dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion and conclusions Although quantitatively different, our results, based on an appropriate statistical methodology, confirm those presented in the original study by Rosenberg et al. [1] , indicating that the nature of the codon has some influence on the fine details of the local conformation in proteins. While the correlation between synonymous codons and secondary structure in the translated proteins is a well known phenomenon, differences at the (ϕ, ψ) level for the most populated conformational states remain an intriguing and somehow counterintuitive observation. In fact, we cannot exclude artifacts related to the procedures. In particular, the nature of the dataset used could explain some of the subtle differences that we have observed. This dataset was derived from a limited set of Escherichia coli proteins for which the structure has been experimentally determined, and it was assumed that the gene used for the production of the protein was the same as in the original organism, which is a reasonable assumption in this case, but probably not in general.
Moreover, high-resolution crystallographic structures are elucidated in a highlypacked context and at low temperature, severely reducing their inherent conformational fluctuations. We believe that the detailed understanding of the codon effect on the fine structural features of proteins will only be achieved when extensive structural databases including the corresponding gene sequence are available. With the availability of extensive and accurate datasets, the comparative analysis of codon-specific Ramachandran plots at the amino acid and/or triplet level will be possible using the statistical methods presented here, thus enabling an unambiguous assessment on the influence of the gene sequence on polypeptide structure.
Study participants We used data from three French and Belgian prospective cohorts of people with type 1 diabetes  [19] [20] [21]. The three studies complied with the Declaration of Helsinki, and the study protocol of each cohort was approved by the Ethics Committee of Angers University Hospital (Angers, France). All participants from the 3 cohorts gave written informed consent. Characteristics of participants at baseline in each single cohort were previously published  [22] [23] [24] [25] , and are shown in Additional Table  1. Briefly, the Survival Genetic Nephropathy (SURGENE) study was a single center, prospective cohort of all volunteers with type 1 diabetes attending the diabetes clinic at the university hospital of Angers, France  [19]. Participants were selected from 1989 to 1996 based on a diagnosis of type 1 diabetes before the age of 40 years, and a duration of diabetes longer than 3 years.
Participants were followed from enrolment in each corresponding cohort until death or the latest clinical visit up to May 31, 2019. Data from the 3 cohorts were pooled together for the current analysis. Among 1347 participants enrolled in the 3 cohorts, we excluded 35 participants without LLA data at baseline and 98 subjects without a known vital status or follow-up data within the study period (Additional file 4: Fig. S1 ). We also excluded 45 participants according to the definition of the exposure (see below).
Study endpoint The endpoint was any death occurring during follow-up, regardless of the causes. The vital status was obtained from hospital case records or by contacting the family physician of the participants. It was cross-checked by contacting the civil registry of the birth place of participants. Death cases were centrally reviewed by an independent adjudication committee by using hospitalization records or all other relevant supporting documents. Data regarding causes of deaths were available only in a subset of participants. Cardiovascular mortality was defined as deaths resulting from arrythmia, acute myocardial infarction, heart failure, stroke, cardiovascular haemorrhage, PAD, end-stage kidney disease, dead in bed syndrome, sudden deaths or undetermined causes. Non cardiovascular mortality was defined as deaths resulting from cancer, infectious, pulmonary, gastrointestinal, hepatobiliary or pancreatic disease, metabolic complications, suicide, drug reaction or overdose, and trauma or unintentional injuries.
Analyses were adjusted for age, sex and cohort membership (model 1), plus other relevant confounding variables: history of tobacco smoking, duration of diabetes, HbA1c, BMI, systolic blood pressure, UAC, eGFR, and use of antihypertensive and lipid-lowering drugs at baseline (model 2). The Schoenfeld residuals method was used to check the proportional hazards assumption for the association between exposure and all-cause death. We have also estimated the loss in life-time expectancy within study period in participants with baseline history of CVD, LLA or both conditions, compared to those without these conditions, using the pseudoobservation approach on survival data  [26, 27]. We performed a series of sensitivity analyses. First, we evaluated the association between the exposure and the endpoint after adjusting for total cholesterol and history of diabetic peripheral neuropathy and retinopathy stages at baseline (on top of model 2) in a subset of participants from whom these data were available.
0 × 10. 5 cm as the in-vivo rat capnoperitoneum. Optimization of the operating parameters to maximize IP aerosol deposition were determined by real time scintigraphy (Gamma Imager, Biospace, Mesures, France with IRIS Software Aries Nucleaire, France). For this purpose, 99 m Tc-labelled human plasma albumin scintigraphic studies was chosen due to its low absorption by both lymphatic and blood capillaries in view of the later in-vivo studies. The freeze-dried radiopharmaceutical was dissolved under vacuum into 3 mL saline. After 5 min, 2 GBq of 99m Tc sodium pertechnetate (Cis bio, France) were added. After further 5 min incubation at room temperature, the resulting solution was used to load the liquid chamber of the USN. At a CO 2 flow of 2 L/min, an optimum of 99m Tclabelled human plasma albumin aerosol deposition of 30 MBq/1.
0% isoflurane (Iso-Vet, Piramal Healthcare, UK) anesthesia in air carrier at 0. 5 L/min and maintained at 37 °C. The imaged data were reconstructed (HiSPECT NG software. Scivis GmbH, Göttingen, Germany) and analyzed with image analysis software (InVivo Scope, Bioscan Inc. , USA). For quantitative spatial distribution analysis 99m Tc labelled human plasma albumin, SPECT/CT full imaging along the main body axis (250 images of 92 × 92 pixels) were stored in Digital Imaging and Communications in Medicine (DICOM ® ) files. A custom computer program (source code available on GitHub at https:// github. com/ grezn iczek/ SPECT alyzer) was used to process these files. First, the 100 consecutive slices that contain the highest combined signal intensities were identified. Then, data points were ordered from highest (top) to lowest signal value and the number of data points (from the top) were counted that were needed for their cumulative signal to exceed a given percentage (30%) of the total cumulative signal in the 100 slices.
This number, the TVE30 (top voxels exceeding 30%), is represented as the fraction of the total number of data points. Thus, higher TVE30 values correspond to a more uniform distribution. SigmaPlot14 (Systat Software Inc. , San Jose, CA) was used for statistical analysis. The Shapiro-Wilk test was used to assert normal distribution of experimental data. Paired t-test was used to compare within experimental groups. All reported p-values are two-tailed.
Radionuclide decay was automatically corrected between each bed position. The energy window was set to 250-700 keV. Images were reconstructed and visualized by MMWKS Image Software (4. 7 Build 452). 3D FORE/2D OSEM algorithms were used for image reconstruction. Thirty days post induction of the human colon cancer cells, all rats were sacrificed by an overdose of anesthesia and then autopsied to characterize metastases and to compare their anatomical locations to foci observed by BLI and 18F-FDG-PET.
18 F-FDG-FDG-PET resulting in intense fixation of the tracer in HCT116 tumor cells allows millimetric resolution for imaging metastatic dissemination with reliable quantitation of the aerobic glycolysis expressed as SUVs ranging from 2. 5 to 12. Therefore, for future interventional studies, 18 F-FDG-PET for therapy response quantification is mandatory before and at the end of any anti-cancer treatment. To verify both BLI and 18 F-FDG-PET results, all rats underwent also necropsy. During necropsy, tumor tissue was observed in particular on the visceral and parietal peritoneum that was similar to the received information from BLI and 18 F-FDG-PET imaging studies. To our knowledge, there are no data comparing 18 F-FDG-PET and BLI for PM detection of orthotopic colorectal cancer in the rat. However, we observed 18 F-FDG-PET to be more sensitive than BLI for the detection of early PM in our rat model.
Acknowledgments This study was funded to the greatest extent by institutional funds. Granulometric verification was partly supported by the  Development Bank of Saxony (SAB)  under grant  100374636. The authors are deeply thankful to the whole team of the  CIPA-TAAM, CNRS Orleans  for their scientific and administrative support. Furthermore, we would like to thank  Dr. -Ing. Fernando Munayco  ( FVM Electronic Company, Biopharmimaging Division; Palaiseau, France ) for the design and optimization of some of the instrumentation used in this.
a b s t r a c t Tractography combined with regions of interest (ROIs) has been used to non-invasively study the structural connectivity of the cortex as well as to assess the reliability of these connections. However, the subcortical connectome (subcortex to subcortex) has not been comprehensively examined, in part due to the difficulty of performing tractography in this complex and compact region. In this study, we performed an in vivo investigation using tractography to assess the feasibility and reliability of mapping known connections between structures of the subcortex using the test-retest dataset from the Human Connectome Project (HCP). We further validated our observations using a separate unrelated subjects dataset from the HCP. Quantitative assessment was performed by computing tract densities and spatial overlap of identified connections between subcortical ROIs.
Reliability studies can evaluate and increase the confidence of methodological approaches used to study structural connectivity, while reproducibility studies can validate findings by comparing results produced with other techniques. In this work, we recapitulate pathways of the subcortical connectome in the Human Connectome Project (HCP) test-retest dataset. We aimed to assess the feasibility and reliability of mapping the subcor-tical connectome, with a specific goal of recapitulating known connections, through application of subcortical structure segmentations and probabilistic tractography. Furthermore, we sought to develop a framework that enabled evaluation of reliability for the subcortical connectome moving forward. Additional validation was performed using the unrelated subjects dataset of the HCP.
Materials and methods Processing of the data was performed in containerized computing environments on a high performance compute cluster. An overview of the general workflow is shown in Fig. 1. Briefly, publicly available minimally pre-processed test-retest data from the Human Connectome Project was used to assess reliability of connections (identified via tractography) between subcortical structures and feasibility of identifying connections of known subcortical circuits. Analysis included evaluating tract overlap, changes in tract density, and examining identified connections with trajectories previously described in the literature. Furthermore, processing and analysis was replicated on an unrelated subset from the Human Connectome Project.
Dataset Minimally pre-processed subjects as part of the test-retest dataset (n = 36; 11M/25F, aged 22-35) of the Human Connectome Project (HCP)  ( Glasser et al. , 2013 ; Van Essen et al. , 2013 )  were used to assess the reliability of subcortical connections identified via tractography. Briefly, T1weighted (T1w) MRI scans were acquired with a 3D MPRAGE sequence  ( Mugler and Brookeman, 1990  ): resolution = 0. 7 mm isotropic voxels; repetition time/echo time (TR/TE) = 2400 / 2. 14 ms, while dMRI scans were acquired in opposite anterior-posterior phase-encoding directions with a pulsed gradient spin-echo sequence  ( Stejskal and Tanner, 1965 ) : resolution = 1. 25 mm isotropic voxels; TR/TE = 5520 / 89. 50 ms; bvalues = 1000, 2000, 3000 s/mm 2 (90 directions per shell) with 18 bvalue = 0 s/mm 2 images.
All data was acquired on customized Siemens Skyra 3T MRI systems  ( Sotiropoulos et al. , 2013 ; Van Essen et al. , 2012 ). Full acquisition details are described in the HCP1200 reference manual 1. As part of the minimal pre-processing pipeline data release, all subjects underwent FreeSurfer processing (v5. 3. 0-HCP;  Fischl et al. , 2004 ) , where the cortical ribbons were retained for further processing. Further, subjects part of the HCP unrelated dataset that did not overlap with test-retest dataset were selected (n = 85; 35M/50F; aged 22-35) for validation. Acquisition and minimal pre-processing steps from the HCP release of the unrelated dataset were identical to the test-retest dataset.
A second exclusion mask was created from an inverted convex hull surrounding the subcortical structures to discard streamlines outside of the convex hull. FreeSurfer processing was performed in the subject's native space, while the atlas was transformed to the subject's native space using the Advanced Normalization Tools  (ANTS v2. 1. 0; Avants et al. , 2009 ). Briefly, the atlas was transformed to the subject's native space in a 3-step process: (1) linear affine transformation, (2) non-linear symmetric normalization (SyN), and (3) HCP provided subject-specific transformations. The first two steps transform the labels from MNI2009bAsym space to MNI152NLin6Asym space, while the final step transforms the labels to the subject's native space. Transformations between the two spaces can be found in the available repository (see data availability).
Tractography All tractography processing was performed using the MRtrix3 software suite (v3. 0_RC3;  Tournier et al. , 2019 ). First, individual tissuespecific response functions were estimated for each subject in both test and retest sessions using an unsupervised approach  ( Dhollander et al. , 2016 ). From here, an averaged group response function was computed from the individual response functions. Fiber orientation distribution (FOD) maps were estimated for each subject with a multi-shell, multitissue constrained spherical deconvolution (MSMT-CSD) algorithm  ( Jeurissen et al. , 2014 ) , with group average response functions independently computed for the test-retest and unrelated datasets. The use of a group average response function minimizes biases in FOD maps  ( Raffelt et al. , 2012 ) , improving the comparability of tractography within datasets with observed differences attributed to the underlying diffusion data of an individual.
Tract density Streamlines weighted by their contribution to the underlying diffusion signal were summed to calculate the tract density (also referred to as apparent fibre density (AFD);  Raffelt et al. , 2012 )  of the connection between two subcortical structures. A connectivity matrix for each subject was created with the AFD representing the edge strength between two ROIs (nodes). Further, the percent change in AFDs were calculated between test and retest sessions using Equation  1 : % 𝐴𝐹 𝐷 𝐷𝑖𝑓 𝑓 = 𝐴𝐹 𝐷 𝑇 𝑒𝑠𝑡 -𝐴𝐹 𝐷 𝑅𝑒𝑡𝑒𝑠𝑡 𝐴𝐹 𝐷 𝑇 𝑒𝑠𝑡 × 100% (1) Additionally, intraclass correlation (ICC) was computed for the tract densities between the two datasets as a metric of consistency using a two-way, mixed effects model  ( McGraw and Wong, 1996 ). Prior to computing an ICC, an analysis of covariance (ANCOVA) was first performed to identify and account for covariates (age, subject motion, brain volume) with a significant effect on the tract density via linear regression.
The numerator of Equation 2 computes the sum of overlapping non-zero voxels between A and B, while the denominator calculates the total sum of non-zero voxels in A and B respectively. Computed overlaps from wDSC follow similar indicators of agreement as the conventional Dice similarity coefficient: poor ( < 0. 2), fair (0. 2 -0. 4), moderate (0. 4 -0. 6), good (0. 6 -0. 8) and excellent ( > 0. 8;  Kreilkamp et al. , 2019). In addition to comparing the tract overlap, a Spearman's correlation was also computed between the average AFD across the two datasets. Similar to AFD, a one-way ANOVA was performed to compare the wDSC for connectivity of the basal ganglia with connectivity between the basal ganglia and thalamus.
Validation with unrelated dataset Processing and analysis of the HCP unrelated dataset followed the same workflow as before with the test-retest dataset. As before, known connectivity of the subcortical connectome was both visually and quantitatively assessed. AFD matrices were computed for each subject as before, and further separated by hemispheric connectivity to compare with previous findings. With only a single acquisition session in the unrelated dataset, an average AFD matrix was computed across subjects, and a Pearson's correlation was performed against the average AFD matrices of the test and retest sessions to evaluate the similarity of the subcortical connectome. As with the test-retest dataset, the relationship between AFD and the size of the subcortical structures was also evaluated.
Observations in HCP unrelated dataset An identical analysis was performed on a subset of the HCP unrelated dataset, where similar observations were noted. An average connectiv- ity matrix was computed and compared against the test-retest dataset, where notably a Pearson's correlation coefficient of 0. 99 was demonstrated against both the test (p < 0. 05) and retest (p < 0. 05) sessions, indicative of highly similar AFDs with the unrelated dataset. Full details of the results from this validation can be found in the Supplementary Material.
Reliability of the subcortical connectome Upon visual inspection, the connectivity matrices demonstrated subjectively similar tract densities (AFDs), that is the sum of weighted streamlines that comprise a connection, across subjects and datasets. For a given subject in the test-retest dataset, the tract density was expected and observed to be similar across sessions. After correcting for brain volume, a covariate with significant effect on tract density, this was reinforced quantitatively, where no significant difference was identified between test-retest subjects and an average ICC of 0. 49 and 0. 48 was observed for left and right hemispheric connectivity respectively, with a noted higher reliability in connections between basal ganglia structures (see section 3. 1. 2 ). While AFDs were not perfectly identical between sessions, changes in AFD were likely due to differences in the acquired data between sessions.
62 was demonstrated, suggesting our findings within the subcortical connectome are comparable. To validate our findings, we also replicated the analysis on the HCP unrelated dataset, observing similarities between the two datasets from visual inspection and quantitative comparison. We demonstrated high similarity of connectivity matrices across datasets, with a Pearson's correlation coefficient of 0. 99 between the averaged connectivity matrix of the unrelated dataset as well as test and retest connectivity matrices. Due to a lack of subcortical connectome reliability studies, direct comparison of our findings was challenging as often different connectomes were investigated, typically focused on cortico-cortical or cortico-subcortical connections. However, in an investigation of cortical connectome reliability across different different resolutions, Pearson's correlation coefficients between 0.
To our knowledge, while no previous work has evaluated tract overlap of the subcortical connectome, wDSC has been used to demonstrate the reproducibility in the cortical connectome  ( Boukadi et al. , 2019 ; Cousineau et al. , 2017 ). Our observed wDSC in connectivity between basal ganglia structures was within the reported range (wDSC = 0. 71 to 0. 82) of four examined cortico-cortical tracts identified using similar techniques  ( Boukadi et al. , 2019 ). Similarly, wDSC has been employed to examine test-retest reliability of cortico-cortical tractography in the Parkinson's Progression Markers Initiative dataset, where a wDSC of 0. 72 was identified as a threshold for good overlap in their study  ( Cousineau et al. , 2017 ). The same study also examined reliability of cortico-subcortical connections using an ROI defining a general cortical region (e.
Application of connectivity thresholds As previously described, defining a threshold is a non-trivial task (see section 2. 4. 4 ). While there are different methods for defining a threshold, including selection of an arbitrary value or by sweeping through a range of values, the present study uses a test-retest technique to determine an appropriate threshold value. Such an approach is limited to test-retest datasets. However, the threshold can be applied to other datasets processed with the same techniques, as was performed on the HCP unrelated dataset. To apply the threshold to other datasets, an analysis should first be performed to assess whether the defined threshold is appropriate.
Acknowledgements JK was supported by the postgraduate scholarship from the  National Sciences and Engineering Research Council (NSERC). RH was supported by a  BrainsCAN postdoctoral  fellowship. AK was supported by the  Canada Research Chairs program  # 950-231964  ,  NSERC   Discovery  Grant # 6639  , and  Canada Foundation for Innovation (CFI)   John R. Evans Leaders Fund Project # 37427  , the  Canada First Research Excellence Fund  , and  Brain Canada. JL was supported by research start-up funding through the  Department of Clinical Neurological Sciences at University of Western Ontario. This research was enabled in part by the support provided by  Compute Ontario  ( www. computeontario. ca ) and  Compute Canada  ( www. computecanada. ca ). Data was provided in part by the  Human Connectome  Project,  WU-Minn Consortium  (Principal Investigators:  David Van Essen  and  Kamil 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.
europa. eu/eurostat/web/nuts/overview) or GADM2 (http://gadm. org/) levels, with an average area of 5,675 km 2 ; henceforth: department). Each entry comprises cultivated area, production and yield data. The crops are barley, maize, oats, potatoes, rapeseed, sugarbeet, sunflower, durum wheat, soft wheat and wine. Four of them (barley, oats, rapeseed and soft wheat) have distinct spring and winter cultivar records, resulting in a total of 18 crop-cultivar types. This unique data set contains a total of 375,264 data points on department level that have been collected and manually digitized (until 1988) over the course of two years from regional statistical offices in France. Yields (in tonnes dry mass, t DM) were calculated from production and area data since the annotations in the statistical year books were often erroneous.