dataesr/scientific-paragraphs-categorization huggingface dataset:
Optimization The optimization target is the negative dataset ELBO L dataset ¼ X n sub i¼1 X n j¼1 L ij ð29Þ where L ij is the ELBO associated with a subject i and region j, defined by Eqs. 10 to 12. We minimize the cost function over the weights of the LSTM networks h 1 , h 2 , weights of the neural network f, means and variances of the subject-specific parameters and of the external input time series M 1 , M 2 , V 1 , and V 2 ; external input time scale τ, system; and observation noise variances σ 2 s and σ 2 o (in log scale), and forward projection parameters A and b. The method is implemented in Keras 2. 4 parameters of the method and of optimization procedure are given in table  S1. For optimization, we use the Adam algorithm (66) while relying on the reparameterization trick (67) for backpropagation through the random node of the variational autoencoder.
Additive Gaussian noise η is included in the equations, with SD β. To generate the synthetic dataset, we use the structural connectome matrices of the first eight subjects from the group described above. We simulate eight subjects, with increasing coupling coefficient G spaced linearly between 0 and 0. 7. The intrinsic frequency f i of all nodes is sampled randomly from uniform distribution on [0. 03, 0. 07] Hz. The bifurcation parameter a is sampled randomly from uniform distribution [-1, 1]. The initial conditions of the system for all subjects and both variables are chosen randomly from normal distribution N(0, 0. 3), and the system is then simulated for 205 s. The first 25 s are then discarded to avoid the influence of the initial conditions, leaving 180 s of data. The system is simulated with Euler method with time step Δ t = 0.
This was determined by performing 4-min-long simulations with subject-specific connectome and fixed regionally heterogeneous parameters, repeated for 31 values of G between 0. 17 and 0. 22 (where optimal value was expected to lie), and picking the value where the mean of FC from the last 2 min was the highest. With this value of G, the activity of each subject was simulated for 16. 4 min, first two of which were discarded to avoid the influence of the initial conditions. The Euler method with time step Δ t = 10 ms was used for the simulation. The resulting time series of S i were temporally averaged over windows of size 0. 72 s, leaving 1200 time points in every time series. The data are normalized to zero mean and variance equal to one (calculated across the whole dataset).
In addition, we have used neuronal density estimated from the BigBrain model  (27). The BigBrain model is a 3D-reconstructed dataset with a spatial resolution of 20 μm isotropic, i. e. , nearly cellular. The regional cell densities were estimated by randomly sampling at least 15 3D chunks per region from the cortex of the BigBrain model. For each region in the parcellation map, between 15 and 140 random coordinates were drawn in the ICBM 2009c nonlinear asymmetric space depending on the size of each region  (70). Each coordinate was transformed to the BigBrain histological space using a nonlinear transformation guided by sulcal constraints  (71)  and then shifted to the closest position around the cortical midsurface, as defined by the BigBrain layer segmentation  (72). A square cube extending across the full cortical depth was sampled at each mid-surface location, and layer-specific histograms of Big-Brain gray values were extracted from each cube.
The gray values are 8-bit integers in the range of 0…255, where dark values indicate the presence of cell bodies due to the silver staining that was applied to the BigBrain tissue sections. To convert gray values into actual density estimates, a collection of 120 BigBrain cortical image patches scanned at 1-μm resolution [e. g. ,  (73) ; full list of data references in data S1] with explicit layer-specific density estimates in numbers of segmented cell bodies (74) per 0. 1 mm 3 was used to compute an individual calibration function per layer. These calibration functions were applied to the mean gray value per layer in each sampled cube. From the resulting layerwise densities in each sampled cube, average cortical cell densities were determined by weighting each layer density with its relative volume.
The final cell density estimate for each parcellation region was determined as the average cortical density across all cubes sampled from it. Next, the RSFC principal gradient (28) was obtained from resting-state fMRI of large cohort of healthy adults by means of diffusion embedding, a nonlinear projection technique. It was proposed as a proxy measure for processing hierarchy. We use the numerical values provided by  Kong et al. (4). The T1w/T2w ratio is used as a measure of myelin content. We use the map in Desikan-Killiany parcellation (4) obtained from the HCP dataset of 1200 healthy adults  (29). Last, we consider two features derived from gene expression profiles (5) from the Allen Human Brain Atlas  (30) : First is the first principal component of 1926 brain-relevant genes. Second is the EI map, estimated from the expressions of genes coding for the excitatory AMPA and N-methyl-D-aspartate receptors and inhibitory GABA A receptors.
Acknowledgments: We wish to thank  J. Jha ,  A. N. Vattikonda , and  M. Woodman  for helpful discussions. Funding: This work was supported by a public grant overseen by the  French National Research Agency (ANR)  as part of the second " Investissements d'Avenir" program  (reference:  ANR-17-RHUS-0004 ), by the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement no. 945539  ( SGA3 )  Human Brain Project , and by the  Fenix infrastructure , funded under the EC's  H2020 R&I programme  through the  ICEI  project under the grant agreement no. 800858. Data were provided by the  HCP, WU-Minn Consortium  (principal investigators:  D. Van Essen  and  K. Ugurbil ;  1U54MH091657 ) funded by the 16  NIH Institutes  and Centers that support the  NIH Blueprint for Neuroscience Research  and by the  McDonnell Center for Systems Neuroscience at Washington University.
Subjects Data were obtained from 25 adult male mice (six L7-PKCI with seven control littermates; six L7-PP2B with six control littermates), aged from three to six months during the recordings. Mice were housed singly in a standard transparent polycarbonate cage (25x16x14cm; LxWxH) on a 12-hour light/dark cycle. All the recordings were performed during the light cycle. Animals were food-restricted during recording days (minimum 85% of normal body weight measured during ad libitum diet) to increase their foraging performance. The experimenters were blind in respect to animal genotype. All experiments were performed in accordance with the official European guidelines for the care and use of laboratory animals (86/609/EEC) and in accordance with the Policies of the French Committee of Ethics (Decrees n 87 848 and n In the L7-PKCI mouse line, the pseudosubstrate PKC inhibitor (PKCI) is selectively expressed in Purkinje cells under the control of the pcp-2 (L7) gene promoter.
A speaker generating 80-85 dB white noise sound and an infrared camera were centered above the arena on the ceiling. A pulley system was used for counterbalancing the cable weight. Electrical signals recorded on the tip of tetrodes were amplified by a unity-gain operational amplifier on two headstages (HS-18-LED for four-tetrodes microdrives, and HS-36-LED for eight-tetrodes micro-drives, Neuralynx Inc. ) above the animal head and transferred to a 64-channels data acquisition system (Digital Lynx Combo Board, Neuralynx Inc. ) using ultralight conner wires. The signal from each tetrode was differentially recorded against a tetrode with a low activity to detect low-noise cell activity. On the data acquisition system, electrical signals were band-pass filtered (600 Hz to 6 kHz). The filtered signal was passed through a preset amplitude-threshold to detect the extracellular action potentials and digitized at 32 kHz using a 24-bit analog-to-digital converter (Digital Lynx Combo Board and Cheetah software, Neuralynx Inc).
After each standard session, detected spikes were sorted into isolated clusters (representing individual neurons) using an automated spike sorting software (Snap, Neuralynx Inc. , USA). The firings of cells were analyzed to detect head-direction cell activity using a custom-made program in MATLAB. If any isolated clusters exhibited HD cell activity in at least one of the two sessions, the animal was conditioned to a full-protocol experiment; otherwise, tetrodes were advanced 15-60 m, and the animal was returned to the homecage. Tetrodes were lowered only once at each recording day. A full-protocol experiment was composed of a sequence of six consecutive 10-min foraging sessions. The six sessions involved two standard light sessions in the presence of a proximal cue card (sessions L1 -L2 ), a counterclockwise 90° cue rotation under standard light condition (session R3 ), another standard light session (session L4 ) followed by a dark session with cue removal (session D5 ) and a final standard light session (session L6 ).
Between every two sessions (except before the dark session), animals were removed from the arena and allowed to rest for 5 min on the remote resting pot. During the dark session, however, while the animal was kept in the environment from the previous light session, the cue card was removed, the environment lights were turned off, and the recording began. The light and dark sessions were respectively carried out under 8-10 lux and 0. 00 lux conditions (measured with a lux meter on the arena's surface). Data analysis was performed offline. The automatically sorted spikes (Snap, Neuralynx Inc. , USA) were refined manually using a graphical user interface software (Offline Sorter, Plexon Inc. ) except for speed cells, for which, due to abundance, analysis was performed on stable automatically-sorted clusters.
HD samples were converted to radian to bound within (0, 2𝜋). The covariate matrix was built by including all covariate elements according to the model order and the intensity functions. Model fitting was performed in MATLAB using the function glmfit under a Poisson distribution assumption with a log link function. The maximum order numbers were preset at 𝒩 ℋ =4, 𝒩 𝒮 =3, 𝒩 𝒜 =3 and all combinations of models with orders of zero up to these maximum orders were tested.
The value on zero lag was set to zero. The normalized distribution of autocorrelogram was generated by subtracting the mean value from all values divided by the sum of all values. AThe autocorrelation signal was tapered with a Hanning window to reduce spectral leakage. The FFT was scaled to the length of the autocorrelation signal. The power spectrum was generated by FFT in MATLAB. The power spectrum was set as the square magnitude of the FFT signal. Theta index was defined as the ratio of the average power spectrum in the theta frequency band (4-11 Hz) to the average spectrum in frequencies between 0 Hz to 125 Hz. Cells with theta index greater than 5 were considered theta modulated. Information (bits/spike): Analogous to information entropy, head-direction information was defined as  (Skaggs et al.
AHV and Speed cells As a first approach, the GLM framework was used to classify AHV and Speed cells along with HD cells. As a second approach to generate a Speed or AHV score, we modeled the firing rate of the cells with a single-component (speed or AHV) input using the maximum likelihood estimator  (Hinman et al. , 2016; Sharp et al. , 2001; Stackman and Taube, 1998). We assumed the firing rate of a speed cell follows either a linear or a saturating exponential function  (Hinman et al. , 2016) , and for an AHV cell, either a linear (symmetric AHV), parabola, or hyperbola (asymmetric AHV) function. The fitted functions were optimized using the mle function in MATLAB and custom-made code adopted from the codes available online  (Hinman et al. , 2016). F-statistic and p-values for each fit were calculated from log-likelihood against an intercept model.
To evaluate whether the speed or AHV component significantly accounts for some variability in observed spiking of the cell, we assessed whether the fitted model performs significantly better than a chance-level statistic. A spike shuffling procedure created the chance-level statistic described earlier. The selected model was then compared to models with the same orders fitted to the shuffled data, and the 99 th percentile of Pseudo-R 2 values of shuffled data was used as the criterion. Finally, we included an additional criterion to limit speed and AHV cell comparisons only to the structures where HD cells are recorded. For speed cells of the ADN group, due to abundance and sufficient sample size, we limited the speed cell samples to the clusters which were co-recorded with at least one cluster that was identified as HD cell in at least one of the first two standard light sessions.
Temporal coordination between HD cells by spike cross-correlogram A deterministic correlation for a cell pair (two simultaneously recorded cells) was generated from spike trains binned in 100ms or 200ms with lags up to 20s using xcorr in MATLAB  (Bassett et al. , 2018; Peyrache et al. , 2015). To create a comparable situation between different pairs and remove the effect of various firing rates, which can lead to variable peaks in the histogram, cross-correlograms were normalized by a chance-level cross-correlogram  (Bassett et al. , 2018). In this regard, the spike train of one of the cells in a pair was time-shifted by a variable randomly chosen between 20 s and the total trial length minus 20 s. The shifted spikes that were passing the original size of the trial were wrapped to the beginning.
ACKNOWLEDGMENTS This work was supported by the  Fondation pour la Recherche Médicale   DEQ20160334907 -France  and by the  National Agency for Research   ANR-17-CE37-0015-01-NaviGPS  (LRR),  CNRS ,  Inserm  and  Sorbonne Université. We thank all members of the  CEZAME team  for helpful discussions of the experiments and manuscript. We gratefully acknowledge the  IBPS animal facility  staff for their support.
Imaging and quantification for assessing dendritic morphology Images of ddaC, one of the class IV neurons, in A3-A5 segments were acquired in live wholemount larvae as described  (Hattori et al. , 2013; Parrish et al. , 2009; Matsubara et al. , 2011). Protocols for single-cell labeling (MARCM) were as previously described  (Shimono et al. , 2014). For quantification of the number of dendritic branching terminals, we drew an outline of the dendritic field as a region of interest (ROI) by connecting the outermost dendritic terminals with the Adobe Photoshop path tool. Then, dendritic branching terminals inside the ROI were semiautomatically counted by using DeTerm  (Kanaoka et al. , 2019). In addition, the area size of the ROI was measured as the dendritic coverage size. Some representative control images and control data are shared by multiple figures.
ACKNOWLEDGMENTS The reagents and genomic datasets were provided by the  Drosophila Genetic Resource Center at Kyoto Institute of Technology, National Institute of Genetics , the  Bloomington Stock Center, Vienna Drosophila Resource Center and FlyBase. We thank  T. Kondo  and  Y. Sando  for performing RNA-sequencing;  J. A. Hejna  for polishing the manuscript;  T. Kambe ,  R. Niwa ,  T. Jovanic ,  N. Yamanaka ,  S. Goulas  and other members of the  Uemura laboratory  for discussions and their technical assistance;  M. M. Rolls ,  A. Wodarz ,  T. Igaki ,  T. Ito ,  M. Nakamura ,  M. Yamazaki  and  M. Sato  for kindly providing reagents; and  Y. Xiang  for sharing unpublished results. This work was supported by  AMED-CREST  ( JP18gm1110001  to  T. Ue ),  Japan Society for the Promotion of Science  (JSPS;  15H02400  to  T.
Alternative splicing of repeats in proteins provides a mechanism for rewiring and fine-tuning protein interaction networks. In this work, we developed a robust and versatile method, ASPRING, to identify alternatively spliced protein repeats from gene annotations. ASPRING leverages evolutionary meaningful alternative splicing-aware hierarchical graphs to provide maps between protein repeats sequences and 3D structures. We re-think the definition of repeats by explicitly accounting for transcript diversity across several genes/species. Using a stringent sequence-based similarity criterion, we detected over 5,000 evolutionary conserved repeats by screening virtually all human protein-coding genes and their orthologs across a dozen species. Through a joint analysis of their sequences and structures, we extracted specificity-determining sequence signatures and assessed their implication in experimentally resolved and modelled protein interactions.
We recently proposed a solution for matching protein isoforms across species toward comprehensively describing ASinduced diversity and assessing its evolutionary conservation  (Zea et al. , 2021). To do so, we introduced evolutionary splicing graphs (ESGs) as a generalization of the notion of splicing graph  (Heber et al. , 2002)  to many genes/species. Each node in an ESG is a spliced-exon, or s-exon, defined as a set of aligned exonic sequences coming from several orthologous genes (Fig. 1 , top left panel). The s-exons are minimal building blocks for transcripts in evolution. In this work, we report on a robust and versatile method, ASPRING (Alternatively Spliced Pseudo Repeats IN-Gene), to identify protein repeats across a set of genes/species with evidence of evolutionary conserved alternative usage.
It identifies repeats based on sequence similarity and on the topology of the ESG. By applying ASPRING to the human protein coding fraction, we detected over 5,000 repeats defined across a dozen species spanning 800 million years of evolution. We extracted specificity-determining sequence signatures, providing novel insights into the mechanisms of protein-protein interactions. To further elucidate the functional and structural implications of these repeats, we mapped them onto experimental 3D structures of monomers and complexes available in the Protein Data Bank (PDB)  (Berman et al. , 2000) , as well as 3D models predicted by AlphaFold2  (Jumper et al. , 2021; Sommer et al. , 2022; Varadi et al. , 2022; Burke et al. , 2023). Overall, this comprehensive analysis demonstrates the importance of considering alternative splicing patterns in these regions.
By identifying specificitydetermining sequence signatures and investigating their 3D structure, this study sheds new light on the mechanisms of protein-protein interactions and paves the way for targeted design of repeat-mediated interactions. ASPRING is freely available at https://github. com/PhyloSofS-Tea m/aspring and accessible to all as a Python package and a Docker image. Curated data associated with the study are available at https ://doi. org/10. 6084/m9. figshare. 22722682.
And it defines evolutionary meaningful and AS-aware entities, the ASRUs, thereby going beyond repeat enumeration. ASPRING takes as input a Gene Ensembl ID and an evolutionary splicing graph (ESG) computed by ThorAxe  (Zea et al. , 2021)  for this gene and its one-to-one orthologs in a set of species. The ESG summarizes the transcript diversity observed in this set of orthologous genes (Fig. 1A , top left panel). Each node in the ESG, called a s-exon, is an alignment of translated exonic sequences coming from different genes/ species. An edge between two nodes indicates that the corresponding sexons co-occur and are consecutive in at least one observed transcript isoform. Hence, the s-exons can be viewed as the minimal building blocks of the isoforms in evolution. Starting from the input ESG, ASPRING extracts a set of ASRUs (Fig.
1A , top right panel). Each ASRU is defined by a collection of s-repeats, where an s-repeat can be a single sexon or an ordered list of several s-exons showing sequence similarity and evidence of alternative usage. ASPRING outputs several tables summarizing information about the ASRUs, the s-repeats, the similar pairs of s-exons and the raw alignments, and also annotated 3D models of the protein isoforms corresponding to the input transcripts (Fig. 1A , top right panel). ASPRING algorithm unfolds into four main steps (Fig. 1A , see also Supplementary Methods). It first performs an all-to-all comparison of the s-exons in the input ESG using profile HMMs with the HH-suite3 (Steinegger et al. , 2019) (Fig. 1A , step 1). Secondly, it identifies similar sexon pairs by considering four criteria, namely the significance of the pvalue associated with the profile HMM-HMM alignment, the sequence identity and the coverage shared between the s-exons, and their evolutionary conservation (Fig.
1A , step 2). By default, we require a high level of shared sequence identity (45%) to allow for contrasting amino acid (aa) divergence across isoforms versus species. All filtering criteria can be changed by the users depending on their system of interest. As this point, since our motivation is to assess the AS-induced modulation of the composition and number of protein repeats, we need to verify the existence of some alternative s-exon usage. For this, ASPRING explicitly accounts for the AS events encoded in the topology of the ESG. Following  (Zea et al. , 2021) , we define as AS event as a variation from a reference canonical transcript chosen for its high conservation and length (Fig. S1A ). Given a pair of s-exons, ASPRING considers four types of AS-mediated relationships, namely MEX, ALT, REL and UNREL, inferred from the role of each s-exon in the AS events (Fig.
The s-exon 4_9 is not part of the ASRU because, although it shares high similarity with 4_8, we cannot establish an AS relationship between them (Fig. S1B ). step, the ASPRING algorithm aims at extending these seeds with adjacent s-exons, according to the topology of the ESG, to obtain more homogeneous ASRUs in terms of length (in aa) of their s-repeats (Fig. 1A , step 4). We ensure that the s-exons used for extending the seeds comply with the same sequence similarity constraints as the seeds themselves. On top of the ASRU detection, ASPRING maps the s-exons and s-repeats onto the 3D models of the input isoforms available from the AlphaFold database  (Varadi et al. , 2022)  (Fig. 1A , top right panel). This functionality allows users to easily visualise the sequential and spatial localisation of the s-exons and s-repeats.
Application over the human coding fraction across a dozen species We ran ThorAxe  (Zea et al. , 2021)  on the ensemble of 18 228 human protein-coding genes and their one-to-one orthologs across 12 species, namely three primates (Homo sapiens, Gorilla gorilla, Macaca mulatta), two rodents (Rattus norvegicus, Mus musculus), four other mammals (Bos taurus, Sus scrofa, Ornithorhynchus anatinus, Monodelphis domestica), one amphibian (Xenopus tropicalis), one fish (Danio rerio), and one nematode (Caenorhabditis elegans). We downloaded the corresponding gene annotations from Ensembl release 105 (December 2021)  (Cunningham et al. , 2022). We used the default parameters except that we did not apply any filtering of the transcripts based on Transcript Support Level. On average, each human gene had one-to-one orthologs in 8 of the other species (Table  S2 ).
For the evolutionary analysis, we focused on the information contained in the alignments generated by the HH-suite. For the structural analysis, we predicted disorder using the protein Language Modelbased method SETH  (Ilzhoefer et al. , 2022). To characterize interactions, we looked at all the physiologically relevant experimental 3D complex structures involving the ASRU-containing proteins or their close homologs. We detected interfaces based on a distance criterion and we mapped them to the sequences of the s-repeats. Experimental PDB structures are often partial and may contain modifications, which makes the mapping sometimes difficult. To circumvent this issue, we used the full-length transcript 3D models available from the isoform. io database  (Sommer et al. , 2022). In addition, we explored a resource of 3D complex models predicted by AlphaFold2  (Burke et al.