dataesr/scientific-paragraphs-categorization huggingface dataset:
This data suggested a nuclear cullin-RING E3 ligase was responsible for the observed degradation. Due to lack of signal in competitive chemoproteomics experiments, a FLAG pulldown approach was used to enrich proteins associated with KBO2-SLF compared to DMSO. These experiments identified DCAF16, an understudied nuclear E3 ligase. Control experiments with shRNA and CRISPR/Cas9 knockout confirmed a dependency on DCAF16 for KBO2-SLF activity. Although the exact cysteine modified by KBO2-SLF could not be identified, the utility of DCAF16 to degrade endogenous proteins was showcased with the design and testing of KBO2-JQ1. This PROTAC was capable of degrading BRD4 at modest mid-mM concentrations. The success of DCAFrecruiting PROTACs suggests the exciting possibility that recruitment of some E3 ligases may allow for cellular compartment-specific degradation.
The studies described above are exciting, however the expression of VHL, CRBN, and cIAP are rather broad, necessitating the discovery of ligands for E3 ligases that have more specific expression profiles. These E3 ligases could be systematically identified by searching all perspective E3 ligases (found here 103 ) in the Human Protein Atlas database. 108  The Human Protein Atlas combines RNA expression profiling with immunostaining to provide detailed tissue expression data for a majority of the human genome. It is a great platform for triaging perspective tissue specific E3 ligases. Another exciting possibility that may be realized within the next decade is the recruitment of E3 ligases with disease-specific expression (Fig. 5 ). PROTACs engaging these E3 ligases should have decreased cytotoxicity since the target protein would be spared in healthy cells.
117  Due to the collapse of the PEG linker, the VHL ligand and JQ1 of MZ1 were brought in close proximity upon binding BRD4. Therefore, the authors identified a vector for linker attachment that would enable the use of a shorter linker, which was hypothesized to better discriminate between BRD4, BRD2, and BRD3 due to decreased flexibility. The resulting molecule, AT1 retained reasonable binding to VHL and BRD4, but did not induce degradation of other BRD proteins like its parent PROTAC MZ1. Similarly, in the development ACBl1, the previously discussed SMARCA2/4 degrader, enhancing rigidity was used to improve potency. 28  Thanks to the structural data for PROTAC1, the authors were able to arrive at a potent PROTAC with just two iterative design modifications, the most important being replacement of the flexible PEG linker with a more ridged 1,4 substituted phenyl ring.
121  This suite will be a valuable tool to elucidate SAR trends during PROTAC development. The above model describes ternary complex formation at equilibrium. Although this will be useful for many systems, it may not accurately describe degradation kinetics for proteins with rapid turnover or in which deubiquitination occurs rapidly. Binding equilibria may not be reached as the pool of protein to be ubiquitinated is in constant flux. In an attempt to account for these more complex systems, a kinetic proofreading model for TPD molecules was recently developed. 122  The system was developed using the commercially available MATLAB Sim-Biology software and used previously published BTK degradation data to develop the model. This program predicts how changes in permeability of a PROTAC will affect its DC 50 and D Max.
However, to more fully expedite PROTAC discovery, programs are needed that will allow researchers to dock proposed degrader molecules and visualize the resultant ternary complex. These in silico modeling programs are already in development and have showed promising results. The two available models can be run using the Molecular Operating Environment (MOE) or Rosetta. [123] [124] [125]  Development of the MOE based software was a pioneering achievement of in silico docking of PROTAC ternary complexes. The program uses four different methods to predict ternary complex formation: (1) Sampling the entire ternary complex at once, (2) sampling PROTAC conformations independently, (3) one binary interaction is modeled, to which second protein's binding pose is modeled, and (4) PROTAC conformations are sampled independently of E3 ligase and POI, with E3-POI arrangements determined by independent protein-protein docking.
123  This program was able to accurately predict active PROTACs based on previously published BRD degraders. Recent improvements to the MOE program allow for a fifth prediction method. Clustering of ternary complex ensembles has been found to increase the accuracy of identifying viable ternary complex structures. 124  Additionally, two crystal structures of binary protein-ligand complexes can be used to predict optimal linker lengths to bridge the gap between the E3 and POI. The new Rosetta program, PRosettaC, uses similar strategies to dock PROTACs into ternary complexes. 125  Its protocol consists of sampling the distance between two anchor points on the E3RE or POI ligand. This can be based on already existing ternary complexes or one can use binary complex structures. Then, global protein-protein docking is used to predict the nascent E3 ligase-POI interactions.
Local docking, followed by conformational sampling of PROTAC linkers, and finally clustering of complex poses, allows for the identification of plausible ternary complex structures. Excitingly, this program was able to accurately predict the formation of the BTK-CRBN E3 ligase complex, which had yet to be predicted by in silico docking. 125  These data suggest that this program may be amenable to predicting ternary complexes of novel E3 ligase and POI ternary complexes. However, caution should be taken when interpreting data from either the MOE or PRosettaC programs, especially when considering new E3 ligase and POI ternary complexes. Crystallography artefacts, such as crystal packing interfaces, may unduly influence the PROTAC configurations within the top ensembles.
Considering the strides in ternary complex structure determination and in silico PROTAC docking, one can imagine a robust workflow for expediting PROTAC identification (Fig. 6 ). First, binary protein-ligand co-crystal structures can be determined or can be accessed via the Protein Data Bank. If these co-crystal structures do not exist, programs like AlphaFold can be used to predict protein structure and compounds can be subsequently docked. 126  Then, programs like PRosettaC, can be used to model possible linkers and predict novel PPIs. These predictions can also Fig. 6  Flowchart for expedited PROTAC discovery. Improved in silico modeling of ternary complexes can predict which PROTACs will form suitable ternary complexes to induce ubiquitination. The top predicted PROTACs can then be synthesized.
Acknowledgements We would like to thank  Dr John Hines  and  Dr Brian Linhares  for their careful reading and editing of this manuscript. C. M. C. is funded by the  NIH  ( R35CA197589 ) and is supported by an  American Cancer Research Professorship. M. J. B. acknowledges support from the  NIH  ( F31CA232477  and  5T32GM067543 ).
We show that when one imposes zero factors and requests the 1SIV estimator, the xtivdfreg command can replicate the results of the popular ivregress command. Essentially, the two-stage least-squares (2SLS) estimator of the two-way error-components panel-data model can be viewed as a special case of the proposed 2SIV approach in that the former does not defactor the instruments. Notably, unlike ivregress, xtivdfreg permits estimation of the two-way error-components panel-data model with heterogeneous slope coefficients. We illustrate the method with two examples. First, we use a panel dataset consisting of 300 U. S. financial institutions, each one observed over 56 time periods. We attempt to shed some light on the determinants of banks' capital adequacy ratios. The results are compared with those obtained by using popular panel methods, such as the fixed-effects and 2SLS estimators, as well as the CCE estimator of  Pesaran (2006).
In the second example, we use macrodata used by  Eberhardt and Teal (2010)  for the estimation of cross-country production functions in the manufacturing sector. The dataset is unbalanced, containing observations on 48 developing and developed countries during the period 1970 to 2002. The remainder of the article is organized as follows. Section 2, outlines the 2SIV approach developed by  Norkute et al. (2021)  and  Cui et al. (2020)  and discusses implementation with unbalanced panel data. Section 3 describes the syntax of the xtivdfreg command. Section 4 illustrates the command using real datasets. Section 5 concludes.
Unbalanced panels When the panel-data model is unbalanced, that is, some observations are missing at random, our procedure needs to be modified to control for the unobserved common factors. Following  Stock and Watson (1998)  and  Bai, Liao, and Yang (2015) , we may distinguish between X i and X * i. X * i is a T × K matrix containing the true values of the regressors, and it is defined as in (2). Let x * (k) i,t denote the (t, k)th entry of X * i , and ι (k) i,t denote a binary indicator that takes the value unity if the kth variable for individual i at time t is observed and zero otherwise. Thus, we set x (k) i,t = x * (k) i,t if ι (k) i,t = 1 and x (k) i,t is unobserved otherwise, k = 1,. , K. 10 Let f (0) x,t and γ (0) ki denote some initial values for the factors and factor loadings, respectively.
Example 2: Estimation of cross-country production functions To illustrate additional features of the xtivdfreg command for unbalanced panels, we use the macropanel dataset of  Eberhardt and Teal (2010)  for estimating cross-country production functions in the manufacturing sector. The dataset contains observations on 48 developing and developed countries during the period 1970 to 2002. These data are available as an ancillary file for the xtmg package, developed by  Eberhardt (2012) :. use http://www. stata-journal. com/software/sj12-1/st0246/manu_prod (Manufacturing productivity analysis ) Following  Eberhardt and Teal (2010) , we focus on the following model, which imposes constant returns to scale: ln Y it L it = β ln K it L it + u it ( 13 ) u it = η i + τ t + γ yi f y,t + ε it The dependent and independent variables denote the log value added per worker and the log capital stock per worker, respectively, for i = 1,.
C. Testbed Measurements The proposed scheme of Sections III-IV (with the simplified conflict graph construction in Section IV) was also tested on the FIT/CorteXlab testbed (http://www. cortexlab. fr/), which is composed of SISO or MIMO software defined radio (SDR) nodes  [37]. The testbed is hosted in a 180 m 2 shielded room, which is partly covered with electromagnetic wave absorbing  material, and its layout is shown in Fig. 22. User nodes are placed over a regular grid with an inter-node distance of 1. 8 meters, and allow any physical layer implementation on both hardware and software. A unified server is available for starting, coordinating, and collecting the results of the experiments. As a development tool, the GNU Radio software is employed for real-time experimentation. The physical layer of CorteXlab is described in Table  I  and is closely related to IEEE802.
RNA from uninfected and infected brain organoids was extracted using Direct-zol RNA MicroPrep (Zymo) and submitted to the JP Sulzberger Columbia Genome Center for library preparation and sequencing. Strand-specific RNA-Seq libraries were prepared using a poly-A enrichment and were sequenced on an Illumina NovaSeq instrument with paired-end 2 Â 100-bp reads. After quality and adapter trimming, transcript abundance quantification was performed using Kallisto v0. 44. 0 (71) with GRCh38 as the reference genome. To understand the brain organoid developmental stage, we used the BrainSpan data set (79 v10 annotations, we mapped R1 sequencing reads to GRCh37 annotation of the human genome using Bowtie2  (72)  and quantified gene-level RPKM levels using featureCounts  (73). Genes were filtered based on an arbitrary gene-level RPKM sum of greater than 1,000 across all 539 RNA-Seq experiments (524 BrainSpan, 15 MeV infections).
All-by-all correlation matrices of log 2 -transformed RPKM values with a pseudocount of 1 were generated in R v3. 6. 2. The top 100 BrainSpan samples with the highest correlation coefficients with uninfected FA10 brain organoid replicate 1 were pulled, and a heatmap of correlation coefficients was generated using pheatmap (https://github. com/raivokolde/pheatmap). Differential gene expression analysis was performed using the Kallisto transcript abundances and the R Bioconductor package DESeq2  (74). The code for analysis is available at http://www. github. com/ greninger-lab/MeV-brain-organoids. Low-count filtering was performed on genes with an average of less than one count per sample. Batch effects and biological differences between organoids were incorporated into the design formula as confounders, and normalization and differential expression analysis was performed using DESeq2 with default parameters.
The expression of the 50 genes with the lowest Benjamini-Hochberg adjusted P value between MeV IC323-EGFP-F L454W and uninfected organoids was plotted on a heatmap generated using the R package pheatmap, with bar graphs generated in the R package ggplot. Enrichment of differentially expressed genes (padj, ,0. 0001, absolute value log 2 fold change,. 1) between MeV IC323-EGFP-F L454W and those uninfected in KEGG pathways was evaluated using the R package ReactomePA  (75). To calculate the reads per million (RPM) values for MeV reads, each sample was aligned against the NC_001498 MeV reference sequence using Bowtie2 with default parameters  (72). MeV RPM values were calculated using the number of mapped reads in the resulting BAM file. RNAseq of mouse brain slices. RNA extracted from brain slices was prepared and sequenced as for organoids, as described above.
Reads were pseudoaligned with Kallisto v0. 44 to mouse reference transcriptome mmGRCm38. Differential expression analysis was performed in DESeq2, incorporating batch effects into the design formula. An expression heatmap was generated as for brain organoids, described above. MeV RPMs were calculated as described above. Brain organoid genome-specific RT-qPCR. Specific reverse transcription targeting the MeV genomic strand was performed with the SuperScript III first-strand synthesis system (Thermo Fisher), according to the manufacturer's instructions, on 500 ng of total RNA using reverse primer for human GAPDH and forward (FW) 59 tagged-MeV primer 59-gcagggcaatctcacaatcaggAAAACTGGTGTTCTACAACAA-39 containing MeV sequence of the antigenomic strand with a TAG sequence from Nipah virus. The obtained cDNAs were then diluted 1:10.
ACKNOWLEDGMENTS The work was supported by grants from the  NIH  ( AI121349 ,  NS091263 ,  NS105699 , and  AI146980 ) to M. P. , from  French ANR NITRODEP  ( ANR-13-PDOC-0010-01 ) to C. M. , and from  Region Auvergne Rhône-Alpes  and  LABEX ECOFECT  ( ANR-11-LABX-0048 ) of  Lyon University , within the program " Investissements d'Avenir"  ( ANR-11-IDEX-0007 ) operated by the  French National Research Agency (ANR)  to B. H. This research was funded in part through the  NIH/NCI Cancer Center Support  grant  P30CA013696. The research was conducted using the Genomics and  High Throughput Screening Shared Resource.
The THTM project aims to reconstruct the architectural and technical history, via industrial archaeology, on the one hand, and the demographic and social history of the city's main working-class district, on the other. Belfort indeed experienced very rapid industrialisation after the Franco-Prussian conflict of 1870, which makes it very interesting to study its population from a demographic point of view through the various sources of historical demography: as a boomtown, it does not seem to have experienced a deterioration in living standards during its phase of rapid urbanisation: see  Heyberger [2013]. Data provided in this project is composed of unlabelled-scanned images of French vital records, an example of which is displayed in figure  1. These data are hosted in the Belfort archives website available at the following https URL.
The French vital records include data that are of interest to historical research, here mainly: the date and place of birth of individuals (neighborhood and street, but also home or hospital), the identity and occupation of the two witnesses to the declaration of birth, the date and place of death of individuals, the occupation of the father and mother of individuals, their sex, and the date and place of marriage of individuals. The use of these data, which had already been started manually, has made it possible to produce some very interesting initial results regarding the longevity of the Belfortains born around 1900 as explained in  Haton [2020]. However, here as elsewhere, the development of more automated processing of sources would make it possible to break one of the most important barriers facing cliometry and demographic history.
The gathering of this data can be assisted by tools such as the European project Transkribus as shown by  Massot et al. [2019]  and  Schlagdenhauffen [2020]. The latter gives access to efficient HTR and OCR algorithms through an ergonomic user interface in order to perform the transcription process. This solution is, however, restricted in its charge-free version and use a closed-source HTR engine that can make it much less accessible. Other well-known software such as Tesseract offers efficient OCR algorithms but as explained by  Rakshit et al. [2010]  and  Rakshit and Basu [2010] , the engine might be less accurate on handwritten data. Finally, to effectively help humanities research, the need of proper text data extraction is unavoidable. In the current study, the gathering of precise sample information regarding the individuals in question is needed.
The model used is instead trained on data that shares common characteristics with the one to recognise. This solution has been tested on 18th-century handwritten documents in French and Italian and gives interesting results that are, however, insufficient for our scenario. Romero et al. [2011]  has proven that the use of hidden Markov models can be a solution to historical handwritten text recognition. The solution described by  de Sousa Neto et al. [2020]  is a new architecture for HTR that is giving very good results over many datasets including historical handwritten records. It has been tested with English and French handwritten documents and performs the best when the input data is at word level with a character error rate of around 2. 5% and a word error rate of around 8. 5%. This method then seems well adapted to our context since the data to be recognised, which will be provided to the network, are segmented by words.
Ground truth creation To train deep learning models, one needs to create a consequently large dataset that fits with production data characteristics. This task can be heavily time-consuming as a result of the required manual operations. For this reason, solutions to accelerate this process has been developed such as the one presented by  Fischer et al. [2010]. This method presents a tool for semi-automatic ground truth creation. It covers this process from segmentation to annotation with alignment to its corresponding segmented area. Nevertheless, this approach would not fit perfectly our context since it requires the use of external software during the process but also because the segmentation method cannot be modified to include the one chosen for our solution.
Text Segmentation Our text segmentation strategy is divided into two parts, one for the training data creation process, the second for the production pipeline process. The need for precision and robustness to deal with partially unstructured layout makes the use of machine learning algorithms unavoidable. A dataset, therefore, has to be produced from accurate production-based data. This step can be done through manual work, however, this task can be highly time-consuming when only a small workforce is available. The creation of dedicated tools aiming to help this task is a good trade-off that enables strong time savings and requires a lot fewer contributors to establish training sets. This might also be applied for the segmentation since text words bounding box annotation can be redundant and error-prone for the contributor.
Image preprocessing The input data of the process is raw scanned grayscale images containing scanner artefacts and frames. This initial condition makes the segmentation process harder due to background noise. Therefore, several preprocessing steps are applied whose goal is to enhance text while reducing at most non-text parts. To achieve this, the use of the background detection based on the process described in figure  4  allows to robustly finding the document outline on our dataset. Then, a gamma correction is performed to improve the general luminosity of the image. Gaussian Blur (1) Large horizontal dilation (2) Inverted threshold (3) Biggest contour extraction (4).
Its properties are particularly well suited to text segmentation since the contrast of the image can be variable. The latter would cause parts of the text to be wiped out by a standard thresholding strategy. This method mainly allows the erasure of paper-related data and noise, while mostly retaining textual information as shown in the binarization process in figure  5. The quality of the segmentation is, therefore, less linked to the complete image features but rather to local information which provides accurate and robust results throughout its use on our dataset. This method requires, however, to parameterise the size of the blocks that leads it to be less robust to strong image changes since it will be linked to input image properties. Finally, figure  6  shows the result of the full preprocessing pipeline and illustrate how the chosen adaptive binarization performs compared to the method introduced by  Otsu [1979].
Histogram projection based segmentation Once the preprocessing steps are applied to the image, the background removing allows to decrease a lot the analysis complexity since the text is the main remaining information. This enables the development of segmentation heuristics which is the basis of the dataset creation tools that aims to ease the manual work needed for machine learning algorithms. Indeed, in order to provide accurate enough tools and save users' time, the segmentation needs to only require small tweaks during manual inspections. Heuristics developed for this part are then strongly linked to the data since it will only be used in a preproduction process that will not involve the robustness of the final method. This can, however, be easily adapted for other use cases without major pipeline transformation.
This can be moreover quickly implemented thanks to the simplicity of the process. This is especially needed to easily and accurately build a dataset. Zohrevand et al. [2019]  validated this method as well with the support of deskew algorithms which are not needed in the current dataset since lines generally have horizontal levels. The text area's main shapes are then found based on white and black pixel distribution. This method remains, however, highly sensitive to changes in text areas breakdown. Consequently, we chose to guide the general process by computing image histogram projections only on specific image areas derived from the most common layout as we can see in figure  8. The selection of the less significant element provides finally a way to find paragraph borders. As shown in figure  8b , the side panels of each sample are segmented through the same strategy.