dataesr/scientific-paragraphs-categorization huggingface dataset:
Background: Urea cycle disorders (UCDs) are rare inherited metabolic defects of ammonia detoxification. In about half of patients presenting with a UCD, the first symptoms appear within a few days after birth. These neonatal onset patients generally have a severe defect of urea cycle function and their survival and outcome prognoses are often limited. To understand better the current situation of neonatal onset in UCDs, we have performed a multicentre, retrospective, non-interventional case series study focussing on the most severe UCDs, namely defects of carbamoyl phosphate synthetase 1 (CPS1), ornithine transcarbamylase (OTC), and argininosuccinate synthetase (ASS). Methods and results: Data of 63 patients were collected (27 patients with ASS deficiency, 23 patients with OTC deficiency, and 12 patients with CPS1 deficiency, one patient definite diagnosis not documented).
More recent reports indicate improved patient outcome with respect to the likelihood of survival for neonatal onset patients  [7, 21]. This improvement has been hypothesized to be due to earlier diagnosis and better treatments  [7]. Since data on the current prognosis of neonatal onset UCD patients are still scarce and often derived from only small cohorts, we collected information on UCD patients treated after the year 2001 in leading European centers. We performed a multicentre, retrospective, non-interventional case series study of neonatal onset UCD patients with an observation period of up to five years or until death. We focused on UCDs where the presentation was more likely to be severe and in the neonatal period: carbamoyl phosphate synthetase 1 (CPS1, OMIM *608307) deficiency (CPS1D, OMIM #23730), ornithine transcarbamylase (OTC, OMIM *300461) deficiency (OTCD, OMIM #311250, and argininosuccinate synthetase (ASS, OMIM *603470) deficiency (ASSD, OMIM #215700).
Study population To characterise the clinical course of neonatal onset UCDs, we performed a multicentre, retrospective, noninterventional study collecting data on patients affected by symptomatic hyperammonemia in the neonatal period and treated according to local protocols in the years 2001 until 2013. Inclusion criteria were: neonatal onset CPS1, OTC or ASS deficiencies with enzymatic or molecular genetic confirmation, demographic data and medical history, and data until death, orthotopic liver transplantation (OLT), or at least six months of followup. Exclusion criteria were: birth before 2001, severe chronic or systemic disease other than UCD, and participation in the liver cell transplantation study SELICA (Safety & Efficacy of Liver Cell Application, Cytonet, Weinheim, Germany). The observation period of patients according to the study protocol was up to five years or until death, but the analysis period was only 550 days.
Data collection, ethics, consent and permissions Study sites identified records from all UCD patients treated in the years 2001 to 2013. Patients with CPS1, OTC and ASS deficiencies were included if the observation period was at least six months or until death or OLT. Documentation of the patients included information on basic clinical and biochemical parameters, hyperammonemic events (HE, defined as plasma ammonia levels ≥ 150 μmol/L together with clinical symptoms probably related to hyperammonemia), medication, diet, and clinical outcome. Parameters evaluated for this study included the number, severity and duration of HEs, concentrations of plasma ammonia and glutamine levels, and, if applicable, age in months at the time of death or at the time of OLT. Primary endpoints were the time to the first HE after the initial HE, the number and duration of HEs, and peak plasma ammonia concentrations; events of increased plasma glutamine (= "hyperglutaminemic events"), defined as glutamine levels ≥ 1000 μmol/L (reference ranges for glutamine varied between laboratories and are agedependent), time to the first hyperglutaminemic event, and peak plasma glutamine concentrations; hospitalizations due to HE; survival for at least six months.
Statistical analyses Data were analysed as indicated using descriptive statistics and Kaplan-Meier plots. A Microsoft Windows Server 2003 operating system and several working stations with Microsoft Windows XP Professional operating system were used for data management and statistical analyses. SAS 9. 2 and SAS 9. 3 for Microsoft Windows were the statistical packages in use. All programming of tables, figures, listings, and statistical analyses were performed using the statistical software package SAS® versions 9. 2 and 9. 3. After preparation of figures with SAS, the graphical presentations were produced using alternative software packages (e. g. EXCEL).
Study population and baseline characteristics The individual retrospective observation and data collection period in this study was until death, OLT, or at least 6 months (maximum five years) but the analysis period was limited to 550 days. The first and last dates documented were February 2001 and February 2013, respectively. In total, 190 patients from the eight study centres were assessed for eligibility. After exclusion of 127 patients based on different reasons (reasons for exclusion of patients are given in Fig. 1 ), 63 patients remained as the study population and were further analysed. The screening logs indicated that participating sites went thoroughly through their patient records and indeed considered all patients potentially eligible for inclusion into the study. Nevertheless, it cannot be excluded that some patients were missed.
Analysis of screening logs further revealed that four patients who died early would have been eligible for the study but were recorded only on the screening log with no documentation in the database. Within the study population, the most frequent diagnosis was ASSD accounting for 27 of 63 patients (42. 8 %), followed by OTCD for 23 patients (36. 5 %), and CPS1D for 12 patients (19 %). In one patient, no definite diagnosis was documented in the study records; nevertheless, to avoid data bias, this patient remained included in this study (Table  1 ). Out of the 23 OTC deficiency patients, 21 (91. 3 %) were male, resulting in a total of about 60 % male patients included in the study, despite the fact that slightly more female than male patients were included with CPS1 and ASS deficiencies (63.
Discussion This was a European multicentre, retrospective, noninterventional case series reporting the natural course of neonatal onset UCD patients treated with standard care within the first five years of life. Data evaluation focussed on the basic clinical course and outcome, and on plasma levels of ammonia and glutamine at manifestation and during the course of the study. Our study is the largest of its kind in recent years in Europe, describing the natural course of 63 UCD patients treated in eight metabolic centres in Austria, France, Germany, Italy and Switzerland according to local management standards.
Orthotopic liver transplantation Definitive treatment of the underlying UCD using OLT was performed in 11 patients (17. 5 % of the study population; 5 OTCD, 4 ASSD, and 2 CPS1D patients). Except for two patients, this procedure was performed in the first 550 days of the study period, which likely reflects the early decision in favor of OLT although this definite treatment was done relatively late in some of the patients (mean 453 days, range 116-663). The time point of OLT in our study is in agreement with data from the US registry, which reports OLT in 42/66 patients before 2 years of age  [25]. However, the number of patients receiving OLT in this study is lower than in another study from the US that reports liver transplantation in 66 % of CPS1D and OTCD patients  [26]. A similar proportion of UCD patients (24 %) received liver transplantation in Japan  [7].
Conclusion This study provides data on the clinical course of 63 UCD patients with a neonatal onset of disease. The mortality rate during the entire study period of 30. 2 % is higher than reported before in other recent case series. A possible explanation for this may be underreporting of severe cases in other studies. The data from this retrospective study confirm the high risk of neonatal onset UCD patients not only for an initial life-threatening decompensation but also for recurrent hyperammonemic crises, which may likewise result in permanent neurological damage. We therefore conclude that novel therapeutic approaches are still highly needed for this group of patients. Until these are effectively in place, current treatment protocols, which recommend start of extracorporeal detoxification if ammonia levels exceed 500 μmol/L, should be re-evaluated since earlier start of dialysis may be beneficial.
More recent work have shown that good results can be obtained imposing sparsity rather than independence to spatial decomposition  [1] , relying on dictionary learning formulation  [2]. All these techniques suffer from their lack of scalability, as they were initially designed to be applied to small datasets. The recent increase in publicly available dataset size (e. g. HCP  [3] ) has revealed their limits in terms of memory usage and computational time. Efforts have been made to make decomposition methods available for large scale studies, possibly with several groups. They involve using a hierarchical model for dictionary learning  [1]  or incremental PCA techniques  [4]. However, the former only proposes PCA+ICA based decomposition methods, which do not naturally yield sparse maps, and the latter suffers from its computational complexity.
Running a satisfying decomposition algorithm on the full HCP dataset currently requires a very large workstation. In this paper, we focus on dictionary learning methods for fMRI, and show how to make them more scalable in both time and memory. Uncovering the computational limitations of dictionary learning when analysing very large datasets, we propose to perform randomprojection based hierarchical dimension reduction in the time direction before applying dictionary learning methods. As a result, time and memory consumption are reduced, avoiding out-of-core computation. We introduce a measure of correspondence to relate results obtained from compressed data to those from non compressed data, and show that substantial gain in time and memory can be obtained with no significant loss in quality of the extract networks.
Scalability challenge Following  [6] , online dictionary learning has an overall complexity of O(n p k 2 ), as convergence is typically reached within one epoch on rfMRI. In theory, the dictionary learning problem is thus computationally scalable. However, on large rfMRI datasets, online dictionary learning faces two main challenges detailed below. Out-of-core requirements for large datasets For datasets like HCP (t = 2000, n = 1200, p = 20000, 1. 92TB), typical computers are unable to hold all data in memory. It is thus necessary to stream the data from disk, which is only reasonably efficient if the data are stored in the same direction as it is accessed. Yet online DL algorithm require to pass 3 times over data, during which it is streamed in different directions (row-wise for initialization, columnwise for DL and final Lasso solving), while fMRI images are naturally stored row-wise.
Grid search in parameter setting The sparsity of the maps obtained depends critically on parameter λ, that scales non trivially with p. It is therefore impossible to set it independently from the experiment size, and several runs must be performed to obtain best maps, relative to their neurological relevance or a validation criterion. Grid search should be run in parallel for efficiency, which is a serious issue when doing out-of-core computation, as simultaneous access to the disk from different processes makes the pipeline IObound. Reducing dataset size therefore reduces disk and memory usage, which permits the efficient use of more CPUs. Both issues suggest to reduce memory usage by reducing datasets size while keeping the essential part of its signal: being able to keep data in memory avoids drastic loss in performance.
TIME-COMPRESSED DICTIONARY LEARNING Reducing time dimension Good quality maps are already obtained using small datasets with standard number of samples (ADHD dataset, n = 150). For this reason, we investigated how large datasets can be reduced to fit in memory while keeping reasonable map accuracy compared to the non-reduced version. Indeed, the n time samples per subject are not uniformly scattered in voxel space, and should exhibit some low dimension structure: we expect them to be scattered close to some low rank subspace of R p , spanned by a set of m vector X s r ∈ R m×p. We thus perform a hierarchical rank reduction : X s is first approximated by a rank m surrogate P T X s r , and a final rank k decomposition is computed over concatenated data. We show that such reduction is conservative enough to allow good map extraction.
VALIDATION Reference result-set Validation of dictionary learning methods for rfMRI is challenging, as there is no ground truth to assess the quality of resulting map sets. However, we can assess how much a result-set V obtained on a reduced dataset Xr from Eq. 5 is comparable to a result-set V 0 obtained on X from Eq. 2. Result-set comparison Two sets of maps V 0 and V can only be compared with an indicator invariant to map ordering. Two sets are comparable if each map from the first set is comparable to a map in the second set. We find the best one-to-one coupling between these sets of maps and compute correlation between each best assigned couple of maps: corr(v 0 i , vj) = |(v 0 i ) T v j | v 0 i 2 v j 2 to measure similarity between two maps vj (held in column Cj(V)) and v 0 i. We set d to be the mean correlation between best assigned maps: d(V, V 0 ) = max Ω∈S k Tr V T Ω V 0 (6) where S k is the set of permutation matrices.
Ω can be computed efficiently using the Hungarian algorithm. Comparing random results Eq. 2 and 5 admits many local minima that depend on algorithm initialization, and on the order used for streaming dataset columns. For any dataset Y ∈ {X, (Xr) reduction method }, we expect obtained maps Vi = DLi(Y) to capture a neurological/physical phenomenon for any run i corresponding to a streaming order. As in  [8] , we perform l runs numbered on S l ⊂ N of the algorithm to obtain different maps, and compare the concatenation V l (Y) = [(Vi)i∈S l ] of these maps to the concatenation of reference maps V 0 l (X) = [(V 0 i ) i∈S 0 l ] with runs numbered on S 0 l : d l (X, S 0 l , Y, S l ) = d V 0 l (X), Vp(Y) (7) We thus take into account non unicity of DL solutions: different maps are obtained when performing the dictionary learning algorithm over the same data with the same parameters.
We model result maps (vi)i to be part of a larger full result-set V: V(Y) = vi = Ci(V) ∈ R p s. t. V ∈ R p×k , ∃U ∈ R n×k , (U, V) ∈ argmin U,V Y -UV T 2 F + λ V 1 (8) When result-sets are concatenated over all possible streaming orders, we expect dp to converge toward a S (0) p independent measure: d∞ V(X), V(Y) = lim l→∞ d l X, S 0 l , Y, S l (9) It is expected that d∞(V(X), V(X)) = 1, but p is finite in practice. Ensuring S 0 l ∩ S l = ∅, we measure mean result-set correspondence d l (X, S 0 l , X, S l ) over different runs on the same dataset X, and compare it to d l (X, S 0 l , Y, S l ) to assess the reduction effect.
Tools and datasets We validate our reduction framework over two different datasets with different size: ADHD data, with 40 records, n = 150 time steps per record; a subset of HCP dataset, using 40 subjects, 2 records per subject, subsampling records from n = 1200 to n = 400 to obtain reference X. Dictionary learning output depends on its initialization, and the problem of choosing the best number of components k is very illposed. We bypass these problems by choosing k = 70 for HCP dataset, k = 20 for ADHD dataset, and use reference ICA-based maps RSN20 and RSN70 from  [9]  as initialization -we prune unused dictionary atoms on HCP dataset. For benchmarking, we measure CPU time only, i. e. ignore IO time as it is very platform dependent. To limit disk access in out-ofcore computation, small memory usage is crucial for IO time.
We use scikit-learn for computation, along with the Nilearn neuro-imaging library. Code for the methods and experiments is available at http://github. com/arthurmensch/nilearn/tree/isbi. Indicator and reduction validity Fig. 1  shows d l behavior as l increases. The results demonstrate the relevance of random rangefinding as it out-performs simple subsampling. We first obtain a reference set of maps V 0 p from non-reduced X, choosing λ to obtain little overlapping maps (λ = 1, 6 for ADHD, HCP). Secondly, we compute d l (X, S 0 l , Y, S l ) setting Y = {X, (Xr) rf,m , (Xr)ss,m}, for various m ∈ [n/40, n]. As the relationship between λ and a given level of sparsity depends on m, we run DL on Y on a range of λ so as to find the value that matches best the reference run. We observe that running DL several times does produce sets of maps that overlap more and more, as they cover a larger part of the result-sets V defined in Eq.
Both methods perform similarly for small compression factors, which shows that subsampling already provides good large low-rank approximation of X. Using a range-finding algorithm is therefore useful when drastically reducing data size, typically when loading very large datasets in memory. Qualitative accuracy We validate qualitatively our results, as this is crucial in DL decomposition: maps obtained from reduced data should capture the same underlying neurological networks as reference maps. In Fig. 2 , we display matched maps when comparing two result-sets. For this, we find matchings between sets (V l , V 0 l ), and we display the maps corresponding to the median-value of this matching. Maps are strongly alike from a neurological perspective. In particular, maps do not differ more between our reduced dictionary learning approach and the reference algorithm than across two runs of the reference algorithm.
Time and accuracy tradeoff For efficient neuroimaging data analysis, the important quantity is the tradeoff between quality of the results and computation time. On Fig. 3 , we plot d l (X, Y) -omitting S l , S 0 l in notation -against computational CPU time, for various Y. Using range-finding algorithm and to a lesser extent time subsampling on data before map decomposition does not significantly deteriorate results up to large reduction factor, while allowing large gains in time and memory. Compression can be higher for larger datasets: we can reduce our HCP subset up to 40 times, ADHD up to 20 times, keeping d l (X, Xr) within the standard deviation of d l (X, X). The range-finder algorithm adds a time overhead that shift performance curve towards higher time for large compression. However, it allows 4 times lower memory usage and thus higher overall efficiency when considering IO.
Moreover, benchmarks were performed on a single core, while reduction can be parallelized over subjects to reduce its overhead. We outline best time/accuracy trade-off reduction ratios in Fig. 3  and Table  1. They depend on chosen k and on dataset, but any reasonably low reduction (with m k) ratio is likely to produce good results with little accuracy loss. Following this strategy, we set α =. 025 and performed the entire processing of 100 subjects of the HCP dataset (384GB) on a single workstation (64GB RAM) in less than 7 hours. Table  1. Time/accuracy with most interesting method for each dataset, comparing to reference DL run. RF α: range-finder ratio.
CONCLUSION We introduce the use of a randomized range finding algorithm to reduce large scale datasets before performing dictionary learning and extract spatial maps. To prove efficiency of time reduction before dictionary learning, we have designed a meaningful indicator to measure result maps correspondence, and have demonstrated that fMRI time samples have a low rank structure that allows range finding projection to be more efficient than simple subsampling. This approach enables a 40-fold data reduction upon loading of each subjects. It thus makes processing large datasets such as the HCP (1. 92TB) tractable on a single workstation, time-wise and memory-wise.
Luminescence Measurement for Estimation of Ab-Nluc or scFv-Nluc Fusion Concentration Due to the inherent difficulty to quantitate scFvs we chose to validate the quantification of fusion proteins via Nluc with complete antibodies for which alternative dosing strategies were readily available. Four different monoclonal antibodies in the IgG1 format were fused to Nluc (mAb-Nluc1 to 4) and compared to the luminescence signal of purified Nluc. To this end a new vector was designed and the Nluc gene fused to C-terminal end of the CH3 domain via a flexible linker (Figure  2B ). IgGs were expressed in transiently transfected CHO EBNA cells, purified and further characterized by SDS-PAGE and size exclusion chromatography. All IgGs showed a monomeric fraction of >95% and no instability of the heavy chain-Nluc fusion was observed (data not shown).
ACKNOWLEDGMENTS The authors thank  Dr. K. V. Wood  and  Dr. L. P. Encell  from  Promega Corporation  for kindly providing them with purified NanoLuc R luciferase and NanoLuc R expression vectors. The authors thank  C. Bertaux ,  S. Bernois ,  L. Revy ,  K. Brost Dit Feuillet ,  Virginie Robillard ,  Anne Viaud  for their technical assistance and  I. Parfier  for her assistance in the preparation of the manuscript.
Little is known about the incidence of stroke in HF-PEF, particularly in the absence of AF. To investigate this further, we therefore combined and analysed patient-level data from two large HF-PEF trials, the Candesartan in Heart failure Assessment of Reduction in Mortality and Morbidity-Preserved trial (CHARM-Preserved, ClinicalTrials. gov NCT0 0634712)  5  and the Irbesartan in Heart Failure with Preserved Systolic Function trial (I-Preserve, NCT00095238),  6  to provide a robust estimate of the current incidence of stroke in patients with HF-PEF, with and without AF. We also tested a simple clinical model, developed in HF-REF,  7  for predicting the risk of stroke in patients without AF in this pooled dataset. Easy identification of those at highest risk of stroke coupled with the availability of new oral anticoagulants with a low risk of bleeding might allow for a stroke prevention strategy which has an acceptable benefit/risk balance in patients with HF without AF.
Trial patients In order to have a sufficiently large number of HF-PEF patients without AF for analysis, we pooled data from the CHARM-Preserved (NCT00634712) and I-Preserve (NCT00095238) trials. Each was a randomized, double-blind, placebo-controlled, multicentre trial and was approved by the appropriate institutional review boards. CHARM-Preserved and I-Preserve enrolled 3023 and 4128 patients, respectively. 5, 6 Together, these trials included a broad spectrum of patients with chronic HF-PEF. CHARM-Preserved enrolled patients aged !18 years in New York Heart Association (NYHA) functional class II-IV with a left ventricular ejection fraction (LVEF) >40% (although for the purposes of this study we included only patients with an LVEF !45%). I-Preserve enrolled patients aged !60 years in NYHA functional class II-IV with an LVEF !45% and corroborating ECG, echocardiographic or radiologic evidence.