dataesr/scientific-paragraphs-categorization huggingface dataset:
Since the efficiency of registration depends heavily on how accurately the points are identified, the points can usually be interactively selected by the user. The points can represent something other than salient and precisely locatable points of the morphology of the visible object structure. Any matching points that the user can confidently and unambiguously identify in both images will serve this purpose. One of the disadvantages 9 https://reference. wolfram. com/language/ref/ImageCorrespondingPoints. html of manual methods is that they require a time-consuming and tedious task to be performed by a user who is knowledgeable in the field. This disadvantage, along with the arguably more significant disadvantages of repeatability and variability, justifies using existing algorithms for this purpose.
For these reasons, we used the function ImageCorrespondingPoints of the software Mathematica ® , which can find these points, or at least find these points in the same way in different images. Thus, we have automatically converted complex font shapes into a vector of numbers of corresponding points. The experiment reported in this paper was divided into two parts. The first part compares Voynich letters among themselves to verify that this approach is valid. To accomplish such a task, we used a specific architecture of a neural network called autoencoder  [20] [21] [22] [23] , Fig. 7. The auto-encoder reduces the dimension of the Voynich data set to vectors of three dimensions (hidden layer with three neurons). By this reduction, the data can be plotted in a 3D coordinate system with all the points linked to their corresponding images; therefore, visually, we can identify how close the images are to each other.
The data sets Two basic data sets were selected for our experiment. The first contains only the Voynich alphabet itself. On this data set, we wanted to test how our methods could compare the letter's similarity of the same alphabet and how accurately it compares graphic objects. The second data set (for the 2nd experiment) consists of selected old dialects compared mutually to each other. As already mentioned, several ancient Indian dialects were chosen, namely Assamese (Fig. 19 ), Gujarati (Fig. 20 ), Hindi (Fig. 21 ), Khojki Jiva (Fig. 22 ), Konkani (Fig. 23 ), Panjabi (Fig. 24 ) and Urdu (Fig. 25 ). The first goal is to identify any similarities among the Voynich letters themselves. Fig. 8  shows the alphabet, which is composed of 25 letters and special characters. For visualization purposes,  the letters in the article will be shown with a white background (numerically 1s); on the other hand, the shape of the letters will be composed numerically by 0s.
Experiment 2 -How much are ancient alphabets similar? The second experiment aims to compare the Voynich Alphabet with other alphabets. It is possible to see their corresponding images in the figures below (Figs. [19] [20] [21] [22] [23] [24] [25]. For visualization purposes, likewise the Voynich Alphabet, we decided to set the background with 1s (saturated pixels) and the actual shape of the letter with 0s. The second part used some of the ideas from the first part, but since the number of images to compare is more extensive, we could not correctly predict by shrinking the data into a vector of three dimensions. This second part, by experimentation, has been network settings, so the bottleneck produces a vector of 16 dimensions presented as 4 × 4 pixel images. The structure of a suitable auto-encoder is on (Fig.
Figs. 29-32 are four samples of how the images look numerically when entering the auto-encoder and the numerical output of the auto-encoder. The Latent Space -letter representation of the given alphabet is below each figure as a matrix of 4 × 4. As shown previously, our data set is currently constituted of a set of 4 × 4 pixels, i. e. a 1D vector of 16 values. In order to compare the VM alphabet with other alphabets, the vector from the Voynich alphabet must be extracted. See some samples in Figs. 33 34 35 36. We also transformed all the letters from the other alphabets to 1D vectors; we used then the Pearson coefficient to calculate how strong the correlation between each Voynich Letter is in its 1D representation to the letters of the rest of the alphabets in their In other words, we propose a unique value representing the correlation between each letter from any of the alphabets to one of the Voynich letters, see Fig.
Acknowledgements The following grants are acknowledged for the financial support provided for this research: grant of  SGS  No. SP2023/050 ,  VSB-Technical University of Ostrava, Czech Republic. This work was supported by the  European Regional Development Fund  in the '' A Research Platform focused on Industry 4. 0 and   Robotics in Ostrava Agglomeration' ' project, reg. No. CZ. 02. 1. 01/0. 0/0. 0/17-049/0008425  within the  Operational Programme Research, Development   and Education. This work was supported by the  European Regional Development Fund in the Research Centre of Advanced Mechatronic Systems  project, project number  CZ. 02. 1. 01/0. 0/0. 0/16-019/0000867  within the  Operational Programme Research, Development   and Education.
Mechanical characterisation The quasi-static tensile characterisation was performed using an MTS Criterion 45 Series 4 screw-driven frame following the ASTM D6387 standard under stroke control at a cross head speed of 2 mm/min. Additionally, a 2D video extensometer was used to acquire the local longitudinal and transverse strains. The polymer specimens (black due to their carbon filler), were speckled with white paint to enable contrast and capture the local strain field using an Imetrum camera at 5 fps and 1392 × 1038 pixels resolution. The analysis was performed with the software VIC 2D provided by Correlated Solutions. Window size and distance were selected to provide at least 2600 tracking points per image. 5 specimens were tested per configuration (layer height, printing angle and nozzle diameter).
The force and strain data were processed with a Python script to obtain the Young's modulus, the ultimate strength, and the elongation at break of every sample. Post-mortem specimens were inspected using a TM4000Plus Scanning Electron Microscope (SEM) manufactured by Hitachi (Tokyo, Japan) with a 15 kV accelerating voltage and a working distance of 50 mm. The fractographies of the cracked surfaces were taken to identify the failure modes of each configuration and see the welded filament before failure, see Fig. 2.
Implementation The previous model has been implemented in the software Python. The complete source code is available for download on GitHub 1. All the simulations have been run over 4 s, except the worst case scenario (1 mm nozzle and 0. 2 mm layer height) that has been run for a longer period of time (10 s) to ensure thermal equilibrium is reached to prove the assumptions of the model are reasonable. The PDEs have been solved using the pdepe function solved with the ode15s solver with a relative error tolerance of 10 -5  [44, 45]  in combination with the boundary conditions aforementioned and the following geometrical dimensions and material properties. The value of the thermal diffusion coefficient, 𝑎 = 0. 105 ⋅ 10 -6 m 2 ∕s in Eqs. (  4 ) and (  6 ) has been taken from the literature  [46]  considering 1 https://github.
To elaborate, perturbation effects tend to be sparse in that most perturbations affect only a small number of genes or co-regulated gene programs  2. In this scenario, rather than assaying each perturbation individually, we can measure a much smaller number of random combinations of perturbations (forming what we call "composite samples") and accurately learn the effects of individual perturbations from the composite samples using sparsity-promoting algorithms. Moreover, with certain types of composite samples, we can efficiently learn both first-order effects (i. e. , from single gene perturbations) and higher-order genetic interaction effects from the same data. We have previously shown that experiments that measure random compositions of the underlying biological dataset can greatly increase the efficiency of measuring expression profiles  20  and imaging transcriptomics  21.
We found that 5-6 conventional channels were needed to achieve equivalent validation accuracy (correlation) as one cell-pooled channel (Figure  3E ). The relative efficiency gains of the compressed screen were consistent when varying the number of effects being compared (Figure  S2C ). We also assessed the sensitivity of each approach by testing whether the significant effects determined from the validation set were recovered by the down-sampled conventional or cell-pooled screens. We constructed precision-recall curves, calling "true positives" the 79,100 significant effects from the validation dataset and varying the classification threshold by the significance of the effects in the down-sampled conventional or cell-pooled datasets. One cell-pooled channel had comparable AUPRC to 4 conventional channels (Figure  3F ), with consistent efficiency gains when varying the number of true positive effects (Figure  S2C ).
For the left plot, all effects with FDR q-value > 0. 2 are whited out, whereas this threshold is relaxed to 0. 5 for the middle and right plots. (E) (Left) Scatterplot of all significant effects (FDR q < 0. 05; N = 19,909) from the cell-pooled screen (X-axis) versus the same effects in the conventional screen (Y-axis). Effects are represented in terms of log-fold changes in expression relative to control cells. R, Pearson's correlation. SC, sign concordance. (Right) Held-out validation accuracy of down-sampled conventional screen and cell-pooled screen. X-axis: Size of down-sampled conventional screen by number of channels. Y-axis: Validation accuracy (in terms of correlation of effects with held-out validation dataset) of top 19,909 effects from the down-sampled conventional screen. Effects are estimated using FR-Perturb, elastic net regression, or negative binomial regression.
The same method is used to estimate effects in both the down-sampled conventional data and validation data. The results from the cell-pooled screen (dotted line) are shown for estimation with FR-Perturb only (see Figure  S2D  for results using other methods). (F) (Left) Precision-recall curves computed from down-sampled conventional screen and cell-pooled screen (dotted line). True positives are the 79,100 significant effects from the held-out validation dataset, while the classification threshold being varied (X-axis) is the significance of the effects in the down-sampled conventional screen. All effects displayed are learned using FR-Perturb. (Right) AUPRCs (Y-axis) computed from the down-sampled conventional experiment when varying the number of channels (X-axis). Effects are computed using all three inference methods.
For all gene pairs in which gene i exerts an effect on gene j (i. e. , has a significant knock-down effect in our Perturb-seq screen), we would expect that gene i and gene j are enriched for cis-by-trans eQTLs. (C) Using data from an eQTL study closely matching our cell type and treatment  26  , shown is the probability of observing significant cis-by-trans eQTLs among the top 15 perturbed genes from our knock-down screen and their affected downstream genes (red) compared to random downstream genes (grey). (D) Enrichment of significant cis-by-trans eQTLs among various sources of gene-gene pairs: significant KO/KD effects (representing significant gene-gene effects from our KO and KD screens, respectively), curated transcription factor (TF) and target gene pairs  61  , and the top 1,000/10,000 most co-expressed gene pairs (based on correlation of expression across samples) from the eQTL dataset.
Enrichment is computed relative to random trans genes for each cis gene, then averaged over all cis genes. (E) Selective constraint on trans genes from (D) plus all significant cis-by-trans eQTLs from the Fairfax dataset. Each point represents a cis gene, while the x-axis represents the proportion of the trans genes for each cis gene that are under selective constraint (determined as having a pLI > 0. 5).
Conversely, we did observe significant enrichment of cis-by-trans eQTLs in gene pairs coexpressed in the same eQTL study (Figure  6D ), as has been observed in other trans-eQTL studies  63. Notably, co-expression in eQTL datasets is dominated by environmental effects rather than genetic effects  67. Thus, given that the two effects are independent across samples, we would not ordinarily expect the most strongly co-expressed genes to be enriched for cis-by-trans eQTLs, suggesting that they may be confounded in part by unmodelled technical artifacts or inter-cellular heterogeneity (Supplementary Note). We also observed that the level of negative selection on the trans gene mirrored the patterns of cis-by-trans eQTL enrichment (or lack thereof) we observed in the previous analyses (Figure  6E ), suggesting that our power to detect cis-by-trans eQTLs was affected by selection-induced depletion of SNPs affecting the trans genes  54, 68  (Supplementary Note).
By integrating data from GWAS, our screens highlighted perturbed genes with downstream genes enriched for disease heritability. Many of these perturbed genes are under strong selective constraint and would require up to millions of samples to detect in GWAS  71. Thus, our analysis represents a potential way to circumvent the issue of negative selection removing GWAS signal from large-effect disease-relevant genes, a key challenge for biological interpretation of commonvariant GWAS. Gene-gene effects learned from our Perturb-seq screens were not enriched for cis-by-trans eQTLs in a closely matched cell type and treatment. Many possible explanations exist for this observation, including (1) insufficient power to detect trans-eQTLs in the eQTL dataset, (2) biological differences between our cell line and primary monocytes used in the eQTL study, (3) large differences in the magnitude of perturbation between experimental KO/KD and eQTLs, and (4) confounders in the eQTL dataset (Supplementary Note).
COMPUTATIONAL PROCEDURES Selecting genes to be perturbed A set of perturbed genes was compiled from several sources (Table  S1 ). These included: a manually curated list of 35 canonical LPS response genes; the top 100 genes from a previous genome-wide CRISPR screen for regulation of TNF expression after LPS stimulation  25  ; 100 genes identified as being a cis eQTL target of SNPs that were (in total) associated with trans eQTL effects for at least 4 downstream genes in primary monocytes treated with LPS 26 ; 95 genes near high confidence variants in IBD GWAS loci  73  ; 108 genes associated with Mendelian disorders identified by search for "bacterial infection" in the Online Mendelian Inheritance in Man (OMIM) database  74  and 115 Mendelian genes similarly identified by "NF-kappa-b" search; and 173 genes reported in studies identified by a GWAS Catalog 75 search for "infection" with diseases/traits related to liver disease and HIV-1 infection excluded.
Generating expression and perturbation design matrix Starting with raw Illumina BCL files from the sequencing output, the "cellranger mkfastq" command with default parameters (from the 10x CellRanger tool v6. 0. 1; https://support. 10xgenomics. com/single-cell-gene-expression/software/downloads/latest) was used to generate FASTQ files. The "cellranger count" command with default parameters was used to align the expression reads to the GRCh38 build of the human transcriptome and generate a gene expression count matrix (see below for details on normalization of expression counts). To generate the droplet by perturbation design matrix, paired-end reads (in FASTQ format) containing a droplet barcode and UMI on read 1 and sgRNA sequence on read 2 were aligned using Bowtie2 as follows. Read 2 reads were aligned to a reference constructed from the labeled sgRNA sequences using the --local option with default parameters, which performs local read alignment.
Covariates Covariates corresponding to the proportion of mitochondrial reads, the total read count per cell, and cell cycle state (as determined by the CellCycleScoring function from the Seurat R package 77 ) were accounted for when estimating effect sizes using FR-Perturb, by regressing the covariates out of the expression matrix according to the linear model 𝒀′ = 𝑪𝑫. Here, 𝒀′ represents the 𝑁 × 𝐺 normalized expression matrix (where 𝑁 is the number of cells and 𝐺 is the number of sequenced genes), 𝑪 represents the 𝑁 × (𝐶 + 1) covariate matrix including an intercept term (where 𝐶 represents number of covariates with all covariates centered to mean 0), and 𝑫 represents the fitted (𝐶 + 1) × 𝐺 matrix of covariate effects on gene expression. All downstream inference was performed on the residual matrix 𝒀.
Hyperparameters for FR-Perturb The spams R package  78  was used to perform the steps of factorize-recover, including sparse PCA and LASSO. Three hyperparameters are set in FR-Perturb: the rank 𝑅 of 𝒀′, a tuning parameter 𝜆 4 for sparse PCA during the factorize step (which is the solution of min.
Inference using negative binomial regression Using the glmGamPoi R package  79  , 𝑩 was inferred by separately running differential expression analysis for each perturbation (i. e. , column of 𝑿), where the two groups being compared were droplets containing only non-targeting control guides and droplets containing a guide for the perturbed gene of interest. For droplets containing multiple guides, other guides present in the droplet were ignored when forming these groups. Analytic p-values and false discovery q-values were obtained for all effect sizes from the method output.
For each 𝑖, 15 different version of 𝒙 𝒊 were generated, corresponding to all possible subsets of the 4 guides. For each version, any cell receiving a guide within the given subset of guides is labelled as containing a perturbation for the gene, while the remaining guides are ignored. Only 𝒙 𝒊 in 𝑿 was modified and the remaining columns were kept the same. Next, the dataset of interest was randomly split in half by samples (cells). FR-Perturb was used to infer effect sizes for all perturbed genes within each half. Then, the 𝑅 $ of 𝜷 X + was computed between the two halves (restricting to only effects with an FDR q-value < 0. 2), and the specific guide subset that produced that highest 𝑅 $ was retained. The same procedure was repeated for each 𝑖 to learn the optimal guide combination for each perturbed gene.
Simulations Perturb-seq datasets were simulated at various levels of overloading using real expression counts and perturbation effect sizes estimated from our data. Simulating cell-pooled data. To simulate expression data for 𝑛 droplets containing 𝑚 cells each, the expression of 𝑛 ⋅ 𝑚 cells (each containing 1 guide) were first simulated by randomly sampling control cells from our experiment, scaling their expression counts by the fold change effect sizes of a given perturbed gene (estimated from our conventional knock-out Perturb-seq screen). A 10% probability of receiving a control guide (i. e. , no change in expression) was simulated to match the proportion of control guides in the real data. Next, the expression counts of 𝑚 cells were randomly averaged at a time to create cell-pooled data.
Clustering and dimensionality reduction For Figure  5C , dimensionality reduction was performed using PCA on the log(TP10K + 1) expression counts of all cells, where the expression values of each gene are scaled and centered to mean 0 and variance 1. The rows and columns of Figure  5D  were clustered using Leiden clustering  80. First, the Euclidian distance between all pairs of genes was calculated by their perturbation effect sizes, and the FindNeighbors function from the Seurat R package  77  was used to compute a shared nearest neighbor graph from these distances (k=20), followed by the FindClusters function to perform Leiden clustering on the graph with resolution parameter = 0. 5, selected by visual inspection of the resulting clusters. GO enrichment analysis of the genes in the resulting clusters was performed with the ClusterProfiler package 81 with gene sets obtained from the C2 (curated gene sets) and C5 (ontology gene sets) collections of the MSigDB 82.
eQTL analyses Raw genetic data for 432 European individuals and gene expression data for primary monocytes from these individuals profiled 2 hours after treatment with LPS was obtained from Fairfax et al. 26. For each cis-trans gene pair, plink  88  was used to compute marginal association statistics of all SNPs within 1 megabase of the promoter of the cis gene with the expression of both the cis gene and trans gene. All our analyses were restricted to cis genes with at least one significant cis-eQTL (q < 0. 05) in the Fairfax dataset. Next, coloc  65  was applied to the association statistics to estimate the posterior probability (with the default prior) that the cis and trans gene have a shared eQTL within 1 megabase of the cis gene, setting a posterior probability threshold of 0. 75 to determine significant colocalization (varying this threshold does not change downstream results, Figure  S7D ).
Bivariate Haseman-Elston regression as implemented in the GCTA software tool 66 was also used to compute the genetic correlation between the expression of the cis gene and the trans gene when restricting to the region 1 megabase around the promoter of the cis gene. Again, the top 20 principal components of the gene expression matrix were included as covariates. The method outputs a genetic correlation estimate 𝑟̂ and standard error estimate 𝑆𝐸(𝑟) for each cis-trans gene pair. In order to obtain a combined genetic correlation estimate for all downstream genes of a given perturbed gene, all 𝑟̂ estimates were first squared and then combined using inverse variance weighing. The variance of 𝑟̂$ was estimated from 𝑆𝐸(𝑟) using the Delta method: 𝑉𝑎𝑟(𝑟̂$) ≈ 4𝑟̂$𝑉𝑎𝑟(𝑟).
Acknowledgements We thank  Atray Dixit  for early discussions on efficient screens and  Orit Rozenblatt-Rosen  for discussions and help. BC was supported by the  Broad Fellows program  and a  Merkin Institute Fellowship  at the  Broad Institute. DY was supported by the  NSF   Graduate Research Fellowship Program  (Grant # 1745303 ). AG was supported by  R01 HG012133  and  R01 HG006399. AR was supported by an  NHGRI Center of Excellence in Genome   Science grant  (CEGS;  RM1HG006193 ; AR), the  Howard Hughes Medical Institute , and the  Klarman Cell Observatory  and  Klarman Incubator  at the  Broad Institute. AR was a  Howard Hughes Medical Institute  Investigator when this study was initiated.
This is commonly done by modelling space use to evaluate how an animal occupies space and is influenced by environmental and biotic factors  [117, 164, 169]. Conventionally, two levels (or isopleths) of space use are reported, the core-or 50% area  [64] , which refers to the most frequently visited part of a range containing the features most important to the individual, and the home range or 95% area  [108] , which fits the traditional definition of home range. However, despite vast technological improvements in acoustic telemetry, no existing technique allows recording continuous longterm data without gaps or errors, and home range should be studied using methods that include these sources of uncertainty  [117]. Examples of this are location error and the variation in detection range of acoustic receivers (hereon "receivers"), gaps from irregular or spaced out sampling, and missing data resulting from technical errors like code collisions, loss of receivers, battery failure, among others  [115, 132, 154].