dataesr/scientific-paragraphs-categorization huggingface dataset:
For replication, we used the UKB, a large population-based cohort which consists of 502,492 unselected volunteers from the United Kingdom, recruited between 2006 and 2010, of which 487,409 underwent genotyping and extensive phenotyping. 17 Phenotypic data of interest include age, sex, body mass index, Townsend deprivation index, and the age they completed full time education. Cholesterol data was measured from blood collected in the baseline visit. Informed consent was obtained from all participants registered in the UKB. In the UKB, we included the 1,005 patients with a reported diagnosis of AD, the 2,268 patients with PD, and the 2,732 patients with a report of dementia. The clinical outcomes (Parkinson's field ID 42030, Alzheimer's field ID 42020, all dementia field ID 42018) were generated by the UKB (category 42), by crossing UKB baseline self-reports with linked hospital admissions and death register data.
We relied on the Anatomical Therapeutic Chemical (ATC) Classification System  18, 19  available directly in the THIN database. In the UKB, we used the medication-use data collected by trained nurses during interviews. The UKB classifies medications into 6,745 categories of which 1,809 were reported by 10 or more people. 18 We mapped the medication categories into ATC codes (1,752 over 1,809 were mapped) as previously done in a genetic analysis of the UKB. 18.
Statistical Analysis We first used the THIN database to estimate the association between the preselected list of health conditions or medication classes and each specific neurodegenerative disorder. We estimated the odds ratios (ORs) using a logistic regression corrected for sex and age at index date. We reported the OR estimates for different time windows prior to the neurodegenerative onset: 0 to 5 years, 5 to 10 years, and 10 to 15 years, in order to show their progression over time. The index date for each AD-control pair was defined as the first date of AD diagnosis of the corresponding patient with AD. The index date for PD and DLB was defined as age of onset. We corrected for multiple comparison using Bonferroni corrections. This led to using a significance threshold of p < 0. 004 (p = 0.
05 with Bonferroni correction for 12 potential exposures, 2-tailed test). All analyses were conducted using the StatsModel library in Python. Next, we compared the ORs among the 3 neurodegenerative disorders (PD vs. AD, DLB vs. AD, and DLB vs. PD). We also compared the association of prediagnostic medication use between patients with PD who developed dementia in the 5 years after a first PD diagnosis and those who did not. For this analysis, we excluded patients with a diagnosis of dementia before a first PD diagnosis. We compared these 2 groups in terms of medication usage in the 0 to 5 years before PD diagnosis. In the UKB, we compared the AD and PD groups to replicate the significant results for all classes of medications considered in the THIN study. We used logistic models and controlled for sex, assessment center, age at diagnosis, age at entry in the cohort, and also for the Townsend deprivation index, body mass index, and education level.
Results In the THIN database, our full initial analytical cohort included 20,214 patients with a final diagnosis of AD, 28,222 patients with a diagnosis of PD, 4,682 patients with a diagnosis of DLB, and 20,214 healthy control patients. Table  1  describes the characteristics of the different populations. The median age at diagnosis was younger for PD (77 [70-83] years) than for DLB (80 [74-85] years) and patients with AD (82 [76-87] years) and for healthy controls (82 [76-87] years). There were more men than women in the PD and DLB cohorts (sex ratio of 57%) and the opposite in the AD and controls cohorts (sex ratio of 31%). There were 13,625 patients with AD, 14,539 patients with PD, 2,279 patients with DLB, and 10,205 healthy controls who had data available for more than 10 years before diagnosis.
UKB Replications We used the UKB database to replicate the comparison in terms of medication usage between patients with AD and patients with PD (Table  4 ). A lower prescription of statins was also observed in the years prior to diagnosis of PD as compared to AD (OR = 0. 77, p = 0. 01). As in the THIN database, there was no significant difference between patients with AD and patients with PD regarding agents acting on the renin-angiotensin-aldosterone system. When patients with PD were compared with dementia cases (including vascular dementia and AD), prescriptions of all families of antihypertensives except diuretics (calcium channel blockers, beta blockers, and agents acting on renin-angiotensin-aldosterone system) and statins (see Table  2 ) were higher in individuals with subsequent dementia.
As above, the design of the study did not allow us to distinguish between a direct effect of benzodiazepines on the risk of dementia or the consequence of the need to treat prodromal anxiety. Our observed association of benzodiazepines with PD and DLB suggests that this association is not specific to AD. More surprisingly, we found a lower prescription of statins in the years before the diagnosis of PD compared to dementia-related disorders (AD and DLB). These differences remained significant when adjusting for potential confounding variables, such as APOE status, body mass index, education level, and Townsend deprivation status, and in the replication (UKB) dataset. This observation could have several explanations. First, because statins are prescribed in secondary prevention of cardiovascular diseases, it may be the result of higher vascular comorbidities in the AD and DLB groups.
It may argue toward a specific role of statins in the development of dementia. Finally, we also identify other prescriptions associated with future development of dementia in the PD population (drugs against constipation and depression). Using past prescriptions of the GP may be useful in the future to design approaches to distinguish patients with PD who will develop dementia shortly after their first initial PD diagnosis. This study is, to our knowledge, the largest addressing on the same database as the prodromal phase of the 3 neurodegenerative diseases allowing to compare their clinical profile and treatment prescriptions as observed by GPs. The observations of increased nonmotor, non-cognitive prodrome in patients with DLB might help GPs who encounter these symptoms in their clinics.
Indeed, many are facing challenges of knowing when to refer for subspecialist evaluations, especially in underserved areas. Another notable strength of this study is the replication on the UKB database, with consistent results obtained for the 2 analyses. Our work has also several limitations. First, in the THIN database, the diagnosis of neurodegenerative diseases was made by GPs, which may result in a delayed diagnosis, explaining why we can observe disease-specific symptoms before their diagnosis. However, most patients have likely been seen by a specialist because previous studies showed that it was the case for 78% of patients with a PD diagnosis. 37 Initial diagnosis of probable PD was later confirmed in 83% cases. 37 In addition, we used ICD codes, which cannot capture disease severity, which might under-detect soft clinical signs.
However, the size of the database offers the opportunity to start progressing our understanding of prodromal risk factors of DLB in France and the United Kingdom. Conversely, in the UKB database, diseases' records came from hospitalization or death registers, which may be bias toward more severe disease forms. Second, the duration of the study is long. It represents one of the major strengths of this work, because it enables study of possible associations several years before the first diagnosis. However, over that time, diagnostic and prescribing behaviors of generalists have evolved, making the overall cohort heterogeneous in their ascertainment. Third, the frontiers of each complex neurodegenerative disease may not be defined perfectly, and their distinction is a simplification with respect to disorders heterogeneity  38  and distinction between developing a DLB or co-existence of PD and AD might be difficult to do in clinical practice.
Odds Ratios Corresponding to Alzheimer's and Parkinson's Disease and Dementia with Lewy Bodies and to the Comparison between Parkinson's and Alzheimer's Disease 0 to 5 Years before a First Diagnosis on the THIN Database and Odds Ratios Corresponding to the Comparison between Parkinson's Disease and Alzheimer's Disease and between Parkinson's Disease and All Dementias with Presentations before Diagnosis of the Corresponding Neurodegenerative Disorders in the UK Biobank Replication Sample The p values are corrected for multiple comparisons in the THIN analysis. In the THIN analysis, odds ratios are corrected for age at diagnosis and sex and in the UK Biobank replication sample, for age at diagnosis, age at first visit, sex, center, BMI, education level and Townsend deprivation index. **Cannot be calculated because an insufficient number of presentations was recorded.
Acknowledgments The research leading to these results has received funding from the joint program in neurodegenerative diseases ( JPND )  ANR-21-JPW2-0002-01  ( LeMeReND ) and the program " Investissements d'avenir "  ANR-10-IAIHU-06. This work was funded in part by the  French government  under management of  Agence Nationale de la Recherche  as part of the  "Investissements d'avenir" program , reference  ANR-19-P3IA-0001  ( PRAIRIE 3IA Institute ). B. C. D. is supported by a  CJ Martin Fellowship  ( NHMRC ,  APP1161356 ) and the  CNRS.
Edge-Path bundling is a recent edge bundling approach that does not incur ambiguities caused by bundling disconnected edges together. Although the approach produces less ambiguous bundlings, it suffers from high computational cost. In this paper, we present a new Edge-Path bundling approach that increases the computational speed of the algorithm without reducing the quality of the bundling. First, we demonstrate that biconnected components can be processed separately in an Edge-Path bundling of a graph without changing the result. Then, we present a new edge bundling algorithm that is based on observing and exploiting a strong relationship between Edge-Path bundling and graph spanners. Although the worst case complexity of the approach is the same as of the original Edge-Path bundling algorithm, we conduct experiments to demonstrate that the new approach is 5-256 times faster than Edge-Path bundling depending on the dataset, which brings its practical running time more in line with traditional edge bundling algorithms.
Accordingly, there exists an Edge-Path bundling algorithm equivalent for every algorithm that computes a graph spanner. Here we propose an algorithm based on the greedy spanner algorithm [ADD*93]. For a graph G = (V, E ) and a particular drawing D G of it to be bundled, our invariant and new algorithm (S-EPB) still have a worst-case time complexity of O(|E| 2 log |V |), but the practical runtime performance is 5-256 times faster than EPB [WAA*22] depending on the dataset. Moreover, its bundling quality in terms of the three originally proposed metrics ink ratio, distortion and ambiguity is comparable. Our spanner approach does not incur independent edge ambiguities and has performance similar to previous CPU-based edge bundling approaches  [LBA10, HvW09]. However, all previous approaches, other than EPB and our new approach, incur independent edge ambiguities.
Experiments Our experimental evaluation primarily compares Edge-Path bundling (EPB) [WAA*22] to the Spanner Edge-Path bundling (S-EPB) introduced in this paper. We hypothesize that S-EPB is faster and produces a bundling that is of commensurate quality. All datasets, images, algorithm implementation and code to reproduce the experiments can be found in the supplementary material on OSF (osf. io/t4h6j/).
Datasets and experimental procedure We test our S-EPB approach on the same datasets used in Wallinger Statistics about the datasets can be seen in Table  1. The biconnected component decomposition decreases the input size for computing a valid Edge-Path bundling drastically. As a graph needs at least three edges to form a valid Edge-Path primitive all components with less than three edges can be ignored. Interestingly, all datasets have similar structural property of many small (|c i | < 100) components and one large component (c max ). As mentioned in Section 3. 1, we can process biconnected components in parallel. However, due to the aforementioned structure of the graphs, the opportunity for load balanced parallelization is not present. The experimental setting was the following: all runtime experiments were executed sequentially on an AMD Ryzen 5 5600x, with 3.
7-GHz base clock 6-core CPU, but limited to one core. The L1 cache of 64KB and L2 cache of 512KB are available to each core exclusively while the L3 cache of 32 MB is shared among all cores. Thirty-two gigabytes of memory were available on the system; however, peak memory allocation never required that amount. The used operating system was Ubuntu 22. 04LTS. Algorithms were implemented in C++ using the Open Graph algorithms and Data structures Framework (OGDF) [CGJ  *13]. Graph datastructures, the biconnected component decomposition and Dijkstra's shortest path algorithm were used from OGDF. The code of the implementation was compiled with GCC 11. 2. 0 for optimized performance with compiler flag -O3. We measured the wall clock time of the bundling algorithms, i. e. including the biconnected component decomposition and assignment of control points but excluding the loading of the graph, calculation of Bézier curves for the curved edges and rendering of the output.
Our experiment procedure performed 100 runs of each individual bundling algorithm and we averaged the runtime over all results. Both Amazon200K and Panama Papers were drawn with FM 3 [HJ04] beforehand. As these datasets are large, we only ran the algorithm three times. For measuring memory allocation, we used Ubuntu's time command which summarizes system resource usage of a programme. Here, we were mainly interested in the resident set size which includes heap, stack and shared library memory allocation. For the ambiguity experiments, we rendered the output exactly the same as presented in Wallinger et al. [WAA*22]. For each edge, we exported a list of 50 control points representing a polyline approximation of a cublic Bézier curve.
We also ran all experiments once with the biconnected component decomposition and once without biconnected component decomposition. These variations are all meant to improve performance. Table  2  presents the runtime of these variants and EPB. The biconnected component decomposition drastically decreases the runtime of the Edge-Path bundling computation. The only exception here is the Air Traffic dataset as the largest component contains approximately 80% of all vertices. While all variants are still considerably faster than EPB, the overhead of computing the biconnected component decomposition and the respective sub-graphs neglects most of the speed-up when comparing S-EPB variants with and without biconnected component decomposition For all datasets, the proposed approach to compute multiple shortest paths, instead of performing a shortest path computation for each edge, improves S-EPB.
However, the two variants to compute the spanner are less clear and no variant clearly outperforms the other. This behaviour can be explained with the overhead of tracking the shortest distance between vertices and the correlation between density of the graph and succesful look-ups. 2  shows the results of EPB and S-EPB on the large datasets. Here, we were mainly interested in the scalability of S-EPB. Generally, we see that S-EPB is 5-256 times faster depending on the dataset. S-EPB with and without biconnected component decomposition scales better with increased input data size. Memory. For the panama dataset approximately 1. 45 GB of memory are allocated for EPB and 1. 55 GB for biconnected S-EPB. A table with the respective memory allocation for each dataset and variant can be found in the supplementary material.
06 0. 76 0. 44 0. 15 0. 07 0. 05 0. 79 1. 06 1. 02 0. 54 0. 10 0. 00 S-EPB has parameters t = 2, κ = 2. Column dist gives mean and median. Columns amb δ are only shown for 1 ≤ δ ≤ 5 if there are non-zero entries. Comparison to other bundling algorithms. Table  3  compares the original edge path bundling algorithm to the graph spanner approach on the same machine as in the previous study  [WAA*22]. Additionally, the original Edge-Path bundling algorithm was reimplemented in C++ using OGDF for increased performance. The S-EPB variant used in comparison computes the spanner with the FG-Greedy algorithm and uses V -Dijkstra to compute shortest paths. As seen in the table, S-EPB is on par with or outperforms all of the other bundling algorithms that are CPU-based. The imageand GPU-based approaches are still the fastest of all approaches, taking less than a 100 ms even for the panama dataset.
However, all of these edge bundling algorithms have the independent edge ambiguity. For the approaches that do not have independent edge ambiguities, S-EPB is always faster than EPB by a factor of 5-21 times as it operates on a sparser graph. Summary. The performance of S-EPB compared to EPB is between 5-256 times faster depending on the dataset. On the two larger datasets, S-EPB was at least 110 times faster and shows increased scalability compared to EPB. The speed-up of S-EPB compared to EPB can mainly be explained by the fact that shortest path calculations are performed on sparse sub-graphs of the input graph. The variance of speed-up between datasets is mainly due to structural differences between the graphs. Especially, sparsity and  distribution of lengths of shortest paths is an indicator of the magnitude of speed-up.
As Dijkstra's shortest path computation stops once the target vertex is found, a longer path usually correlates with a higher number vertices explored. Similarly, sparsity of the spanner correlates with fewer vertices explored during bundling in the S-EPB algorithm, thus, an overall speed-up. Biconnected component decomposition did help on certain datasets, but its performance was more variable and dataset dependent as expected. Especially, the Panama Papers and Amazon200k datasets profit from the biconnected component decomposition as an Edge-Path bundling is computed on a much smaller sub-graph; see Table  1  for details.
Quality metrics Tables  4  and 5  show the results of the quality metric calculations on the three small real-world datasets. As presented in Wallinger et al. [WAA*22] , the ambiguity metric is costly to compute and could not be computed on the larger datasets. Overall, all approaches are comparable, especially for higher values of delta. S-EPB without all pairs shortest path on the spanner tends to bundle less than the other approaches (seen by higher ink ratio and some lower ambiguity). Therefore, we can conclude that S-EPB has a similar performance to EPB on these quality metrics, but with an improvement in terms of runtime.
Comparison of image results Figures 4-6 show the results of EPB and S-EPB on the Airlines and Migrations datasets, respectively. S-EPB is consistently faster when computing a bundling of these images. Even though EPB and S-EPB will compute different bundlings, the results look very similar as can be seen from the images. There are small differences between the two (burgundy bundles near Texas in Figure  4 , thickness of bundle near the centre of the United States in Figure  5 , and bundles across the Atlantic and Pacific in Figure  6 ), but the overall structure is similar with both bundlings free of independent edge ambiguities. Therefore, performance, in terms of speed, is greatly improved with the drawing quality remaining the same. In Figure  7 , we vary and compare the t and κ parameters to see how the stretch factor and bundling parameter influence the quality of the drawing.
This resulted in a minor runtime improvement for some experiments, but it did not generalize to all experiments. For the larger datasets (Amazon200k, Panama Papers), this memory overhead was too high causing the approach to run out of memory when constructing this additional data structure. As we are mainly interested in bundling long edges, we tested introducing a threshold length on the edges for bundling, which could be used to instantly add shorter edges to the spanner. For example, we sorted the edges in increasing order and added the first 20% of edges to E before proceeding with the greedy algorithm on the remaining edges. In practice, this approach added unnecessary edges to the spanner that increased the cost of the shortest path computation and resulted in slower total runtimes. Finally, we experimented with replacing Dijkstra's shortest path algorithm with A*  [HNR68]  and the Euclidean distance between vertices as heuristic.
Conclusion In this paper, we presented Spanner Edge-Path bundling. This approach uses graph spanners to accelerate the computation of edge path bundling while achieving commensurate results in terms of visual quality. The approach provides a 5-256 times speed increase when compared to Edge-Path bundling, depending on the dataset used. Although the approach has the same worst case complexity of O(|E| 2 log |V |), as the graph spanner is sparser than the full graph G, the shortest path computations take less time. The bundling computed by S-EPB is not the same as that of EPB, but both are of the same class of algorithm that do not produce independent edge ambiguities. We have improved the computational performance of Edge-Path bundling to bring it in line with other bundling approaches. However, the approach still cannot compete with image-based approaches, such as CUBu  [vCT16] , which can bundle large datasets in less than a second but suffers from independent edge ambiguities.
Summary βthalassemia is an haemoglobinopathy characterized by a defective synthesis of the βglobin chain. To assess the current state of health of paediatric patients with βthalassemia, data from the French national registry regarding children born between 2005 and 2020 with βthalassemia intermedia (TI) or major (TM) were collected. A total of 237 patients (median age 7. 1 years at last visit) were analysed, of whom 156 (65. 8%) were born in France and 162 (68. 4%) had a TM phenotype. The probability of survival for children with TM born in France was 98. 3% at 15 years. Fifty-four (22. 8%) children received a haematopoietic stem cell transplant with a success rate of 88. 8%. Hepatic and cardiac iron overload monitoring in non-transplanted patients showed moderate overload in 15. 7% (18/115) and 7.
PATIE N TS A N D M ETHODS This study included patients with a diagnosis of βthalassemia enrolled in the French National Thalassemia Registry (NaThalY). 3 Since 2005, this database collects prospectively and periodically updates data on βthalassemia patients on a nationwide basis. Patients are classified at inclusion as βthalassemia major (TM) or βthalassemia intermedia (TI) according to the need for regular blood transfusions (>8/year) or by the absence of or occasional need for transfusions (<8/year) before the age of 4, respectively. Children born between 2005 and 2020 were included. Data at last visit until December 31, 2020 were analysed. The type of complications collected in the registry are described elsewhere. 3 Definition of endpoints are detailed in supplemental data in the Patients and Methods section.
Statistical analyses were performed using IBM SPSS Statistics 20 (SPSS Inc) software. P-values below 0. 05 were considered statistically significant. Quantitative variables were expressed as proportions (%) and numbers and quantitative variables as mean with standard deviation or median with interquartile range or minimum and maximum. Children's height, weight and BMI were compared with the French population using paired Student t-tests, according to children's age and sex. Survival analysis was performed using a Kaplan-Meier curve.