dataesr/scientific-paragraphs-categorization huggingface dataset:
"interspecific" density dependence) by adding, an interaction term between n i and the number of all the females from other mosquito groups collected with the same trap ( m i ) (Table  1 ; Fig. 1c ). Model 4 was similar to Model 3, but we considered all the other K i taxonomic groups separately. Therefore, Model 4 included all the pairwise interaction terms between n i and the number of females of each kth mosquito group (s k i ) (Table  1 ; Fig. 1d ). Our analysis mainly focussed on three mosquito groups, but we collected a higher number of species hence K > 3 (Addi- tional file 1: Table  S1 ). The analysis was performed in the statistical environment R  [62] , with Bayesian model fitting to the data done using the program JAGS  [63]  interfaced within R via the package rjags  [64]. For parameters β 1 , β 2 and β k we used  A) Model 1 B) Model 2 C) Model 3 D) Model 4 N n N N N n n n 1 2 3 Fig.
In the specific field trials from which data were obtained here, density dependence between and within mosquito species influenced the performance of only one trap, the BGS, but not any others. An interactive Shiny App calibration tool was developed for this and similar applications. We conclude that this calibration approach provides a valuable framework for assessing human exposure from different outdoor trapping methods. As the performance of candidate traps relative to the HLC varied between mosquito taxa, there was no single optimum. While all the candidate traps underestimated HLC catches, and thus HBRs, the calibration tool created here enables a mathematical definition of the traps relationship as well as model-fitting limits. Further studies of trapping methods and associated evaluation criteria should focus on consistency and representativeness as opposed to simply finding traps that catch as many mosquitoes as HLC.
Genetic alteration of this activity is associated with extensive neurological defects and early lethality  17, 18. In addition, dysfunction of GPHN-mediated neurotransmission has been implicated in severe disorders, such as Alzheimer's disease, autism, schizophrenia, epilepsy, and also in hyperekplexia  [19] [20] [21] [22] [23]. The Gphn gene exhibits a complex intron-exon structure that consists of 29 exons, of which 9 are subjected to alternative splicing in tissue-specific manners  24. Although alternative exons inclusion in Gphn transcripts were proposed to change the properties and functions of GPHN protein , only 2 and 16 expressed sequence tags (EST) are currently annotated for mice and humans respectively. Most significantly, patients with temporal lobe epilepsy without any genetic mutation express four abnormal GPHN splice variants  25.
: Extensive splicing regulation of Gphn during brain development in mice. To explore the repertoire of Gphn transcripts during mouse brain development, we performed a targeted Gphn RNA analysis using Third Generation Sequencing technology developed by Pacific Bioscience (PACBIO). This approach provides high throughput long-read sequencing of full-length transcripts, and therefore the complete analysis of exon combinations incorporated in distinct splice variants. Gphn cDNAs were amplified from the mouse cortex and cerebellum at four postnatal stages (Fig. 1A ). Among the 114458 sequences, our custom-made bioinformatic pipeline retained 42033 high-quality circular consensus sequences (CCS; and Fig. S1A ). They were further grouped by clusters of identical sequences, each defining a Gphn alternative mRNA transcript (Fig.
S1B  and 1B ). In total, we identified 277 unique transcripts (Gphn-1 to Gphn-277, ordered by descending abundance; Fig. S1B  and Table  S1 ), demonstrating that Gphn is subjected to extensive alternative splicing regulation of 40 exons (Fig. S1C  and S1D ). Among the 9 splicing cassettes described in the literature, splice cassettes G1, C4b, and E1 remained undetectable in our dataset, suggesting that they are expressed outside the brain and the E2 cassette was only detected in two splice variants (Gphn-197 and Gphn-181) (Fig. S1D ). Our data further unveiled five entirely novel Gphn exons, as well as multiple exons annotated to alternative transcript(s) but, so far, without experimental validation. We also detected hundreds of new Gphn splice junctions, as well as several unannotated alternative 3' splice sites (ss) (Fig.
S1E to S1I ). Each exon is part of a myriad of different transcripts, emphasizing how difficult it is to detect a unique transcript by conventional method. in particular to amplify a specific PCR product and assess its expression level (Fig. S1C ). Using available public database, we built a library of 2. 9 billion short reads sequenced to analyze the global gene expression, and found that a significant part of the exon-exon junctions identified in this study were not detected previously, unveiling the importance of targeted long read sequencing to capture the complexity of Gphn expression (Fig. 1C  and Table  S2 ). Our data completely reframes our understanding of Gphn expression. In contrast to the current view that counts 20 Gphn exons as constitutive we found that all internal exons were alternatively spliced.
To the best of our knowledge, Gphn is the first gene (>10 exons) for which all internal exons are subjected to alternative splicing regulation (SM1 and Fig. S1B  and S1C ). Next, we analyzed the expression of specific transcripts in space and time comparing SMRT sequencing in cerebellum and cortex at the four post-birth developmental stages (Fig. 2D ). The most abundant transcript Gphn-1 was stably expressed in both tissues (less than 15% variation between P6 to P39). In contrast, many other abundant transcripts (Gphn-2 to -60) displayed distinct expression profiles across developmental stages, and several of them got different regulation in cortical and cerebellar cortices. By limiting RT-PCR assays only to transcripts containing unannotated alternative 3'ss, we validated their expression including those detected with only one CCS (Gphn-192, -203 and -244, Fig.
1E, S1E-I). Several variants displayed a differential expression in brain versus muscle or heart, supporting a fine-tuning tissue-specific regulation of Gphn transcripts diversity. To further explore this possibility, we used publicly available RNA-seq data to expand our analysis over eight different cell types of the central nervous system, including neurons and astrocytes (Fig. 1F , Table  S3  and 26  ). We detected several of the unannotated Gphn exons identified in our PACBIO sequencing (E9, E16, E22), and acquired evidence for cell-type specific regulation of Gphn exons through alternative splicing in the brain. Altogether, our data redefines the Gphn locus with forty, rather than thirty exons as described previously (Fig. S1D ) and reveals a previously unforeseen number of Gphn transcripts regulated during brain development.
Gphn splicing remodels functional domains of GPHN through a complex isoform proteome. Whether the Gphn transcriptome is translated to protein isoforms or rather retained in the nucleus as "transcriptional noise" remains a critical question 27. To tackle this question, we first used purified polysomes from the mouse brain, we performed RT-PCR analysis and confirmed that novel Gphn transcripts are associated with the translation machinery, independently on their expression levels (Fig. S2A ). Second, we generated the theoretical proteome of GPHN in silico by tracking open reading frames (ORFs) spanning at least three hundred nucleotides (Fig. 2A  and Table  S4 ) and filtered Translation Initiation Starts (TISs) with ribosome profiling data as biological evidence of their activation 28. Our approach retained 9 out of 31 potential TISs and 253 out of 402 ORFs.
4E ). We also showed that the density of inhibitory synapses in dendrites was influenced by the expression of specific GPHN isoforms in vitro (Fig. 4F ), suggesting that a change in the level of a specific GPHN isoform could modulate the density of synapses. To explore how change in the level of GPHN isoforms affect synapse formation in vivo, we injected lentiviral constructs in the mouse brain, and quantified the number of inhibitory synapses in cerebellum (Fig. 5A  and 5B ). Our experimental conditions led to an increase in the expression of GPHN isoform 12 days post-injection, mimicking therefore an up regulation of a specific splice variant in the pool of all alternative Gphn transcripts expressed endogenously. Exogenous expression of GPHN-1, GPHN-6, GPHN-10, GPHN-32 and GPHN-49 led to a similar synapses density than the control Scarlett (Fig.
5C ). In contrast, GPHN-14, GPHN-28 and GPHN-42 exhibited a decreased number of synapses, while GPHN-8 enhanced the density of synapses, unveiling negative and positive dominant activities of several isoforms. These data demonstrated how the balance of Gphn alternative transcripts can direct the inhibitory synapse formation and/or maintenance in vivo. In addition, the remodeling of functional domains by alternative splicing is a mechanism to enrich selectively distinct GPHN isoforms to GABA A circuitry and control their clustering properties. These results sustain our initial observation about the heterogeneity of endogenous GPHN epitopes and further document that distinct types of inhibitory synapses are assembled with an array of different GPHN isoforms. To further test that specific GPHN isoforms are heterogeneously distributed, and are not present at all synapses of an individual cell.
7F  and Table  S6 ). Overall, our data revealed a much more diverse and regulated expression of human GPHN than the 16 alternative transcripts currently annotated in databases. Among the GPHN transcriptome, we sought transcripts containing exon-exon junctions mimicking the genetic variations of GPHN previously characterized in patients with neurological disorders (Fig. 8A  and 8B ). Surprisingly, almost all pathogenic exon-exon junctions were detected in healthy human brain samples and we noticed that they were all included in transcripts expressed at low level, from 0. 02 to 2. 7% of the total GPHN expression (Fig. 8C ). Although many of these exon-exon junctions have been classified as irregular splicing events uncovered in patients, our work demonstrates that they are actually canonic splicing events expressed in healthy individuals.
PacBio, ONT and short reads analysis: PacBio analysis: PacBio data were processed using informatic and manual approaches because the Isoseq 3. 1. 1software provided by PacBio did not provide homogeneous clusters of identical Circular Consensus Sequences (CCS). CCS were obtained from the Cold Spring Harbor sequencing platform and aligned to the Mouse genome GRCm38. p6 using STARlong 2. 5. 3a 59. Genomic intervals showing alignments with the CCS (corresponding to potential exons) were extracted using bedtools 2. 27. 1merge 60 from merged bam files generated with samtools 1. 6 61. Each potential exon was extracted using bedtools 2. 27. 1 getfasta and checked by eyes to keep only those framed with canonical GT-AG dinucleotides at their splice sites. Retained exons were then aligned to CCS with blastn-short algorithm of blastn 2.
2. 31 62 (using an evalue threshold of 0. 001 and a penalty for nucleotide mismatch of -2) to annotate each CSS with a binary code 1 and 0 for the presence or absence of distinct exon. Theoretical transcripts were constructed by concatenating the exon sequences and according to their annotation, CCS were next aligned against each theoretical transcript sequence (using a gapped global alignment, similar to the Needleman-Wunsch algorithm with Exonerate 2. 4. 0) 28 to provide an alignment score used as quality control. Similar sequences were clustered together and poorly aligned CCS were removed. Homogeneity of each cluster was checked visually using Snapgene software. Number of CCS present in each cluster was quantified in order to establish an overall assessment of alternative Gphn transcript expression.
R 3. 3. 2 and ggplot were used to produce the overall isoform composition plot displayed Fig. 1A. Analysis of Gphn expression in brain cell types: Gphn exons expressed in distinct neuronal cell types were analyzed using Illumina data from 26 corresponding to total gene expression. An artificial sequence containing all Gphn exon junctions was used to align short reads using Hisat 2. 1. 0 (with the -no-spliced-alignment option). Using FeatureCounts 1. 6. 4 63 , the aligned reads were quantified to assess the expression of each exon. To compare exon expression between cell subtypes, we normalized their quantification by the overall total Gphn expression in each cell type. Analysis to validate exon-exon junction of Gphn expression in mouse brain: Gphn exon junctions detected using short reads reported in 82 publicly available data sets (listed in Table  S1 ).
ONT analysis: Raw data were processed for base calling using GUPPY and long read sequences were analysed with a homemade pipeline. Demultiplexing of samples was processed similarly to the PacBio analysis and 149K up to 397K raw sequences were obtained per human tissue sample. Using LAST version 1205 64 , the sequences were aligned on Human Genome GRCh38 and the corresponding BAM files were reviewed manually to annotate all exons. Exons were used to produce a universal reference and all reads were aligned on this particular reference with LAST. Using standard genomic tools, samtools 59 , bedtools 65 , a specific transcript barcode was generated to each sequence depending on its exon architecture, then pooled in clusters defining each alternative GPHN transcripts. Given the library preparation, we only retained the sequence containing at least the first and last exons in their barcode.
DNA constructs: The cDNAs corresponding to selected Gphn alternative transcripts were built using QuikChange II Site-Directed Mutagenesis Kit and cloned in a modified version of pLVX-puro vector for which CMV promoter was replaced by human Syn promoter. Each cDNA is cloned in 5' fusion with Scarlet and Flag-V5 tags. Lentiviral particles were produced using HEK-293 cells co-transfected with pLVX-FV5-Scarlet-GPHNs, p8. 7 and pCMV-VSV-G, then concentrated by gradient centrifugation. All constructs used in this study are listed in Fig. S4B  and available on request. Expression of exogenous proteins were probed by western blot using anti-V5 (Invitrogen).
Data Analysis MS data were analyzed with PEAKS Studio X+ (Bioinformatics Solutions Inc. , Canada). Raw data refinement was performed with the following settings: Merge Options: no merge, Precursor Options: corrected, Charge Options: 1-6, Filter Options: no filter, Process: true, Default: true, Associate Chimera: yes. De novo sequencing and database searching were performed with a Parent Mass Error Tolerance of 10 ppm. Fragment Mass Error Tolerance was set to 0. 02 Da, and Enzyme was set to none. The following variable modifications have been used: Oxidation (M), pyro-Glu from Q (N-term Q), phosphorylation (STY), acetylation (protein N-terminal) and carbamidomethylation (C). A maximum of 3 variable PTMs were allowed per peptide. Data were searched against a fasta database concatenated from UniProt_mouse (UP000000589, reference proteome, 55408 proteins, 5-Nov-2019, all variants), predicted (XP_) and validated (NP_) gephyrin sequences from NCBI and a custom ORF database constructed as described above.
Tissues were then glued using surgical skin glue (Vetbond). Mice were given buprenorphine (Vetergesic) for analgesia. Animals were kept Hippocampus neuron cultures were repeated at least three times, and infected neurons were analyzed. For each neuron, proximal dendrites (50-100µm) were used as ROI. Analyses were made with Fiji (ImageJ) software. Images were thresholded using MaxEntropy Auto-Threshold command. GPHN clusters or GAD-65 puncta were annotated and counted. Measure Particle function was used to measure GPHN cluster area. For immunofluorescence analyses at least three mice were used and at least three slices per mice were imaged. For every image, ROI of identical size was selected within the region of interest. For cerebellar slices, images were taken so as to contain all three layers.
Statistical analysis All statistical analyses were performed using GraphPad Prism 8 (GraphPad Softwares). Normality was assessed with the Shapiro-Wilk normality test. Homoscedasticity was assessed with the Barlett's test. Parametric data with the same standard deviation were analyzed by t-test, one-way ANOVA, or two-way ANOVA followed by comparison of multiple samples with Tukey post hoc analysis. Parametric data with significantly different standard deviation were analyzed by Welch's ANOVA followed by comparison of multiple samples with Dunett's T3 post hoc analysis. Non-parametric data were analyzed by the Kruskal-Wallis one-way analysis of variance on ranks followed by comparison of multiple samples with Dunn post hoc analysis. P values <0. 05 were considered statistically significant. *p<0.
Splicing regulation of Gphn expression in mouse brain. (A) Procedure developed to analyze Gphn expression by long read sequencing. Gphn transcriptomes expressed at each developmental stage were prepared separately and combined in a multiplexed library sequenced with the PacBio technology. Number of retained Circular Consensus Sequences (CCS) and unique sequences are indicated at the bottom of the draw. (B) Distribution and percentages of alternative transcripts expressed by Gphn in Mouse brain. (C) Graph displaying the Gphn exon-exon junctions (EEJ) found in this study (grey), in 2,872. 10 9 short read sequences obtained by global gene expression analysis (red), in the Ensembl database (yellow). (D) Heat map graphical representation of the 60 most expressed Gphn transcripts at P6, P9, P15 and P39 in cortex and cerebellum.
Each sub-system is respectively composed of components (e. g. , electrical motor, bearings, clamping unit). The instantiation of the proposed RPL concept is realized for the GROB BZ560 machine tool (RENAULT case) and focuses only on the Electro-spindle subsystem (critical subsystem) Fig. 3. This specific machine tool is able to manufacture two types of cylinder-blocks (i. e. , 4626 and 4369) and uses respectively, for each type, 15 and 17 different cutting tools realizing milling, boring, drilling and tapping operations. For each profile mission there is a set of cutting tools that performs the operations on the product (cylinderblocks). Each product is associated with a set of quality characteristics (e. g. , surface location, diameter) with corresponding tolerances. Also, trajectory followed, and the cuts made that are materialized in the machine program depends on the type of cylinder-block to be machined.
PyLlama is a handy Python toolkit to compute the electromagnetic reflection and transmission properties of arbitrary multilayered linear media, including the case of anisotropy. Relying on a 4 × 4-matrix formalism, PyLlama implements not only the transfer matrix method, that is the most popular choice in existing codes, but also the scattering matrix method, which is numerically stable in all situations (e. g. , thick, highly birefringent cholesteric structures at grazing incident angles). PyLlama is also designed to suit the practical needs by allowing the user to create, edit and assemble layers or multilayered domains with great ease. In this article, we present the electromagnetic theory underlying the transfer matrix and scattering matrix methods and outline the architecture and main features of PyLlama.
4 × 4 matrix formalisms experienced many developments over the years  [31, 32, 33, 34, 35, 36, 37, 38] , including faster algorithms  [39]  and a correct treatment of degeneracies causing singularities  [40, 41]. Besides commercial solutions, such as the powerful program WVASE  [42]  dedicated to ellipsometry analysis  [43] , freely-available and open-access codes  [44, 45]  generally exploit the elegant transfer matrix method  [46]  to propagate the solution from layer to layer, as originally proposed by Berreman  [30]. Unfortunately, such an approach can become numerically unstable for large systems in presence of evanescent modes due to the coexistence of exponentially decaying and growing waves (in the forward propagating direction) in the multilayer. This shortcoming was addressed by Ko and Sambles  [33] , who suggested to use a scattering matrix to treat all evanescent modes as forward or backward decaying waves.
Scattering matrix formalisms have gained popularity in the framework of the rigorous coupled wave analysis (RCWA) for periodically-corrugated multilayered media  [47, 48, 49, 50, 51]  to avoid numerical instabilities. Some freely-available RCWA codes  [52, 53, 54]  can be used for uncorrugated multilayered anisotropic media, yet with an unnecessary complexity. The scattering matrix method has been implemented recently for the study of cholesterics but at normal incidence only  [55]. Here, we present a freely-available and open-source Python toolkit named "PyLlama" 1  that implements both the transfer matrix and the scattering matrix methods to compute the reflection and transmission properties of arbitrary (linear and uncorrugated) multilayered media in all possible situations. While the transfer matrix method proves being faster, the scattering matrix method ensures numerical stability in all cases.
PyLlama can thus deal with systems composed of an arbitrary number of layers of any thicknesses, mixing isotropic and anisotropic, absorbing and non-absorbing materials, studied at all possible incident angles and wavelengths for both linear and circular polarizations. Surface waves may be excited by modelling the Otto-Kretschmann configurations  [56, 41]. In addition to robustness, we designed the code to be convenient to use and included tools that are particularly suitable for the modelling of cholesteric liquid crystals. First, layers are treated as building blocks. The optical calculations occur internally at the layer level and the user can write their own routines, in form of new classes, to pile up layers into stacks according the parameters of their choice without having to handle optical parameters.
Second, the code is organised in modules. Periodic structures can be easily combined into master structures with sub-periodicities, and different pre-defined structures can be stacked onto each other, in order to enable the modelling of complex situations, such as beetle cuticles made of a combination of cholesterics and absorbing layers  [12, 57]  or stacks of cellulose nanocrystal cholesteric structures separated by a nematic layer  [58]. Third, once the user has defined which structures they need to model, a level of automation has been added to allow for the calculation of spectra and the export in MATLAB and/or Pickles with only a few lines of code. Lastly, more specific to cholesterics, we incorporated a physical model  [10]  that describes the cholesteric helicoid in various situations (choice of handedness, tilt, vertical compression of tilted helicoids).
4. 1) and in MATLAB (version R2019b). 2. Factorizing ∆ = PQ ∆ P -1 where Q ∆ is a diagonal matrix containing the eigenvalues and P the eigenvectors of ∆. This leads to an eigendecomposition of R as R = PQP -1 , with identical eigenvectors and eigenvalues straightforwardly related those of ∆, see below. This was also suggested by Berreman  [30]  and is implemented in Passler and Paarmann's code  [41, 45]. 3. Using an algorithm based on Sylvester's formulas applied to biaxial crystals, as proposed by Palto et al. [39] , which is expected to converge faster and requires less calculation steps than Padé's decomposition. In the second approach, which we will follow here, the fields in the layer are decomposed into four partial waves  [61]. The eigendecomposition has a clear physical meaning, as sketched in Fig.