dataesr/scientific-paragraphs-categorization huggingface dataset:
1b , Fig. S1b-f , Supplementary Data 1). On the other hand, performing 2-day experiments, even when 300 cells were used, led to significant RLS differences compared to the benchmark experiments (Fig  S1a-f , Supplementary Data 1). As a result, we performed 3-day-long (72 hours) microfluidic aging experiments for the current study by analyzing 200 cells pooled from two biological replicates. While the inherent stochasticity associated with lifespan determinants led to some differences between the replicative lifespan outcomes of the two biological replicates, we saw a high degree of correlation (Pearson Correlation Coefficient (PCC) = 0. 937) between the two replicates, confirming the reproducibility of our assay (Fig. 1c , Supplementary Data 2). Using this approach, we obtained the full survival distributions and statistics associated with the 307 single gene deleted yeast strains (Supplementary Data 3); not surprisingly, the mean and standard deviation of the RLS values were positively correlated among all strains (PCC = 0.
12E-10 and 3. 70E-32, respectively). However, when the SIS2 gene was deleted in the PPZ double deletion background, we observed a relative increase in RLS of the same magnitude as the increase in RLS when the SIS2 gene was deleted in the wild-type background (Fig. 3d, e , Supplementary Data 4). These results indicate that the SIS2 gene's lifespan-extending effect was independent from its PPZ-inhibiting role. Ground truth RLS mean of several strains was measured using the benchmark approach (5-day experiments and analysis of 300 cells per strain) and compared to the results obtained from 3-day experiments, with 200 or 300 cells analyzed per strain prior to Weibull fitting and predicting the full survival statistics. Solid bars indicate the measured or predicted RLS mean, while the error bars indicate the SEM (n = 200 or 300).
c Scatterplot of the RLS mean measured from each replicate of each strain analyzed. The red dot corresponds to the wild-type strain. The dotted line lies across the x = y axis and the Pearson's correlation coefficient (PCC) of the data distribution from two replicates is indicated. d Scatterplot of the RLS means measured using our microfluidic platform versus a micro-dissection platform (for shared strains). The red dot corresponds to the wild-type strain, and the Pearson's correlation coefficient (PCC) across the data points is indicated. e Scatterplot of the RLS means measured using our microfluidic platform versus the SEBYL technique (for the strains that were present on both studies). The red dot corresponds to the wild-type strain, and the Pearson's correlation coefficient (PCC) across the data points is indicated.
When we compared our RLS dataset with results from a previously published CLS dataset  48  , we saw that only 12 of the genes we identified as RLS-extending (8. 8% of all strains we analyzed) also turned out to be extending CLS (Supplementary Data 2). This was not very surprising as CLS and RLS are two intrinsically different metrics to measure lifespan. Different pathways may control these lifespan metrics for each mutant, and regulation of CLS or RLS by one pathway does not guarantee that the other lifespan metric will also be regulated by the same pathway in the same manner. A small fraction of the yeast genes we identified as lifespanextending when deleted had orthologous C. elegans genes whose deletion, hypomorphic mutation or RNAi knockdown caused extended lifespan in worms. The differences between our set of yeast-worm orthologs and the previously published set that resulted from the manual micro-dissection assay  19  are due to the major differences between lifespan outcomes measured by the two assays.
Overexpressed genes belonging to the 'cell cycle' gene set of the KEGG pathways are pointed in the plot. b Volcano plot showing the log 2 (CPM foldchange) of the expressed genes in the sis2Δ strain over the wild-type strain versus the negative decimal logarithm of the BH-adjusted p-value for every expressed gene. The data points highlighted in red show the transcripts of the genes that are repressed in the sis2Δ strain relative to their wild-type expression, while the data points highlighted in blue show the transcripts of the genes that are overexpressed in the sis2Δ strain relative to their wild-type expression. Overexpressed genes belonging to the 'cell cycle' gene set of the KEGG pathways are pointed in the plot. Output of the GSEA analysis performed using the list of DEG identified as a result of RNA-seq analysis of the sis2Δ background relative to the wild-type, exploring the enriched KEGG pathways (c) and the Biological Process domain of the Gene Ontology database (d).
Growth conditions and media Complete synthetic media (CSM) supplemented with amino acids was used to grow cells overnight and in all microfluidic chip experiments; 2% glucose was included in the media as the carbon source. Since the RLS-extending strains were curated mainly from solid agar plate-based micromanipulation studies, strains were checked for their growth in liquid CSM media; a handful of strains were dismissed from the strain list due to various reasons (Supplementary Data 9). For each strain whose RLS was measured microfluidically, 10 ml of yeast culture in a 50 ml conical tube was first shaker-incubated at 30 o C for 20-24 hours overnight in aerobic conditions using Innova-42 orbital shaker. To avoid cells' entering into stationary phase at the end of the overnight growth, cells were properly diluted right before the overnight growth started so that the OD 600 readings at the end of overnight growth was ~0.
Microfluidic measurements of yeast RLS For all strain-specific RLS measurements by our microfluidic chip, 200 cells (pooled from two replicates) were analyzed and included in the final results. During a ~2-year time period, we performed (on average) 8-10 independent lifespan measurements per week, corresponding to successful characterization of lifespan from 4 different strains per week as we included 2 replicates for each strain. While it was possible to increase this throughput by simply using additional microscopes, manual analysis of the single-cell lifespan data was a bottleneck; 100 cells were analyzed by a researcher per day. While not a commercial platform, our microfluidics setup is user-friendly: new students and postdocs in our lab were able to use this platform by themselves in 1-2 weeks without any major difficulties after detailed instructions were provided to them by senior lab members.
These flow rates cycled repeatedly until the end of the 72-hour RLS experiment. For tracking the cells, bright field images were acquired with 10 min intervals. Using NIKON's Elements software, mother cells were analyzed for the total number of daughter-production events by starting from their first generation until the end of each RLS experiment; we also marked each mother cell as dead or alive at the end of the 72-hour period. Typically, 100 cells were analyzed from each replicate experiment, and we performed and analyzed two replicate experiments per strain. In a few cases, the number of cells analyzed from a replicate was close to but not exactly 100; in those cases, we analyzed slightly more than 100 cells from the other replicate, so that the total number of cells analyzed per strain was consistently 200.
This procedure was performed systematically for all strains by using the code provided in Supplementary Data 7. For determining the reproducibility of replicate experiments for each strain, we applied the above procedure to cells of a replicate, instead of two replicates combined for a strain. Using 100 cells for the Weibull-fitting and predicting had similar predictive power for obtaining the full lifespan statistics compared to using 200 cells. The final lifespan distributions we measured turned out to be Gaussian or Gaussian-like distributions. For a Gaussian distribution, the mean and median of the distribution are the same number. Therefore, while we reported the mean RLS values in this work, the median RLS values would be very close to the means.
Acquisition and analysis of the flow cytometry data Cells were grown in CSM media supplemented with amino acids and 2% glucose as described above. 500 μl of cultures were transferred to 5 ml polystyrene round bottom tubes compatible with the cytometer inlet and placed on ice. Flow cytometry measurements were taken using FACSVerse (Beckton Dickinson). We measured 10,000 events for each replicate and used two independent biological replicates for each strain. Raw data were converted to CSV files with FCSExtract 1. 02 (Earl F. Glynn, Stowers Institute), and then custom analyses were performed on spreadsheets. For EGFP fluorescence measurements, a small gate (corresponding to ~20% of all events) was consistently placed on the densest region in the FSC-SSC chart to avoid cell-size-based variations.
6 mm HPLC column (Mac-Mod, USA). The column temperature, sample injection volume, the flow rate was set to 30 °C, 5 μL, and 0. 5 mL/min respectively. The HPLC conditions were as follows: Solvent A: Water with 0. 1% Formic Acid (v/v), LC/MS grade and Solvent B: Acetonitrile with 0. 1% Formic Acid (v/v), LC/MS grade. Gradient condition was 0-2 min: 5% B, 5-16 min: 90% B, 17 min 5% B, 30 min: 5% B. Total run time: 30 mins. Detected metabolites with a clean peak matching their expected retention time for these conditions were quantified by integrating their peak intensities using Analyst 1. 7. 2 software. Integrated intensity of the metabolites of interest was normalized by the total ion chromatogram (TIC) of its corresponding run and all runs were relativized to the average values detected in the wild type triplicates (Supplementary Data 8).
org), except for two genes (one being SIS2 itself, which was of course very downregulated in the sis2Δ strain, and the other one was YIL082W-A, a transposable element) which were clear outliers with respect to the rest of the data in terms of their fold-change value. In quantifying the fold-change, we divided sis2Δ read counts over BY4741 read counts, so a positive log 2 (fold-change) represents a gene overexpressed in the sis2Δ background. With WebGestalt, we have evaluated the Biological Process domain of the Gene Ontology database, as well as the KEGG pathways. We have selected Saccharomyces cerevisiae as the organism of interest and GSEA as the analysis method, keeping the advanced parameters at their default levels; we selected to obtain the top10 categories positively or negatively enriched in each of the domains/database.
Acknowledgements We thank  Prof. Dr. J. Ariño ,  Dr. E. Sarnoski ,  Prof. J. J. Collins  and  Acar Lab  members for comments and feedback on various stages of this work,  Prof. Dr. H-J. Schüller  for kindly providing yeast strains, the  Yale Center for Genome Analysis  staff for preparing and sequencing the RNA-seq libraries,  Dr. H. Baniasadi  at  UT Southwestern  for help on running LC-MS samples, and  Prof. Dr. M. Aldea  for providing resources and lab space to  D. F. Moreno  while the  Acar Lab  was moving. T. T. Ölmez  and  M. Acar  gratefully acknowledge the use of the services and facilities of the  Koç University Research Center for Translational Medicine (KUTTAM) , funded by the  Presidency of Turkey, Directorate of Strategy and Budget. This work was supported by the  National Institute of General Medical Sciences of the National Institutes of Health  under Award Number  R01GM127870  to  M.
This work is the outcome of a joint workshop organised by the three H2020-NMBP-35 projects, where experts and stakeholders, cooperating under the umbrella of EC-funded projects and various platforms, were brought in a common table to discuss about emerging scientific and technological topics and share their knowledgeable insights. The views of 28 experts were collected, representing multiple sectors in the materials field and 10 different countries in Europe, UK, and Norway: 10 experts from universities, institutions, and Research and Technology Organisations (RTOs), 1 from a national standardisation body, 1 from a Small-Medium Enterprise (SME), 8 experts from the industry, and 9 representatives from European initiatives and platforms. The main topics covered were characterisation and standardisation, the suites of modelling and characterisation tools, as well as data exchange procedures.
It is encouraging that many European projects are engaged in standardisation processes, because their consortia can allocate their technical expertise and power to help advance and implement the standards. The scientific community has identified the need and benefits of cooperating with Standardisation Committees to find ways to exploit the results of Research and Innovation (R&I) activities. Also, developing FAIR (Findable, Accessible, Interoperable, and Reusable)  [15]  databases and common strategies for storing and sharing data will contribute to dealing with some of the current challenges in research. However, a standardisation framework yet to be realised; ongoing work within IUPAC task groups and the Committee on Publications and Cheminformatics Data Standards is moving towards this direction, emphasising on FAIR data, data interoperability, and machine readability  [16].
NMBP-35 project CHARISMA is contributing to such objective under the framework of open science, developing models that will be accessible and open to public; for example, models related to reference materials for calibration. Ab-initio methods and DFT are used to build the physical models of the systems that are studied. There is planned effort to calculate their Raman spectra and identify the spectral features and validate calculations with experiment. The project will also support the acquisition of spectra without influence of the optical part or any other contribution by the environment, aiming to the pure material response. The spectra data generated will be made available in the NOMAD repository and all project outcomes will be open and accessible there. On the digitalisation pillar, the applications/graphical user interfaces (GUIs) that are being developed, can be integrated with AI and lead to exciting applications in the use cases; however, it was argued that performance bottlenecks could appear, and then it would be challenging to address them and investigate whether it would be the data, the model, or both that would be the real problem.
AI and data exchange procedures Process parameter optimisation is a big case study of AI exploitation, because even a slight percentage of improvement in the efficiency of a model can be a deal-breaker. In that case, after the user sets the target, machine learning algorithms can give useful output on necessary adjustments in order to achieve the objectives set in the design phase of the manufacturing process. Thus, since data access is a critical aspect for data science, it is crucial to have suitable databases where data integrity is protected and preserved. There is a gap on organised databases or datasets, as commented for the glass manufacturing sector, where there is potential for data exploitation opportunities utilising inhouse produced data and data used by industry, but this also requires a framework with open access for exchange procedures and external management.
Ansys, for example, is conducting a lot of work utilising artificial intelligence in exploiting small-to big-size data, and apart from the commercially available and very restricted data, it also works with open-access tools, such as PyAnsys, a rich open-access Python environment with multiple possibilities offered to the users. However, data exchanges between big industry players are difficult since IP issues can arise. The current strategy of EU industry is to exchange and share data only in the framework of collaborative projects; even there, companies and industry, in general, are eager to share their data. Another cornerstone of the NMBP-35 joint workshop discussions was the importance of AI and alternative methods to help with data exploitation since AI is a great interpolation system.
g. Raman spectroscopy, and generate spectra corresponding to material structural properties via the development of physics informed machine learning and explainable machine learning. Therefore, industry does not underestimate the power of basing some AI prediction on, for example, a Raman peak ratio. Physics informed and explainable machine learning can achieve high impact in the materials science landscape if there is integration with databases and access to vast amounts of annotated semantic data  [25, 26]. An interesting point is that AI can, in some cases, fill in data gaps, and thus, dataset completeness can be increased, which is crucial when indexing data in public repositories. In other cases, AI is going to be an ever-extending body of knowledge for an industrialist. At present, there are good examples of applications in additive manufacturing (AM), where a lot of data is generated for every build and product.
Digitalisation policy in Europe At high level of policy, everything is driven towards green and digital in various technologies that are contributing with data and knowledge generation to this field. The first message, as brought from AMI 2030 initiative, is that an end-to-end approach is taken about generated materials data by both modelling and characterisation. Industries have already joined the Materials Digitalisation working group 1, contributing to setting the stakeholder-driven priorities towards the strategic outcomes in a stakeholder-driven EU economy. Regarding the AMI 2030 Digitalisation working group  [31] , a starting point has been to investigate what data we have today, and what is the need to access and use that data. Then the second priority is the constantly generated new knowledge.
To achieve AMI2030′s objectives, there is the need for its goals to be well-defined to deliver on what these innovation markets need and beyond, and ultimately to realise the benefits for the end-user that actually is the society. Since the priorities can only be a few, there is an ongoing effort to capture the current state of the art across different fields. The vision of the necessary developments integrates and homogenises the diversity of approaches indicated by experts in the new version of the Materials 2030 Roadmap, building on the previous accomplishments of EC FP and Horizon Europe programmes. At more specific level of digitalisation, the semantics, ontology, EMMO, CHADA, and interoperability to modelling, AI, and characterisation could have a positive impact IoT in industry. The characterisation (meta)data structure (CHADA) is a unique result which has been standardised, and is used to extract the semantics for characterisation.
NanoMECommons is taking specific use cases of characterisation to extract the specifications and links/interdependencies of methods according to end-user needs. There is an effort to simplify the completion process because currently completion includes free text, which can make it almost impossible to transform human readable forms into machinereadable language to represent the domain knowledge. Thus, one emerging need is to have a glossary that describes the characterisation and the background knowledge around it, so CHARISMA and Nano-MECommons projects are documentating the terminologies used for each characterisation technique in a collaborative environment, i. e. Wikidata, which can be used for open collaboration between experts and research community. It is important to engage more experts in this effort of trying to determine widely accepted terms for describing the experiment of characterisation, and such great impact could be the outcome of a synergy between NMBP-35 and other characterisation projects, such as OntoTrans and Big Map projects, on characterisation semantics.
This is the foundation that end-users are laying today and with open innovation digital ecosystems within their companies and with a clear business case for reusing precious knowledge and data generated in the past. The second story is about Japan's paradigm. Japan provided every company and industry with access to a centralised database created to build a common know-how in depth, which provided security and strategic advantage for the development of advanced research and technology in Japan back in 1980 s′ and till today  [40]. The European Community was suggested to see the benefits in mimicking this and focus the efforts across a common research line, specifically on data saving and ensuring availability with adequate guidelines and clear ownership rights, and case specific management for stakeholders owning precious data.
Regarding materials data uniqueness, stemming from different instrument suppliers and hardware features, an example was given by CHARISMA project on Raman spectroscopy development and standardisation. It was noted that through benchmarks of reference materials, algorithms, and protocols developed, accurate, and consistent data collection and handling is possible, regardless of the Raman equipment used. The transforming society and the undergoing digitalisation activities are pushing for new solutions to cope with different forms of crisis, including the supply chain, energy, health, and environment (already confronted to some extent). It was shown that standard assessment methods can also have a role in social exploitation of new projectderived technologies. In a context of projects achievements and their corresponding new technologies, people can have in the future firsthand experience of the application of physical and chemical principles.
In order to envision the latter, i. e. , the robotics reproducing experiments, the community has to define a strategy to (i) capture the thoughts of the scientist, (ii) conceptualise knowledge and (iii) assess correlative information to perform evidence-based decisions, and (iv) assure that the cognitive knowledge for the methods can be stored and reused in an interoperable manner (i. e. , according to EMMO guidelines). This is much in relation with another European initiative, MAPs, coupled with a collaborative spirit from USA and Canada, and coming from the concept of autonomous or self-driving labs, articulated through the workshop report Materials Acceleration Platform  [41]. Besides that, it should be noted that the community needs the structured data and terminology to be in machine-readable form and accessible to achieve IoT wireless connection, which is a mean of improving the application of the technologies; if you can connect a handheld Raman device to the network, the user can get instant comparison of your data utilising the power of databases, in order to automate and simplify the analysis task for the users.
Acknowledgements The Workshop was supported by  EU   H2020  project  Nano-MECommons ,  GA 952869 ,  CHARISMA ,  GA 952921 ,  EASI-STRESS ,  GA 953219 , and  EsSENce COST   ACTION CA19118. This article/publication is based upon work from  COST Action EsSENce COST   ACTION CA19118 , supported by  COST (European Cooperation in Science and Technology). Miguel A. Bañares ,  Raquel Portela ,  Nina Jeliazkova ,  Enrique Lozano ,  Bastian Barton  and  Iván Moya  have received financial support from the  EU   H2020  project  CHARISMA , GA n. 952921 ,  Bojan Boskovic ,  Ennio Capria ,  Costas Charitidis ,  Donna Dykeman ,  Spyros Diplas ,  Gerhard Goldbeck ,  Marco Sebastiani ,  Elias Koumoulos ,  Silvia Giovanna Avataneo ,  Miguel A. Bañares ,  Raquel Portela ,  Anastasia Alexandratou ,  Athanasios Katsavrias ,  Fotis Mystakopoulos  have received financial support from the  EU   H2020  project  NanoMECommons , GA n.
952869 ,  Nikolaj Zangernberg  and  Ennio Capria  have received financial support from the  EU   H2020  project  EASI-STRESS , GA n. 953219 ,  Natalia Konchakova  has received financial support from the  EU   H2020  project  VIPCOAT , GA n. 952903 ,  Costas Charitidis ,  Elias Koumoulos , and  Spyros Diplas  have received financial support from the  EsSENce COST   ACTION CA19118. All authors would like to specially acknowledge  Anastasia Alexandratou ,  Athanasios Katsavrias  and  Fotis Mystakopoulos  for their support.