dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments This work builds partly on the classical 7-gene MLST database, which has been curated by  Virginie Passet ,  Radek Izdebski ,  Carla Rodrigues , and  Federica Palma. We thank  Adrien Le Meur  for help with the genome data set constitution and initial analyses of intra-outbreak variation. M. H. was supported financially by the PhD grant "Codes4strains" from the  European Joint Programme One Health , which has received funding from the  European Union 's  Horizon 2020 Research and Innovation Programme  under Grant Agreement No. 773830. This work was supported financially by the  French Government 's  Investissement d'Avenir program Laboratoire d'Excellence "Integrative Biology of Emerging Infectious Diseases"  ( ANR-10-LABX-62-IBEID ) and by an award from the  Bill and Melinda Gates Foundation  ( INV-025280 ).
Data availability All genome assembly accessions are provided in Supplementary Tables  S1  and S9. The allele sequences for each of the 629 loci of the scgMLSTv2 scheme are available at https://bigsdb. pasteur. fr/cgi-bin/bigsdb/bigsdb. pl? db=pubmlst_klebsiella_seqdef&page=downloadAlleles& scheme_id=18. The source codes of MSTclust and LINcoding are available at https://gitlab. pasteur. fr/GIPhy/ MSTclust and https://gitlab. pasteur. fr/BEBP/LINcoding, respectively.
Logical modeling Software GINsim We used the freely available software GINsim (Gene Interaction Network simulation, http://ginsim. org) for the edition, analysis and simulation of the regulatory graph  [49]. Once the model defined, several simulation tools are proposed to simulate the State Transition Graph, the Hierarchical Transition Graph, do reduction of model or compute the stable states. GINsim also enables the simulation of mutants (loss-of-function, ectopic gene expression, and combinations) by blocking the levels of expression of the corresponding variables. To further ease the analysis of multiple perturbations, we have written a set of scripts in Python and R, which iteratively compute the behaviour of our model for every input values and mutants considered, and generate heatmaps representing their behaviour.
Reproducibility of the results In order to allow reproducing the results, we have developed an application allowing to: 1. load the logical model (in the GINsim format) corresponding to the current study 2. after choosing N nodes of interest, build a set of mutant models (which contains the "wild type" and all the single mutants, double mutants,. , N-uple mutants) 3. compute the dynamics of each model using GINsim This application is installable directly from source from gitlab and can be run via executing a docker file containing all the required dependencies. It is available at https: //gitlab. com/Laurent. Tichit/fe-s-cluster-biogenesis-logical-model/.
Background: Despite the efficacy of psychosocial interventions in minimizing psychosocial morbidity in breast cancer survivors (BCSs), intervention delivery across survivorship is limited by physical, organizational, and attitudinal barriers, which contribute to a mental health care treatment gap in cancer settings. Objective: The aim of this study is to develop iNNOV Breast Cancer (iNNOVBC), a guided, internet-delivered, individually tailored, acceptance and commitment therapy-influenced cognitive behavioral intervention program aiming to treat mild to moderate anxiety and depression in BCSs as well as to improve fatigue, insomnia, sexual dysfunction, and health-related quality of life in this group. This study also aims to evaluate the usefulness, usability, and preliminary feasibility of iNNOVBC.
Methods: iNNOVBC was developed using a user-centered design approach involving its primary and secondary end users, that is, BCSs (11/24, 46%) and mental health professionals (13/24, 54%). We used mixed methods, namely in-depth semistructured interviews, laboratory-based usability tests, short-term field trials, and surveys, to assess iNNOVBC's usefulness, usability, and preliminary feasibility among these target users. Descriptive statistics were used to characterize the study sample, evaluate performance data, and assess survey responses. Qualitative data were recorded, transcribed verbatim, and thematically analyzed. Results: Overall, participants considered iNNOVBC highly useful, with most participants reporting on the pertinence of its scope, the digital format, the relevant content, and the appropriate features.
However, various usability issues were identified, and participants suggested that the program should be refined by simplifying navigation paths, using a more dynamic color scheme, including more icons and images, displaying information in different formats and versions, and developing smartphone and tablet versions. In addition, participants suggested that tables should be converted into plain textboxes and data visualization dashboards should be included to facilitate the tracking of progress. The possibility of using iNNOVBC in a flexible manner, tailoring it according to BCSs' changing needs and along the cancer care continuum, was another suggestion that was identified.
This lack of human-centeredness in the development partly explains the high attrition rates and poor engagement often reported in clinical trials and configures a limitation that needs to be addressed to effectively impact survivorship supportive care provision  [39, 40]. The aim of this study is to report on the development, usefulness, usability, and preliminary feasibility of iNNOV Breast Cancer (iNNOVBC), a guided, internet-delivered, individually tailored, ACT-influenced CBT program developed to treat mild to moderate anxiety and depression in BCSs, as well as to improve fatigue, insomnia, sexual dysfunction, and HRQoL in this group. Besides informing iNNOVBC's further development and refinement, the contribution of this paper resides in the description of mental health professionals' (MHPs') and BCSs' perspectives on the use of digital technology to support cancer survivors and the design implications that arise from considering these.
Data Collection Procedures Data were collected between November 2019 and February 2020 (ie, before the COVID-19 pandemic). Up to 2 to 3 interviewers or moderators (CMS, Ana Alves, and Elsa Oliveira) participated in data collection. The research protocol included an exploratory semistructured interview (N=24; ie, 11/24, 46% BCSs and 13/24, 54% MHPs; Multimedia Appendix 2), a laboratory-based usability test (Multimedia Appendix 3), followed by the completion of a self-reported usability survey  [56]  (N=24; ie, 11/24, 46% BCSs, and 13/24, 54% MHPs), and a debriefing post usability test interview (N=24; ie, 11/24, 46% BCSs, and 13/24, 54% MHPs; Multimedia Appendix 4). The exploratory interview aimed to investigate the usefulness of iNNOVBC and gather requirements for further refinement of the program.
Interviews and usability tests were audio and video recorded, respectively. Approximately 36 hours of audio recordings and 9 hours of video recordings were created. The average duration of pretest interviews was 49 minutes (range 26-81 minutes) for MHPs and 74 minutes for BCSs (range 39-106 minutes). Usability tests were completed on average in 20 minutes and followed by 28 minutes (range 10-46 minutes) of debriefing interviews. Interview recordings were transcribed verbatim (CMS and Ana Alves) in parallel to data collection, using oTranscribe (created by Elliot Bentley; a project of the MuckRock Foundation)  [58]. Usability tests were assessed using predeveloped observation grids and by registering participants' voiced remarks. Data were stored in a pseudoanonymized format in a secure password-protected location.
Usefulness Semistructured interview guides (Multimedia Appendix 2) were developed based on a literature review to assess participants' perceived usefulness of the program. The interview guides covered the following domains: (1) survivorship main challenges, unmet care needs, and self-care strategies developed to address those challenges and needs; (2) the provision of psychosocial survivorship care to BCSs and the main barriers impacting it; (3) knowledge and use of DMH; and (4) attitudes toward DMH programs aimed at providing survivorship support. In addition, a questionnaire developed to assess the quality of iNNOVBC's treatment modules was used for this purpose (Multimedia Appendix 6).
Analysis A combination of quantitative and qualitative methods was applied to assess iNNOVBC's usefulness, usability, and perceived feasibility. Descriptive statistics, namely, counts, percentages, medians, and IQRs, were used to characterize the study sample, evaluate performance data, and assess preference data collected via the System Usability Scale and the treatment modules' assessment questionnaire. No efficiency metrics were computed because the think-aloud method was applied. Microsoft Excel was used to compute quantitative variables. Qualitative data resulting from the interviews and BCSs' written comments were transcribed verbatim and analyzed using the thematic analysis method of Braun and Clarke  [59]. First, a deductive approach was adopted based on three predetermined high-level themes: usefulness, usability, and feasibility.
Usability Both the quantitative and qualitative data collected during the usability tests and interviews identified design and functionality issues that could be improved to enable more effective and efficient use of iNNOVBC and increase users' satisfaction with the program. In general, participants' effectiveness, as measured by completion rates, was high in both groups (range 69. 2-100; Tables  2  and 3 ), but the System Usability Scale median score was 65 (IQR 35) for BCSs and 47. 5 (IQR 25) for MHPs, which may be considered below average in terms of usability. These results are aligned with the usability issues identified during the usability tests. According to the classification by Zahabi et al  [62] , the issues identified during the usability tests (Multimedia Appendix 7) were mostly related to inefficient interaction (12/43, 28%), ineffective information presentation (9/43, 21%), cognitive overload (7/43, 16%), ineffective use of language (6/43, 14%), and lack of naturalness (ie, lack of familiarity or matching between users' usual workflow and the system; 6/43, 14%).
[because] All the little windows appear right away, and the images are clear and appealing to me. [BCS4] Similar to BCS4, various participants considered changes to iNNOVBC's user interface could be performed. Some participants believed the program could be redesigned to display "a unique dashboard with all key features available at login" (BCS8), whereas other interviewees suggested that a tour providing an overview of the platform or adding labels and preview options to most used functionalities would suffice. Furthermore, the use of familiar and interactive design was also appointed as a strategy that could facilitate navigation: I found it a little monotonous, everything looked the same. and that confused me. Maybe using more colour and highlighting some parts. it would be easier to understand how the materials are organized.
This was also clear from the participants' behavior during the usability tests, where sometimes errors were committed because participants assumed that modules (eg, the anxiety, sleep, and relaxation modules) could be used interchangeably and not sequentially. This discrepancy between the program's original concept and how participants appropriated the program or intended to use it was emphasized during the field trials, impacting participants' perception of iNNOVBC's feasibility. XSL • FO.
Feasibility Many participants reported that integrating iNNOVBC into their routine, as prescribed, would be feasible but challenging. MHPs worried about the implementation of the program at their workplaces owing to interoperability as well as practical and attitudinal limitations, whereas BCSs anticipated difficulties in "finding the time to fit the program into the day-to-day life" (BCS1): Considering the hustle of professional life, I believe it wouldn't be easy for me to comply with everything that is asked. to enter the site and make daily registrations because I would have to be available to complete several steps and to pay close attention to it. Nowadays, people want simple, fast, and short tasks. and as is, I am not sure I would be able to do it straightforwardly. [BCS6] It is important to me to have some flexibility.
I don't want it to be another obligation. [BCS3] As mentioned by BCS3 and BCS6, various interviewees underlined the role that flexibility, accessibility, and ease of use could play in iNNOVBC's uptake. During the field trials, various participants used iNNOVBC on their smartphones, owing to its portability and usability. A participant who was retired justified that she did not "use computers in a long time, being more acquainted with mobile devices" (BCS4). Another participant who was on sick leave mentioned not using computers regularly, because she did not usually carry it with her anymore and some "movements, like pulling the plug or pressing the mouse buttons, still hurt due to hormonal treatment" (BCS9). Another working participant revealed being receptive to use the web version of the program but showed some resistance in spending additional hours in front of the computer while at home (BCS10).
The positive impact of having dedicated professionals, conceivably digital navigators, supporting the implementation of the program was also discussed by BCSs. Some participants considered it would be important to have an appointed professional to introduce them to iNNOVBC's content, structure, and features, at their cancer centers. Such support could help them in overcoming usability issues and attitudinal barriers toward the program and, ultimately, facilitating adoption: After being admitted or having a first appointment with the doctor, he could say "now you are going to meet a colleague of mine, that will show you how to use a tool that can help you deal with your situation" and then the designated colleague, that has to be an appealing person, possibly a psychologist to better know how to convey the information to the person, would explain the purpose of the program, show how the program works and even trial it with the patient.
Some participants mentioned that using chat or email could facilitate self-disclosure in BCSs and promote the timely discussion of sensitive topics often avoided, thus having a positive impact on the established therapeutic alliance and process. Nevertheless, few studies have addressed chat-based internet interventions targeting cancer survivors. Previous research has focused on chat groups for patients with prostate cancer  [67]  or adolescents treated for cancer and reported mixed results. Thus, it is necessary to conduct further research on one-to-one chat-based programs to evaluate the role chat sessions could have in survivors' treatment progress and engagement in digital supportive care. Despite participants' perceived usefulness of iNNOVBC, both BCSs and MHPs identified aspects hindering their experience while using the program and changes to be performed to achieve better effectiveness, efficiency, and satisfaction with iNNOVBC.
Participants anticipated that such a strategy could facilitate the handling of the program and the assessment of BCSs' treatment progress, promoting their engagement with iNNOVBC. Previous research corroborate these findings  [71] [72] [73]. In a previous study by Igelström et al  [71] , the importance of delivering content in different formats and adding a graphical display of self-reports was emphasized by participant survivors. In another study by Wagner et al  [72] , BCSs stressed the importance of developing a my progress page to display didactic content and tools that had been completed and chart anxiety scores to facilitate tracking of progress. Nevertheless, and according to Kuijpers et al  [74] , although professionals might be primarily interested in dashboards indicating a worsening of symptoms to help patients reduce symptom burden, survivors seem to be interested in monitoring changes in their symptom experience and functional health, preferring to see both worsened and improved scores depicted in such visualizations.
These results underline the importance of tailoring DMH programs to the profile and unique preferences of each user to better address their concerns and needs. The option to tailor iNNOVBC along the cancer continuum was a salient development requirement identified during this study. Being conceived as an individually tailored program, iNNOVBC permits some degree of tailoring, namely, in what concerns the treatment modules. However, participants considered further layout and content customization should be allowed so that the program could adapt to the idiosyncrasies of each of the survivorship trajectory stages. Participants considered that comprehensive support should be provided along this continuum, not compartmentalizing survivors' needs but shaping and transforming the program according to its evolution.
This discrepancy adds to the technical, usability, funding, attitudinal, and training limitations identified by participants and the literature  [51]  as potentially hindering successful implementation of iNNOVBC and underlines the need to further assess and refine it in clinical contexts before scaling up the program. Thus, iNNOVBC will soon be pilot-tested in cancer settings  [78]  not only to assess its preliminary efficacy but also to further assess its feasibility and gather requirements for the design of a patient-centric service that fits into BCSs' lives, professionals' evidence-based practices, and cancer centers' workflows. After piloting and further refining iNNOVBC, the program will be tested for its efficacy and cost-effectiveness using a multicenter, randomized, waiting list, controlled design  [78].
Acknowledgments The authors wish to acknowledge the  Erasmus+ program  of the  European Union :  Phoenix Joint Doctoral Program on Dynamics of Health and Welfare, Fraunhofer Portugal Assistive Information  and  Communication Solutions , and  Foundation for Science and Technology  (individual research grant  2020. 09045. BD ) for supporting CMS. The authors thank  Linköping University  for covering the open-access publication fees. The authors thank all participants, as well as  Elsa Oliveira  and  Ana Alves  for their assistance in the data collection. The authors alone are responsible for the views expressed in this study, and they do not necessarily represent the decisions, policies, or views of the  International Agency for Research on Cancer-World Health Organization ,  Linköping University, NOVA National School of Public Health, Fraunhofer Portugal-Centre for Assistive Information  and  Communication Solutions , or  Karolinska Institutet.
BOX 1 Formal distinctions between statistical and mechanistic models A statistical model assumes that a dataset generated by the phenomenon of interest contains realizations of a random variable drawn from a particular distribution. This distribution is characterized by its parameters, such as a mean μ and a variance σ. Subsequently, distribution parameters are modeled as functions of one or more environmental predictor variables x (boldface means a vector), for example, in the case of generalized linear models, where g is the "link function. " The model parameters β-the effect of each variable x on the phenomenon of interest μ-can be estimated by finding those values that maximize the likelihood of observing the dataset y, where the likelihood function is given by the initially assumed distribution.
Bayesian estimation, by comparison, blends the prior, expert knowledge about parameters with the likelihood function in order to estimate the most likely value β given the dataset y. In contrast, mechanistic models take the form where a vector of system state variables x (e. g. , an individual's body temperature and water balance) is predicted through time t as a function of a vector of exogenous forcings u (e. g. , radiation, wind speed, humidity, air temperature), where β is a vector of model parameters (e. g. , surface area, body insulation, solar absorptivity). The vector function f is a collection of physical laws in functional form, one for each state variable in x (e. g. , the processes of convective, radiative, and evaporative heat transfer). Many biophysical parameters can be measured directly but the parameter set β is, in general, estimable via the maximum likelihood method or Bayesian estimation applied to the measurements y, after establishing a functional relationship between y and the state variables x, y = F(x), and assuming a probability distribution for measurement errors.
| Microclimates Until recently, gathering input data for microclimate models involved searching, downloading, and tailoring the relevant climate/ weather data. Recent implementations of R packages for microclimate modeling provide convenient access to online datasets such as NCEP  (Kemp et al. , 2012)  and ERA5  (Klinges et al. , 2022)  climate datasets for use in microclimate models. The NicheMapR  (Kearney & Porter, 2017)  package allows users to specify a location and time window of interest, extract the input data, and run an expanded version of the Niche Mapper microclimate model  (Porter et al. , 1973). The microclima R package consists of functions for pre-adjustments of such input forcing data for important "mesoclimate" effects such as wind sheltering, coastal influences, cold air drainage, and elevation-associated lapse rates  (Maclean et al.
, 2017 (Maclean et al. , , 2019)). These two complementary packages have now been integrated  (Kearney et al. , 2019) , highlighting the value of collaborations between research groups. Another challenge is the relatively coarse temporal and spatial resolution of online climate databases compared to those of animals  (Potter et al. , 2013; Sears et al. , 2011). For many applications, hourly resolution data can be extrapolated from daily minimum and maximum values  (Kearney et al. , 2014). However, when organisms are sensitive to extreme temperatures or rare environmental combinations, hourly resolution is needed  (Levy et al. , 2015). Integrating high spatial resolution thermal landscapes with biophysical models can inform how organisms are constrained by thermal transients and trade-offs in their ability to access environments  (Basson et al.
There are at least three potential solutions to this problem. First, statistical models  (Maclean et al. , 2021)  or Gaussian process emulation techniques  (Conti et al. , 2009)  can estimate complex dynamics of microclimates over short time periods, eliminating the need to run microclimate models in time increments and reducing run times. Second, modern computationally efficient programming languages, such as Julia (see below), offer the ease and expressiveness of high-level languages with performance comparable to Fortran or C++  (Bezanson et al. , 2018). Finally, it may not always be necessary to model microclimate in a spatially explicit manner-often knowledge of the mean, variance, and/or range of microclimatic conditions at a given locality may be sufficient to answer the research question  (Bütikofer et al.
| Functional trait data Biophysical models require detailed organismal trait data spanning morphology, physiology, behavior, and life history, to tailor predictions to specific taxa or questions. These are necessarily "functional traits" because they act as important parameters or thresholds for models of an organism's performance  (Dawson et al. , 2021; Kearney, Porter, et al. , 2021). Biophysical models are often criticized for being parameter hungry  (Buckley et al. , 2010; Kearney & Porter, 2009) , but with the rapidly increasing availability of trait databases, this criticism has become less valid. Functional trait databases have developed rapidly for plants, with the number of entries for functional traits increasing from 2. 07 to 11. 85 million between 2007 and 2020 across nearly 280,000 plant species  (Kattge et al.