dataesr/scientific-paragraphs-categorization huggingface dataset:
5. "Longitudinal ethnography". An online conversation could be revamped over time (for example every year) to keep track of how its collective point of view evolves. The combination of these elements requires a cultural shift from practitioners. Ethnographers tend to work alone, and there is as yet no culture of open data in the discipline, as access to coded interviews and fieldnotes belongs to the ethnographer alone. To the best of our knowledge, the OpenCare dataset is the first-ever open dataset including primary and secondary data. Yet, the payoff of such a shift is substantial. Ethnography could bring its unique methodological advantages to new problems, that demand a scale and consistency it now cannot supply. For example, we could imagine a version of Eurobarometer based on an open online conversation.
Data We test KMR on the data of the DREAM 8 Toxicogenetics challenge 2  , a joint crowdsourcing initiative of Sage Bionetworks, DREAM and scientists at UNC, NIEHS and NCATS to assess the possibility of developing predictive models in toxicogenetics. The effect of 156 chemical compounds was tested on 884 lymphoblastoid cell lines derived from participants in the 1000 Genomes Project and representing 9 distinct geographic subpopulations. The toxicity response was measured in terms of EC10, i. e. , the concentration in chemical compound at which the intracellular ATP content is decreased by 10 percent. During the challenge, participants had access to a subset of these data, corresponding to 106 chemicals tested on 620 cell lines. The goal of the challenge we focus on was to predict the effect of these 106 chemicals on the 264 cell lines that were not given to the participants.
For 15 among the 106 chemicals tested, no effect on cytotoxicity was observed across the entire population  [5]. To avoid the introduction of noise in the ranking, these compounds were not considered to score the predictions during the challenge, and we also removed them from our experiments below. As all training and test data were released after the challenge was over, we combine them together to obtain a set of 91 chemicals tested on 884 cell lines, the largest toxicogenetics dataset available so far. For each cell line we have access to three covariates (population, batch and sex), to DNA variation profiles (approximately 1. 3 million single nucleotide polymorphisms or SNPs), and to gene expression levels by RNA sequencing for a subset of the cell lines (46 256 transcripts). Each chemical also comes with a set of structural attributes obtained by standard chemoinformatics methodologies, including 160 descriptors based on the Chemistry Development Kit (CDK) and 9272 descriptors based on the Simplex Representation of Molecular Structure (SIRMS).
In addition, we compute for each chemical 881 binary descriptors encoding the presence or absence of 881 substructures defined in the PubChem database  [12] , and 1554 descriptors describing the ability of the chemicals to interact with 1554 human proteins known to be potential targets for drugs and xenobiotics  [29]. To use these various, heterogeneous data as inputs to KMR, we transform them into positive definite kernels for cell lines and chemicals, as follows. • For each vector representation (vector of RNA-seq expression after log transformation, and vector of SNP for cell lines; vector of CDK descriptors, vector of SIRMS descriptors, and vector of predicted targets for chemicals), we compute 10 Gaussian RBF kernels of the form K i (v, v ) = exp -||v -v || 2 /(2σ 2 i ) , for i = 1,. , 10, where v and v are two such vectors scaled such that the maximum distance ||v -v || between all pairs of cell lines (or chemicals) in the training set is equal to 1, and the bandwidth σ 2 i takes a value in the grid {1/20, 1/10, 1/5, 2/5, 3/5, 4/5, 1, 2, 4, 8}.
• From the 3 covariates for cell lines (population, sex and batch), we build 4 kernels: one linear kernel for each covariate, and one linear kernel for the vector of covariates. This amounts to fitting a linear model based on a single covariates, or on all covariates together. We denote these kernels respectively by KcovariatesPopulation, KcovariatesSex, and KcovariatesBatch for the kernels based on each individual covariate, and Kcovariates for the kernel based on all covariates together. • For chemicals, we include two kernels based on detecting substructures in their 2D representations: a marginalized graph kernel  [10]  as implemented in the ChemCPP package with defaults parameters 3  , and a linear kernel obtained from the PubChem fingerprint which characterizes each chemical compound by a binary vector indicating the presence or absence of 881 substructures  [12].
When different kernels can be thought of, we note that a simple way to combine them is to sum them together (what we refer to as the integrated kernel Kint), providing a simple default solution if one wants to pick a single kernel to summarize heterogeneous data such as genotype, gene expression and covariates for cell lines. As for chemicals, which correspond to tasks in the challenge, we note that some kernels can depend on descriptions about the chemicals (such as their 2D structure), while other kernels such as the multitask kernel Kmultitask* and the empirical kernel Kempirical use no information about the chemicals. Using chemical information may be relevant if one believes that a particular chemical feature is important for toxicity, and if one wants to be able to make toxicity predictions for chemicals not seen in the training set; however, if we just want to make toxicity predictions for compounds of the training set, Kempirical and Kmultitask* may be good default kernel.
Performance on the DREAM8 toxicogenetics dataset We now assess the performance of KMR to predict the toxicity of known chemicals on new cell lines on the data of the DREAM8 Toxicogenetics challenge. As explained above, we collected from the challenge 80,444 EC10 toxicity measurements of 91 chemicals on 884 cell lines. To mimic the setting of the challenge on this dataset, we assess the performance of different methods by 5-fold cross-validation over the cell lines, repeated 10 times, meaning that we split 50 times the cell lines into a training set (80% of the cell lines) used to train a model, and a test set (20% of the cell lines) used to assess the performance. Following the method used in the challenge, we assess the performance of prediction in terms of concordance index (CI) on the test set per chemical, averaged over the chemicals.
This suggests that on this dataset, there is little benefit to share information between chemicals. For example, the performance of the plain multitask kernels Kmultitask* keeps degrading slightly as more information is shared between tasks. The only kernel near the top of the list that differs a lot from the Dirac kernel is the empirical kernel, which could be capturing some interesting signals between chemicals with similar toxicity profiles on the training set. Since the RNA-seq data was only available on 337 cell lines, we also analyze the behavior of KMR by cross-validation on the subset of cell lines with RNA-seq data (Figure  6 ). In spite of the reduced set of the training set, we notice no decrease in performance compared to the results on the full set of cell lines (Figure  3 ), if not a slight increase.
The relative performance of the different kernels is overall conserved compared to the experiment on all cell lines, except that, unsurprisingly, the performance of kernels based on RNA-seq information increases and, for example, now outperforms the performance of the covariate kernel. However, the integrated kernel remains the best overall. To assess the performance of KMR compared to other, standard methods for regression, we compare it to regression with elastic net, lasso and random forests  [9]. Since these methods have no obvious multitask version, we run them in a single task mode, fitting a model on each chemical independently. For all methods, we give as input the Kint matrix to represent cell lines; for KMR, we additionally use Kempirical to share information among chemicals. We note that not using a multitask formulation is probably not a strong disadvantage on this dataset, as we showed that the Dirac kernel has a good performance with KMR, and as the winner of the challenge trained a RF model for each chemical separately, with specifically handcrafted features.
Here our goal is just to get a sense of how different methods behave if given the same input data. Figure  7  summarizes the distribution of CI values over the 91 chemicals for the different methods. While elastic net, lasso and RF regression are not significantly different, KMR significantly outperforms them (paired t-test p-value < 10 -13 ). Furthermore, KMR is about 30 times faster than lasso and elastic net, and more than 100 times faster than RF trained with only 100 trees (Table  2 ). In summary, this analysis by cross-validation on DREAM toxicogenetics dataset of the different combinations of kernels shows that (i) the absolute performance remains limited but varies with the combination of kernels used, (ii) there is some predictive information in the population information, and in the RNA-seq information, (iii) the integrated kernel Kint consistently outperforms all individual cell line kernels, (iv) there is limited benefit of using a multitask learning strategy, and Table  2 : CPU time to train a model on half of the full dataset, including parameter optimization by cross-validation, and to predict the toxicity on the second half.
All methods were run from R, using the glmnet package for elastic net and lasso, the randomForest package for random forests, and the kmr package for KMR. All methods were used with default parameters, except for the number of trees of the random forest which was reduced from the default ntree=500 to ntree=100 to reduce by a factor of 5 the total CPU time, which is already two orders of magnitude slower than KMR.
Results during the challenge We participated as a team named CASSIS to the DREAM Toxicogenetics challenge. During the challenge, we only had access to toxicity measurements for a training set of 620 cell lines, and each participant had to submit toxicity predictions for the remaining 264 cell lines. Submissions were scored and ranked by decreasing mean CI over the 91 chemicals. We performed an analysis similar to the one described in the previous section, but restricted to the training set, in order to inform our submission. Results were overall similar, except for a strong batch effect that was present in the training set but, as shown above, is much less marked when all data are considered together. We submitted a prediction based on KMR using Kint for cell lines, and Kempirical for chemicals.
A. S. -G. reports nonfinancial support from Astellas, during the development of this supplement; nonfinancial support and personal fees from Astellas, outside associations in prospective, investigator-sponsored, observational trials at the individual patient level. This new understanding could lead to future tests, interventions, and clinical trials, supported by the industrial investment that is needed for optimal progress. Randomized controlled trials (RCTs) have facilitated many improvements in transplantation and continue to represent the standard we should strive to achieve. However, as a data source, the RCT has some significant disadvantages, particularly the inclusion of highly selective populations that may not adequately reflect the real world. These populations are often excessively 'front-end loaded', being recruited at the time of transplantation, with most events occurring in the first few months posttransplantation, yet they are being used to develop principles for long-term management.
5, 6 The key question is to understand the basis of this association and the phenotype and mechanism of the failures. Associations with donor age are complicated by the inherent bias in organ allocation. Because of clinician reluctance to give older organs to low-risk young recipients, old organs are allocated preferentially to high-risk older recipients, often with serious comorbidities that are difficult to capture in databases. This strongly links the influence of old donor age and old recipient risks, a bias that is not readily corrected by statistical methods. 7 In kidney transplantation, the relationship between recipient age and donor age is so strong that it is often impossible to ascribe a phenotype to either variable alone. 7 One result could be to overestimate the adverse effects of donor age, triggering unnecessary organ discards.
This illustrates the problem of "data drift"; that is, associations and risks change as practice changes. Conclusions reached in earlier eras must be constantly reassessed for validity and relevance. Analysis of "big data" from large administrative databases and electronic health records has been used effectively in some areas of medicine and may have applications in transplantation, provided the same critical standards are applied and confounders are sought. The predictions from such studies must be validated in prospective studies, and the mechanisms of the associations and the disease phenotypes should be understood at the level of the individual patients. In conclusion, there are many opportunities beyond phase III trials and much of the new insights will come from such opportunities in the next decade.
Not only do registries provide us with a large amount of data that continues to expand over time, they also provide long-term follow up beyond that of RCTs. The European Liver Transplant Registry (ELTR) was inspired by the vision of Henri Bismuth, and was established to create a database comprising the whole experience of European liver transplant centers. Data are collected prospectively using a standardized 2-part questionnaire,  15  which is regularly updated by a scientific committee. Part 1 comprises date and indication for the liver transplantation, donor and recipient data, surgical technique used and immediate postoperative immunosuppression therapy, and Part 2 comprises graft and patient outcomes and the longer-term immunosuppressive regimen. The data held in the registry are subject to strict internal and external quality control, with annual audits of randomly selected centers.
By making changes to the immunosuppressive regimen and reducing the variability of tacrolimus exposure, further improvements in long-term outcomes for kidney transplant recipients may be achievable. When we assessed the variability of tacrolimus exposure in our clinic, we did not expect high variability to be identified in our patients. However, data from 255 prospectively compiled patient records 1 year posttransplant confirmed significantly higher rates of AR and graft loss for patients with variability above the median of all levels between 6 and 12 months posttransplantation, compared with patients with variability below or equal to the median. 75 In the first 6 months posttransplant, exogenous treatments with steroids and antibiotics to treat graft failure, AR and infection are frequently applied, and could affect tacrolimus levels by artificially raising the variability of exposure.
With any data set or analysis it is important to recognize the limitations when interpreting and applying the findings to clinical practice. The data on variability of tacrolimus exposure that are currently available have generally been generated via retrospective analysis and covariation with factors, particularly nonadherence, and are impossible to fully dissect. The ADMIRAD study demonstrated that adherence to tacrolimus improved by 10% when patients were converted from immediate-release to prolonged-release tacrolimus. 67 Converting patients from immediate-release to prolonged-release tacrolimus preparations has been shown to reduce intrapatient variability in both South Asian  76  and European populations. 77 The clinical consequences of this degree of improved adherence cannot be easily mapped, but logically, a high-variability cohort should benefit in terms of clinical outcome measures from a once-daily, prolongedrelease formulation of tacrolimus.
Pretransplant risk factors include age, sex, previous cardiac and vascular disease, total time on renal replacement therapy, smoking status, and hyperlipidemia. Posttransplant risk factors include left ventricular hypertrophy, hypertension, renal transplant dysfunction, NODAT and hyperlipidemia. Some of these risk factors can be modified by the immunosuppressive regimen and treatments that are administered posttransplant. Data from the United States Renal Data System database show a prevalence of 42% of NODAT in kidney transplant recipients. 88 NODAT is associated with increased cardiovascular risk and has a negative impact on graft function. 89 Both pretransplant diabetes and NODAT are associated with significantly poorer patient survival compared with absence of diabetes. 90 A number of immunosuppressive therapies, including corticosteroids, tacrolimus, CsA and mammalian target of rapamycin (mTOR) inhibitors, have been associated with a higher risk of developing NODAT.
A decline in renal function is thought to be one of the main causes of poor overall outcomes postliver transplant. Data from the US National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) liver transplantation database suggest that the influence of renal failure on long-term outcomes increases over time, with renal failure associated with a hazard ratio of 7. 5, >5 years posttransplant. 154 Some complications affecting long-term patient survival after liver transplantation are associated with the immunosuppressive regimens used,  167  although data from the NIDDK suggest that diligent management of modifiable factors, including diabetes, hypertension and renal insufficiency, could improve long-term patient survival. 154 he risk of impaired renal function and the need for renal replacement therapy in liver transplant recipients is thought to be increasing due to the use of organs from ECDs and transplantation in older patients with higher MELD scores.
In terms of exposure, lowering the exposure to CNI can be achieved by dose minimization or delaying the administration of CNI until several days posttransplantation. Data from the ReSpECT study showed that patients receiving reduceddose (target trough level, ≤8 ng/mL), immediate-release tacrolimus delayed until day 5, in combination with daclizumab, MMF and corticosteroids had less kidney impairment after 52 weeks compared with those receiving a higher-dose (target trough level, >10 ng/mL), immediate administration of the same formulation of tacrolimus (without daclizumab and MMF). 170 It was unclear from the results of the ReSpECT study whether the reduced renal impairment was due to the delayed administration of tacrolimus or the overall reduction in exposure over time. The DIAMOND study (ADvagraf studIed in combinAtion with MycOphenolate mofetil aND basiliximab in liver transplantation) was designed to investigate administration of prolonged-release tacrolimus-based regimens further.
169 Data from this study indicate that trough levels of 7 to 10 ng/mL are efficacious in preventing AR and are associated with longer-term graft survival compared with patients whose trough levels are outside that window. 169 A systematic review of 64 liver transplant studies suggested that tacrolimus trough levels between 6 and 10 ng/mL during the first 4 to 6 weeks after transplantation could reduce renal impairment without increasing the incidence of moderate/ severe ACR. 173 arly immunosuppression seems necessary for optimal immunologic outcome in liver transplant recipients, especially in light of the role of de novo DSAs and the association between DSAs, AR and graft loss. The concept of tacrolimus minimization as a protective factor in liver transplantation is not based on evidence or data from RCTs in liver transplantation, but seemingly on traditional views of CNIrelated side effects (predominantly on kidney function).
The Reference Group Trial (www. clinicaltrials. gov) is being conducted at all of the sites mentioned above, and serves as an independent, multi-center, phase IV clinical trial that is designed to corroborate historic renal transplantation statistics and generate 'baseline' immunologic health-related quality of life, and health-economic data sets for The ONE Study cell therapy trials. The Reference Group Trial uses a standard immunosuppressive treatment regimen that is similar to the one that is being used in the Cell Therapy Trials. The Reference Group Trial has finished recruitment, and has now entered the follow-up phase. In parallel, 6 Cell Therapy Trials have begun and will continue to recruit patients, the goal being to recruit 8 to 16 patients into each trial. All of the groups conducting Cell Therapy Trials will essentially follow the same immunosuppressive treatment regimen, with the exception that their specific cell therapy products will be applied to the kidney transplant recipients; cell infusion replaces basiliximab induction therapy being used in the Reference Group Trial.
The immune monitoring has already been fully developed and implemented in the Reference Group Trial, demonstrating the feasibility and value of this testing concept. For instance, The ONE Study flow cytometry panels have been developed and standardized with a third party (Beckman Coulter). 225 The concept of The ONE Study immune monitoring has generated significant interest and is presently being adopted by other international immune monitoring laboratories as a means of standardizing immune monitoring performance. 226 Standardized electronic data collection systems have also been developed in parallel to ensure consistent data gathering. This conceptual approach to immune monitoring is a major accomplishment of The ONE Study. In summary, the ONE Study is progressing well toward the planned objectives.
Summary of Key Learnings in Kidney Transplantation Associate Prof Jonas Wadström In light of the use of marginal donors in kidney transplantation, new techniques are required to optimize donor management, improve organ preservation after retrieval and attenuate reperfusion to improve graft survival. However, there is a gap in the literature between experimental research addressing IRI and data that can be translated to clinical practice. 43 It is clear that to improve graft and patient survival, donor management strategies need to be used within the context of patient management and alongside pharmacologic therapies. CNI minimization, with the aim of reducing nephrotoxicity, was traditionally common practice in kidney transplantation; however, data suggest that underimmunosuppression has a negative impact on graft survival.
25, 78 Registry data show that tacrolimus trough levels less than 5 ng/mL 1 year posttransplant are associated with inferior graft survival, indicating that low tacrolimus trough levels should be avoided. 25 Steps need to be taken to modify the risk factors associated with underimmunosuppression, including improving adherence to treatment and minimizing the variability of CNI exposure to improve long-term outcomes. Data suggest that DSA now supersede CNI toxicity and chronic allograft nephropathy in their ability to cause chronic deterioration of the transplanted graft. 50 Information to assist us in understanding the intricate nature of the histologic patterns that can be indicative of subclinical AMR and kidney graft failure is emerging. 54 The implementation of effective screening procedures and improved kidney allocation policies in our clinics will enable us to move to prevent rather than treat AMR and to improve therapeutic outcomes.