dataesr/scientific-paragraphs-categorization huggingface dataset:
PCR primers were removed and sequences with sequencing errors in the primers were excluded. The reads were clustered into Operational Taxonomic Units (OTUs) using the Swarm clustering method. Chimerae were removed, and 580 OTUs were assigned at different taxonomic levels (from phylum to species) using the RDP classifier and NCBI Blast+ on the Silva_138. 1_16S database. When needed, the phylogeny was checked using the Phylogeny browser (https://www. ncbi. nlm. nih. gov/Taxonomy/Browser/wwwtax. cgi). 35538 reads were randomly selected from each sample to normalize the data. Raw reads were deposited into the SRA database (ID: PRJNA888063). The R packages "Biom" and "Phyloseq" were used for data analysis and plotting  (43). Rarefaction analysis was performed to compare the relative abundance of OTUs across samples.
Acknowledgments We would like to thank  Sylvie Berthier  ( Cytometry Platform, CHUGA ),  Dr. HervéLerat ,  Kevin Escot  and  Laurie Arnaud  ( PHTA Animal Facility ),  Cleḿent Caffaratti  and  Heĺène Coradin  ( TIMC Lab ), and  Theó Ziegelmeyer  ( Inserm U1209 ) for their technical assistance. This work benefited from the facilities and expertise of  @BRIDGe  for 16S sequencing ( UniversitéParis-Saclay ,  INRAE ,  AgroParisTech , GABI, 78350 Jouy-en-Josas, France).
Association of proteins with nanoparticles Bovine serum albumin (BSA, Sigma Aldrich, France) was used as a model protein. The loading of BSA in nanoparticles was performed by mixing both components in an aqueous solution at room temperature for 1 h. Different ratios (w:w) of nanoparticles/BSA were used from 1/0 to 1/5. The size and zeta potential were determined as described for the characterisation of the nanoparticles. The association of BSA with the nanoparticles was analysed by non-denaturing PAGE as previously described  [10]. Gels were scanned and quantified using the ImageJ software, setting the 0% of association over the BSA alone input while 100% of association was set to the blank protein lane (NPPS or NPPG alone).
Statistics All experiments were done at least three times. The distribution of the quantitative variables was assessed by the Shapiro-Wilk test completed by the calculation of the residuals and the coefficients of Skewness and Kurtosis. Student's tests and ANOVA with Tukey-Kramer correction were used to compare quantitative values. For kinetic analyses, multivariate regression with repeated measurements was used. All tests were performed with a risk alpha of 5% using the SAS Software (version 9. 04. 01M6P11072018). Only statistically significant differences are indicated in the figures.
Consequently, NP + were first labelled with FITC and then filled with either DPPS or DPPG to produce FITC-labelled NPPG or NPPS, respectively. Nanoparticles were incubated for 30 min with Alexa Fluor 647-labelled annexin V before flow cytometry analysis. The nanoparticles were successively gated according to (i) size and structure (more than 450,000 events), (ii) FITC fluorescence, and (iii) Alexa Fluor 647 fluorescence. Different ratios of NPPS/annexin V and NPPG/ annexin V (mass ratio: 1/0, 20/1, 4/1, and 2/1) were examined. With a 20/1 ratio, about 60% of NPPS or NPPG were associated with annexin V. With a 4/1 ratio, 95% of NPPS or NPPG  were associated with annexin V, while all nanoparticles interacted with annexin V with a 2/1 ratio (Figure  6 ). These data indicate a clear interaction between nanoparticle and annexin V which do not depend on lipid composition.
5-6 h of exposure. No difference in BSA-DQ enzymatic degradation was observed between NPPS and NPPG at any time of the experiment (Figure  10a ). Second, the potential role of lipids in pH-induced vesicular protein degradation was studied (i) through a  30 min pretreatment with chloroquine, which is described as an inhibitor of endosome acidification, or (ii) by using BSA-pHrodo, which is a pH-sensitive labelled protein that fluoresce at lower pH. No difference in protein degradation within chloroquine-pretreated macrophage-like THP-1 cells was observed when BSA was delivered by NPPS or NPPG (data not shown). BSA-pHrodo was formulated with NPPS and NPPG, exposed to macrophage-like THP-1 cells for 0. 5-6 h and the acidification of BSA was then analysed by flow cytometry. Similarly, there was no difference in the kinetics of acidification when BSA was carried by NPPS or NPPG (Figure  10b ).
Acknowledgements: We would like to thank the  Flow Core Facility of BioImaging Center Lille - UAR2014-US41-PLBS -(F-59000   Lille, France ) for the expert technical assistance. Funding information: No specific funding was received for this study. R. C. , C. B. , and L. D. were supported by the  University of Lille , INSERM, and  CHU Lille. PRIMACEN was supported by the  University of Rouen Normandy, Inserm, IRIB, Région Normandie  ( RIN plate-forme "7D microscopy "), the  European Regional Development Fund (ERDF  " 7D Microscopy "), and the  GIS IBiSA. Authors contributions: Conceptualisation: R. C. , L. D. , L. G. , and M. T. ; methodology: C. B. , R. C. , and L. G. ; validation: C. B. , M. B. , A. L. , and D. S. ; formal analysis: C. B. ; investigation: C. B. , M. B. , A. L. , and D. S. writingoriginal draft: R.
In additional studies beyond the scope of this report, we have noted efficacy of SQL in spontaneous atopic dermatitis in dogs (abstract)  27  , in the MRL/lpr murine model of systemic lupus erythematosus (abstract)  28  , and in a T-cell mediated model of colitis in mice. We believe that the mechanism that accounts for this benefit is multifactorial and probably varies for each model. This mechanism includes a reduction in the production of Th2 (see Figures  1B, 1E, 2F,  and 3G ) and Th17 cytokines (see Figures  3G, 4D, 4E,  and 4F ). An attractive feature of SQL is relative preservation of Th1 production at lower dosages of SQL  9, 29, 30  which should reduce the potential for infectious complications due to immunosuppression. Indeed, in an ongoing clinical trial with SQL for the treatment of relapsed, refractory T cell lymphoma, we have demonstrated anti-tumor activity without an increased incidence of opportunistic infection (NCT03952078)  30.
Statistics The data are displayed as means ± SEM, unless otherwise indicated. All statistical analyses were performed using GraphPad Prism Software. Significance was determined using an unpaired twotailed Student t-test, unless otherwise indicated. Differences were considered statistically significant at p<0. 05. P values are reported as: ns. not significant, p> 0. 1, *p <0. 05, **p <0. 01, ***p <0. 001, ****p,0. 0001. FIGURE LEGENDS.
This modelling framework is designed to consider that the generation of final outputs (income) depends on loans, which are supported by loan loss reserves. Fukuyama et al. , (2023)  not only contributed to the DEA banking literature by proposing an innovative behavioural model, but they were also pioneers in proposing a causal analysis to validate the framework of the production process. Their work provided a good example for future DEA studies as the proposal of inputs and outputs in the production process is the basis for the implementation of the model. The choice of inputs and outputs is typically based on relevant economic theory, but due to different types of observational data used for different sectors of the economy, there could be a causal interpretation problem. The proposal and estimation of a causal model would be able to solve this issue and provide validity to the DEA model proposed.
Data description The current paper collects data from 64 Chinese banks that operated between 2013 and 2020. The sample includes five different bank types: 22 foreign banks, 21 city banks, 7 rural banks, 9 joint-stock banks, and 5 state-owned banks. The data was collected from the Fitch Connect database, which provides financial data for over 30,000 banks worldwide. Three original inputs were used: (1) labor, (2) deposits, and (3) physical capital (fixed assets), to produce two intermediate products: loans and securities investments. We argue that loan loss reserves are produced together with the intermediate products of loans and securities investment, but will not directly affect the next stage of production. These two intermediate products are used as inputs in the next stage of the production process, together with loan loss reserves from the previous period, to generate income.
p(x t , z t , c t , c t- The decomposition (13) may or may not be supported by the data; this is particularly a cause for concern when the sample has the annual data, however the panel dataset is characterised in our sample. In order to deal with the curse of dimensionality, we approximate the probability distributions to test the decomposition. Let u (k) and F (k) be, respectively, an inefficiency and the machine-learning approximation dependent on parameter θ (k) , which are parameters for adjusting the values u (k). Let u (k) = F (k) y t , c t , c t-1 , z t , x t ; θ (k) , k ∈ {1, 2, 3, 4}, (14) represent the four machine-learning approximations of probability distributions obtained from the DEA (in)efficiency scores. Figure  1  indicates the following: (A) F (1) depends only on z t but not c t , c t-1 , x t.
We collected the data regarding the non-performing loan ratios of 25 different economic sectors over the period 2013-2020 from the annual financial statements published by the China Banking Regulatory Commission. The detailed figures can be found in Table  4. 6 We can see from the table that the level of credit risk derived from allocating credits to international organizations is quite low compared to that of other economic sectors, with nonperforming loan ratios for most years being 0. On the other hand, the data clearly indicates that The NPLR is calculated as the ratio between non-performing loans and gross loans and it is in the unit of % the level of credit risk derived from allocating credits to the agricultural sector is very high. Our aim is to investigate the impact of credit risk at the industry-level on bank efficiency and examine whether the impact of risk at the industry-level on bank efficiency differs between sectors with a higher risk level and those with a lower level of risk.
Our results are reported in Table  7. The odds against the restrictions of the causal structure A and B, as reflected by the results in Table  7 , are overwhelming, which indicates that without deconfounding, there would be an invalid causal structure in the dataset. This is within our expectation due to the fact that some important variables may be missed in the data generation process. Quadratic deconfounding in Eq. (  16 ) provides the marginal results. Finally, quadratic (dynamic) deconfounding in Eq. (  17 ) provides the results which are largely consistent with a causal structure. Furthermore, we can test the causal interpretation of the truncated regression results in Tables  5  and 6  (bootstrapped truncated regression, and Tobit regression, respectively). Since specifications A, B, and C are rejected, we focus on specification D and we res-estimate the specifications in Tables 5 and 6 by adding the dynamic factor and its square into the specification.
We tried to address this issue in our study, however, the annual financial statements provided by the banks do not disclose enough information about this. Thus, further studies could collect more data through an alternative channel and confirm that all the banks in the sample provided credits to all the selected economic sectors. This will further strengthen the accuracy and robustness of our results. Shen et al. (2022)  proposed an interesting nonparametric approach to estimate the shadow pricing of non-performing loans, which can be regarded as the measurement of risk performance in the banking sector. Future studies could use our dataset, together with the nonparametric approach proposed, to evaluate the shadowing pricing of loan loss reserves. This would help check and obtain the robustness regarding the level of risk performance in the Chinese banking sector.
Learning Process The learning process, proposed in this study, allows to generate learning curves for a given set of caregivers. Let's take the example a set of beginner caregivers (c: 1,…, m) who start a new task T and have to repeat it n times. The service time records, performed by these caregivers, are saved in the database. Each caregiver has a fixed number of tasks to perform, generated by the solver. The learning process consists of extracting the learning rates l c from the database and predicting the future service times. Fig. 1  illustrates the learning process. -The extraction model is based on the following equation: l c ¼ ½lnðT n Þ=lnðnÞ À ½lnðT 1 Þ= lnðnÞ ( 17 ) where l c is the learning rate of caregiver c, T n is the service time at the nth task, T 1 represents the service time at the first task and n represents the number of repeated tasks.
Experimentations The proposed LGA-mTSP is implemented in Java language with the Integrated Development Environment Eclipse IDE 4 , version 2020-03. The algorithm is executed on a 2. 50-GHz Intel(R) Core (TM) i5-7200 CPU computer under Windows 10 with 8 GB of RAM. The proposed MIP model is testing on illustrative example of a HHC company with 3 caregivers who have to perform medical services at patients' homes. To our knowledge there is no standard benchmark in the literature for the considered problem. Therefore, based on the benchmark data of the Single-Depot Multiple Traveling Salesman Problem (multiple-TSP) 5 , several instances are generated and reported in Tab. 1. The benchmark is generated from TSPLIB 6 library. It contains 4 TSP instances: eil51 7 , berlin52 8 , eil76 9 and rat99 10.
Computational Results This section describes computational experiments carried out to investigate the performance of the proposed solution approach LGA-mTSP. Eclipse IDE : https://www. eclipse. org/ 5 https://profs. info. uaic. ro/∼mtsplib/ 6 http://comopt. ifi. uni-heidelberg. de/software/TSPLIB95/index. html 7 https://profs. info. uaic. ro/∼mtsplib/TSPLIB/eil51. tsp 8 https://profs. info. uaic. ro/∼mtsplib/TSPLIB/berlin52. tsp 9 https://profs. info. uaic. ro/∼mtsplib/TSPLIB/eil76. tsp 10 https://profs. info. uaic. ro/∼mtsplib/TSPLIB/rat99. tsp.
Discussion In this sub-section, a comparative study is provided in order to evaluate the effectiveness of GA-mTSP as well as the robustness of the elaborated MIP model. First, a comparative table is given by Tab. 6 in term of routing results. This paper proposes a new mathematical formulation for HHCRSP extended of mTSP. Thus, the authors cannot compare directly the proposed solution with another work in the literature. In order to evaluate the performance of GA-mTSP, the authors remove the constraints related to the considered problem in expectation of returning to the basic formulation of mTSP. After that, they solve it by GA-mTSP with instances taken from the benchmark data of multiple-TSP. Finally, they compare the obtained results with two similar works from the literature. Tab. 6 shows the obtained results of GA-mTSP.
As the number of published MAs is increasing exponentially  [2, 12] , multiple MAs are now often available on a specific topic, reporting a broad range of results with sometimes conflicting conclusions. For example, more than 10 MAs (each including several dozen studies) have been conducted to evaluate the impact of agroforestry on soil organic carbon compared to arable systems without trees  [14, 15]. It is therefore often necessary to consider the results of multiple MAs in order to answer a given question, on the basis of all available evidence. To synthesise multiple MAs, a first approach is to retrieve the original individual effect sizes (or original experimental data) of all the MAs and make a new MA of the whole dataset. This approach can be quickly implemented only if all individual effect sizes (and their standard errors) used in each first order meta-analysis are available but, in practice, this is often not the case  [2, 26].
g. , partial redundancy between MAs, lack of statistical power, risk of bias) and the reliability of their results is barely studied. Thus, there is a need to assess the performances of different time-saving methods for synthesizing results of MAs in order to inform effective environmental policies. The evaluation of these methods is all the more important since syntheses of MAs are potentially subject to different types of bias. Although the systematic review methodology was designed to both reduce bias in syntheses and assess risk of bias in primary datasets, different types of bias may occur at different stages of the process, i. e. , in the individual experiments included in a systematic review (e. g. , bias arising from the randomisation process, bias in measurement of the outcome), in the individual meta-analyses (in particular, publication bias resulting from the selection of studies during the publication process), and during the synthesis of the MAs (e.
This method is attractive as it allows one to summarize the results of the K first-order MAs by a single mean value ( SOMA ) and to describe the uncertainty by a single confidence interval [ L SOMA , U SOMA ]. However, the hypothesis of independence of the k , k = 1,…,K is violated if the first-order MAs were performed from overlapping datasets (i. e. , datasets sharing some primary studies). The second method (MAMA) consists in selecting a single MA among the K first-order MAs. Here, we select the MA leading to the most accurate mean effect size estimate, where the accuracy is measured through a coefficient of variation (CV) defined as the ratio of the standard error of the estimated mean effect size to the absolute value of the estimated mean effect size, i. e. , CV k = σ k |� k | , k = 1, …, K.
Finally, the reference method (REMA) consists in performing a meta-analysis of all the individual studies taken into account by the K first-order MAs. Thus, if each MA is based in N studies and if all studies are different, REMA estimates a mean effect size based on the K × N primary studies. If some of the studies are common among the K MAs, the redundant primary studies are removed before the analysis and the total number of primary studies used by REMA is then lower than K × N. As with SOMA, the result of REMA is an overall mean effect size and its corresponding 95% confidence interval REMA , L REMA and U REMA. Based on REMA, the effect is then be considered as significantly positive if L REMA > 0 , significantly negative if U REMA < 0 , and not significant if L REMA < 0 < U REMA. Note that, contrary to the other methods, REMA relies on the primary data, while SOMA, MAMA, and COMA do not request this type of data and rely only on the results provided by the MAs.
Simulations assuming that the MAs are unbiased Simulated data  [20]  are traditionally used to compare the performance of different statistical methods (e. g. , methods used to estimate some parameters of interest, for example a mean effect size). Simulated data are generated with an explicit statistical model and 'true' parameter values chosen by the scientists conducting the assessment. The statistical methods considered are applied to the simulated data and their results are compared to the true values. This approach is a standard practice in statistics because it offers a practical way to compare estimated parameter values to true parameter values. Such a comparison is impossible with real data because the true parameter values are unknown in real case studies. In order to explore a larger diversity of scenarios, datasets were simulated using a hierarchical statistical model widely used in MA  [4, 13] , defined as y i = θ i + ε i , with θ i ∼ N µ, σ 2 θ and ε i ∼ N µ, σ 2 εi.
In this model, y i is the individual effect size (typically, a log ratio) reported in the ith study. The parameter µ is the true mean effect, i. e. , the quantity that we want to estimate as accurately as possible or, at least, classify in three categories "positive" ( µ > 0 ), "negative" ( µ < 0 ), "no effect" ( µ = 0 ). The vari- ance σ 2 θ represents the between-study variance of the true effect size of the ith study ( θ i ) , and σ 2 εi is the within-study variance (each study is assumed to have a specific variance in order to reflect the fact that some studies may be more accurate than others). The statistical model is used to generate datasets and compare the methods SOMA, MAMA, COMA and REMA, as described in Fig. 1. The datasets are generated according to different scenarios, each characterized by a true mean effect size ( µ = -0.
69, -0. 29, 0, 0. 22, 0. 41 , expressed as a log ratio, corresponding to relative change of -50%, -25%, 0%, + 25%, + 50%, respectively), a number of first-order MAs (K = 3, 5, 10), a number of primary data in each first-order MA (N = 10, 15, 25, 50), a proportion of common data among the K first-order MAs (P = 0, 10, 25, 50%), and a level of precision of primary data (low, medium, high within-study variances σ 2 εi ). The total number of scenarios is thus 5 × 3 × 4 × 4 × 3 = 720. At each iteration for a given scenario, K virtual datasets are generated using the statistical model defined above. Each one of these K datasets is specified such as it includes N data (N pairs of y i and σ 2 εi ), with a proportion P of common data among the K datasets. A MA is then performed using each dataset in turn with a random-effect model, leading to K estimated mean effect sizes ( � 1 , � 2 ,.
, � k. , � K ) and their standard errors (σ 1 , σ 2 ,. , σ k. , σ K ) (Fig. 1 ). These results are then used to implement the method SOMA, MAMA, and COMA as explained above. Finally, the K subsets of data are merged together and used to implement a single global meta-analysis-after the removal of the P% of redundant data-in order to implement the method REMA (Fig. 1 ). The procedure is repeated 100 times for each scenario. The computations were done with the R software (R Core Team, 2021). The code used to simulate data is shown in Additional file 1: A and all codes used in the analysis are available at https:// github. com/ davem akows ki/ CodeP aper2 ndOrd erMAs.
1  Implementation of the methods SOMA, MAMA, COMA, and REMA (see Table  1 ) to simulated data. For a given scenario (characterized by a true mean effect size µ , a number of datasets (K), a number of data per dataset (N), a level of redundancy (P), and a level of precision), a hierarchical Gaussian model is used to generate K datasets, each including N data (effect sizes and standard errors). A 1st order MA is performed using each dataset in turn, generating K mean effect sizes ( � 1 , � 2 ,. , � k. , � K ) and standard errors (σ 1 , σ 2 ,. , σ k. , σ K ). These quantities are used to implement the methods SOMA, MAMA, and COMA (see text). In addition, the K datasets are merged to produce a single global dataset used to implement the method REMA whatever the direction of the effect. With the second (third) types of bias, we consider that an individual study is published only if it shows a significant negative (positive) effect.
The conditions of publication are thus more restrictive with the second and third types of bias than with the first one. The datasets are generated considering 27 scenarios, each characterized by a true mean effect size ( µ = -0. 69, -0. 29, 0 , expressed as a log ratio, corre- sponding to relative change of -50%, -25%, and 0%, respectively), a number of first-order MAs (K = 3, 5, 10), and a type of publication bias (i, ii, or iii, as explained above). The number of primary data in each first-order MA was set to N = 50, P was set equal to 0, and the level of precision of primary data was set to medium withinstudy variances in all scenarios. The same procedure as above is implemented to generate K datasets each including N data (pairs of y i and σ 2 εi ), at each iteration. However, here, the data are generated to reflect publication bias, considering a statistical significance at a level of 5%.