dataesr/scientific-paragraphs-categorization huggingface dataset:
Data description We analyze Vietnam Labor Force Surveys (LFSs) conducted between 2015 and 2020. LFS data are the official source of labor statistics in Vietnam, collected annually by the country's General Statistics Office (GSO). The LFSs rely on a two-stage stratified cluster design and contain 126 strata comprising of urban and rural areas in 63 provinces throughout the country. 6 The surveys are nationally representative on a quarterly basis and at the urban/ rural and provincial levels. The sample size is equally allocated throughout the year, with around one-twelfth of the sampled households being surveyed each month. Aside from collecting basic individual demographic information, the LFSs collect detailed data on employment and wages for people aged 15 and older as well as data on unemployment.
The East Asian financial crisis in the late 1990s was also found to affect poor rural Indonesian households less than higher-income households, perhaps because of their ability to produce food (Friedman and Levinsohn, 2002) or to switch more easily from wage work into self-employment (Smith et al. , 2002). 17 Data on the import and export values for provinces were obtained from Ministry of Trade and Industry, available at https://thongke. idea. gov. vn. The data on GDP for provinces are available from the General Statistics Office of Vietnam at: https:// gso. gov. vn. Finally, we further explore the geographic distribution of the pandemic effects on monthly wages for all 63 provinces in Vietnam and graph the point estimates in Fig. A. 6. The impacts ranged from À0. 173 to 0. 003 and were strongest for Da Nang, which was under a second lockdown in August 2020.
Acknowledgements We would like to thank the editor  Chuan Liao , two anonymous reviewers,  Anna Aizer ,  Upasak Das ,  Thai-Ha Le ,  Jongwook Lee ,  Edmund Malesky ,  Harry Moroz ,  Andy Sumner , and participants at seminars and conferences organized by  Duke University, Fulbright University  ( Ho Chi Minh city ),  Korea Development Institute ,  Seoul National University, University of Economics and Business (Hanoi ),  UNU-WIDER (Bogota), Vietnam-Japan University (Hanoi), and International Centre for Economic Analysis (ICEA)  for useful feedback on earlier versions. We would also like to thank  Thai Minh Pham  and  Toan Ngoc Pham  for their helpful assistance with the LFS data. We are grateful to the  UK Foreign Commonwealth and Development Office (FCDO)  for funding assistance through a  Knowledge for Change  ( KCP) Research Program.
a b s t r a c t Noise pollution is becoming a critical health risk for city life. In 2020, the COVID-19 pandemic forced many cities to implement several mobility restrictions. These restrictions changed human activity patterns and decreased the noise levels and noise pollution that often affect urban settings. As the number of infections decreased, so did the outdoor activities, influencing the population's perception of noise. This paper aims to evaluate the changes in noise levels associated with mobility restrictions between 2020 and 2021 in Guayaquil, Ecuador. This study used crowdsourcing with the help of smartphones and mobile applications to collect geo-referenced environmental noise data. The data was used to generate noise maps in different time frames. Finally, noise level maps were created using GIS-based tools to identify the urban areas that experienced the highest noise level variation during the study period.
Data scrubbing Once the noise measurements and recording were completed, the resulting data was filtered to identify the months in which complete measurements were obtained. First, a statistical criterion was applied to work with representative data. Then, a geographic positioning criterion was used to obtain the data within the areas of interest. The first filtering criterion analyzed the continuity of the monthly data, which made it possible to ensure that the participant had performed at least 70% of the measurements during the month of study. Accordingly, 11,528 and 4,533 records were obtained between June and August for 2020 and 2021, respectively. On the other hand, the second criterion evaluated the geolocation of the data using the ArcGIS Pro application (version 2. 8), where the sound pressure records located  within the study areas were extracted ( Fig.
Maps The Kriging interpolation method was used to visually represent the data. The Kriging method is a univariate geostatistical method that uses  the weighting principle of the sample values for the predictions and considers the sample points' spatial arrangement for the weight value  ( Munyati and Sinthumule, 2021 ). Kriging is widely employed in the field of prediction of environmental variables  ( Xu et al. , 2022 ) , for instance, to estimate the danger posed by noise exposure of on-site workers  ( Ellis et al. , 2022 ). In addition, the interpolation method has been used to assess the impact of traffic-generated environmental pollution in countries like India and Pakistan, where they produced spatial noise prediction maps  ( Banerjee et al. , 2009 ; Latif et al. , 2022 ). The noise maps were created by importing the refined data and establishing the World Geodetic System (WGS 1984) as the datum.
To address this issue, we have sequenced and identified the bacterial 16S rDNA from liver tissue of a cohort of 36 Romanian, 17 Spanish, 19 Italians and 10 Austrian patients with different stages of liver fibrosis, notably at their early stages. We could design hypotheses regarding the putative causal role of liver microbiota in the development of liver fibrosis. We used this database to evaluate the efficacy of Principal Coordinate Analysis (PCoA) to visualize the different liver fibrosis group scores using Wilcoxon-Mann-Whitney statistical tests  [23]. Eventually, since the overall database of patients issued from different separated cohorts we anticipated some degree of heterogeneity of the overall cohort therefore, we adapted and developed a specific statistical approach i. e. L1 spectral clustering with fairness.
Linear Discriminant Analysis (LDA) Effective Size (LEfSe) The bacterial profiles were further compared between the three groups using LEfSe pairwise analysis with an alpha cut-off of 0. 05 and an effect size cut-off of 2. 0. The bacterial diversity analyses (alpha and beta diversity, MDS ordinations and taxonomic composition barplots) were generated using the Phyloseq (v1. 14. 0), vegan (v2. 4. 0) and ape (v3. 5) packages under R environment v3. 3. 1. LEfSe analysis was performed on the OTU table using the online Galaxy interface to identify bacterial taxa that were differentially abundant in the three liver fibrosis groups  [27]. Respective cladograms were generated with genus at the lowest level. Quantitative plots of differential features were generated from genus level percent relative abundance data showing means with standard deviation using GraphPad Prism 6 software.
To visualize the distribution of patients according to their clinical parameters, we performed a Principal Component Analysis (PCA) using FactoMineR and factoextra R packages. For the study of 16SrDNA diversity, we first filtered the less abundant OTUs to reduce the noise within the matrix before running the PCA. We eliminated those with abundance <0. 01. We then normalized the OTU table by using the Cumulative Sum Scaling normalization followed by a log transformation, using mixOmics package (https://pubmed. ncbi. nlm. nih. gov/29099853/). To explore the metagenomic data and identify the largest sources of variation, another Principal Component Analysis was conducted. Also based on the projection of the dataset into a space of lower dimension and originally designed for regression we performed a Partial Least Square Discriminant Analysis (PLS-DA) and its sparse version (sPLS-DA) on the normalized OTU table count to predict and select the most discriminative features in the data that help to classify the samples according to the fibrosis variable (package mixOmics).
Since we observed the influence of the metagenomic data on the outcome, we used alternative method of classification such as random forest (package randomForest). The random forest is built from a multitude of different decision trees and classifiers at training time thereby predicting and storing the predicted target outcome. Cluster graphical analyses. The abundance matrix of OTUs can be modeled by a graph using PLNmodels package under R where nodes represent OTUs and edges interactions between each pair of nodes. We developed an analysis in clusters i. e. the L1-spectral clustering, implemented in R, a robust variant of the well-known spectral clustering that aims to detect the natural structures of a graph by taking advantage of its spectral properties. The adjacency matrix modeling the variable associations of the graph is used as an input of the l1-spectralclustering algorithm.
Functional metagenomic prediction Metagenome inference and predicted functional analysis were initiated by analysis of the OTU clustered 16S sequence count table data and the OTU representative sequences using the PICRUSt2 tool  [26]  (https://pubmed. ncbi. nlm. nih. gov/32483366/) version 2. 3. 0b for each sample. The metagenome prediction process included four main steps: 1) The input OTU representative sequences were aligned against the PICRUSt2 reference alignment, 2) From this alignment, the input OTU were placed into the PIC-RUSt2 reference phylogenetic tree, 3) The metagenome functions were inferred by the hidden state prediction method using this phylogenetic tree. During this inference process, the abundance values of each OTU were normalized to their respective predicted 16S_rDNA copy numbers and then multiplied by the respective gene counts of the target bacteria, 4) The predicted functions were mapped to the MetaCyc database to determine the minimum set of pathways present in the samples.
Analyses of the liver bacterial 16SrDNA ecology. To identify whether the graphical differences between the three liver fibrosis scores are associated with a differential liver bacterial DNA signature, we then performed PCA on the OTUs as entries in the database. The analysis using countries as groups shows that the different cohorts poorly overlapped suggest-   Fig1 A, B, C ). At the family taxonomic level, the most prominent taxa were the Enterobacteriaceae and the Pseudomonadaceae which accounted for more than 50 % of the overall taxa (Fig  2E ). Group comparisons showed that the Caulobacteraceae, Flavobacteriaceae and Propionibacteriaceae families were statistically different when comparing F0 and F1 (Supplementary Fig1 D,E,F). To further identify whether liver fibrosis scores could be characterized by specific signatures we explored indexes of alpha and beta diversity of 16SrDNA in liver tissue.
The data show that differences in abundances at the phylum, and family taxonomic levels were also associated with differences of the alpha diversity (Supplementary Fig 2A  , B, C ). Notably, the Observed, Shannon and Simpson indexes were significantly different between the F0 and F1 groups at the phylum and family levels. In addition to alpha diversity, we analyzed beta diversity and performed a principal coordinate analysis (PCoA) considering distances between variables (using Bray-curtis distance). The PCoA analyses showed that the F0 group was distant from the two others which suggests a specific 16SrDNA signature (Supplementary Fig  2D, E ). It is noteworthy that outlier patients were also detected. Although, when analyzed together the three groups could not be classified clearly. The F0 group differed graphically from the F1,F2 groups suggesting a specific signature discriminating between F0 and F1,F2.
We noted that the frequencies of the majority of OTUs equal 0 or are extremely low (<0. 01%) thereby, most of these variables do not bring information. Similarly, a minority of the variables of high frequencies were common to all liver fibrosis groups and did not provide discriminant information neither. Such OTUs could be considered as the core variable of liver microbiota. Conversely, a subset of OTUs could be considered as discriminant that was identified on a different heatmap follow- Altogether, some degree of graphical classification of the liver fibrosis score could be observed using the clinical database and the 16SrDNA database. However, in both instances the individuals appear to be still distributed according to the countries. Therefore, to overcome this issue we developed an ad hoc fairness statistical strategy allowing the classification of variables i.
Identification of clusters of cohort-independent 16SrDNA associated with different mild scores of fibrosis In front of these numerous signatures and the influence of confounding factors such as the impact of the cohort set there is a need to identify clusters of variables specific to each liver fibrosis score but independent of the cohort impact. To this aim we considered three different fair approaches on the overall cohorts and then defined clusters of OTU variables independent from the cohort. The first fair approach consists in identifying principal components from the metagenomic dataset as signatures of the cohorts and removing them to generate a new dataset where no components would be cohort sensitive. To this aim we compared the largest cohort i. e. from Romania to the others. Principal components conditional distributions with respect to the cohorts were visualized (Fig  4A ).
Then, we removed the principal components the most correlated with the cohorts when the absolute value of Pearson correlation was above a threshold. The remaining non-overlapping components are cohort-insensitive and used to identify the variables associated with the mild fibrosis score. Remarkably, more than 78% of the variation from the original data was still included into the selected principal components suggesting that the discriminant information was only marginally affecting our previous results. On this "fair" dataset we applied the standard random forest classification to predict fibrosis scores. From the variable importance plot, indicating the contribution of the variables to classify the data, we selected the 10 more predictive principal components and identified 3 significantly associated with fibrosis scores (Fig  4B, C, D ).
We then represented an listed all selected enzymes and pathways highly expressed in the two major discriminant components (Fig  6G-J ) and (Table5). Three pathways were highly and negatively associated with the liver fibrosis score of F1-2 when compared to the F0. We identified from the MetaCyc database (https://metacy. org/) that the preQ 0 biosynthesis (PWY-6703), specific to Enterobacteriaceae such as E. coli, is involved notably in the synthesis of tetrahydrofolate and a class of nucleoside analogues that often possesses antibiotic, antineoplastic, or antiviral activities  [29, 30]  (Fig  6K ). In addition, two other pathways related to glucoryranose (PWY 6737) and glycogen (GLYCOCAT-PWY) degradation were identified probably providing energy to the main preQ 0 biosynthesis pathway. On the other hand, six major metabolic pathways were positively associated with the F0 score from both components.
Discussion We here report a mathematical approach to identify a bacterial 16S rDNA signature in liver tissue and corresponding putative biochemical pathways in patients with low scores of fibrosis. Our main finding is that even low scores of fibrosis (F0 vs F1-2) can be classified by biomarkers from the Proteobacteriaceae family within the liver. The second observation is related to the importance of cohort heterogeneity in term of size and data variability which could be major confounding factors that must be taken into account in multi-centric clinical trials or database. We here present a mathematic approach that could help solving this major and common issue. A gut metagenomics signature of liver fibrosis in humans has been recently described, suggestive of its causal role in the disease  [21].
PLS-DA aims to classify a data set according to the values of a qualitative variable by maximizing the covariance between linear combinations of the observed variables and the qualitative outcome. The sparse version, on the other hand, delivers variables per each component, only selected in the OTU dataset, that are the most discriminatory for the liver fibrosis scores. We focused our attention on the identification of the OTU frequencies within and across each group of patients and on the understanding of the importance that OTUs carry within and across the cohort. We found that the data set is mostly populated by a few high frequency OTUs. However, beside the level of information gained form this approach where overrepresented OTUs we identified cannot rule out that some more information could be obtained from OTUs rarely represented.
Therefore, some information could be hidden in the low frequency OTUs. To test this hypothesis we introduced a new normalization approach called TF-IDF  [36]  originally developed for text mining, to attenuate the effects of the high frequencies OTUs in the data set. Furthermore, aside from the fibrosis scores, it reveals some new predominant taxa at the different taxonomic levels. In conclusion, the first evidence of the existence of a liver microbiota opens alternate routes for novel therapeutic strategies since specific bacteria could be involved in the process of liver fibrosis. However, to generate information which could serve as a substratum to reach this aim, we here adapted predicted metagenomics and mathematical approaches to the original and novel nature of the tissue metagenomics data set.
Acknowledgements: We wish to extend our gratitude to the study participants, investigators, monitors and study nurses who enabled this study. We are grateful to  Bogdana Dorcioman MD  and the corresponding team from the  Laboratory Department and Emergency of the Mures County Hospital , who performed blood analysis and who provided some technical help. This grant was supported by subsides from the  Agence Nationale de la Recherche ,  Novo-Nordisk and Sanofi-Aventis , and the  Région Midi Pyrénées  to R. B. This research program was partly funded by  VAIOMER SAS  (project no. 6869/12. 06. 2014 ) through the  University of Medicine and Pharmacy Tirgu Mures , A subside was allocated to  Camille Champion  from  Institut National des Sciences Appliquées  and the  Région Occitanie. HT is supported by the  excellence initiative VASCage  ( Centre for Promoting Vascular Health in the Ageing Community ), an  R&D K-Centre (COM-ET program -Competence Centers for Excellent Technologies ) funded by the  Austrian Ministry for Transport, Innovation and Technology , the  Austrian Ministry for Digital and Economic Affairs and the federal states Tyrol, Salzburg and Vienna.
A major reason for this is the difficulty of obtaining databases with a sufficient number of well-documented cry recordings in these everyday contexts, for babies of a given age, and with enough replicates per baby  20. Most studies testing whether cries carry information about their cause have therefore been conducted on a limited number of cry databases (see  24  for an overview). Problematically, most of the used datasets include a mixture of cries from healthy and sick or disabled babies, or babies with a high pathological risk. For example, The Baby Chillanto database  24  lists five types of cries including "deaf", "asphyxia", "normal", "hungry", and "pain". Few cry databases are focused solely on healthy babies. One exception, the Donate a Cry dataset  25  , is still relatively undocumented  20.
Using only 150 baby cries from this database, a previous study  26  classified five cry types ("hunger", "attention seeking", "lack of ease", "stomach issue", "unidentified reasons") with 81. 27 % reliability. However, in addition to the very small sample of cries, the fact that the babies included in the database ranged between 0 and 2 years of age makes the result of the study difficult to interpret, as age may explain substantial variance in cry acoustics. A similar bias is found in another study  27  where the authors, despite having obtained good classification scores, also mixed cries from babies of very different ages (1-22 months; 320 cries extracted). The Dunstan Baby database 28 is another database of cries traditionally used to test cry classification methods, and was established for commercial purposes.
The database contains a limited number of cries recorded at different ages (between 1 day and 6 months), classified into five categories defined without scientific validation (« Neh » hungry, « Eh » Pain/burp-me, « Owh » sleepy, « Eairh » Pain, and « Heh » discomfort). After a meticulous review of the cries available in this database, some authors  29  were able to extract only 400 s of usable audio recordings (83 cries from 39 babies; see also  30  ). They obtained excellent recognition scores for all five cry causes. As the authors point out, however, the small number of cry recordings they used increases the risk of overfitting in the learning models. Using the same database but different analysis methods, other studies  31, 32  obtained comparable results. However, the problem of mixing cries from babies of different ages, and the fact that it is probably not the same babies who were recorded longitudinally, makes the interpretation of these results very difficult.
Another recurrent problem with available databases of infant cries is that the identity of the babies is often not known. It thus cannot be excluded that some cries in certain categories may have been produced by the same babies, leading to pseudo-replication issues and falsely inflating recognition accuracy. Taken together, the search for automatic methods to identify the causes of baby cries cannot be considered to have been successful to date. This is not because acoustic analysis and machine learning methods are inefficient-on the contrary, they are now particularly elaborate. Rather, a key problem is that modern analytical methods have not been previously applied to a large and well-controlled corpora of cry recordings. Here, we present a dataset containing cries from a cohort of babies recorded systematically and longitudinally at multiple ages during the first months of life and whose causes of crying have been labeled in a bottom-up, systematic manner.
We also test the prediction that there are consistent acoustic differences between baby cries caused by different events. To achieve these objectives, we audio recorded 24 male and female babies during several continuous 48-h sessions in their homes at 15 days, 1. 5 months, 2. 5 months, and 3. 5 months after birth. We classified cries into three key contexts (hunger, isolation, or discomfort), relying mainly on the parental behavior that ceased the cry, which also corresponded to the parental evaluation of cry cause in 75% of cases. Crying bouts were segmented into 39201 individual cries, from which we measured ecologically relevant acoustic parameters including fundamental frequency (perceived as pitch), perturbation and noise parameters, roughness, and duration. These cries and their associated metadata have been collated into a database available online: EnesBabyCries1 (see Methods and Supplementary Data).
Methods The research was performed under the authorizations 18CH085, no. IDRCB 2018-A01399-46 (recordings of cries) and IRBN692019/CHUSTE (psycho-acoustic experiments) and approved by the French national human ethics committee: Comité de Protection des Personnes. The study, including sample characteristics, design, procedures, and outcome measures was preregistered on https://clinicaltrials. gov/ under no. NCT03716882. The preregistration did not specify the hypotheses or the full analysis plan. Informed consent was obtained from all parents. Parents consented to public sharing of the cry-data. The playback experiment was approved by the local ethics committee (Comité d'Ethique du CHU de Saint-Etienne, Institutional Review Board: IORG0007394), and informed consent was obtained from all participants.