dataesr/scientific-paragraphs-categorization huggingface dataset:
The search for HIC1 consensus binding sites (HiRE) in the CXCR7 regulatory region identified 11 putative HiREs to which HIC1 could bind directly  [195]. HIC1 gene knockdown, CXCR7 promoter HiRE mutations and ChiP-seq approaches demonstrate that CXCR7 is a direct target of HIC1, which acts as a direct repressor of CXCR7 expression  [195]. This suggests that in tumors with loss of HIC1 expression, the subsequent increase in CXCR7 may participate in tumor progression. Although, similar to its partner CXCR4, CXCR7 expression can be regulated by epigenetic mechanisms involving miRs in different tumor types  [195] [196] [197] [198] ; to date, no data in the literature have demonstrated the involvement of a miR to regulate CXCR7 expression in CRC.
AMD3100 alone can induce β-arrestin recruitment to CXCR7. Moreover, and in contrast to the antagonistic effect observed for CXCR4, AMD3100 increases 125 I-CXCL12 binding to HEK293 cells expressing CXCR7 and CXCL12-facilitated recruitment of β-arrestin to CXCR7, recruitment that is also possible in the absence of CXCL12, albeit at relatively high concentrations (≥10 mM)  [235]. To date and to the best of our knowledge, no molecular mechanism has been proposed to justify the agonistic property of AMD3100 on CXCR7. Data about mode of action of AMD100 are limited in CRC. An in vitro study in the SW480 colon cell line demonstrated that the anti-tumor effect of AMD3100 was mediated through the reduction of VEGF and MMP-9 expression, but not MMP-2  [236]. Further evidence comes from a study on mammary stem cells that identified among the proteins showing CXCL12-induced phosphorylation, up to 22% are involved in signaling pathways related to cell adhesion and migration, actin and microtubule association in cytoskeletal remodeling.
PepR Peptide R (PepR) is a new CXCR4 antagonist peptide, effective mainly in combination with conventional chemotherapies such as 5-FU and oxaliplatin. In subcutaneous xenografts of HCT116 or HT-29 cells, mice treatment with PepR potentiates the inhibitory effect of chemotherapy on the proliferation and activation of EMT  [242]. As a proposed mechanism, an analysis of TCGA dataset RNA-Seq indicates that adding the PepR compound to chemotherapy reverted the increased expression of the mesenchymal markers as well as PD-L1, all markers being induced by chemotherapy alone  [242]. This suggests a role for CXCR4 in controlling EMT marker expression. In addition, treatment of colon cells with chemotherapy/radiochemotherapy induced a population of CD133+CXCR4+ cells, supposed to be stem-resistant cancer cells, while adding Pep R reduced this population.
A theory of receptor desensitization has been proposed to explain the agonist/antagonistic effect of the molecule. CCX771 would not stimulate chemotactic activity but rather induce internalization of CXCR7 from the cell surface. This has been observed for a CCR5-targeting molecule in search for anti-HIV-1 agent  [251]  or described for a CCL7 agonist non-glycosaminoglycans (GAGs) binding and evaluated for its antiinflammatory effect  [252]. The lack of data on the efficacy of these antagonists can be explained by the following studies showing that those molecules initially designed to inhibit CXCR7 activation also act as agonists in different pathologies  [253]. Likewise, some CXCR4 receptor antagonists are agonists for the CXCR7 receptor, such as the cyclic peptide TC14012  [254]. There may be several reasons why molecules presented as antagonists/agonists, exert inverse physiological activity.
The combination of NOX-A12 and PD-1 checkpoint inhibitor acts synergistically to facilitate T cell infiltration into spheroids. These observations were validated in vivo in a mouse model of syngeneic CRC in which treatment with NOX-A12 improved the response to anti-PD-1 therapy to reduce tumor size  [259]. A Phase I/II clinical trial is underway to study the effects of the NOX-A12 and anti-PD-1 combination in patients with advanced CRC or pancreatic carcinoma (NCT03168139).
Among the drugs tested in clinical trials, CXCR4 small molecule antagonists, fully humanized anti-CXCR4 antibodies and CXCR4 or CXCL12 peptide inhibitors represent the most advanced programs of CXCR4 inhibition in solid tumors. Galsky et al. published the first in-human phase I study in patients with advanced or metastatic CRC that explored the safety and tolerability of LY25110924 among other solid tumors  [266]. To date, AMD3100 is the only approved CXCR4 inhibitor drug  [231] , while multiple antagonists are in different stages of development. Presently, the clinical trials are mainly ongoing phase I/II trials. They mainly concern the CXCR4 peptide inhibitor LY2510924 (NCT02737072), the anti-CXCR4 antibody LY2624587 (NCT01139788), the small molecule inhibitors Plerixafor (NCT20179970, NCT03277209), MSX-122 (NCT00591682, suspended), USL311 (NCT02765165); however, for a number of these trials, the cancer type is not always indicated, as it only specified that the targeted diseases are solid tumors.
The only available data from completed phase I/II trials evaluated the application of the NOX-A12 molecule (OPERA trial, NCT03168139), first as monotherapy, and then continued with pembrolizumab in patients with advanced stage pretreated metastatic colorectal or pancreatic cancer. The NOX-A12 was well tolerated and allowed for a disease control rate of 25%, and an overall survival close to 12 months could be achieved  [267]. This effect was mediated by a transformation of the tumor immune microenvironment with the expression of a specific cytokine signature consisting of IL-2, IL-16 and IFN-γ as an indicator of activation in tumor tissue. Following this success, a phase II trial is currently underway to evaluate the effect of the combination of NOX-A12 and pembrolizumab in glioblastoma and pancreatic cancer.
Conversely, treating patients with CRC for seven days with continuous infusion of the CXCR4 inhibitor AMD3100/Plerixafor induces an integrated immune response with enhanced intratumoral immune B and T cell responses as observed in paired biopsies of metastatic lesions (NCT02179970)  [268] , an immune response that is predictive of a clinical response to T cell checkpoint inhibition. For other trials, no results are currently available, due to the required time to exploit the data. Although several CXCR7 antagonists (CCX771, CCX662, CCX733, CCX754, and CCX777) have been investigated in preclinical models  [253, 269] , to date, CXCR7 modulators have not been clinically investigated.
Acknowledgments: Work in the author's laboratory is supported by the  Inserm  ( AD10 Grand Est ) and the  Ligue Contre le Cancer  ( Comité du Grand Est ). Aïssata Aimée GOÏTA is a predoctoral fellow from the  French Ministère de la Recherche et de l'Enseignement Supérieur. Dominique Guenot  is a research director at the CNRS.
In addition, heavy machinery and high ground pressure equipment can often result in a high soil compaction and further on in poor plant growth and establishment. Plants growing in highly compacted soils will become stagnant as they cannot develop their root system properly or with limitations due to the soil compaction. Although, SWBE is known for the use of natural materials, sometimes conventional materials, such as for example petroleum-based products, are used in SWBE structures. These materials are not biodegradable and will eventually end up within the river environment. This is a significant problem in some parts of the world, where engineers in general don't seem to understand or realize the long-term consequences of using such products. The reason therefore could be the lack of knowledge and availability of data quantifying the resistance of plant material both top growth (roughness) and roots (soil cohesion, tensile strength etc.
Upregulation of PD-1, PD-L1, and PD-L2 has been demonstrated in the peripheral blood mononuclear cells of patients with MDS  [14, 15]. This upregulation is enhanced by epigenetic modifiers such as azacitidine and has been associated with poor survival  [14, 15]. The PD-1/PD-L1 pathway thus represents an attractive target in patients with MDS who have experienced failed first-line treatment with an HMA. We present data from the MDS cohort of the KEYNOTE-013 study of pembrolizumab in patients with hematologic malignancies.
KEYNOTE-013 (NCT01953692 ) is an open-label, nonrandomized, multicohort, phase 1b study of pembrolizumab in patients with hematologic malignancies. The cohort described here includes patients with MDS. Eligible patients were aged 18 years and had primary or secondary MDS with an IPSS score of intermediate-1, intermediate-2, or high-risk that failed to respond to 4 previous cycles of HMA treatment (defined as worsened cytopenia, increased percentage of bone marrow blasts, or disease progression [per International Working Group (IWG) criteria] to a more advanced MDS French-American-British subtype than at pretreatment). Patients had to have Eastern Cooperative Oncology Group (ECOG) performance status 0 or 1, life expectancy long enough to be assessed for response to or benefit from therapy, bone marrow biopsy/aspirate material for biomarker analysis or newly obtained bone marrow biopsy/aspirate, and adequate organ function.
By the database cutoff date of June 26, 2020, 25 patients (89%) had died; 9 deaths (32%) resulted from disease progression. The median duration of follow-up, defined as the time from the first dose to the date of death or the database cutoff date, was 5. 6 months (range, 1-78 months). Three patients (11%) were on study for 1 year (Figure  1 ).
Data availability statement Merck Sharp & Dohme Corp. , a subsidiary of Merck & Co. , Inc. , NJ, USA (MSD) is committed to providing qualified scientific researchers access to anonymized data and clinical study reports from the company's clinical trials for the purpose of conducting legitimate scientific research. MSD is also obligated to protect the rights and privacy of trial participants and, as such, has a procedure in place for evaluating and fulfilling requests for sharing company clinical trial data with qualified external scientific researchers. The MSD data sharing website (available at: http://engagezone. msd. com/ds_documentation. php) outlines the process and requirements for submitting a data request. Applications will be promptly assessed for completeness and policy compliance.
Feasible requests will be reviewed by a committee of MSD subject matter experts to assess the scientific validity of the request and the qualifications of the requestors. In line with data privacy legislation, submitters of approved requests must enter into a standard data-sharing agreement with MSD before data access is granted. Data will be made available for request after product approval in the US and EU or after product development is discontinued. There are circumstances that may prevent MSD from sharing requested data, including country or region-specific regulations. If the request is declined, it will be communicated to the investigator. Access to genetic or exploratory biomarker data requires a detailed, hypothesis-driven statistical analysis plan that is collaboratively developed by the requestor and MSD subject matter experts; after approval of the statistical analysis plan and execution of a data-sharing agreement, MSD will either perform the proposed analyses and share the results with the requestor or will construct biomarker covariates and add them to a file with clinical data that is uploaded to an analysis portal so that the requestor can perform the proposed analyses.
Acknowledgments The authors thank the patients and their families and caregivers and all primary investigators and their site personnel. The authors also thank  Brooke Burton  (employee of  Merck Sharp & Dohme Corp. , a subsidiary of  Merck & Co. , Inc. , Kenilworth, NJ,  USA ) for clinical study support and  Luana Atherly-Henderson, PhD , CMPP (employee of  Merck Sharp & Dohme Corp. , a subsidiary of  Merck & Co. , Inc. , Kenilworth, NJ,  USA ) for medical writing and/or editorial support. Additional medical writing and/or editorial assistance was provided by  Dominic Singson, MD , and  Matthew Grzywacz, PhD , of  ApotheCom (Yardley, PA, USA ). This assistance was funded by  Merck Sharp & Dohme Corp. , a subsidiary of  Merck & Co. , Inc. , Kenilworth, NJ,  USA.
The ELECTRO-PAD study was registered on Clinical-Trials. gov (NCT03795103). A Clinical Research Officer mandated by the promoter ensured participant safety and good practices of the clinical study. The coordinating center was certified to conduct such randomized controlled trial. and approved by the sponsor and the applicable French institutional ethical committee (CPP: "Comité de Protection des Personnes") with respect to scientific content and compliance with applicable research and human subjects' regulations, according to the French law and the Declaration of Helsinki (CPP reference 2018/07). The ELECTRO-PAD study was registered on ClinicalTrials. gov (NCT03795103). A Clinical Research Officer mandated by the promoter ensured participant safety and good practices of the clinical study.
All the instructions for wearing the monitors are summarized in a handbook (File S3) that is sent to the participants together with the monitors. Participants are asked to report their bedtime and waking time every day in the handbook. In addition, at the end of each day, the participants report the main indoor and/or outdoor activities carried out throughout the day with a timestamp (File S3). Data from accelerometers will be processed following previously published procedures  [34, 35]. The main metrics computed to assess daily PA level will be the following: mean time per day of total PA; mean time per day of light, moderate, and vigorous PA; mean number of steps per day; mean number of steps per day (or mean daily stepping time) accumulated in different duration bouts; and mean number of steps per day (or mean daily stepping time) accumulated in different cadence bands.
After the 7-day PA monitoring, participants send back monitors and the PA guide by mail using a prepaid package. GPS-measured outdoor walking capacity. During the 7-day free-living PA measurement, participants are asked to complete two 30 min outdoor walking sessions while wearing and using the PA monitors as described above. The instructions to perform the walk were previously reported  [37] [38] [39]  and are summarized in the handbook (File S3). Data from the GPS receiver will be directly analyzed to process indicators of walking capacity, as previously reported  [36] [37] [38] [39]. For each participant, the main GPS parameters recorded will be: the total distance walked over the outdoor session; the whole duration of the outdoor session; the number of walking and stopping bouts; the mean walking speed calculated over each walking bout; the total time and distance over each walking bout; the total time over each stopping bout; the bout with the highest time and distance walked; and the mean time and distance over all the walking bouts.
Data Collection, Management, and Analysis Data collection and management. The data collected are in an electronic Case Report Form (eCRF) and data monitoring is carried out on a regular basis (~every six months). Statistical analysis (see also File S1, item 20a). Descriptive statistics will be performed to describe the baseline characteristics of interest at the level of the two groups and for the different outcomes. Continuous data will be expressed as mean +/-standard deviation or median values with interquartile range, depending on the distribution. A normality test will be carried out to judge the Gaussian or non-Gaussian distribution of the data of the continuous variables studied. Two-sample, two-sided t-tests (or Mann-Whitney U test) and χ2 tests will be used to compare continuous and categorical characteristics of participants across the two groups at baseline, respectively.
For statistical analysis of the primary and secondary outcomes after study completion, two -sample, two-sided t-tests (or Mann-Whitney U test if the data is not normally distributed) will be used to compare changes in outcomes between baseline and the 3-month follow-up between the NMES and the CONT groups. However, in case of imbalance in baseline characteristics between the 2 groups, outcome data will be analyzed using linear regression models adjusting for significantly different baseline data between the two groups  [46]. Firstly, an intention-to-treat analysis will be performed. As recommended  [47] , a missing at random (MAR) assumption will be made for missing data for principal analysis by performing complete cases and multiple imputation analysis. Deviation from MAR will also be assessed with sensitivity analysis under the missing not at random (MNAR) assumption, using controlled multiple imputation with different controlled scenarios.
A per-protocol analysis will be also performed considering only participants with data available both at visit #1 and visit #2. A subgroup analysis will be performed to explore the potential association between change in treadmill MWD and reported walking activity changes during the follow-up. In the NMES group, potential association between the total dose of NMES over the program and change in treadmill MWD will be assessed. Statistical analysis will be carried out using R software  [48]. The level of significance will be fixed at p < 0. 05.
Acknowledgements We thank all participants of the  ANRS PRIMO cohort. This work was granted by the  ANRS (Agence Nationale de Recherches sur le SIDA  et les h epatites virales), the  Investissements d'Avenir program  managed by the  ANR  under reference  ANR-10-LABX-77  and  EHVA  (No. 681032 ,  Horizon 2020 ). Work in S. B. 's laboratory is supported by grants from the  Agence Nationale de la Recherche (ANR)  ( ANR-11-LABX-0070_TRANS-PLANTEX ), the  INSERM  ( UMR_S 1109 ), the  Institut Universitaire de France (IUF) , all the  University of Strasbourg (IDEX UNISTRA) , the  European regional development fund (European Union )  INTERREG V program  (project no. 3. 2 TRIDIAG) and  MSD-Avenir  grant  AUTOGEN. The funders had no role in study design, data collection and interpretation or the decision to submit the work for publication.
Ethical Approval This study was approved by the ethics committee of the Keio University Faculty of Pharmacy (approval No 191218-2, 190301-1). All procedures were performed in accordance with the Ethical Guidelines for Medical and Health Research Involving Human Subjects (settled by the Ministry of Education, Culture, Sports, Science and Technology and the Ministry of Health, Labour and Welfare in Japan) and the Declaration of Helsinki and its later amendments. Consent to use the data from Life Palette for research purposes was obtained at the time of user registration. In this study, all data were analyzed anonymously and informed consent for this research was waived due to the retrospective observational design of the study.
Task and Metrics A multilabel task was performed to classify five labels simultaneously. The performance was evaluated in terms of precision, F score, and exact match accuracy, which indicates the percentage of correct predictions for all labels. As a way to use the research, we envision the construction of an information provision system tailored to each patient's problems. Therefore, we focused on precision so as not to provide unmatched information and inadvertently impose a burden on patients with breast cancer. The data set was divided into training data and test data in a ratio of 4:1, and the model was evaluated using the average of 5-fold cross-validation results to confirm its robustness. Moreover, to examine the effect of the upper limit of the number of input words on the model performance, the performance for blog posts with over 512 words, that for all posts, and that for posts with 512 words or less were compared.
The terms "model" and "tool" can be distinguished as follows: • An exposure model (here "model") is a conceptual or mathematical representation of the exposure process  [10, 11]. As such it encompasses a concept, set of input parameters and mathematical equations that are defined based on a dataset or another source of past experience regarding exposure phenomena. • A modelling tool (here "tool") is an embodiment of one or more exposure models within a standalone or web-based software. An individual exposure model may be integrated in different tools. Models can rely on describing mathematically the physical principles that determine the transfer of the substance from its source to a receptor (mechanistic models), but also on the use of historical data (empirical models) and probability distributions (stochastic models), as well as expert judgement.
g. developing evaluation criteria). Next to the mathematical description of physical processes, models rely on data. Therefore, for improving models, a central element is to ensure access to existing data and to advise on data collection. Ideally, all data should be made available in an open database that would include all the exposure data collected/ measured over the last few decades. Improvement of models is also needed regarding specific processes. One example is spray application, e. g. hand held spraying for surface coating or application of disinfectants, but also the application of plant protection products (PPP): spraying results in a direct release of substances into the air, so that they are available for human inhalation and deposition on the skin. The deposition of aerosols in the lungs is size dependent  [24] , so that exposure depends on the size distribution.
A modelling tool (Spray Drift Calculator) was therefore developed by the Joint Research Centre to calculate the drift, so that appropriate mitigation measures can be recommended  [27]. Development of new methodologies and support for understudied research fields. With the rise of big data technology and omics measurements, a wealth of data becomes available that stays useless if not structured  [28]. To use this data efficiently, new modelling strategies need to be further developed, e. g. Bayesian approaches to integrate data and model  [29, 30]. In the new EU partnership on chemical risk assessment PARC  [31]  starting in 2022, data sharing is considered a key element and it is planned to develop common databases for Europe. Furthermore, the partnership intends to work on aggregate exposure and enhancing models for regulatory risk assessment.
Thus, by combining QSPRs with monitoring databases, a full framework for read-across can be established  [34]. Recent approaches also include the construction of quantitative structure use relationships for screening  [35]. Below are specific areas where new developments are considered most urgent. Dermal exposure: Both in occupational and consumer exposure settings, data and modelling approaches for dermal exposure are underrepresented compared to inhalation exposure. There are some tools available (e. g. RISKOFDERM, MEASE, ECETOC TRA, Crème cosmetics, PACEM), all of which require a number of input parameters that are often not available or difficult to obtain (e. g. the dermal absorption). In addition, only very limited information regarding the evaluation of dermal exposure models is available  [36].
However, the scope of such analyses is restricted to the chemicals included in the monitoring programmes based on concerns regarding hazard, and hence does not cover all "real-life co-exposures". Complementary exposure modelling thus is necessary to account for substances that are not monitored or monitored infrequently. Ideally, what is needed for the assessment of combined exposure is the ability to predict the likelihood of specific co-exposures, so that the most likely co-exposures can be considered in a cumulative risk assessment. For exposure through diet, some methodologies have recently been developed and applied  [53, 54]. But for most substances, and their sources, data is lacking on occurrence that prohibits similar approaches. Creating occurrence databases and advancing mixture modelling therefore is crucial, as well as co-exposure to physical nuisances  [55, 56].