dataesr/scientific-paragraphs-categorization huggingface dataset:
A leak detection scheme based on an unsupervised learning technique was presented in Aksela,  Aksela, and Vahala (2009). Specifically, Self Organizing Maps or SOMs  (Kohonen, 1990)  were exploited. This kind of techniques have the advantage of not needing information about all the possible leaks, as the SOMs are trained so that the vectors of the model represent training flows so that they are independent on the detected leaks, i. e. , similar flows would be mapped into the same or close model vectors. The method is evaluated through a realistic case study, using actual flow data from the studied WDN. The performance on the tested events was excellent, highly reducing the appearance of false positives, although more events would be required to fully assess the method's performance. A typically exploited technique in mixed model-based/data-driven methods like SVM was used in  Mounce, Mounce, and Boxall (2011)  in an online training fashion, complemented with the necessity of historical datasets.
Specifically, Support Vectors Regression (SVR) is considered, and the method is tested over a real WDN in UK, working in parallel to the method explained in  Mounce et al. (2008). Although the SVM-based technique seemed to perform faster than the AI/FIS one, several non-leak abnormal events (like unusual demands) raised the alarm of the SVM method, increasing the ratio of false notifications. Years later,  Laucelli, Romano, Savić, and Giustolisi (2016)  proposed an Evolutionary Polynomial Regression (EPR) paradigm to model the behaviour of WDNs for leak detection purposes. EPR is based on a multi-objective optimization problem, solved using GAs, that aims to maximize the fitness of prediction to data and minimize the number of required explanatory variables and polynomial terms. It is trained using pressure and flow data from datasets covering a week, to then test the obtained model with another set of datasets.
The achieved nominal EPR model would be used for comparison with the entering hydraulic measurements, raising alarms when thresholds are surpassed. A case study with available real data was proposed to test the method. In average, the results were acceptable, although smaller leaks became undetectable when another large leak was occurring. A CNN-based method was proposed in Fang, Zhang, Xie, and Yang (  2019 ), with the aim to solve the multi-leak detection problem. The CNN-scheme is able to extract features from a historic leak dataset and then predict if there exist a new leak in the network by considering the learned features. An experimental setup was constructed to test the method, leading to high true positive rates even for three leaks. However, several simplifications are considered, like the simplicity of the experimental setup (only 21 nodes), a high density of sensors (all the nodes in one case and 8 in the other).
Historical flow measurements at the inlet were used in  Eliades and Polycarpou (2012)  to perform leak detection. The methodology adapts to the unknown and time-varying inflow dynamics through an approximation technique based on the update of the coefficients of a Fourier series. Then, the bias term of the generated series is considered, analysing changes in its value through the CUSUM technique. Finally, the features are compared with a threshold for the leak detection purposes. The leak size estimation is done by comparing the actual flow consumption and the Fourier series prediction. The methodology is compared to a night-flow analysis method, applying both to historical data from a DMA in Limassol, Cyprus. The comparison showed the superiority of the new method for detecting small leaks, leading to less false negatives, although additional work was necessary to reduce the false positives and the detection time.
In van Thienen (2013), a method to compare the current inlet flow measurements with the historical ones, called Comparison of Flow Pattern Distribution (CFPD), is used to create a plot with the aim of facilitating the decision for an expert about the detection of a leak and their size estimation. Although it is not an automated method, it is not computationally expensive, easily implemented and independent on a hydraulic model. The method is tested in datasets provided by actual water companies, analysing several cases of leakage, unusual demand events, as well as theoretical experiments. The results show the usefulness of the methodology, despite the necessity of the decision from a human operator reduces the impact of its advantages. A heuristic burst detection method was proposed in Bakker, Vreeburg,  Van De Roer, and Rietveld (2014).
Using flow and pressure measurements as input information, the approach continuously compares observed and predicted values for nodal demands and pressures. The prediction for the demands is obtained through an adaptive water demand forecasting model, whereas the expected pressures are achieved by means of a dynamic pressure drop -demand relation estimator. The former method learns the nominal demand patterns, considering week characteristics and other possible sources of outliers related to special dates. The pressure estimator analyses the relation between the pressure at the inlet and the pressures at other locations. Leak detection is achieved through the deviation with respect to a threshold. To evaluate the method, data from three supply areas at Netherlands were collected from a historic dataset.
This problem may affect model-based strategies too, although their exploitation of the network model allows them to study how the effects of leaks are added, searching for the best combination of leaks to explain the network behaviour, leading to feasible multi-leak solutions  (Berglund et al. , 2017; Sophocleous et al. , 2019). Data-driven strategies may be affected by this issue, although normally they can be easily extended to solve multi-leak cases, considering that they do not depend on the possible leak scenarios for their operation  (Rajeswaran et al. , 2017; Wang et al. , 2020). Some relevant examples of multi-leak methods are those taking part in the BattLeDIM2020 competition  (Vrachimis et al. , 2022) , which were required to solve the multi-leak problem due to the characteristics of the proposed benchmark and the provided datasets, containing several overlapping leaks of different nature and magnitude  (Daniel et al.
Flow sensors are typically used in detection schemes, mostly installed in the DMA inlets in order to detect changes on the overall consumption  (Buchberger & Nadimpalli, 2004; Palau et al. , 2012). Until recently, online demand data using AMR has not usually been considered in the literature, as their installation on real WDNs is not currently extended. To remark the usage of this kind of meters in the BattLeDIM2020 articles, as the facilitated benchmark included them in one area of the WDN. It is interesting to remark that the BattleDIM2020 case study did involve a section with AMRs, so that the methods in this contest also use this information.
, 2016; Ye & Fenner, 2011) , which mainly require data from the DMA inflows. This data is normally already gathered by water utilities for control purposes, so the availability of historical datasets is more common. Besides, these techniques are usually trained and calibrated with leak-free data, which is easier to obtain. Finally, let us highlight the existence of several benchmarks that have been used through the literature. They allow to reproduce the results and compare previous methodologies with new ones. In this category, it is necessary to highlight the recent L-TOWN benchmark from the BattLeDIM2020 competition. The top-performing methods from this contest have been described through the text and table.
Case study: a comparison between model-based and datadriven methods To complete the discussion, a practical comparison between a model-based and a data-driven technique is presented, using the Bat-tleDIM2020 benchmark (see  Vrachimis et al. (2022)  to complete the information about the BattLeDIM2020 characteristics). The network is represented in Fig. 4. The dataset of 2018 is used for the comparison of both methodologies, because the 2019 dataset is composed of numerous overlapping leaks that would hinder the extraction of conclusions. The features of the leaks appeared during 2018 are summarized in Table  2. The compared methodologies have different requirements and philosophies: • The model-based approach requires a calibrated model of the WDN to be implemented into a hydraulic simulator, as well as pressure and demand measurements from a set of distributed sensors.
Methods This retrospective observational multicenter cohort study used the Greater Paris University Hospitals Group's data warehouse (38 hospitals). Patients aged ≥ 75 years with a con rmed COVID-19 diagnosis and hospitalized from March 2020 to January 2022 were included. The study period was divided into ve waves. The fth wave (January 1st to 31st 2022) was considered as the Omicron wave as it was the predominant variant (≥ 50%), and was compared with waves 1 (March-July 2020), 2 (August-December 2020), 3 (January-June 2021) and 4 (July-December 2021). Primary outcome was in-hospital mortality. Secondary outcome was occurrence of ICU admission or in-hospital death. Multivariate logistic regression was performed, with a sensitivity analysis according to variant type.
To date, this data warehouse includes anonymous recorded data on the demographic characteristics of hospitalized patients such as date of birth, sex, vital status and date of death. It also contains, for each hospitalization, the hospital discharge summary (principal and related diagnoses coded according to the International Classi cation of Diseases 10th edition ICD-10), medical procedures and administered drugs (Anatomical Therapeutic Chemical (ATC) codes), comorbidities of patients from medical records and ICD-10 codes, and laboratory results. Data extraction was performed the 15th of April 2022.
Study population, follow-up and COVID-19 waves The study cohort comprised all adults aged 75 years or over, hospitalized in one of the 38 participating hospitals between 1st of March 2020 to 31st of January 2022, with a con rmed COVID-19 diagnosis (hospital stay with an ICD-10 code for COVID-19: U07. 1 except U0713, U10. 9 and/or at least one SARS-CoV-2 positive RT-PCR). If a patient was hospitalized more than once for COVID-19, only the rst hospitalization at AP-HP was included for analysis. Patients were included the day of admission, and followed-up until discharge, death or end of study period (15th of April 2022). One hospitalization could include transfers in different acute care departments (intensive care units, acute medical wards, intermediate care units). According to the French epidemiological data on SARS-CoV-2 circulation in the Greater Paris area  [14]  and by variant dominance, the overall study period was divided into 5 COVID-19 waves, de ned as follows: rst wave, from March to July 2020; second, from August to December 2020; third, from January to June 2021; fourth, from July to December 2021; fth, from 1st to 31st of January 2022.
Statistical analysis Data are presented as numbers (percentages) for categorical variables, mean (standard deviation) and median [ rst to third quartiles, interquartile range, IQR] for quantitative variables with normal and nonnormal distribution, respectively. Normality was assessed with the Kolmogorov-Smirnov test and a graphical representation of the distribution. Chi-squared test or Fisher test were used for categorical variables and Kruskal Wallis test was used for continuous variables. Association analyses were conducted using logistic regression models using 5th Omicron wave as reference group. Adjusted odds ratios (aORs) with 95% con dence intervals (CIs) were calculated. All variables with a P-value < 0. 20 on univariate analysis were included in the multivariate analysis. Age, sex, comorbidities (coronary heart disease, non-valvular atrial brillation, organ transplantation, cancer or hematologic malignancy evolving since 5 years or less, chronic kidney disease, dementia, chronic obstructive pulmonary disease and diabetes), baseline CRP and baseline lymphocyte count were evaluated as potential confounders.
Acknowledgements This work was supported by the  National Science Centre, Poland  (grant no. 2020/36/C/ST5/00247  and  UMO-2020/39/ D/ST4/01182 ) and  IDUB-AMU  programme "50x50: fifty grants for 50 young scientists- 038/04/NŚ/0040 " as well as the the  Agence Nationale de la Recherche  through the  Interdisciplinary Thematic Institute SysChem via the IdEx Unistra  ( ANR-10-IDEX-0002 ) within the  program Investissement d'Avenir , the  International Center for Frontier Research in Chemistry (icFRC) , and the  Institut Universitaire de France (IUF).
The numbers of JDP sequences identified as a consequence of efficient sequencing techniques has exponentially increased where it is now beyond the ability of careful manual curation. In this work, we first provide a broad view of the JDP repertoire accessible from public databases, and then we then use an automated classification scheme, based on Artificial Neural Networks (ANNs), to demonstrate that the sequences of the J-domains carry sufficient information to recover with high reliability the phylogeny, localization and domain composition of the corresponding full length JDP. Exploiting the interpretability of the ANNs employed, we find that several of the sequence positions that are most useful for the classification match to residues that form the interaction interface between the J-domain.
We then used Artificial Neural Networks (ANNs) to show that even the relatively short and simple sequences of the J-domains generally carry enough phylogenetic and functional signatures to reliably identify the architecture of the corresponding full length JDP, its cellular localization and the taxonomic clade. These findings allowed us to use amino acid sequence variations embedded in the J-domain to develop an ANN-based automated solution to curate existing and new JDP sequences reported in various organisms with high precision. By further identifying the residues that are most important for the classification, we pinpointed the structural positions where the class-and organism-specific signatures localize. From a more general perspective, we highlight how the combination of interpretable large-scale sequence analysis techniques and of coarse-grained annotations can unlock the wealth of information that would otherwise remain hidden in exponentially growing, and thus manually unmanageable, sequence databases.
Construction of an extensive annotated J-domain protein sequence dataset The number of deposited JDP sequences in the UniProt repository  (36)  has been exponentially growing over the past two decades, increasing by an order of magnitude every 6-7 years (Fig. 2A ), ranging from. This compound procedure allowed us to assign to each JDP sequence in the dataset a set of ground-truth attributes comprising phylogenetic, architectural and cellular localization features. In this context, the features describing for instance the human DnaJA1 are, alongside its sequence, that it belongs to class A JDPs (based on the full-length domain composition), that it is a eukaryotic/metazoan/mammalian protein, and that it is predominantly cytoplasmic. The correct identification and proper functional assignment of such a plethora of JDPs is, and will increasingly be, incompatible with any careful manual curation.
Overall characterization of the diversity of the J-domain protein dataset Based on the overall domain composition of JDPs, the three established functional classes were clearly identifiable: Class A and Class B (members with the canonical Hsp40 domain architecture, Figs. 1C  and D ) represented 24. 4% and 13% of the dataset, respectively (Fig. 2B ), with the remaining JDPs assigned to class C. We found that there are 1945 different domain architectures that compose the ~140'000 class C JDPs, with roughly half of them (78'282) have a domain-architecture consisting, according to the Pfam-based annotation, only of the J-domain (Fig. 2F ). Collectively, the 12 most populated domainarchitectures (including class A and B JDPs) made up roughly 90% of the total dataset (196'719 sequences). Visual inspection of class A and B JDP sequences showed that beyond the clear difference due to the presence of the ZFLR, we could distinguish sequences belonging to the two classes by the composition of their G/F-rich regions.
Indeed, the amino-acid profiles of residues in the G/F regions of classes A and B are remarkably different, with the class A G/F-rich regions showing a more marked segregation of phenylalanine, arginine, glycine, and glutamate residues compared to the ones of class B JDPs (Fig. 2C  and D ). This was quantitatively confirmed by a simple statistical model which showed that the amino-acid sequences of the G/F region alone were able to successfully discriminate class A and B JDPs (data not shown). This significant difference further supports a functional role played by the G/F-rich regions, beyond being simple flexible linkers. Agreeing with previous observations  (22) , class C members dominated the dataset (Fig. 2B ) mainly due to their overwhelming abundance in eukaryotes (Fig. 2E  and Supplementary Fig.
1A  for a more detailed analysis of class C abundance in the dataset). The 1945 different architectures present in class C were built from 1558 distinct domains, often classified as Domain of Unknown Function (DUFs), with little co-occurrence of the most common ones (Supplementary Fig. 2 ). The only notable exceptions are JDPs containing multiple tetratricopeptide-repeats (TPR) associated to several different Pfam families (in Fig. 2F  and Supplementary Fig. 2  all these TPR domains are collected into a single group). Inspection of the most frequently observed JDP domain architectures within class C revealed a very disparate phylogenetic makeup (Fig. 2F ). For instance, JDPs of the bacterial (E. coli) DjlA family, which formed the most abundant class C-subtype architecture, are present only in bacteria.
The HscB family (human DnaJC20, Fig. 1F ), which participates in the universal iron-sulfur cluster assembly pathway, is instead present in both prokaryotes and eukaryotes. Others, such as the endoplasmic reticulum (ER)-localized yeast Sec63 (human DnaJC23), DnaJC18 (which shares almost the same domain composition as DnaJB12 and DnaJB14), DnaJC11 (DUF3995) and DnaJC3/DnaJC7 were found only in eukaryotes. Some highly represented architectures obeyed an even finer phylogenetic origin, with Djp1/Caj1, JJJ1 and JJJ3 (yeast names) present only in yeast (e. g. in S. cerevisiae). We then analyzed the predicted subcellular localization of JDPs to assess if the classes are differently represented in various cellular compartments. The analysis of the organellar makeup of the dataset revealed that mitochondrial JDPs were mostly of class A and C (Supplementary Fig.
The output values are eventually transformed into the predicted probability that the input sequence {xi} belongs to each given class. The training of the ANNs consisted in progressively modifying the network parameters (weights and offsets) until the output probabilities of the sequences of the dataset were, on average, as similar as possible to the ground truths established previously (Fig. 4B ). For example, in the A/B/C classification task, the J-domain of HscB belongs to class C with certainty, corresponding to probabilities (0,0,1), which is the set of values that the ANN should reproduce. To prevent the networks from simply memorizing features "by heart" and force the networks instead to learn useful information, the dataset was split in two parts. One part was used for the training, while the other (test set) was used to check the predictions on sequences not seen during the learning phase.
An ANN could be reliably used as classification tool only if the performance over the training set was comparable to that over the test set. For each one of the tasks (A/B, A/B/C, etc. ), the output probabilities were used as coordinates for a low-dimensional representation of the dataset, similarly to the analysis performed in Fig. 3. As an example, this kind of representation is shown in Fig. 4C  in the case of the classification task A/B/C (including a further dimensionality reduction from three to two dimensions using the UMAP algorithm), providing evidence that the ANN had found information embedded in the J-domain sequence that allowed it to easily distinguish the three classes. To move beyond a visual, but not quantitative appraisal of the performance of the ANNs, we computed their confusion matrices (Fig.
Indeed, the degree of uncertainty encountered in this work is mostly due to two factors. On the one hand, several architectural C subclasses are too poorly represented in the dataset that it is difficult to train a network to tell them apart from more abundant subclasses. This problem, though, is likely to be solved through the exponential growth of the number of reported JDP sequences. On the other hand, a large swath of class C JDPs comprise domains that have not yet been annotated in Pfam, or just some disordered regions of various lengths. Yet, the advent of novel deep learning approaches on scales even larger than the present ones are recognizing domains that were previously uncharacterized  (52) , paving the way for a better assignment of class C JDPs to precise, well-populated architectural classes.
At the same time, this also creates a tension between the indiscriminate use of sequences from all sources and the more cautious desire to use only (possibly manually) carefully curated sequences. The new era of exponentially increasing genomic datasets and their Machine Learning exploration calls for an intermediate approach, where stringent, conservative automated criteria go hand-in-hand with manual curation at higher scales (e. g. domain composition). In this respect the present work can be considered as an example where this strategy allows for an unbiased, semi-unsupervised coupled functional/phylogenetic-structural investigation: it is supervised for the classification task, but unsupervised for the emergence of the relevant structural features. a region comprised of 55 positions defining the pseudoZFLR domain.
We then constructed a hiddenmarkov model profile of the novel domain using the hmmbuild utility from the hmmer suite  (59)  and used to profile to query all the 223'124 JDP sequences in our curated dataset to identify JDPs containing a pseudoZFLR domain. Because of the similarity between this domain and the canonical ZFLR domain, we used a more stringent E-value =10 -4 cutoff to identify pseudo ZF domains (see Supplementary Figure  4 ). This resulted in a total of 1421 JDPs identified as containing a pseudoZFLR domain, of which 1336 possess a DnaJ-DnaJ_C-pseudoZF architecture.
in year 2003 to 10 5 in year 2018, and projected to reach 10 6 sequences in years 2023-2024 and 10 7 by year 2030. Using stringent homology-search criteria, we extracted a total 223'124 JDP sequences from existing genome databases (see Methods). The associated annotations allowed us to assign all JDPs to different phylogenetic classes at various taxonomic levels. Using sequence domain models from the Pfam database (37), we further characterized the domain composition of the full-length sequences, assigning to each JDP a tag describing the domain architecture of the protein. Proteinlocalization prediction algorithms were used to infer the most probable organelle of residency of eukaryotic JDPs (see Methods).