dataesr/scientific-paragraphs-categorization huggingface dataset:
ETHICS AND DISSEMINATION Approvals This protocol received ethical approval from the Delegation for Clinical Research and Innovation (Délégation à la Recherche Clinique et à l'Innovation-DRCI) of the Nancy University Hospital, in accordance with the National Commission for Computing and Liberties (CNIL) (protocol number 2019PI256-107). The CNIL is an independent administrative authority responsible for ensuring the protection of personal data contained in computer or paper files and processing. It ensures that data processing is at the service of the citizen and that it does not infringe on human identity, human rights, privacy, individual or public freedoms. All procedures performed in this study involving human participants were in accordance with the ethical standards of the institutional and national research committee and with the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards.
Data management and confidentiality The first author (IS) is the only person who has knowledge of the identity of the recruited patients and the results of patients' investigations. Data are collected in a protected computerised database only accessible to first author. In accordance with the third paragraph of Article 56 of the Data Protection Act, the presentation of the data processing results may in no case allow the direct or indirect identification of the persons involved in the research. Data collection will be carried out in accordance with the National Commission on Informatics and Liberties (CNIL) policy. Patient's families give their oral consent to anonymous treatment of data to the first author (IS). Oral information as well as written details has been supplied to the family in order to provide the principles of the study (aims, data treatment modality).
Sample preparation A metabolomics workflow generally involves a few crucial steps, for instance, robust sample preparations with rapid quenching of metabolism, metabolite extraction, possible chemical derivatization, and instrumental analysis, processing of data with or without database information and additional statistical analysis and data interpretation. Quick and efficient quenching protocols are essential for accurate quantitative snapshots of metabolic information as the metabolites are in a network that is continuously undergoing various biochemical reactions within a cell  (da Luz et al. , 2014). Microbial cells are usually quenched with solvent solutions at extreme pH or temperatures in attempt to rapidly arrest intracellular biochemical reactions  (Doran et al. , 2017). However, such solutions can damage the cell wall which confines the intracellular metabolites within the cell, causing the leakage of intracellular metabolites into the extracellular cell culture medium.
Sampling is executed systematically through the division of a sample into a grid of pixels  (Alexandrov, 2020). Metabolites are then desorbed from every pixel in the grid through a laser, subsequently, a mass spectrum is produced displaying relative molecular intensities within the pixel (Fig. 2 ), imaging MS results in large datasets, with 10, 000 to 1,000,000 spectra each relating to their own pixel  (Nguyen et al. , 2021). There are also other approaches in spatial metabolomics such as spectroscopy  (Movasaghi et al. , 2008; Langer et al. , 2020)  and molecular cartography  (Protsyuk et al. , 2018). As discussed in the previous section, MS is the most common technique for metabolomics  (Alseekh et al. , 2021)  since it offers good sensitivity, specificity, and molecular coverage. This makes imaging MS the most preferred method for spatial metabolomics.
Modelling approaches Constraint-based and dynamic-based modelling can be used to model the metabolomics data that has been generated experimentally. In doing so, researchers can gain distinct and complementary insights into intracellular metabolism (reactions) that is not obvious based on the concentrations of metabolites alone. Extracellular metabolite concentrations can be measured via analytical approaches (as discussed earlier) over various timepoints, and their rate of exchange with the modelled biological system can be evaluated as the change in their bioreactor concentration per unit time, further divided by the dry cell weight per unit bioreactor volume. The intracellular rate of substrate consumption and product formation (i. e. , reaction) can be similarly evaluated. For constraint-based flux balance analysis of a stoichiometric metabolic model, the calculated rate of metabolite exchanges is used to constrain the model in deriving feasible flux solution.
, 2019). ML can also be utilized for processing MS spectra during analysis of metabolites, where supervised machine learning methods can aid in picking peaks, missing data imputation, and normalization  (Liebal et al. , 2020). Omics datasets generally have many more variables than samples and are thus saddled with the so-called 'curse of dimensionality'. The latter refers to the overfitting of explanatory model by too many variables, whereby the model appears predictive on training data, but are otherwise not generalizable to new observations. It is also challenging to identify meaningful variables from the large pool. In this regard, ML approaches can be applied to reduce the feature size by projecting the high dimensional data to a lower dimensional space  (Hira and Gillies, 2015; Dekermanjian et al.
, 2022; Faquih et al. , 2020). Some examples of such ML methods are non-negative matrix factorization (NMF), principal component analysis (PCA), t-distributed stochastic neighbour embedding (t-SNE), and autoencoder (a type of ANN), with the last two especially suited for analysing non-linear data. To give some examples of their applications, PCA has been generalized as multi-omics factor analysis (MOFA)  (Argelaguet et al. , 2018) , while NMF-based approaches, such as 'intNMF'  (Chalise and Fridley, 2017) , and 'integrative-NMF' (iNMF)  (Yang and Michailidis, 2016)  have been developed for identifying meaningful sample sub-groups. In addition, t-SNE is used for both clustering multi-omics samples  (Lake et al. , 2018) , and the integration of single-cell transcriptomic datasets  (Butler et al.
, 2018) , whereas autoencoder has been used for identifying biological states that are predictive of phenotype  (Ding et al. , 2018) , as well as for integrating domain knowledge  (Ma and Zhang, 2019). ML can also aid in imputation of missing data for multi-omics datasets. Missing data is common with omics dataset for various reasons  (Misra et al. , 2018). Besides being replaced by the mean or median of other samples, missing values can be imputed with superior performance using regression, k-nearest neighbours (KNN) method  (Kim et al. , 2016) , and singular value decomposition (SVD)  (Linderman et al. , 2022). Correlations have also been used to this end  (Lin et al. , 2016; Mias et al. , 2016). In addition, maximum likelihood estimates  (Allison, 2012)  are widely used for estimating parameter values in linear models.
Model reconstruction ML has greatly assisted the reconstruction of genome-scale metabolic models. For example, unsupervised learning methods have been commonly used to infer metabolic networks from omics datasets, sequence information, and enzyme-enzyme associations  (Vert et al. , 2007; Yamanishi et al. , 2005). In another study, Naïve Bayes, decision trees, and logistic regression were found to perform as well  (Dale et al. , 2010)  as the widely-used PathoLogic algorithm  (Karp et al. , 2011)  in predicting metabolic pathways from genomic sequences. Importantly, Fig. 3. Machine learning models to analyze and interpret multi-omics data. Validation of predictions is required after the application of the appropriate machine learning approach. The new data generated can be used to improve the learned model, enhance prediction performance, and aid in designing new experiments for data generation based on biological hypotheses.
, 2020). ML models generally result in faster and enhanced performance for pathway predictions as compared to parametric and non-parametric computational models  (Quest et al. , 2010). ML models can also be mapped between proteomics and metabolomics using training datasets followed by experimental verification and validation of predictions based on test data  (Costello and Martin, 2018). After computational modelling of omics datasets using machine learning approaches, strains can be re-engineered according to new predictions based on further perturbations or identified gene targets which would improve product yield. For instance, in a study integrating metabolomics, proteomics, genome-scale models, and metabolic engineering of bacterial strains, several genes and metabolic reactions were identified which resulted in re-engineering of strains which produced higher product yields  (Brunk et al.
Conclusion: future challenges and directions There are various challenges faced in the development of metabolomics approaches for metabolic systems engineering. These include metabolite coverage, automation, and throughput. Increasing the coverage of metabolites can compensate the restricted throughput of LC-MS methods whereas the use of robotic liquid handler platforms and microfluidics allow for high throughput analysis of metabolites as discussed in the previous section. However, microfluidic approaches still lack the sensitivity to quantitate metabolites of low amounts. Further developments in this area would drive the production of large datasets of high-quality data that could be coupled to ML algorithms which can enhance the understanding of biological networks to improve the production of a phenotype during strain development.
Broadening the training content represents a contribution to the literature, since much existing research considers curriculum that covers mainly finance or marketing content, rather than strategy and organization science frameworks. Third, we explore whether early-growth entrepreneurs with more ambitious growth goals were particularly likely to benefit from our training intervention. Providing evidence on whether heterogeneity in entrepreneurs' growth goal moderates the effect of training on new venture's financial performance would help reconcile the mixed evidence on the beneficial effects of training. Our data consist of Singapore-based entrepreneurs running new ventures who were trained during 2017-2018 in the three tools of business-model innovation, team structuring, and effective networking, using a randomized control trial (RCT).
| METHODS We implemented a RCT with 103 entrepreneurs in the treatment group and 78 in the control group. Entrepreneurs in the treatment group received training at the beginning of the study, and entrepreneurs in the control group were promised and offered training 10 months after the treatment group's training, by which time all the data for the study were collected. Data and syntax for all results in this study are available online: https://osf. io/rcbpa Data and timeline: Ventures were selected in two stages (Figure  S1 ). In Stage 1, a subset consisting of nearly 14,500 new businesses registered with the Singapore Accounting and Corporate Regulatory Authority (ACRA) in 2016 were mailed invitations asking founders 1 to complete an online recruitment screener survey if they were interested in participating in the study.
Only a few founders received loan financing from banks or other financial institutions (n = 16, mean = SGD 60,000). None of the ventures in our sample were VC backed when baseline data was collected. Given the relatively supportive financial environment of Singapore, consistent with  Belenzon, Chatterji, and Daley (2020) , there was only a single case of eponymy in our sample. In addition, Table  S5  provides insights into the main challenges faced by the founders in our sample. The key themes revolve around managing competition (both domestic and foreign), attracting and retaining customers, and mobilizing other resources such as financing and skilled manpower. In summary, our sample consists of entrepreneurs whose revenuegenerating ventures are in the early-growth stage, are non-VC backed, and represent a broad range of industry sectors in Singapore, a developed economy context.
2 Two robustness analyses suggest this type of response bias is unlikely in our data. First, if social desirability bias in self-reporting of sales revenues were operating in our sample, this bias is likely to be even higher in midline data-information collected 6 months after training and, hence, temporally closer to the classroom sessions when compared to endline data collection. However, as we report in Table  S2  there is no difference in self-reported sales revenue at midline data collection between treatment and control groups. Second, we tested for concordance between self-reported and statutorily reported sales revenues for a sub-sample where the latter was available. Specifically, we examined whether ventures disclosed their revenues to the Singaporean regulator ACRA. Per ACRA guidelines, reporting sales revenues to ACRA is purely optional for small firms, that is, firms having annual revenues less than SGD 10 million or less than 50 shareholders.
Note: (i) Mean and standard deviation in thousand SGD. t-test was performed between treatment and control groups. (ii) Personal finances are defined as personal savings ("savings"), loans from family members ("family"), credit card ("credit"), and loans from friends or work colleagues ("friends"). Professional finances are defined as unsecured personal loans from banks or financial institutions ("loan"), bank overdraft ("overdraft"), and others. (iii) Baseline refers to data collected at the start of the study (April-May 2017). The transformed ACRA sales revenue data were then compared to the self-reported sales (by the entrepreneurs) for the corresponding study period. Data points were flagged as overstated if the self-reported sales were higher than the sales figures reported to ACRA. Examining the 69 observations with self-reported and ACRA-disclosed sales revenues, we find two ventures in the treatment group overstated their self-reported sales revenues; similarly, three ventures in the control group overstated their self-reported sales revenues.
Independent business is an indicator variable that takes the value of 1 if the start-up is an independent business. Full-time employees is the count of full-time employees in the new business. Estimation strategy: We measured Sales and other features of a focal venture/founder at both baseline (April 2017) and endline (February 2018), thus enabling us to construct a panel dataset. This data structure enabled a differences-in-differences estimation strategy with the two groups, treatment and control, using the ITT Panel regression with entrepreneur fixed effects and with standard errors clustered by venture to estimate sales (xtreg in Stata). While randomization would take care of unobserved heterogeneity among founders/ventures, the entrepreneur fixed effects add an additional layer of control for time-invariant heterogeneity across founders/ventures.
Therefore, there is a possibility that the results of the sales estimations may vary over a longer horizon. Second, our self-reported Likert scale type measure of venture growth is not ideal. Entrepreneurs who attended the training may have responded with a biased (higher) sales information. However, analysis of the midline data when the bias may be more prevalent, and a sub-samples analysis of actual sales data (were available) suggest the bias may not be a concern in our sample. Third, only a small fraction of the invited entrepreneurs participated in the study. Our highly selected subset may hinder generalizing to the entire Singapore entrepreneurial ecosystem. More generally, future research should examine the systematic heterogeneity driving participation in field experiments. Finally, while we predict and find that heterogeneity in growth goal is an important driver of who benefits most from training, we note this was an exploratory hypothesis that was not pre-registered.
ACKNOWLEDGEMENTS This project was funded by the  Ministry of Education, Singapore  grant no. 16-C207-SMU-014. We acknowledge and thank the entrepreneurs and their associates that generously gave their time to take part in this research. We also thank  Yun Yao Lin  and  Adam Quek  for outstanding research assistance. We thank  Rem Koning  and seminar participants at the  Duke Field Experiments Workshop ,  Stanford MS&E Ph. D. Seminar  participants,  SMU Strategy & Organization Summer Research Workshop, Imperial College ,  UCL, Tsinghua University, NTU Business School management seminar series  for helpful comments. The remaining errors are our own.
6 0. 6 Travel 1 175 100 0. 6 Telecom 1 0. 6 Total 67 111 100 T A B L E 3Note: (i) The SSIC is the national standard for classifying economic activities undertaken by economic units and is used in censuses of population, household and establishment surveys and in administrative databases. Complete report for SSIC2020 available at https://www. singstat. gov. sg/standards/standards-and-classifications/ssic. (ii) Data on industry classification are missing for six ventures. T A B L E 4.
The internalization of PRDX6 by the parasite was further confirmed by immuno-electron microscopy (EM). As expected, uninfected RBCs showed uniform cytosolic staining of PRDX6 (Figure  1B ), whereas in infected RBCs, PRDX6 was localized in electron-dense, hemoglobin-containing vesicular structures (Figures  1C-1F ). PRDX6-containing vesicles were found at the inner surface of the parasite plasma membrane in structures that resembled cytostomes (Figures  1C-1E , black arrows), adjacent to the FV (Figures  1C-1E , white arrows) and in the parasite cytosol (Figure  1F , striped gray arrow). Taken together, these images demonstrate that host PRDX6 is internalized by P. falciparum. Furthermore, the data suggest that PRDX6 is internalized by endocytosis during HCCU and subsequently transported in hemoglobin-containing vesicles (HCVs) to the FV.
Co-treatment of ART-resistant parasites with ART and Darapladib synergistically reduces parasite survival Resistance against the frontline anti-malarial drug ART is a major threat for malaria control and elimination efforts. Parasites attain deviation (MAD) of DAUC in each dataset and R 2 = 0. 8 cut offs are indicated. Hits surpassing selection criteria, as well as PRDX6, are plotted in black. Thermal stability profiles for these hits are provided in Figure  S2D. GluPho, glucose-6-phosphate dehydrogenase-6-phosphogluconolactonase. Data from two independent experiments. See also Figure  S2E. ART resistance through reduced hemoglobin uptake and digestion, which lowers oxidative-stress levels and precludes activation of ART  (Birnbaum et al. , 2020; Xie et al. , 2020). As inhibition of PRDX6 by Darapladib raises oxidative-stress levels, we investigated whether co-treatment with Darapladib and ART could restore ART susceptibility in ring-stage survival assays (RSAs)  (Witkowski et al.
Red blood cells O + human erythrocytes in citrate buffer were obtained commercially from ICAReB, Paris, France. In vivo infection of mice with P. yoelii prdx6 À/À mice were generated from C57/BL6JRj mice in the lab of Aron B. Fisher, University of Pennsylvania, USA as described earlier and are available under the accession number B6. 129-Prdx6 tm1Abf /Mmjax  (Feinstein, 2019). Animals were housed in the Institut Pasteur animal facilities accredited by the French Ministry of Agriculture for performing experiments on live rodents. Work on animals was performed in compliance with French and European regulations on care and protection of laboratory animals  (EC Directive 2010 /63, French Law 2013 -118, February 6th, 2013). All experiments were approved by the Ethics Committee #89 and registered under the reference 01324.
Membranes were developed with enhanced chemiluminescence (ECL) substrate (ThermoFisher Scientific) and imaged on an Amersham Imager 600 (GE Healthcare). Band intensity was calculated using ImageJ software (NIH, Bethesda, USA)  (Schneider et al. , 2012). Immunofluorescence microscopy P. falciparum 3D7 was cultured as described above and mice were infected with P. yoelii YM as described below. 1mL of a 4% hematocrit, high parasitemia culture for P. falciparum or 20mL of freshly drawn blood in EDTA for P. yoelii YM was pelleted by centrifugation (1. 5 g, 3min, RT) and resuspended in freshly prepared PBS containing 4% paraformaldehyde (Electron Microscopy Sciences) and 0. 0075% gluteraldehyde (Sigma) and fixed for 30 min at RT. After washing in PBS once, fixed parasites were permeabilized with 0.
1% Triton X-100 in PBS for 10 min at RT. Cells were washed in PBS again and blocked for 30 min in PBS with 2. 5% BSA. Antibody dilutions were prepared in PBS with 2. 5% BSA. After blocking, parasites were incubated with mouse a-hPRDX6 1A11 (1:500, Santa Cruz) for 1h at RT. Parasites were washed twice in PBS and incubated with a-mouse AlexaFluor 488-coupled secondary antibody (1:2000, Invitrogen) and Hoechst 33342 (2 mM final) (Invitrogen), washed again twice in PBS and resuspended in 30 mL PBS. From this suspension, 4 mL was transferred onto a glass slide and covered with a glass coverslip. Samples were examined under a widefield Deltavision Elite high resolution fluorescence microscope (GE Healthcare Lifesciences) using DAPI, FITC and differential interference contrast (DIC) channels. The images were deconvolved using the Deltavision software default setting (10 iterations).
The integrated fluorescence density (IDF) and area of both the parasite as well as the whole infected RBC were measured in multiple z-planes using ImageJ software (NIH, Bethesda, USA). To quantify the localization of PRDX6 within the parasite, the integrated fluorescence density (IFD) and area of both the parasite as well as the whole infected RBC were measured in multiple z-planes. For each z-plane, the relative IFD of the parasite within the infected RBC was divided by the relative area of the parasite within the infected RBC to calculate the relative mean fluorescent signal located within the parasite as compared to the whole RBC: (IFD Parasite /IFD iRBC )/(Area Parasite /Area iRBC ). Finally, the average of all measured z-planes for a single parasite was calculated. Hemozoin crystal size was measured in DIC images using ImageJ.
Further 24h later, the overall growth and development of next generation rings was measured. Flow cytometry data was analyzed with the FlowJo 10. 8 software (FlowJo LLC). After exclusion of doublets, a gate for uninfected and infected RBCs based on the size of the cells in the FSC-A channel was set up to exclude debris and merozoites. Finally, gates for rings, trophozoites and multiply infected RBCs, and schizonts were set up. The total parasitemia was defined as the sum of the aforementioned gates. Each experiment was performed with three biological replicates. Relative rate of progression was calculated as the fraction of schizonts in treated and control samples (T/C). Relative growth was calculated as the ratio of parasitemia in the treated and control samples after subtraction of the initial parasitemia (T-I/C-I).
Labelling of P. falciparum for flow cytometry Cultures were diluted to 0. 2% hematocrit and transferred to a 96-well round bottom plate. 5 mL of 2003 SYBR Green I (Lonza, Switzerland) was added to 200 mL of the diluted culture for a final concentration of 53 SYBR Green I. The plate was then incubated for 30 min at 37 C in the dark. Samples were measured on a calibrated MACSquant flow cytometer on the forward scatter (FSC), side scatter (SSC) and B1 fluorescent channel (B1: l Ex 488 nm and l Em 525/50 nm). Data was processed with the FlowJo 10. 8 Software (FlowJo, LLC). Infection of mice with P. yoelii prdx6 À/À mice and age-matched control C57/BL6JRj WT mice (Janvier Labs, France) were infected with the P. yoelii YM lethal strain  (Mwakingwe et al. , 2009)  via the tail vein for an initial parasitemia of 0.