dataesr/scientific-paragraphs-categorization huggingface dataset:
to predict the 3D protein structure of CD1687. As shown in Fig 5c, CD1687 has an alpha helix N-terminal signal peptide and two putative beta domains. To search for possible functions of the beta barrels, the putative structure of CD1687 was analysed in the Ekhidna database through the Dali server (33), but no function was detected. Since the function of CD1687 could be assigned to one of the two beta domains, we complemented the ∆1687 mutant by overexpressing CD1687 with either one of the two domains removed and growing these strains under biofilm inducing conditions (BHISG + 240µM DCA. Complementation of the mutant was not observed, indicating that C. difficile needs both beta domainds of the CD1687 to form DCAinduced biofilms (Fig 5d).
Acknowledgements This work was funded by the  Institut Pasteur, the "Integrative Biology of Emerging Infectious Diseases" (LabEX IBEID)  funded in the framework of the  French Government's "Programme Investissements d'Avenir" and The ANR DifBioRel   AAPCE5. EA is a doctoral fellow of  Université Paris-Cité. LH is a doctoral fellow of  Sorbonne Université.
( 32 ) 32 to predict the 3D protein structure of CD1687. As shown in Fig 5c, CD1687 has an alpha helix N-terminal signal peptide and two putative beta domains. To search for possible functions of the beta barrels, the putative structure of CD1687 was analysed in the Ekhidna database through the Dali server (33), but no function was detected. Since the function of CD1687 could be assigned to one of the two beta domains, we complemented the ∆1687 mutant by overexpressing CD1687 with either one of the two domains removed and growing these strains under biofilm inducing conditions (BHISG + 240µM DCA. Complementation of the mutant was not observed, indicating that C. difficile needs both beta domainds of the CD1687 to form DCAinduced biofilms (Fig 5d).
Methodology The review of scientific literature focused on riverine microplastics in Vietnam. The papers were selected using: Web of Science database, Google Scholar databases, three websites recording Vietnamese scientific journals (http://tapchikttv. vn; https://vjs. ac. vn; http://stdjns. science andtechnology. com. vn), and hard copies of research papers published without online versions; keywords: microplastic AND Vietnam AND fiber OR river OR estuary OR biota; and published status or accepted for publication status until December 2021. After screening, the publications related to wastewater treatment plants, marine environments, or plastic debris, were out of the scope of the study and thus not considered. A total of 15 scientific publications were collected (Figure  1 ). The review of national policies and regulations on controlling microplastic (and plastic) pollution was conducted by collecting policy documents issued by Ministry of Environment and Natural Resources, Ministry of Agriculture and Rural Development, and Ministry of Science and Technology.
2. 2) Reducing the use and release of plastic and microplastics from fisheries and aquaculture activities The Decision No. 687/QĐ-BNN-TCTS dated February 5, 2021 approving the Action Plan for Management of Marine Plastic Waste in the Fishery Sector, period 2020-2030, addresses to (i) develop and implement projects to collect lost and discarded fishing gear in the sea in conservation zones, aquatic resource protection zones and other sea areas; (ii) research the impact of microplastics on the chain of seafood production; and (iii) investigate and evaluate for building and updating the database of ocean plastic waste management in the seafood industry  [64]. The reduction of microplastic release is targeted indirectly, via the reduction of larger plastic items which might then degrade in-situ into microplastics.
Acknowledgement This study was undertaken under the framework of the project " Riverine Microplastics Pollution in ASEAN Countries -Case Study in Vietnam " led by  Dr. Pham Ngoc Bao ,  Deputy Director  of  Adaptation and Water Area, Institute for Global Environmental Strategies  (IGES). The project was funded by  IGES  under the  IGES's Strategic Research Fund  ( 2021-2022 ). The authors would like to thank  CARE  ( Ho Chi Minh City University of Technology HCMUT, VNU-HCM)  for their support of time and facilities.
Introduction The past decades have witnessed an enormous increase in information processing and data transfer fueled by the extraordinary progress of micro-and nanoelectronics devices and circuits. This evolution is accelerated even more currently by the rapid development of the Internet of Things 1 , neuromorphic computing and quantum computing which require substantially more energy e cient electronics. However, the remarkably successful down-scaling of conventional complementary metal oxide semiconductor (CMOS) technology on silicon is reaching physical and technological limitations. Moreover, Si CMOS devices working at deep cryogenic temperatures, which are used as control and readout circuits of qubits in a quntum computing system face a big challenge of scaling of the subthreshold swing to reduce the applied voltage.
Pesticides: understanding pollinator exposure to pesticides Kirsten Traynor: pesticides in pollen: real-world exposure in stored pollen of Apis mellifera High levels of honey bee (Apis mellifera) colony mortality in the USA  (2006) (2007)  increased interest in the risk factors honey bees experience. As such, the Animal Plant Health Inspection Service National Honeybee Disease Survey incorporated pesticide residue analysis into longterm monitoring of honey bee colonies. Traynor presented pesticide residue data collected from honey bee colonies over 7 years from 2011 to 2017. The dataset looked at 218 different active ingredients and their metabolites in 1055 apiary samples of bee bread, investigating five different ways to estimate risk: (1) pesticide prevalence, which looks at absence or presence in an apiary sample, (2) pesticide diversity, how many different residues are detected in an apiary sample, (3) pesticide concentration in parts per billion (ppb) summed across all products found, (4) relevant pesticides that contribute a minimum of 50 points when the detected concentration is divided by the pesticide product's LD 50 , and (5) the pollen hazard quotient.
Jessica Cole: investigating wildflowers as a route of pesticide exposure to bees Loss of habitat is undoubtedly a driver of wild bee declines and as such agri-environmental schemes encourage the planting of wildflowers to promote pollinator health  (Stout and Dicks 2022). However, wildflowers can be contaminated with pesticides, creating a potential trade-off  (David et al. 2016). Here, Cole presented data determining (i) the species of wildflowers most frequently visited by bees in agricultural environments and (ii) if they expressed pesticides. Floral abundance, diversity and visitation were surveyed at 9 transects sites at the University of Vermont Horticultural Center. Plantago lanceolata was preferentially visited by both honey bees and wild bees and Trifolium pratense was preferentially visited by wild bees, but not honey bees.
Acknowledgements This symposium was partially supported by a  USDA NIFA  conference grant to  Drs. Kapheim ,  Harrison ,  Evans ,  Li-Byarlay  and  Giray. We thank the  IUSSI  organizers for substantial assistance in developing and managing this symposium. HS was funded by the  Stengl-Wyer Scholars Program. AF was partially supported by an  USDA NIFA  postdoctoral fellowship and  USDA   NIFA 2022-67013-36285. JH was partially supported by  USDA   NIFA 2022-67013-36285. KMK was partially supported by  NSF  award  2142778  and  USDA NIFA  award  2018-67014-27542. HLB is supported by  USDA NIFA  award  NI211445XXXXG018 ,  USDA   AFRI  award  2020-67014-31557 ,  USDA CBG 2021-38821-34576 ,  USDA SARE NCR  project  LNC21-459. WMF is supported by the  University of Buenos Aires  ( 20020170100078BA ),  CONICET  ( PIP 11220200102201CO ) and  ANPCYT  ( PICT 2019 2438 ) of  Argentina.
NER was supported by the  Ontario Ministry of Environment and Climate Change (MOECC)   Best in Science grant  ( BIS201617-06 ), the  Natural Sciences and Engineering Research Council (NSERC)   Discovery Grants  ( 2015-06783  &  2021-04210 ), the  Food  from  Thought: Agricultural Systems for   a Healthy Planet Initiative , by the  Canada First Research Excellence Fund  (Grant  000054 ), and as the  Rebanks Family Chair in Pollinator Conservation  by the  Weston Family Foundation. GS was supported by the project  Stingless  bees as effective managed pollinators for Australian horticulture funded by the  Hort Frontiers Pollination Fund , part of the  Hort Frontiers  strategic partnership initiative developed by  Hort Innovation , with co-investment from  Western Sydney University ,  Syngenta  and OLAM, and contributions from the  Australian Government.
Background: We report updated data for avelumab plus axitinib versus sunitinib in patients with advanced renal cell carcinoma from the third interim analysis of the phase III JAVELIN Renal 101 trial. Patients and Methods: Progression-free survival (PFS), objective response rate (ORR), and duration of response per investigator assessment (RECIST version 1. 1) and overall survival (OS) were evaluated in the overall population and in International Metastatic Renal Cell Carcinoma Database Consortium (IMDC) risk groups; safety was also assessed. Results: Overall, median OS [95% confidence interval (CI)] was not reached [42. 2 months-not estimable (NE)] with avelumab plus axitinib versus 37. 8 months (31. 4-NE) with sunitinib [hazard ratio (HR) 0. 79, 95% CI 0. 643-0. 969; onesided P ¼ 0. 0116], and median PFS (95% CI) was 13.
8 Findings were confirmed in the second interim analysis (minimum follow-up, 13 months). 9 The median PFS with avelumab plus axitinib versus sunitinib in the overall trial population was 13. 3 months [95% confidence interval (CI) 11. 1-15. 3 months] versus 8. 0 months (95% CI 6. 7-9. 8 months), respectively [hazard ratio (HR) 0. 69, 95% CI 0. 574-0. 825; one-sided P < 0. 0001] and in the PD-L1þ population was 13. 8 months (95% CI 10. 1-20. 7 months) versus 7. 0 months (95% CI 5. 7-9. 6 months), respectively (HR 0. 62, 95% CI 0. 490-0. 777; one-sided P < 0. 0001). 9 The ORR with avelumab plus axitinib versus sunitinib in the overall population was 52. 5% (95% CI 47. 7%-57. 2%) versus 27. 3% (95% CI 23. 2%-31. 6%), and the complete response (CR) rate was 3. 8% versus 2. 0%, respectively. 9 he International Metastatic Renal Cell Carcinoma Database Consortium (IMDC) classification is a commonly used prognostic model for patients with aRCC.
ACKNOWLEDGEMENTS The authors thank the patients and their families, investigators, coinvestigators, and the study teams at each of the participating centers. This trial was sponsored by  Pfizer  as part of an alliance between  Pfizer  and  Merck  (CrossRef Funder ID:  10. 13039/100009945 ). Medical writing support was provided by  Hiba Al-Ashtal  of Clinical Thinking and was funded by  Pfizer  and  Merck. Patients treated at Memorial Sloan Kettering Cancer Center were supported in part by  Memorial Sloan Kettering Cancer Center   Support Grant/ Core Grant  [grant number  P30 CA008748 ]. FUNDING This research was sponsored by  Pfizer  as part of an alliance between  Pfizer  and  Merck  (CrossRef Funder ID:  10. 13039/ 100009945 ) (no grant number). DISCLOSURE JBAGH has provided consulting or advisory roles for  Achilles Therapeutics, AIMM Therapeutics, Bristol Myers Squibb, Immunocore, Ipsen ,  MSD ,  Neogene Therapeutics ,  Neon Therapeutics ,  Novartis ,  Pfizer ,  Roche/Genentech ,  Sanofi, Seattle Genetics , and  Third Rock Ventures ; and has received research funding from  Amgen ,  Bristol Myers Squibb ,  MSD ,  Neon Therapeutics , and  Novartis.
Data collection A database and electronic clinical report form (eCRF) were created for the purposes of the registry. Data were collected at each site as per routine clinical practice and collated in the Oracle Remote Data Capture (OC-RDC) onsite system (v4. 6. 6) developed for the registry and maintained by a contract research organisation (Syneos Health). Adverse events (AEs) were collected according to the WHO guidance for pharmacovigilance of medicines used in the treatment of TB, current at the time of the registry  [23]. All AEs and treatment-emergent AEs (TEAEs) were recorded, including those that were serious or bedaquiline-related, or that led to withdrawal, interruption, or dose reduction of bedaquiline, and deaths and events leading to death. AEs of special interest (AESIs) were identified from the known clinical safety profile of bedaquiline and preclinical data, and included, per Medical Dictionary for Regulatory Activities (MedDRA) version 17, Acute Pancreatitis (Standardised MedDRA Query [SMQ]), Rhabdomyolysis/Myopathy (SMQ), Torsades de Pointes/QT Prolongation (SMQ), and Drug-related Hepatic Disorders (SMQ).
Electrocardiographic (ECG) data were collected to monitor QT prolongation, with QT interval defined with correction using Fridericia's formula (QTcF). Effectiveness/clinical response was assessed in terms of treatment outcome (cured, treatment completed, died, treatment failed, lost to follow-up) as per 2013 WHO guidance  [19] , stratified by drug resistance type; sputum culture conversion, defined as two consecutive negative sputum cultures taken at least 30 days apart; and median time to initial sputum culture conversion. Data relating to dosages, duration, treatment compliance/adherence, treatment history, medical history, and concomitant medications used for conditions other than TB were collected. Baseline resistance profiles were defined per the 2013 WHO guidance  [19] : • MDR-TB excluding pre-XDR and XDR (resistant to isoniazid and rifampicin: MDR-TB H&R ); • pre-XDR-TB due to fluoroquinolones (pre-XDR-TB FQ ); • pre-XDR-TB due to SLIs (pre-XDR-TB SLI ); • XDR-TB (resistance to isoniazid, rifampicin, fluoroquinolones, and SLIs).
Bedaquiline drug susceptibility testing (DST) data were not collected systematically, although DSTs are performed in most culture-confirmed patients in South Korea. Limited DST data (proportions of patients with isolates resistant at baseline) for other MDR-TB drugs only were available for analysis. Concomitant medications of interest were other anti-TB drugs, antidiabetic drugs, antidepressants, cardiovascular medications, and antiretroviral treatment.
Data analysis Three analysis populations were defined: the enrolled population; the safety population of all patients exposed to at least one dose of study treatment; and the evaluable population. The evaluable study population, assessed for treatment success/failure, consisted of those patients who had been exposed to at least one dose of study treatment, had a baseline assessment, and had at least one post-baseline assessment. Accordingly, patients with no baseline assessment were excluded from the analysis of treatment success but were still assessed for safety. Mortality was analysed descriptively as deaths per WHO treatment outcome definition. AEs and TEAEs were summarised descriptively and presented by preferred term. AESIs were grouped by SMQ. Laboratory and ECG data were analysed descriptively.
Patient disposition Data were available for all 172 enrolled patients with MDR-TB. The first patient was enrolled on 3 May, 2016, and the last contact date listed in the clinical database was 1 July, 2020. Eighty-eight patients received a bedaquiline-containing MDR regimen and 84 received a non-bedaquiline-containing MDR regimen (safety population); 171 patients had baseline and at least one postbaseline and/or microbiological assessments (evaluable population) and were therefore assessed for WHO treatment outcomes. Approximately half (51. 2%) of patients were hospitalised at the time of treatment. In the evaluable population, 61. 4% (54/87) of bedaquiline-treated and 54. 8% (46/84) of non-bedaquilinetreated patients completed the study; Table  1  describes patients lost to follow-up, those that died, and patients who withdrew from treatment.
Baseline drug susceptibility Routinely collected baseline DST data for other MDR-TB drugs were as follows for 155 patients overall, and for 84 bedaquiline-treated and 71 non-bedaquiline-treated patients: • MDR-TB H&R : 81 (52. 3%), 24 (28. 6%), and 57 (80. 3%); • pre-XDR-TB FQ : 36 (23. 2%), 32 (38. 1%), and 4 (5. 6%); • pre-XDR-TB SLI : 7 (4. 5%), 5 (6. 0%), and 2 (2. 8%); • XDR-TB: 21 (13. 5%), 17 (20. 2%), and 4 (5. 6%). Baseline DST results suggested a greater proportion of patients with severe resistance profiles (pre-XDR-TB or XDR-TB) in the bedaquiline-treated group than did patients' baseline characteristics. The discrepancy may have been due to differences in accuracy of testing between the reference laboratory (phenotypic DST) and local laboratories (rapid DST based on molecular tests such as Xpert ® MTB/RIF, Cepheid, Sunnyvale, CA, USA, or line probe assay).
In addition, we note that the rate of loss to followup was higher in this registry than elsewhere  [22]. Results from a much larger (> 3000 patients) retrospective South African registry  [28]  showed increased rates of treatment success and reduced mortality in patients receiving bedaquiline. There were higher incidences of treatment-emergent peripheral neuropathy (13. 6% vs. 2. 4%) and paraesthesia (17. 0% vs. 8. 3%) in patients receiving bedaquiline. However, most bedaquiline-treated patients with these TEAEs were also receiving linezolid which is associated with neurological or ophthalmological AEs (including optical and peripheral neuropathy)  [29]. Moreover, unlike the compulsory reporting required for bedaquiline-treated patients, reporting of serious TEAEs (including fatal events) for non-bedaquiline-treated patients to the Janssen Global Safety Database was not required and therefore may be underreported.
Acknowledgements We would like to express gratitude to the patients and their families for their participation and support during the study. We would also like to thank the study centre staff and all  Janssen  study personnel. We would also like to acknowledge  TaeHun Lee  ( Ulsan University Hospital ),  Hae-Suk Seo  ( Seobuk Hospital ),  Jun Sung Cho  ( National Medical Center ),  Ju Sang Kim  ( Incheon St. Mary's Hospital ),  Ji-Yeon Oh  ( Korea University Guro Hospital ),  Young Sam Kim  ( Yonsei Severance Hospital ),  Young Chun Koh  ( Kwangju Christian Hospital ),  Eun Young Choi  ( Yeungnam University Medical Center ),  Ji Young Kang  ( Seoul St. Mary's Hospital ),  Won-Yeon Lee  ( Wonju Severance Christian Hospital ),  Gwang Ju Park  ( Ajou University Medical Center ),  Jin Young Kim  ( Incheon Medical Center ), and  Jin Woo Kim  ( Uijeongbu St.
A young woman carrying freshly picked root vegetables. Photo: UC Davis. The technical assistance project extended to a research project after the first several webinars, once consistent themes began to emerge from the presentations and attendee participation. It was evident that the impact of the pandemic on agritourism operatorsand the opportunities and challenges that it presented -needed to be collected to document the experience of the operators as well as to identify lessons learned for the future. We collected data through field notes taken while reviewing recorded webinars (eight), during in-person workshops (five), and during pre-interviews conducted with the speakers (20 individuals). Notes outlined themes, examples, and anecdotes. Data were also collected through post-training evaluation surveys that were completed by the attendees (93 individual surveys completed).
In total, data were drawn from 113 individuals (20 speakers and 93 attendees), collectively referred to as "participants. " Data were analyzed for key themes drawn from frequency and level of engagement around topics and then coded. Because many findings are not applicable to all participants and the qualitative approach of the study limited uniformity in data collection, analysis was conducted on subsets of the participant pool, illustrated in tables throughout the discussion. After analyzing the data, we conducted follow-up semi-structured phone interviews with eight speakers to gain a more in-depth understanding of their experiences as it connected to the themes and to obtain additional examples and quotes that would illustrate the findings. Information about participants' business and experience level was collected during registration.
Data collection For each patient, demographics, past medical history, trauma characteristics, pre-hospital management data such as time to hospital admission, initial Glasgow Coma Scale (GCS) score, minimum systolic blood pressure, peripheral oxygen saturation, maximum heart rate, need for mechanical ventilation, volume of fluid resuscitation, catecholamines use were collected. We also reported the following scores: Injury Severity Score (ISS)  [14] , details of injuries for each organ (Abbreviated Injury Scale 2015, AIS)  [15]  and simplified acute physiology score (SAPS II)  [16]. Severe organ injury was defined by an AIS for the correspondent organ over 2. On hospital admission, mean arterial blood pressure, heart rate, peripheral oxygen saturation, laboratory parameters (hemoglobin, prothrombin time, fibrinogen, myoglobin) were reported.
Outcome The primary outcome of interest was the transfusion of at least one pack of RBC during the first six hours of admission (pRBC H6 ). The secondary outcome was the need for an emergency hemostatic procedure. It was defined as performing angioembolization or hemostatic surgery within the first six hours of management from admission. Orthopedic surgery for fracture osteosynthesis was not considered as a hemostatic procedure. There is no data regarding ΔPCO 2 fem and SvO 2fem in trauma patients. Since trauma patients present a wide variety of traumatic injuries, we considered that a sample of 60 patients would be representative for this exploratory study.
Time, group (transfusion, no transfusion) and interaction (time x group) were set as fixed effect and patient as random effect. To evaluate the ability of admission lactate, ΔPCO 2 fem and SvO 2 fem to predict the need for pRBC H6 transfusion or an emergency hemostatic procedure, receiver operating characteristic curves (ROCs) were built and their areas under the curve (AUC) were calculated. The best threshold for the prediction of pRBC H6 transfusion or to predict the need for an emergency hemostatic procedure was defined as the value maximizing the Youden index (Y = Sensitivity + Specificity-1). Sensitivity, specificity, positive and negative predictive values (PPV and NPV), were reported for each variable. Two-sided level of significance was fixed at P = 0. 05. Data were analyzed using Prism (GraphPad Software, San Diego, California, USA).
Ice core sites In this study we use ice samples from 11 ice cores drilled in Antarctica and 1 in Greenland. The Antarctic sites are the West Antarctic Ice Sheet (WAIS) Divide core (WDC06A, or WDC), Siple Dome (SDM), James Ross Island (JRI), Bruce Plateau (BRP), Law Dome DE08, Law Dome DE08-OH, Law Dome DSSW20K, Roosevelt Island Climate Evolution (RICE), Dome Fuji (DF), EPICA (European Project for Ice Coring in Antarctica) Dome C (EDC), and South Pole Ice Core (SPC14, or SP). Ice core locations in Antarctica are shown in Fig. 2a. In Greenland, we use samples from the Greenland Ice Sheet Project 2 (GISP2). We shall refer to late Holocene data from these sites as the calibration dataset, analogous to a core-top dataset in the sediment coring literature. Site characteristics, coordinates, and number of samples included in the calibration dataset are given in Table  1.