pts: patient ID fixed: ref slide moving: the slide to transform theta: rotation dx, dy: translation inv: if X, do a flip In R, the transformation for the images are (using EBImage) if (!is.na(trinv)) { x = flip(dta[[i]]im); spots[,"pixel_y"]=dim(x)[1]-spots[,"pixel_y"]; } x = rotate(x, trtheta,filter=none,output.dim=dim(dta[[i]]theta, filter='none', output.dim=dim(dta[[i]]im)[1:2]+600, bg.col='white') x = translate(x, c(trdx,trdx, -trdy), bg.col='white') # The +600 is to have all the slide after rotation. It is removed below Regarding the spot positions: spots[,c("pixel_x", "pixel_y")] = transfo(c(trthetabase::pi/180,trtheta*base::pi/180, trdx, -trdy),as.matrix(spots[,c("pixelx","pixely")]),ctr=dim(dta[[i]]dy), as.matrix(spots[,c("pixel_x", "pixel_y")]), ctr=dim(dta[[i]]im)[1]/2) The slides are then recentered to keep only the part with spots. sp = colRanges(do.call(rbind, lapply(dta, function(i) as.matrix(ispots[,c("pixel_x", "pixel_y")])))); sp[,1] = sp[,1]-50; sp[,2]=sp[,2]+50; for (i in seq_along(dta)) { x = translate(dta[[i]]im, -sp[,1], bg.col='white'); dta[[i]]im=x[300+(0:diff(sp[1,])),300+(1:diff(sp[2,])),];dta[[i]]im = x[300+(0:diff(sp[1,])), 300+(1:diff(sp[2,])), ]; dta[[i]]spots[,c("pixel_x", "pixel_y")] = dta[[i]]spots[,c("pixelx","pixely")]rep(sp[,1],each=nrow(dta[[i]]spots[,c("pixel_x", "pixel_y")] - rep(sp[,1], each=nrow(dta[[i]]spots)) } patches Data on patch sizes distribution. Each RDS is a list, with items Np Number of pixels of each annotation (before dilatation) patches also a list, each item giving the size of each patch for each annotation. rawCountsMatrices tsv files by array obtained directly from the sequencing. Robjects R objects of the various basic data, used for the analyses. counts Counts per gene per spot. Batch-corrected. Lists with items: cnts: matrix of counts spots: ids of the spots (pixels are relative to the "images" files) countsNonCorrected Similar to "counts", but not batch-corrected images Lists with one item per slide (so 2-3 per sample) For each slide there are two items: img: image in EBImage format spots: positions of the spots on that image imagesSmall Same as images, but with smaller images. Usually big enough to display. The "spots" info are rescaled to fit the image size. annotsBySpot Lists with items: annots: matrix, N pixels annotated as class ... on each spot spots: position of the spots annotated annotationRecoded Annotation images (imageAnnotation) recoded as a matrix of values 1 to 17. The values correspond to the order of colAnn2 (see imageAnnotation directory), so 1 = Nothing 2 = Tumor 3 = Necrosis and so on BatchCorrection The negative binomial fit parameters used to correct for the array-specific batch effect. There are also the following files - bulkCount.RDS Counts per gene from the bulk RNA. PB_count.RDS Counts per gene from the pseudo-bulk RNA (obtained by summing values from each spot for a alide)

    Agroforestry-based community forestry as a large-scale strategy to reforest agricultural encroachment areas in Myanmar: ambition vs. local reality

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    Abstract: Context: The high rate of deforestation in Myanmar is mainly due to agricultural expansion. One task of the Forest Department is to increase tree cover in the encroaching farmland by establishing large-scale agroforestry-based community forests (ACFs). Aim: The objectives of this study were to analyze the adoption and performance of the ACFs in the agricultural encroachment areas in the Bago-Yoma region, Myanmar; and to provide recommendations to enhance the adoption of ACFs by farmers. Methods: We inventoried 42 sample plots and surveyed 291 farmers. Survey responses were analyzed by binary logistic regression, one-way ANOVA, and non-parametric correlation tests to evaluate factors influencing the adoption of ACFs. Stand characteristics were calculated from the inventory data to evaluate the performance of ACFs. Results: Our results show that farmer participation in ACFs was lower than stated in the registry of the Forest Department. Farmers practiced four different agroforestry designs in ACFs with different outcomes. The Forest Department strongly determined tree species and planting designs, farmers’ perception and participation in ACFs. Farmland size, unclear and insufficient information on ACFs, and a negative perception of raising trees in crop fields were the major factors limiting the adoption rates of ACFs. Conclusion: We recommend capacity building for farmers and Forest Department staff and raising awareness about the benefits of planting designs and trees on farmland. A stronger consideration of farmers’ preferences for design and species selection could increase their motivation to adopt ACFs and improve the long-term sustainability of ACFs.The data is part of the PhD research of the first author

    Defining the Essence of Buddhism: Appropriating the Dharma for the West

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    The contribution presents the examples of three prominent Western Buddhists who strongly advocated the definition of the nature of Buddhism and argued to transfer the assumed essence of Buddhism without accompanying Asian cultural baggage. The paper selected the influential Buddhist teachers Lama Govinda, highly respected author, artist and founder of the Buddhist order Arya Maitreya Mandala; the British Sangharakshita, founder of the Friends of the Western Buddhist Order, renamed to Triratna; and Stephen Batchelor, well-known meditation teacher, author and advocate of a secular Buddhism. Overall the presentation argues that the definition of an essence is equal to the subjective composition of an essence. And this procedure entails three crucial principles: that of critique, of development and of ideal. The contribution will outline these implications in the second part of the paper, followed by a brief conclusion.+ ID der Publikation: unilu_61884 + Titel der Reihe: Religious Studies in Interfaith Contexts/Religionswissenschaft in interreligiösen Kontexten + Sprache: Englisch + Bemerkungen: keinen Vertrag mit dem Verlag geschlossen, daher wohl open access publikationsfähig nach einem halben Jahr + Letzte Aktualisierung: 2023-04-20 14:56:0


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    Researchers have difficulties accurately and objectively diagnosing stress in patients. The feeling of stress can often lead to serious health consequences if not properly monitored and treated. Therefore, this study aims to compare the effectiveness of various machine learning algorithms at differentiating levels of stress experienced by test subjects. This study analyzed the stress levels experienced by test subjects using electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) while they played a computer game that tested their decision-making and logical processing. Test subjects’ heart rates and average scores across each trial were also monitored and recorded. The EEG and fNIRS data were imported, processed, tested, and verified using MATLAB and created various machine learning algorithms for comparison based on accuracy. The data indicated the test subjects experienced increasing stress levels as the difficulty of the computer game increased. The machine learning algorithms varied in the accuracy in differentiating the levels of stress each test subject experienced but were overall successful in determining the stress experienced by each test subject

    Assembly and annotation of Pinot Noir

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    Pinot Noir is one of the world's most famous grapes, with its late ripening light ruby red fruit., was used for T2T genome assembly. A total of 33,349,412,693bp HiFi reads with ~ 65x coverage were generated by the PacBio platform. The preliminary assembly were conducted using Hifiasm on HiFi reads. A total of 83,958,664,800 bp of Hi-C reads with ~ 160× coverage were used to anchor and remove some short contigs using Juicer. 3D-DNA was then used to obtain the genome at the scaffold level. NextDenovo was used to assemble ONT reads into 15,705,792,031 bp ONT long reads with ~30× coverage into a contig-level genome to fill the gaps. The two T2T gap-free haplotypes of the hybrid Pinot Noir, PN1 (473.43MB) and PN2 (467.09MB). To validate the quality of our assembly, K-mer and BUSCO were conducted. We used K-mer to evaluate genomic heterozygosity, estimated 1.43%. BUSCO to evaluate genomic completeness, about 98.3% in PN1 and 98.4%in PN2 of the core conserved plant genes were found complete in the genome assembly. For genome annotation, the number of genes identified by the genome is similar, more than 37,000 genes were found for Pinot Noir, PN1 (37039) PN2 (37350). The PN1 genome assembly: PNhap1.fa The PN1gene annotation: PNhap1.gff3 The PN1 TE annotation: PNhap1.TE.gff The PN2 genome assembly: PNhap2.fa The PN2gene annotation: PNhap2.gff3 The PN2 TE annotation: PNhap2.TE.gf


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    TMEM163 is a currently understudied protein whose function stills requires elucidation. While information on its nature as a zinc influx or efflux transporter is still debated in the literature, this research sought to focus on its relation to the SLC30/ZNT family of zinc transporters. These ZNTs are known to have 10 members, exist as a dimer, efflux zinc, and share functional similarities with TMEM163. Previous research has shown that certain ZNTs form homodimers or heterodimers. This research aims to demonstrate that TMEM163 is a new member of the SLC30/ZNT family and as such, be able to form heterodimeric interaction with related members. More specifically, the goals of this project include characterizing physical interaction of distinct ZNT proteins with TMEM163 and to determine the amino acids or motifs within TMEM163 that are responsible for heterodimerization. Additionally, determination of both colocalization and possible altered sub-cellular localization was carried out to give credence to the interaction having a cellular purpose. Overall, TMEM163 was found to interact with ZNT4 and ZNT8, both members of the SLC30 family, as well as ZIP1, a member of the SLC39 family that influxes zinc into the cytosol. Deletion mutagenesis of TMEM163 did not reveal the potential interaction site within the protein. While the exact amino acids or motif(s) responsible for dimerization remain elusive, it is deemed likely to be located between transmembrane domain 1 to the first half of loop 5. Further, ZNT4, ZNT8, and ZIP1 all partially co-localized with TMEM163 upon heterologous co-expression in cultured HeLa cells. Interestingly, the confocal imaging data shows that TMEM163 co-expression with ZNT4 or ZNT8 altered normal subcellular localizations of both ZNT4 and ZNT8. In conclusion, the results support that TMEM163 is likely a new member of the ZNT family and should be reclassified as ZNT11, and the data provide information for future work on identifying specific amino acids or motifs responsible for protein dimerization

    Efficiency-Improving Conditions and Strategies for the Management of Educational Counseling in Primary Education

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    The article contains a theoretical investigation with applied elements that can be used in the practice of counseling primary school students and their parents. We present the difficulties and contradictions, their content, the mistakes that occur at primary education level and require multidimensional qualified help, psycho-pedagogical support and educational counseling, as well as a number of principles, psycho-pedagogical conditions and strategies, the exploration of which ensures the efficiency management of educational counseling/MEC. The author mentions that, in order to achieve quality management, it is necessary to know the technology of counseling, elucidating some pedagogical tools that, when applied in the practice of school counseling, have demonstrated their functionality

    Asymmetric decay heat removal in MYRRHA: experiments in the E-SCAPE facility

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    MYRRHA is an accelerator-driven system, coupling a proton accelerator to a subcritical reactor cooled by lead-bismuth eutectic (LBE), under development by SCK CEN in Belgium. The reactor design is based on a pool configuration, all components of the primary system housed within the main vessel. In this context, the thermal-hydraulic experimental program considers separately the performance of individual reactor components, and the integral pool dynamics in steady-state and in anticipated transients. The E-SCAPE (European SCAled Pool Experiment) facility is a thermal hydraulic 1/6-scale model of the primary system of the MYRRHA reactor. Replicas of the main components are placed in the main vessel, while pumps and heat exchangers are located in two external circuits. Operating conditions in LBE are representative of (scaled) decay heat removal in terms of mass flow rate (up to 120 kg/s), core power (up to 100 kW) and temperature (200 – 340 °C). This arrangement allows the study of different scenarios, including forced circulation, natural circulation, flow transitions and asymmetric operation. Two specific asymmetric scenarios, corresponding to possible initiating events for design-basis accidents, are studied in the present experimental campaign, framed within the European collaborative project PASCAL (2020-2024). First, a single pump failure is investigated. Second, partial loss of heat sink is simulated by stopping the flow of secondary coolant in only one circuit. These scenarios are compared to a reference symmetric case. Their evolution depends on the fluid mixing in the cold and hot plena

    Data for: Greening the dike revetment with historic sod transplantation technique in a Living Lab.

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    Data from an in situ experiment on a dike in Living Lab Hedwige Prosperpolder. In the experiment we applied a historic sod transplantation technique. We tested the erosion resistance of the adapted vegetated dike revetment after one growth season. Dataset contains measurements in sods plot S1, S2, S3, S4 and in reference plots R1-R4. In addition, two section were only milled (K1 and F). These sections are not described in the publication, however some data is available and thus included in this dataset. This dataset contains files from the following research steps: 1. Visual observation (vegetation) 2. Soil moisture content & Soil penetration resistance 3. Root indication (doorwortelling) 4. Sod pulling method (grastrek proef) 5. Wave impact test (golfklap proef) & Overflow test (overloop proef) The data is described in the following publication: Greening the dike revetment with historic sod transplantation technique in a Living Lab. Kim van den Hoven; Carla J. Grashof-Bokdam; Pieter A. Slim; Ludolph Wentholt; Patrik Peeters; Davy Depreiter; André R. Koelewijn; Marte M. Stoorvogel; Mario van den Berg; Carolien Kroeze; Jantsje M. van Loon-Steensm


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