dataesr/scientific-paragraphs-categorization huggingface dataset:
Results A total of 161 patients were enrolled in the database. However, 14 patients (11. 9%) in the acute group and 4 (9. 5%) in the chronic group were lost to follow-up. An additional patient was excluded for previous ORIF. Therefore, the acute group contained 104 PHF's, while the chronic group included 38. All but two patients received the Equinoxe Ò Fracture Reverse Implant; two patients received the Equinoxe Ò Standard Reverse Implant (Exactech, Gainesville, FL, USA). In the chronic group, 22 patients had a diagnosis of malunion and 11 patients had a nonunion of the proximal humerus. The remaining 5 chronic patients were indicated for RTSA by worsening displacement, defined by change in fracture alignment on consecutive radiographs indicating grater displacement. Patients in the chronic group had surgery significantly later than patients in the acute group (0.
66 and 1. 0, respectively. In addition, XPS survey was also acquired in each layer, and the Cu 2p peak was determined from the XPS survey with relative sensitivity factor of 25. 39. The analysis area was around 100 m. The data were extracted from the XPS machine and analyzed with Thermo Fisher Scientific Avantage software. UV-visible spectra were collected by the Cary 7000 Universal Measurement Spectrophotometer (Agilent Technologies) with suspended fiber array samples, and the fiber density used for the measurement was 20 fibers/mm. The XRD measurement for silver fibers was carried out by Siemens D500 (SIEMENS) with the 35-kV and 20-mA x-ray setting. The crystallinity was calculated from the area under crystalline peaks over total scattering area, and the average crystalline grain size was determined with the Scherrer equation  (45).
Acknowledgments: We thank  G. Ma  from  Hubei University  for technical support and  A. Casabuena-Rodriguez  from  Engineering Department of Cambridge University  for support in SEM imaging. We thank  Y. Pan  for assisting with the fiber resistance measurements. Funding: This work was supported by the  Engineering and Physical Sciences Research Council (EPSRC ;  EP/M018989/1 ) and  European Research Council (ERC-StG ,  758865 ). W. W. and X. L. were grateful for the support from the  Chinese Scholarship Council. A. L. R. acknowledges support from the  Whitaker International Scholars Program  and the  European Commission 's  Horizon 2020 Marie Sklodowska-Curie Individual Fellowship  (no. 797506 ). M. G. thanks the funding from  Cancer Research UK (CRUK). T. E. N. thanks the funding from  EPSRC Cambridge NanoDTC  ( EP/ L015978/1 ).
Introduction Refractory cardiac arrest, usually defined as persistent cardiac arrest despite 30 minutes of medical cardiopulmonary resuscitation (CPR)  (1) , is associated with an extremely low probability of survival with conventional CPR. In this setting, extracorporeal CPR with venoarterial extracorporeal membrane oxygenation (ECMO) has been found to be associated with better neurological outcome compared to conventional CPR  (2) (3) (4). A recent analysis of the Extracorporeal Life Support Organization Original Article Initial pH and shockable rhythm are associated with favorable neurological outcome in cardiac arrest patients resuscitated with extracorporeal cardiopulmonary resuscitation (ELSO) database showed that the use of extracorporeal CPR (eCPR) has increased during the last decade  (5).
Population We performed a retrospective chart review of all patients admitted to our tertiary regional intensive care unit (ICU) who underwent eCPR implantation for refractory cardiac arrest between February 2010 and March 2017. Cases were identified through a prospectively maintained electronic database. Refractory cardiac arrest was defined as persistent cardiac arrest after 15 minutes of medical CPR  (9). Because of a potentially different prognosis  (10, 11) , patients suffering from post-cardiac arrest cardiogenic shock were excluded. After initial resuscitation, for persistent comatose patients, decision of withdrawing life-support was based upon multimodal evaluation indicating a poor neurological outcome. This decision was made in the ICU during a collegial meeting associating experienced medical doctors and nurses  (12, 13).
Variables assessment Demographic data collected included age, gender, cause of cardiac arrest, and any known comorbidities. Cardiac arrest related data included the out-of-hospital or in-hospital setting, witnessed, duration of no flow and low flow, and initial rhythm. Low flow duration was defined as time between collapse and effective eCPR start. In hospital treatments included percutaneous coronary intervention, use of vasopressors and inotropes, left ventricle unloading, renal replacement therapy, mechanical ventilation and transfusion. Severity related data included blood lactates and pH at the time of admission to the operating room and just before eCPR start, or just after initiation of ECMO, and 24 hours later, SOFA and SAPS2 scores. ICU length of stay were also recorded. ECMO related data included duration of ECMO and successful weaning.
A value of € 0 is specified in the event of  1 , not going to trial, because it is assumed that this decision will incur no further litigation costs. A value different from 0 may be chosen, however, to account for costs of option  1 other than legal fees, if required. Specifying the BDN in this way using, for example, a Bayesian D q G L S D q G (i) (ii) d 1 : out-of-court settlement € 90,000 d 2 : go to trial € 100,000 q 1 : win 0. 8 q 2 : lose 0. 2 G L S (iii) network software such as HUGIN, 20  leads to model output shown in expanded version in Figure  3 (iii). 21 The decision node displays the EMV of the options  1 and  2 , and the chance node  shows the plaintiff's probabilities for the trial outcomes  1 and  2. It may be argued that none of these results are original, because they may also be obtained using paper and pencil.
Adding node L′ as a parent for L requires a modification of the node table of L so that it will copy the negative value 22 of the current state of L′ when the condition  =  2 holds, and the value 0 otherwise. Software environments such as Hugin offer a rich syntax (e. g. , if-then expressions) to define functions in this way. Similarly, there is an additional node G′, acting as a parent node for G. The states of G′ correspond to the different court-ordered settlements, whereas the associated node probability table contains the plaintiff's probabilities for those outcomes in the event decision  2 is made. The node table of G is defined 23 such that it copies the current value of G′ in the case where both  =  2 and  1 holds, and the value 0 otherwise. 22 A negative value is specified here because in Hugin utility nodes are considered as additive contributions to the utility function.
973 and the optimal decision is  2 , with an EMV of € 125,946. One can proceed analogously for the potential outcomes  2 (neutral forensic report) and  3 (unfavourable forensic report). Applying the results in Equation (  12 ) leads to the EMVpI of € 117,100. Comparing this result with the EMV € 100K of the optimal decision without partial information, shown in Figure  6 (i), gives the EVpI of € 17,100. For a summary of the computation of the EMVpI, see also Table  1. Note that this listing of the various outcomes and the optimal decisions in each of these cases is also sometimes referred to as a policy. The direct computational step to obtain the EVpI for the forensic report E is shown in Figure  7 , using the "Value of information" functionality of the software Hugin. The EVpI can be retrieved in an information pane while keeping  8.
1,0. 05,0. 85,0}, for i = 1,2,3,4. In case of  =  2 , not acquiring forensic information, we specify Pr(  | =  2 ) = {0,0,0,1}, regardless of the state of the node. Implementing the Bayesian decision network shown in Figure  8  in a graphical modelling software, such as Hugin, allows one to conduct a variety of analyses. A first important question is: "Should forensic information be acquired?". To answer this question, we need the EMV( 1 ). In Hugin, this value can be obtained by using the iterative algorithm called "Single Policy Update". As shown in Figure  9  (top), the value obtained is 117,000, which corresponds to the EMVpI found at the end of Section 3. 6 (see also Table  1 ). This value is greater than the EMV 100,000 for not conducting forensic analyses (not shown in Figure  9 ). Note that the cost for the forensic analyses has been set to zero here in order to allow for a direct comparison of the output with the results obtained in Section 3.
Plaque Reduction Neutralization Test We used the plaque reduction neutralization test (PRNT) as a reference for this study because neutralization assays are the standard for coronavirus serologic analysis. We tested serum samples for their neutralization capacity against SARS-CoV-2 (German isolate; GISAID ID EPI_ISL 406862; European Virus Archive Global #026V-03883) by using PRNT as described with some modifications (9). We 2-fold serially diluted heat-inactivated samples in Dulbecco modified Eagle medium supplemented with NaHCO 3 , HEPES buffer, penicillin, streptomycin, and 1% fetal bovine serum, starting at a dilution of 1:10 in 50 µL. We then added 50 µL of virus suspension (400 plaque-forming units) to each well and incubated at 37°C for 1 h before placing the mixtures on Vero-E6 cells.
5F. Fig S5C) , consistent with available high-throughput proteomic datasets  (Swaney et al. , 2013 , Iesmantavicius et al. , 2014). Earlier reports showed that two N-terminal lysine residues (K27, K28) in Mup1 were ubiquitinated by Art1-Rsp5 and required for methionine-induced endocytosis  (Guiney et al. , 2016 , Busto et al. , 2018 , Ghaddar et al. , 2014)  (Fig. 5F ). The role of ubiquitination at C-terminal lysine residues was not clear. To determine how these N-and C-terminal lysine residues contributed to Mup1 ubiquitination under different growth conditions, we mutated the N-terminal (K27, K28) or C-terminal (K567, K572) lysine residues to arginine and additionally generated a quadruple mutant (K27,28,567,572R). Equal amounts of immunoprecipitated Mup1-GFP, Mup1 K27,28R -GFP, Mup1 K567,572R -GFP and Mup1 K27,28,567,572R -GFP from exponentially growing cells or after starvation or methionine treatment were subjected to SDS-PAGE and WB analysis to compare their ubiquitination (Fig.
Our screen identified also two de-ubiquinating enzymes, Doa4 and Ubp6 to be specifically required for starvationinduced endocytosis of Mup1. They could act directly on Art2 or Mup1 or help to maintain homeostasis of the ubiquitin pool during starvation. Art2 is subject to extensive post-translational modification, including ubiquitination and phosphorylation. Database searches and our own proteomic experiments identified 68 phosphorylation sites and 20 ubiquitination sites in Art2 (data not shown)  (Swaney et al. , 2013 , Albuquerque et al. , 2008 , Holt et al. , 2009). How these modifications help to control the activity of Art2 remains a complex and open questions. Several arrestins were found to be phospho-inhibited in specific conditions  (MacGurn et al. , 2011 , Becuwe et al. , 2012b , O'Donnell et al.
GuavaSoft 2. 7 software was used for data analysis. The positive/negative cut-off was set for each plate empirically at the intercept of the log/starvation histograms of the WT and art2∆ controls (art2∆, which emerged as a well-reproducible hit early in the screen, was included as a negative control in all further plates). All potential hits were re-examined by fluorescence microscopy. To this end, at least 100 starved cells were analyzed by fluorescence microscopy after starvation and the percentage of cells showing a degradation-deficient phenotype (Mup1-pHluorin at the plasma membrane, in small cytosolic objects, class E-like objects or small objects within vacuoles) of the total number of cells counted was calculated (table  S2 ). Strains with more than 45% cells with retained fluorescence after at least 18 hours of starvation were considered as hits.
Gene ontology analysis Gene Ontology (GO) enrichment analysis  (Harris et al. , 2004)  was performed using 128 genes listed in 21 Appendix Table  S2. They were mapped against the generic GO-Slim: process, generic GO-Slim: component and macromolecular complex terms: component GO sets using the GoSLIM mapper of the Saccharomyces genome database. We calculated the ratio of the observed (cluster frequency) vs. the expected number of genes (genome frequency) associated with the GO term, referred to as enrichment over genome  (McClellan et al. , 2007). Only GO terms with more than one associated gene were reported. The full analysis is presented in Table  S3.
The flow rate was 250 nL/min. The Q Exactive HF mass spectrometer was operating in the data dependent mode selecting the top 20 most abundant isotope patterns with charge >1 from the survey scan with an isolation window of 1. 6 mass-to-charge ratio (m/z). Survey full scan MS spectra were acquired from 300 to 1750 m/z at a resolution of 60,000 with a maximum injection time (IT) of 120 ms, and automatic gain control (AGC) target 1e6. The selected isotope patterns were fragmented by higher-energy collisional dissociation with normalized collision energy of 28 at a resolution of 30,000 with a maximum IT of 120 ms, and AGC target 5e5. Data Analysis was performed using Proteome Discoverer 4. 1 (Thermo Scientific) with search engine Sequest. The raw files were searched against yeast database (orf_trans_all) with sequence of Mup1-GFP added.
TaqMan gene expression assays were from Thermo Fisher (PGK1: Sc04104844_s1; ECM21/ART2: Sc04099967_s1). All probes and primer anneal within coding sequences. Each RT-qPCR analysis was done from 2-3 independent biological samples in 3-4 technical replicates. Data were analyzed with the PikoReal software (version 2. 2; Thermo Fisher) with manual threshold adjustment, and relative mRNA abundance was calculated in Microsoft Excel using the ∆∆CT method. Statistical comparisons were calculated using the Student t-test. Indicated are numbers of PM proteins that are down-regulated, up-regulated or unchanged compared to the exponential growth phase, each exemplified by one representative strain. PM: plasma membrane; V: vacuole. Scale bars = 5 µm. See also Fig. S1  and Table  S1. A) Enrichment of cellular processes and components GO terms in 128 hits from the genome-wide screen for genes involved in the starvation-induced endocytosis of Mup1-pHluorin (table  S2 ).
Acknowledgments: We are greatful to  Claudine Kraft  and  Scott Emr  for reagents. This work was supported by  EMBO/Marie Curie  ( ALTF 642-2012 ;  EMBOCOFUND2010 ,  GA-2010-267146 ) and ' Tiroler Wissenschaftsfond ' to O. S. ,  Austrian Science Fund  ( FWF-Y444-B12 ,  P30263 ,  P29583 ) and  MCBO  ( W1101-B18 ) to D. T and  Agence Nationale pour la Recherche  (" P-Nut ",  ANR-16-CE13-0002-01 ) to S. L.
Input data To obtain the maximum possible results, the model is established in the LINGO optimization tool. To validate the model, 10 farmer nodes and 2 food hubs are inspected in the first case instance. The data for the supply of food from farmer to hub are presented below in (Table  1 ): The distance matrix from the farmer to farmer and farmer to hub nodes are presented in the (Table  2 ) below: The obtained values associated with different parameters are presented in (Table  3 ) below:.
The data sets were acquired from the central part of India (FCI data) for obtaining better insights from the actual problem. Further, the suggested model was tested with LINGO, and the results reveal that LINGO performs better than the metaheuristic approaches (GA and Q-GA) in catering to the transportation, aggregation and collection of food grains. The effectiveness of this study is to assist the food grain aggregator in better addressing the upstream food grain supply chain and also contributes theoretically by establishing better performance of LINGO in addressing the MINLP. The present study reflects some directions for future research. Suggested algorithms can be paralleled with different meta-heuristic techniques such as simulated annealing, ant colony optimization, particle swarm optimization, etc.
Materials and methods We conducted an observational retrospective study. We first collected publicly available data from weather reports (meteorology database Custom Weather, CA, USA) from nine different outbreak locations in four continents, from 1 January to 17 April 2020: Lombardy (Italy), London (United Kingdom), Ile-de-France region (France), Grand Est region (France), Stockholm (Sweden), Tehran (Iran), New York City (USA), Seoul Capital region (South Korea) and New South Wales state (Australia). Maximal temperatures in degrees Celsius (°C) were collected for each day. To limit bias and temperature uncertainties, relatively small regions were studied. Secondly, we assembled publicly available data from official agencies (Italy: Protezione Civile daily bulletins, France: Agence Santé Publique France/French Health Ministry, United Kingdom: NHS England, Sweden: Folkhälsomyndigheten bulletins, Iran: Ministry of Health and Medical Education, USA/ New York: New York State Department of Health, South Korea: Korea Center for Disease Control, Australia: Australian Government Department of Health) for new COVID-19-related diagnoses, over the same period of time, in the same locations.
1 ). As a positive control for this method, we analysed the first weeks of the 2009 pH1N1 influenza pandemic, despite its appearance in the spring later appeared to have a seasonal pattern  [6, 7]. As a negative control we analysed 100 days of the 2015 cholera outbreak in Zimbabwe, which is known to follow a rather different pattern, with a very different relationship to weather  [8]. Each outbreak location was studied independently. Correlation between the number of temperature curve deflections and daily new cases growth rate curve deflections was assessed with a Spearman rho test. Statistical analysis was performed using Matlab R2018a and Statview 5. 0 (SAS institute). The results with P < 0. 05 were considered to be statistically significant.
Conclusion Since the novel coronavirus SARS-CoV-2 emerged in Hubei Province, China, and spread to most countries across the globe, the factors influencing its transmission have been under intense scrutiny and produced a great deal of speculation. It has been suggested that increased temperatures in the spring and summer would slow down the transmission of the virus and lessen its impact. Indeed, most respiratory viruses, including other   A. Rouen et al. coronaviruses, showcase a seasonal pattern of infection with outbreaks during colder months  [4]. However, this had previously not been demonstrated for SARS-CoV-2. In the present study, we collected atmospheric temperature data from weather reports from nine COVID-19 outbreaks across four continents in both hemispheres. Traditional statistical tools used to evidence seasonal patterns in epidemic phenomena, such as distributed lag non-linear models, were not the evident option, because of how recent the pandemic is.
Results Overall 43 MI patients were included in the study. There were no significant differences in baseline characteristics between both groups, except higher left ventricular ejection fraction in the no-MTH group (Table  1 ). Initially, 13 patients were enrolled into the MTH group, however complete data (results from all study time-points) were available for only 7 patients, as 4 of them had died before 24 h from the beginning of the MTH procedure. Additionally, 2 patients from MTH group were excluded from the analysis of the primary end point due to hemolysis in blood samples that precluded complete pharmacokinetic evaluation. Ventricular fibrillation was the first recorded rhythm during cardiac arrest in all 13 patients. The no-MTH group consisted of 30 MI patients without OHCA treated with primary PCI.
Availability of data and material The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. The study protocol was published in Medical Research Journal: Umińska MJ, et al. Platelet reactivity during mild therapeutic hypothermia in patients with acute myocardial infarction treated with ticagrelor: study protocol of a single-centre study. Med Res J. 2016; 1(4): 115-119, doi: 10. 5603/MRJ. 2016. 0021.
Some models or input rainfall configurations provide unreliable results, particularly for Krokodil and Benin-Oueme basins in Africa. For MISDc and HYMOD, the overall results in West Africa are even better than in Europe thanks to the strong seasonality of river discharge observations for such basins that allows us to obtain better performance in terms of KGE (due to the high temporal correlation, R > 0. 9, that is one of the components of KGE  39  ). Among the three rainfall-runoff models, MISDc has been found to perform the best with mean KGE = 0. 83 while HYMOD (mean KGE = 0. 76) and GR4J (mean KGE = 0. 60) show a less good agreement with river discharge observations. Therefore, MISDc model has been selected for the subsequent analyses. Rainfall and hydrological assessment in Europe. The quality of rainfall products has been firstly assessed in Europe through a comparison with the reference datasets, i.
e. , PITA (Italian Rainfall Dataset) for the Italian basins and EOBS (ECA&D rainfall dataset) for Danube basin. Results are summarized in Fig. 2  in which the box plots of R (Pearson's Correlation Coefficient), KGE, absolute value of rBIAS (relative BIAS), |rBIAS|, and rRMSE (relative root mean square error) are illustrated for all products, including EOBS. Long latency products are shown in the grey area to differentiate them from the short latency products. The different scores provide a similar assessment with the products with higher R and KGE also showing lower |rBIAS| and rRMSE. ERA5 is the best long latency products with mean R = 0. 87 and KGE = 0. 84. ERA5 is also characterized by low |rBIAS| and rRMSE. Among the short latency satellite-only products, GPM+SM2RAIN provides the best agreement with the reference rainfall performing slightly better than GPM-FR and GPCC, on average.
Bearing in mind the results of the rainfall assessment, the products performance in terms of river flow prediction in the European basins are analysed. To investigate the variability of MISDc performance for the different products, in Fig. 3 , KGE, NSE (Nash Sutcliffe Efficiency), ANSE (NSE for high flows) and NS(radQ) (NSE for low flows) values for all basins separated by product are shown. In terms of KGE, good performances have been obtained with rain gauge observations from GPCC, ERA5 and GPM-FR with a mean KGE equal to 0. 89,  0. 84 and 0. 86, respectively. Differently, EOBS dataset is performing less good (mean KGE = 0. 83). Among the four satellite-only rainfall products, GPM+SM2RAIN provides the best scores (mean KGE = 0. 85) followed by PRISM-SMOS (mean KGE = 0. 82) and PRISM-SMAP (mean KGE = 0.
4) particularly during the flood period October 2016-March 2017. An additional analysis has been carried out for African basins by comparing the simulated actual evapotranspiration data with Global Land Evaporation Amsterdam Model (GLEAM  49, 50  ) dataset over the six African basins. As the model is calibrated against river discharge observations, this assessment might be considered as an independent validation that also ensures the consistent closure of the water balance. Indeed, we expect that higher quality rainfall products provide a better agreement with evapotranspiration fluxes provided by GLEAM. Results have been evaluated in terms of R to assess products capability to reproduce the temporal variability of actual evapotranspiration. For all basins, performances are relatively good with R values ranging between 0.