dataesr/scientific-paragraphs-categorization huggingface dataset:
Following this rationale (i. e. , favorable clinical implications in managing diabetic patients), the proposed strategy can be easily extended and generalized to other common diabetes complications (e. g. , nephropathy, cardiopathy, etc. ) or other predictive clinical tasks. Therefore, through an additional experiment on a widely employed EHR ICU benchmark dataset (MIMIC-III dataset  [13] ), we also measured the imputation and predictive performance of the proposed ccGAN approach across different missingness rates. The main contributions to biomedical informatics can be summarized as follows: We proposed an ML approach to impute missing values from EHR data and provide the prediction of DR. The data imputation strategy is based on a novel ccGAN architecture that exploits the fully-available clinical features among different patients to infer other missing clinical features.
The prediction phase is realized by implementing and comparing different ML classifiers. We showed how the proposed ccGAN approach overcomes significantly other state-of-the-art methodologies in terms of data imputation (around 19. 79% of gain to the best competitor) and predictive performance (up to 1. 60% of gain to the best competitor) to solve DR tasks using a real MDC dataset. We demonstrated the robustness of our proposed approach across different missingness rates in terms of both data imputation (around 36. 46% of gain to the best competitor in the highest missingness rates condition) and predictive performance (up to 1. 61% of gain to the best competitor in the highest missingness rates condition) on an additional benchmark EHR dataset. The rest of the manuscript is organized as follows: Section 2 provides an overview of the state-of-the-art data imputation techniques in EHR data; Section 3 describes the employed real EHR dataset (i.
Related works We review the work on data imputation techniques tailored to imputing missing EHR data. We decided not to consider strategies that tried to model the temporal dependencies among missing values. This assumption excludes all methods of applying recurrent neural network-based imputation methods for modeling sequential patterns and dependencies in time series. Although these approaches assume potential relevance for ICU data, they cannot be applicable for treating routine EHR data as longitudinal time series  [14] [15] [16]. The rationale motivation is that the amount of observations per patient in our real clinical MDC dataset is limited and sparsely distributed over time (i. e. , on average 19 observations per patient). A non-temporal correlation among univariate characteristics is expected in this particular setting, as confirmed in Section 4.
g. , collection of images, texts, time series, or those similar to the actual observed EHR data); (ii) employ neural networks that allow exploiting any non-linear relation. GANs are state-of-the-art solutions to distribution modeling tasks defined by a collection of data of any complexity. In the direction of problems with missing values, first, advanced GAN-based models are proven to recover a distribution of interest from lossy observations only  [26, 27]. Secondly, recent algorithms propose data imputation strategies in different real-world data domains of study, such as incomplete image imputation -MisGAN  [27] ; medical records data imputation -GAIN  [4] ; and missing view generation -CollaGAN  [28] , VIGAN  [29]. Notably, the last two models tackle the missing view problem in multi-domain datasets, which is challenging.
However, the main interest of particular work is learning with partially observed data -the same objective as for MisGAN and GAIN algorithms. While both provide affordable results for missing data imputation on data with low and high rates of incompleteness, MisGAN architecture, in its turn, employs six neural networks in total to train, which makes it computationally expensive and time-consuming to work with. Accordingly, two main ideas from our method are combined from  [4]  and  [30]. First, we use a generator introduced in GAIN  [4] , which leverages a GAN to generate artificial missing values for data collection of different origins, including medical records. Second, likewise, to cGAN  [30] , which incorporates given auxiliary information into generative and discriminative functions, in our ccGAN formulation, we provide a significant step forward to our generator by conditioning it not only to the observable values but also to those fully-annotated features that are usually available in EHR datasets (e.
g. , age, weight, glycaemia, etc. ). Thus, our proposed ccGAN formulation provides novel insights within the ML and biomedical informatics community by leveraging the idea to combine fully observed with partially observed predictors to improve the data imputation accuracy. The research gaps intended to be filled by the following study can be summarized as follows: • Baseline data imputation approaches usually exploit univariate information (KNN, MissF) or linear information (MICE, matrix factorization) across features. These assumptions may lead to underestimating the true value of missing samples; • Auxiliary clinical information usually fully available in EHR datasets (e. g. , age, weight, glycaemia, etc. ) is not exploited by state-of-the-art GAN data imputation approaches. This clinical information may reduce the estimation error while improving generalization performance.
Multi-center diabetic dataset The real clinical EHR Multi-Center Diabetic (MDC) dataset originally consisted of 120K diabetic patients and is structured in demographics field (i. e. , patient's identification number (ID patient), gender, year of birth, diabetes diagnosis date); pathological field (i. e. , ID patient, ICD-9 codes, pathology diagnosis date); and lab tests field (i. e. , ID patient, lab tests codes, lab tests values, lab tests prescription date).
Definition of control and DR patients The diabetologist selected all the ICD-9 codes of the pathological field associated with DR: the univocal ICD-9 code indicates a non-DR condition, while all the other ICD-9 codes indicate a DR condition. All the ICD-9 codes that did not specify DR or non-DR conditions were removed from pathological field, because they were no longer of interest for the use case. Then, for every patient, both ICD-9 and lab test codes were removed if the pathology diagnosis date and lab tests prescription date preceded the diabetes diagnosis date (i. e. , the use case temporal starting point). Fig. 1  describes the inclusion criteria for selecting the time window of interest (TWOI) for control and DR patients.
DR patients -TWOI A DR patient was defined by at least an ICD-9 code of non-DR followed by one ICD-9 code of DR. A TWOI of a DR patient (see Fig. 1  -bottom side) is delimited by the earliest ICD-9 code of non-DR and the earliest ICD-9 code of DR. A patient was included in the study only if the date of the earliest ICD-9 code of non-DR preceded the earliest date of ICD-9 code of DR.
Features analysis Two diabetologists chose a subset of 48 predictors based on their experience in the clinical task of interest. Thus, the predictors were grouped by the distribution of their missing values (see Table  1 ). Predictors were split into green (  ), yellow (  ), and red (  ) predictors according to the following criteria (see Fig. 2 ): •   contains less than 2% of missing values per patient and less than 50% of missing values for the whole dataset; •   contains between 3% and 40% of missing values per patient and between 50% and 80% of missing values for the whole dataset; •   contains more than 40% of missing values per patient and more than 80% of missing values for the whole dataset.
Patient selection To obtain the   predictors filled (i. e. , no missing values) across all the patients, we removed 2981 patients (i. e. , ∼ 80% control patients, 20% DR patients) that did not contain all the   predictors filled, simultaneously. The subset of patients excluded from the study, which also contains predictors with a percentage of missing values > 90%, corresponds to 7% of the total number of patients and, therefore, can be considered negligible with the full sample size. Table  2  describes the statistics of the MDC dataset after the patient selection preprocessing stage.
Research methodology The amount of observations for each patient in our real clinical MDC dataset is limited and sparsely distributed over time (see Table  2 ). Starting from this evidence, we justify our non-temporal data imputation approach by computing the auto-distance correlation, intending to verify whether there are no temporal dependencies within multiple observations of the same patient across time (see Section 4. 1). Afterward, we present our ccGAN approach for data imputation on the MDC dataset. Diabetes duration years.
Thus, the non-temporal correlation among the predictors was evidenced. Considering this finding, we employed a non-temporal configuration of the MDC dataset, where a single value for each predictor is considered for the th patient. In the current clinical setting, drawing a trajectory of the predictor values across time with respect to the target of the task remained difficult. On the other hand, if we had considered ICU data, catching a temporal correlation among the values of the predictors across time would have been easier. This peculiarity is due to a different necessity of the clinician, which results in another.
Accordingly, we consider a data collection 𝑆 = {(𝐱 𝑦 𝑖 , 𝐱 𝑔 𝑖 )} 𝑁 𝑖=1 = 𝑆 𝐹 ∪ 𝑆 ⟂ of size 𝑁 that consists of two subsets 𝑆 𝐹 = {(𝐱 𝑦 𝑖 , 𝐱 𝑔 𝑖 ) ∈  𝑦 ×  𝑔 } 𝑁 𝐹 𝑖=1 and 𝑆 ⟂ = {(𝐱 𝑦 𝑖 , 𝐱 𝑔 𝑖 ) ∈ 𝑦 ×  𝑔 } 𝑁 ⟂ =1 , where ∪ {⟂}) and symbol ⟂ indicates unobserved components ( =   +  ⟂ and   ≪  ⟂ ). Then further in our explanation, when referring to a sample (  ,   ) drawn from  ⟂ , we will use (x  ,   ). Accordingly, our ccGAN-based imputation strategy aims to simulate a distribution  (  |x  ,   ) by learning a function  that, conditionally on extra information given by green predictors   , generates, from partially available values of yellow predictors x , corresponding complete vector. Our model provides a significant step forward to other generative competitors by conditioning the GAN model to the observable values and the fully-annotated features usually available in the EHR dataset (e.
𝐷 ← (-  ,  ()   , , ) # update of   (+1)  ← (  ,  ()   , , ) # update of  end Algorithm 1: Pseudo-code of ccGAN. Extended code available at https://github. com/michelebernardini/ccGAN. and the imputed   represent the predictors for each patient, while the label is represented in terms of control (0) and DR (1) patients.
Experimental analysis and results In this section we first report both the imputation 5. 1 and predictive (see Section 5. 2) performance comparisons for the MDC dataset. Afterward, we provide the performed statistical analysis to highlight the possible significant predictive performance improvement of the proposed ccGAN approach to state-of-the-art models (see Section 5. 3). Finally, we report an additional experiment on a benchmark dataset (see Section 5. 4) to evaluate the imputation and predictive performance comparisons for different missingness rates.
Additional experiment on a benchmark dataset: MIMIC-III To evaluate the robustness of our proposed ccGAN approach across different missingness rates  [36] , we adopt the benchmark MIMIC-III dataset  [13, 37]. Unlike the real MDC dataset, where the missingness is naturalistic, we artificially generate missing values in the MIMIC-III dataset to purposefully accomplish this aim. We describe the MIMIC-III dataset and the synthetic missing values generation in Section 5. 4. 1. We describe the results in terms of imputation and predictive performance in Section 5. 4. 2 and 5. 4. 3, respectively.
Benchmark MIMIC-III dataset We extracted a subset (i. e. , Feature set A  [13] ) from the original MIMIC-III dataset and then averaged the feature observations over time. The extracted feature set consists of 17 features utilized in calculating the SAPS-II score. To evaluate the robustness of the cc-GAN imputation performance according to different missingness rates, among all the patients, we selected only the 8509 patients with fully observable features (i. e. , no missing values)  [36]. The features such as age, gender, admission type, and Glasgow coma scale were selected as. In contrast, for the remaining ones (  ) different missingness rates were artificially generated as follows: we all the time chosen a proportion of patients (frac_mis = [0. 3, 0. 6, 0. 9]) whose we developed missing values by using a random mask with a binomial distribution with different probabilities (p = [0.
Predictive performance Even in the MIMIC-III dataset, the XGB and RF models achieved the best predictive performance results among other tested supervised classifiers, such as DT, SVM, LR, and KNN. Thus we show only the XGB (see Fig. 4 ) and RF (see Fig. 5 ) predictive performance results in terms of PRAUC for the   +  setting across different missingness rates. The XGB model associated with our proposed ccGAN imputation technique outperformed the other competitors in the most challenging settings (i. e. high missingness rates): frac_mis = 0. 6, p = 0. 9; frac_mis = 0. 9, p = 0. 6; and frac_mis = 0. 9, p = 0. 9. Also, the RF model associated with our proposed ccGAN imputation technique outperformed the other competitors in the most challenging settings (i. e. high missingness rates): frac_mis = 0.
Impact on the multi diabetic center dataset The imputation performance comparisons highlighted how the proposed ccGAN strategy was a reliable solution with respect to baseline GAN-based strategies for imputing missing values in the clinical MDC dataset under the MCAR assumption. Moreover, the predictive performance results and the statistical analysis showed that our proposed ccGAN strategy overcame the other state-of-the-art data imputation approaches for all ML models and settings. Our proposed ccGAN data imputation strategy was robust and effective in dealing with challenging real EHR datasets characterized by high sparsity, imbalanced settings, and noisy and redundant features. Even if the   +  setting achieved the best predictive performance, the experimental results showed that the single   set contains more discriminative power than the single   set.
This fact motivates that a correct and ad-hoc missing values imputation mechanism could be crucial to obtain a satisfactory predictive performance on routine EHR data. The posterior interpretability analysis of the employed ML models also supported this assumption. We computed the feature importance as the impurity accumulation decreased within each tree. The impurity decrease was computed for each feature and was averaged over each CV-10 run (see Fig. 6 ). The microalbuminuria ∈   was the most important predictor in the   +  setting using the ccGAN strategy associated with the RF model. This outcome highlights how the proposed data imputation strategy consistently imputes missing information, representing a discriminative pattern left unseen if we had used the original missing data as predictors.
Robustness for different missingness rates on the benchmark MIMIC-III dataset The imputation and predictive performance results achieved from the benchmark MIMIC-III dataset justified the employment of the proposed ccGAN approach in a setting closer to the real clinical setting. In that scenario, the lab test exams are sparsely prescribed across patients; thus, the missingness rate is significantly high. Indeed, the additional experiment conducted on a benchmark MIMIC-III dataset proved how the ccGAN approach was the best to impute a significant number of missing values (90% of the patients have a 90% probability of containing missing values). Additionally, the ccGAN approach can be easily extended to other types of tasks besides diabetes complications-oriented predictions and this characteristic suggests its potential scalability in several predictive medicine tasks.
Conclusions We proposed a novel ccGAN architecture capable of imputing missing values from routine EHR data collected from a multi-diabetic centers platform. We demonstrated how the proposed data imputation strategy consistently predicted DR in high missingness rates (i. e. , between 3% and 40% of patients have the candidate feature missing). Within a DR screening program, our method is currently integrated into a clinical decision support system (CDSS). It permits discovering the most discriminative predictors by also considering the missing information. The positive effects of the ccGAN strategy encompass various aspects, including unveiling partially annotated features that can discriminate the disease. This outcome allows for a more comprehensive screening process, enabling better identification and understanding of the disease's characteristics.
Overall, these positive effects contribute to the better functioning of territorial presidium structures responsible for providing care to individuals with diabetes. Through the implementation of advanced strategies and interventions, the system becomes more adept at meeting the needs of the diabetic population within their respective territories. Among the limitations, it is worth noting that the performance, as well as the gain of the proposed data imputation methodology, is not uniform across different datasets, tasks and levels of missingness. Future direction in this context might be addressed to further test the generalizability of the proposed approach in a more challenging scenario (e. g. , general practice), where the level of missingness is even higher. As an additional limitation, we did not include other EHR fields, such as exams and drug prescriptions, which may contain a considerable amount of missing information related to the availability of a generic drug code (parent code) and the missing of a specific unique drug code (child code).
Directed evolution The DNA libraries were incorporated into E. coli XL1 Blue cells and transferred onto LB-Agar plates supplemented with ampicillin, obtaining 500-800 colonies per plate. Depending on the size of the library, 20-50 plates were screened in each round. After an overnight incubation at 37 ⁰C, LB-agar plates containing the libraries were further incubated at 4 ⁰C during 24 hours prior imaging to favor protein folding. Using a homebuilt screening platform  38  , the plates were imaged before and after the addition of 10 ml of screening buffer (30 mM MOPS, 100 mM KCl, 50 mM CaCl2, agar 0. 5%, pH 7. 2) maintained at 42 ⁰C during the screening. Images were acquired and analyzed using a customized python routine  38. In general, after each round of screening, the selected indicators were expressed and purified for further characterizations.
First, the absorption and emission spectra of three serial 1:2 dilutions were acquired in the same cuvette. Then, the integrated emission spectrum was plotted against the maximum absorption and the slope was determined. For mNeonGreen a quantum yield of 0. 8 was assumed  19. The Ca 2+ affinity of the indicators was determined using MOPS buffer supplemented with 10 mM EGTA, 1 mM Mg 2+ and increasing concentrations of Ca 2+ as previously described  39. Dissociation constant (KD) values were determined by plotting the log10 values of the [Ca 2+ ] free concentrations in mol/l against the corresponding ΔF/F0 values (normalized to ΔF/F0 at 39. 8 μM Ca 2+ ), and fitting a sigmoidal curve to the plot. Prism was used for data analysis. To determine the pKa of a fluorescent protein (indicating its pH stability), a series of MOPS/MES buffered solutions supplemented with 100 mM Ca 2+ were prepared, with pH values adjusted in 0.
STED and confocal microscopy of hippocampal neurons We used a custom-built 3D-STED/confocal setup  50  constructed around an inverted microscope body (DMI 6000 CS, Leica Microsystems) which was equipped with a TIRF oil objective (x100, 1. 47 NA, HXC APO, Leica Microsystems) and a heating box (Cube and Box, Life Imaging Services) to maintain a stable temperature of 32°C. A pulsed-laser (PDL 800-D, PicoQuant) was used to deliver excitation pulses at 488 nm and a deexcitation laser (Onefive Katana 06 HP, NKT Photonics) operating at 594 nm was used to generate the STED light pulses. The STED beam was profiled to a donut shape using a spatial light modulator (Easy3D Module, Abberior Instruments). Image acquisition was controlled by the Imspector software (Abberior Instruments). The spatial resolution for the excitation beam was 175 nm (x-y) and 450 nm (z) and for the STED beam 60 nm (x-y) and 160 nm (z).
Zebra fish data analysis and statistics All images were ratiometrically processed as described and analyzed using the ImageJ software with a custom-made macro (available upon request). For each fish, n ≥ 6 cells/region of interested were analyzed for the different tissues (Notochord, muscle or epithelium). The mean ratio GreenT-EC/mCyRFP1 was then plotted for each animal. All experiments included between 3 to 5 animals per group. Statistical analyses were performed using Graphpad Prism software. Data was tested for normality using the Saphiro-Wilk test. Normally distributed data is presented as mean with standard deviation and was analyzed using 2-way ANOVA followed by a Tukey's or Šídák's multiple   GreenT-ECb (blue) and lower affinity GreenT-ECc (yellow) were measured by rapidly mixing solutions of protein in MOPS 30 mM, KCl 100 mM, Mg 1 mM with MOPS 30 mM, KCl 100 mM, Mg2+ 1 mM containing increasing concentration of Ca2+.
JI0281 × Caméor A recombinant inbred population derived from the cross between cv Caméor (also designated JI3253) and JI0281 (designated 'P. sativum landrace DCG0248' in Kreplak et al. 5  ) was generated to take advantage of the large genetic distance between these two sequenced genomes (Kreplak et al. 5  ). Caméor is a French field pea variety, and JI0281 is a P. sativum accession from Ethiopia. For the JI0281 × Caméor RIL population, an Axiom SNP array with 84,691 features was used to score markers segregating in the F6 and F7 generations. The F7 individuals were derived from the F6 individuals of which a subset was genotyped. The set of markers which gave useful data was slightly different for the two data sets, presumably reflecting slightly different assay conditions. The F6 data set comprised 375 individuals and 17,936 markers.
F6 and F7 RI Lines Those RILs for which marker data exists at the F6 and F7 generation can be compared to validate the correspondence between the F6 and F7 individuals, their marker scores and the position of crossover events. This analysis showed that some RILs of the same generation share a parent at a generation later than the F1. 366 RILs were genotyped in both the F6 and F7 generations. 10 RILs were genotyped in the F6 alone and 204 were genotyped only in the F7. The relationships between the F6 and F7 individuals is given in Supplementary Table  1  and the allele call frequencies according to call category are given in Supplementary Table  2.
from resequencing data for 42 Pisum accessions. A 10% Minor Allele Frequency additional filter was applied. Corresponding VCF files were submitted to Thermo Fisher Scientific for probeset design and 84,691 SNPs were successfully tiled on the pea Axiom array. SNPs and their position on the pea genome are listed in Supplementary Table  3. Genotyping of the F6 population with the array was performed using the Affymetrix GeneTitan platform following the manufacturer's guidelines. Genotyping of the JI0281 × Caméor F7 RILs and the JI2202 × JI2822 RILs was carried out by Neogen Ltd (Neogen Europe, Ayr, Scotland), whereas the JI0281 × Caméor F6 RILs were genotyped by Gentyane INRAe Genomics platform (https:// short url. at/ zBMZ4,  50  ). Results were analysed with the Axiom Analysis Suite v5. 0. 1.