dataesr/scientific-paragraphs-categorization huggingface dataset:
Polymorphic STR markers (D22S272, D22SAFM205YC11, D22S1157, D22S417, D22S418, primers are available upon request) were used to genotype all available family members to identify and map the linked chromosomal region. STR markers were PCR amplified and alleles were separated in an ABI 3130xl Genetic Analyzer (Applied Biosystems, Foster City, CA, USA) according to the manufacturer's instructions. Results were analyzed using Peak Scanner Software, version 1. 0 (Thermo Scientific, Waltham, MA USA). Genetic analysis. Direct sequencing of ASPM and linkage analysis of WDR62 and CENPJ, using microsatellite markers flanking each gene, was used to exclude the most common MCPH genes in the Pakistani population. Following exclusion of the most commonly involved genes, linkage analysis was performed using FAST-LINK software.
WES was performed on genomic DNA extracted from an affected individual. Capture was performed using the Nextera Rapid Capture Exome Enrichment Kit and sequencing was done using a paired-end 2 × 100 bp protocol using an Illumina Hi-seq 2000 system (Illumina, San Diego, CA, USA). Data were filtered and aligned to the Human Genome reference (hg38) using Burrows Wheel Alignment. Variants were analyzed using the Genome Analysis Toolkit. Variants with minor allele frequency of <0. 01 were filtered against known databases of genomic variants (dbSNP138, ExAC). Of the remaining variants, only rare variants affecting protein structure and predicted deleterious by at least one of the prediction algorithms SIFT and Polyphen2 were considered. The RRP7A mutation c. 465 G > C was confirmed by direct Sanger sequencing of a PCR-amplified exonfragment in all available samples from patients and unaffected heterozygous parents.
The qRT-PCR reactions were performed in triplicate and were analyzed on an ABI 7500 fast system. The data was normalized against the average expression value of two housekeeping genes (GAPDH and B2M, primer sequences available upon request). Human brain tissue samples and immunohistochemistry. Eight brains from 7 weeks post conception (wpc) to 21 wpc were analyzed. Nineteen wpc midgestation foetuses were chosen for the entire immunohistochemical analysis as material from all brain regions was available and all cell types present at this stage. The material was obtained from legal abortions. Written and oral informed consent was given according to the Helsinki declaration II, approved by the Research Ethics Committee of the Capital Region (KF-V. 100. 1735/90). Immediately following operation, the samples were dissected into blocks and fixed for 12-24 h at 4°C in either 10% neutral buffered formalin, 4% Formol-Calcium, Lillie's or Bouin's fixatives.
In brief, tagged forward primer (5′-AGCTGACCGGCAGCAAAATTGCCCGTG GATGGGTGGATG-3′) was used for amplification of the DNA fragment covering the sgRNA-binding sites, and analyzed using the ABI 3010 sequenator (ABI/Life Technologies, USA) under conditions specified by the manufacturer. Raw data was analyzed using the Peak Scanner Software V1. 0 (ABI/Life Technologies, USA). Clones were sequenced by Sanger sequencing using forward primer 5′-CCCGTG GATGGGTGGATG-3′ and reverse 5′-CACCAGGATTCTGTAGGGC-3′ and named according to the total number of nucleotides deleted. Two guides were used to generate clone P19CL6 Rrp7aΔ8/Δ33 : Sg1 5′-AGACAGCACCGAGTTCGCCAA GG-3′ and Sg2 5′-CCCGATCGAACCCTTTTTATCCT-3′. Only Sg2 were used for clone P19CL6 Rrp7aΔ1/Δ18. Two wild-type clones (P19CL6 Rrp7a WT#1 and P19CL6 Rrp7a WT#2 ) were chosen as control cell lines, and their ability to differentiate was confirmed upon 9 days of RA treatment, in which both cell lines readily form elaborate networks of βIII-tubulin positive neurons (Supplementary Fig.
hTERT RPE-1 (RPE-1; ATCC, CRL-4000) cells were cultured in DMEM medium supplemented with 10% FBS and 5% pen/strep. HEK293T cells (ATCC, CRL-3216) were cultured in DMEM containing high L-glucose concentration (GIBCO ® ) supplemented with 10% FBS and 1% pen/strep. Culture medium was changed every 2-3 days for both RPE-1 and HEK293T cells. Constructs and transfection. Using first strand cDNA obtained from a healthy donor as templates, the full-length coding sequence of RRP7A (GenBank accession number NM_015703. 4) was PCR amplified and cloned into the mammalian expression vector pEGFP-n1 using EcoRI and BamHI restriction sites. Two fulllength wild-type expression constructs for RRP7A were generated; one containing a green fluorescent protein (GFP)-tag at the C-terminus and another containing a FLAG-tag at the N-terminus and a C-terminal GFP-tag, using pEGFP plasmid (Addgene).
0 and Adobe Photoshop CS6 version 13. 0. Quantifications of immunofluorescence intensities and cell confluency were performed in Cell Sense (Olympus). Careful outline of the cilium-centrosome axis as well as nucleoli were drawn using the outline tool, and mean intensities were obtained. ARL13B and Acetylated α-tubulin (Ac-tub) were used as ciliary markers to study the formation, length and disassembly of primary cilia. All data were gathered for n ≥ 3.
F3 embryos were raised to adulthood and outcrossed to AB/TL wild-type strain. All analyses were performed on F5-F7 larvae, generated by crosses of heterozygous adult fish. Morphological studies of zebrafish larvae. Survival of the progeny from rrp7a +/-adults was monitored and single larvae were genotyped subsequently. For measurements, lateral and dorsal images of 3 days post fertilization (dpf) zebrafish larvae were taken under Zeiss AxioZoom V16 (Carl Zeiss, Brock Michelsen A/S, Denmark) upon positioning in 3% methylcellulose. Trunk length, eye diameter, and head size (dorsal, lateral) were measured using ImageJ software  74. The measured absolute values were normalized to the body length of the larvae, which refers to the distance between the tip of the snout and the end of the spine. Head area was defined as outlined (Fig.
Acknowledgments We thank  Maria S. Holm ,  Lillian E. Rasmussen ,  Pernille S. Froh ,  Troels Askhøj Andersen , and  Martin Juel Vilhelm  for excellent technical assistance. This study was supported by grants from the  Lundbeck Foundation  ( R221-2016-922 ), the  University of Copenhagen Excellence Program for Interdisciplinary Research , the  Novo Nordisk Foundation  ( NNF18OC0053024 ,  NNF14OC0011535 , and  NNF15OC0016886 ),  Independent Research Fund Denmark  ( DFF-6108-00457  and  8020-00162B ), the  Danish Cancer Society  ( R146-A9590 ) and the  Carlsberg Foundation  ( CF15-0425 ,  CF18-0532 , and  CF-0294 ) to S. T. C. , L. A. L. , and L. B. P. ; grants from the  Lundbeck Foundation  ( R198-2015-174 ) and the  Danish Cancer Society  ( R167-A10943-17-S2 ) to H. N. and N. K. ; a grant from the  Independent Research Fund Denmark  ( DNRF107 ) to L.
Data availability All relevant data supporting the key findings of this study are available within the article and its Supplementary Information files or from the corresponding author upon reasonable request. Source data, including full scan images of blotting data used in this study, are shown in Source Data file, which is provided with this paper. Exome sequencing data from the single individual, which was analyzed in the family, is not available owing to concerns over patient privacy. Sequence data from the following GenBank accession numbers were used to generate expression constructs: NM_001017579. 1 and NM_015703. 4. Source data are provided with this paper.
Recently  (14) , using MD simulations, investigators characterized and compared the dynamics of VCBC and VCBC-Cul5 complexes. They demonstrated the global dynamics of the VCBC complex but found the VCBC-Cul5 complex less flexible. The authors also revealed the efficient binding of VCBC with single-ssDNA and found out that ssDNA stimulates the formation of VCBC dimers. Here, we applied time-lapse high-speed atomic force microscopy (HS-AFM), which has been shown to be successful for studies of the dynamics of proteins and protein-DNA complexes  (19) (20) (21) (22). Our data show that VCBC in complex with DNA is very dynamic and sampled different conformational states with the most favorable ones, including globular, dumbbell, and triangle structures. Based on the data obtained for VCBC in complex with DNA, we identified free VCBC in the same experiments, just dissociated from the VCBC-DNA complex, and followed its dynamics.
Analysis of the HS-AFM data Movies were assembled after frame-by frame recording of VCBC in the VCBC-DNA complex and free VCBC. Analysis of the observed structures was performed by measuring the yield of different structures and their structural characteristics. For globular structures, the volume of the VCBC in the VCBC-DNA complex and free VCBC were measured as described in (31), using the cross section feature from Femtoscan online software (Advance Technologies Center, Moscow, RU). For dumbbell structures, the same cross section feature was used to measure the distance between two clear blobs in the structure  (21). For triangle structures the feature for angle measurement was used from Femtoscan software. On average, for each structure ~10 movies were analyzed, and the data were assembled into histograms and fitted with Gaussian curves.
For the volume fraction of second phases several backscattered electrons SEM images from the cast alloy were measured using Fiji Imaje J software  [30]  to get a good statistical measure of this fraction. The grain size was measured counting a minimum of 500 grains from Electron Backscattered Diffraction (EBSD) maps. X-ray diffraction measurements (XRD) were performed in a Siemens D5000 diffractometer (Bruker AXS, Karlsruhe, Germany) with a horizontal goniometer using Cu-Kα radiation to identify phases presented in the as-cast alloy and after extrusion. Also filtered products from solution after corrosion testing were subjected to XRD (Co-Kα radiation) analysis. Corrosion tests consisted of the measurement of hydrogen release through the immersion of the samples following ASTM-G31-71 (1999) with a ratio of 40 mL of solution per cm 2 of exposed surface.
PROCESS DESCRIPTION AND MODELING A superstructure-based approach is the design methodology followed in this work to assess different wastewater thermochemical conversion routes, corresponding to the use and/or combination of different technologies. A similar approach has been proposed and used by several authors  (Maronese et al. , 2015; Santibañez-Aguilar et al. , 2015). All process units are represented as black-box models, built by assessing the conversion features, either from extensive literature review or real, operating, units. Flowsheeting software (e. g. , Belsim Vali) is used to describe the complex processes and is the base for the linearization of the mass and energy flows, based on a reference size (typically the inlet mass flow). The linear nature of the approach is kept by assuming linear operating and investment costs, as well as process efficiency.
(2004). This index calculates the complexity of the treatment based on the number of drugs prescribed and other factors, such as dosage form, route of administration, dose frequency, or additional instructions given for their use. To analyze the impact of the complexity of the treatments on the repeat visits of the patients with DRP, a number of steps were followed; these included the effect of the number of drugs, the MCRI at discharge from the emergency department on hospital readmissions, and the number of unscheduled visits to public health care centers in Catalonia (including hospitals, long-term healthcare facilities and primary care centers) at 30 days from hospital discharge, based on the data obtained from the clinical notes computer system shared with the Catalan Health Institute.
Bi-level model We now describe the mathematical formulation of the OPTKNOCK  [5]  procedure in detail. OPTKNOCK was developed to identify multiple reaction deletions that may lead to the overproduction of a target chemical while cellular growth is maximized. This hierarchical optimization model comprises two decision or competing strategies, namely a cellular objective and a chemical production objective. It therefore is formulated as a Bi-Level Mixed Integer Program (BLMIP). Binary variables, denoted by y j , j ∈ J, are defined which enable to perform reaction removals inside the model by multiplying both LB j and UB j by (1y j ) in the lower bound and upper bound constraints as follows: y j = 1, if reaction j is removed, 0, otherwise. Assume that K denotes the given number of knockouts. Then the Bi-Level Mixed Binary (linear) Program (BLMBP), which is a hierarchy of two optimization problems, can be mathematically formulated as follows: BLP max y v prod s.
Implementation of MOMO The ideas presented in the previous sections ("Bi-objective model" section "Enumeration" section) were implemented in the software MOMO. The latter is coded in C++ and uses SCIP  [25]  and PolySCIP  [26]  as solvers for the optimization models. MOMO allows to solve instances of the FBA, BOP, ENUMP and ENUMPM models, using exact methods, and further allows for variations of those when there are more than two objective functions, which may include for instance maximizing and/or minimizing other bioproducts. The software is open-source and available at http://momo-sysbio. gforge. inria. fr/. We present below a sequence of steps that can be used with MOMO to predict knockout strategies for the overproduction of a desired product: 1. Solve an instance of FBA in order to obtain a reference flux distribution.
To validate the results obtained by MOMO we considered a reduced set of predictions, among the whole set generated by our software. We eliminated many candidate reactions identified at the Pareto frontier points showing a very small amount of biomass (points F and G) to avoid solutions pointing to increased ethanol production merely by compromising cellular growth. This is a relevant aspect since some of the solutions proposed as means to improve ethanol production could actually be not possible by compromising cellular viability. We also aimed to include, in the set of strains to be tested, those genes that could not be intuitively be linked to ethanol production. We ended up with a set of 19 different strains to be tested on the capacity of yeast cells to produce ethanol under very high gravity conditions (300g/L glucose as starting sugar concentration).
2   Enumeration K= 3 > 604800. 0 For K = 3 the program did not finish before the time-out of one weeK (604800s) enumerated were the same regardless of the FBA solution considered as reference). There are some methods like SIMPLEOPTKNOCK that also have enumeration features. The drawback with SIMPLEOPTKNOCK is the fact that it enumerates all possible deletions ranking them trough a score (wild-type prediction of target production). This results in an unpractical number of deletions to try empirically and often the score is unrelated with the real impact of the deletion. On the other hand, our approach enumerates a reduced number of deletions, instead of all possible ones, thus enabling a more suitable identification of candidates for subsequent experimental approaches. We have tested the effect of the reaction deletions predicted by MOMO for ethanol production, with particular emphasis on reactions whose link with ethanol synthesis was not intuitive and could not be easily anticipated.
In the case of IPT1 and LOT6, which encode respectively a inositolphosphotransferase involved in synthesis of complex sphingolipids and a cytosolic FMN-dependent NAD(P) H:quinone reductase, the improvements that appear to result from their deletions in ethanol production are far less easy to understand. Under the conditions that we have used we could not detect a significant difference between growth of the ipt1 and lot6 strains and of the wildtype strain BY4741 thereby indicating that the improved production exhibited by the mutants does not appear to result from their reduced fitness. Despite the molecular basis underlying the improved production phenotype may not be easily understood, the fact that MOMO has identified non-obvious target reactions is of particular high interest.
Funding This work was partially supported by  FCT and EraNet in Industrial Biotechnology  (6th edition, contract  ERA-IB-2-6/0003/2014 ) and by the  European Union ,  FP 7 BacHBerry  (www. bachberry. eu),Project No. FP7-613793. SV acknowledges support by  Program Investigador FCT  ( IF/00653/2012 ) from  FCT , co-funded by the  European Social Fund (ESF)  through the  Operational Program Human Potential (POPH ). Funding received by  iBB-Institute for Bioengineering and Biosciences  ( UID/BIO/04565/2013 ,  UIDB/04565/2020 ), IDMEC, under  LAETA  (project  UIDB/50022/2020 ), and  INESC-ID  ( UIDB/50021/2020 ), through  Fundação para a Ciência e a Tecnologia (FCT)  - Portuguese Foundation for Science and Technology , and from  Programa Operacional Regional de Lisboa 2020  (Project  N. 007317 ) is also acknowledged.
Availability and requirements Project name: MOMO Project home page: http://momo-sysbio. gforge. inria. fr/ Operating system(s): Linux Programming language: C++ Other requirements: SCIP optimization suite v. 3. 2. 1 License: ZIB Academic Any restrictions to use by non-academics: licence needed for SCIP optimization suite Availability of data and materials All data referenced and analyzed are public and available online.
g. ,  [25, 26] ), biodiversity (e. g. ,  [19] ), and ecosystem functioning (e. g. ,  [27] ) assessments. The main objective of this paper is to introduce a two-step methodology to perform integrated landscape change analyses of interface areas using complementary SRS change detection methods. First, LULC data is used to analyze the major landscape composition changes, and second, three VI-based metrics based on MODIS and Landsat data are used to analyze the productivity, phenology, and structure changes of different natural and anthropogenic vegetation areas where the cover type has remained unchanged. This study also aims to fill a knowledge gap on the landscape dynamics of the Cerrado Biome's interface areas, a critical conservation area network on which we apply the methodology to perform a regional analysis, complemented by a local case study analysis.
1. 2) as an illustrative case study for a regional-level landscape change analysis, due to their critical role in preserving one of the world's biodiversity hotspots from the threats it is facing  [28]  and the little knowledge available on the LULC conversion types and vegetation dynamics (other than the proportion of native vegetation loss (e. g. ,  [33, 34] )) in these areas. One of these CUs, the Parque Estadual das Nascentes do Rio Taquari, and its BZ were additionally used as an illustrative local case study to highlight the usefulness of our methodology in a spatially explicit way and within a more specified context. We carried out the landscape change analysis over two years, 2001 and 2016. This two-year study period meets the following criteria: i) it allows a long-term analysis of landscape dynamics using homogeneous or analogous datasets (i.
Landsat Data We used Landsat data to obtain high spatial resolution images representing the landscapes of each CU and their BZs in 2001 and 2016. The choice of Landsat scenes was made according to these four criteria: 1. Scenes had to be cloud-free over the studied areas; 2. The number of scenes per CU and its corresponding BZ was minimized; 3. If multiple scenes were needed to cover a CU and its BZ, the dates of acquisition between scenes needed to be the closest possible in time; 4. For the same CU and BZ, the acquisition date of the 2001 scene(s) needed to be the closest possible in time to the acquisition date of the 2016 scene(s). Finally, to linearly interpolate the smoothed time series pixel-wise, and place their values at regular intervals of 16 days, we used the Composite Day of Year (CDOY) band of the MOD13Q1 product, that contains the acquisition date of the pixels retained to produce the original composite images  [56].
This step ensured a uniform sampling of the NDVI for each of the evaluated years. The different pre-processing steps applied to the MODIS data are shown in Figure  3. 1 The original data derives from 5 MODIS tiles h12v10, h12v11, h13v09, h13v10, h13v11, which have been cropped to the extent of the study areas and mosaicked to a single image mosaic per date (i. e. , 23 mosaics per year). 2 The final annual time series start and end on the day-of-year 1 and 353, respectively. To attain a valid NDVI range from 0. 0 to 1. 0, a scale factor of 0. 0001 was applied to all the bands in the 2001 and 2016 annual NDVI time series.
Landsat Data We used Landsat data to obtain high spatial resolution images representing the landscapes of each CU and their BZs in 2001 and 2016. The choice of Landsat scenes was made according to these four criteria: 1. Scenes had to be cloud-free over the studied areas; 2. The number of scenes per CU and its corresponding BZ was minimized; 3. If multiple scenes were needed to cover a CU and its BZ, the dates of acquisition between scenes needed to be the closest possible in time; 4. For the same CU and BZ, the acquisition date of the 2001 scene(s) needed to be the closest possible in time to the acquisition date of the 2016 scene(s). A total of 113 scenes were selected (the acquisition details and identification of the Landsat scenes are presented in Appendix C). Since no single Landsat mission was operational in both 2001 and 2016 (except for the Landsat 7 mission in which, however, the Enhanced Thematic Mapper Plus (ETM+) sensor is failing since 2003  [61] ), the scenes were selected from three missions and sensors of the Landsat Data Continuity Mission: Landsat 5 Thematic Mapper (TM) and Landsat 7 ETM+ scenes for the year 2001 and Landsat 8 Operational Land Imager (OLI) scenes for 2016.
1 The original data derives from 5 MODIS tiles h12v10, h12v11, h13v09, h13v10, h13v11, which have been cropped to the extent of the study areas and mosaicked to a single image mosaic per date (i. e. , 23 mosaics per year). 2 The final annual time series start and end on the day-of-year 1 and 353, respectively. A total of 113 scenes were selected (the acquisition details and identification of the Landsat scenes are presented in Appendix C). Since no single Landsat mission was operational in both 2001 and 2016 (except for the Landsat 7 mission in which, however, the Enhanced Thematic Mapper Plus (ETM+) sensor is failing since 2003  [61] ), the scenes were selected from three missions and sensors of the Landsat Data Continuity Mission: Landsat 5 Thematic Mapper (TM) and Landsat 7 ETM+ scenes for the year 2001 and Landsat 8 Operational Land Imager (OLI) scenes for 2016.
• Sankey diagrams  [67, 68] , derived from the transition matrices, in which the total area covered by each LULC category in 2001 and 2016 is represented as stacked vertical bars (the height of each bar representing the relative proportion of each LULC type in the study areas), and in which transition lines, connecting each sequential pair of bars, represent the surface area experiencing LULCC or maintaining the same LULC type (the thickness of each line is proportional to the total surface area experiencing the corresponding LULC transition or remaining unchanged). The 'sankeyNetwork' function of the 'networkD3' R package v. 0. 4. [69, 70]  was used to derive the Sankey diagrams.