dataesr/scientific-paragraphs-categorization huggingface dataset:
In this work, we reevaluate the lowest-order hadronic contribution, a had,LO µ , to the muon magnetic anomaly, and the hadronic contribution, ∆α had (m 2 Z ), to the running α(m 2 Z ) at the Z-boson mass using newest e + e -→ hadrons cross-section data. The BABAR Collaboration has essentially completed a programme of precise measurements of exclusive hadronic cross sections for all the dominant channels from threshold to an energy of 3-5 GeV using the initial-state radiation (ISR) method. Also new results are being produced at the VEPP-2000 facility in Novosibirsk, Russia in the 1-2 GeV energy range. The new data complement the available information on exclusive channels allowing to alleviate the need for estimating missing channels with the use of isospin symmetry. We reevaluate all the experimental contributions using the software package HVPTools  [1] , and add to these narrow resonance contributions evaluated analytically and continuum contributions obtained from perturbative QCD.
2  Figure  1  shows the available e + e -→ π + π -crosssection measurements in various panels zooming into different centre-of-mass energies ( √ s). The green band indicates the HVPTools combination within its 1 σ uncertainty. The deviations between the combination and the most precise individual measurements are plotted in Fig. 2. Figure  3  (left) shows the local combination weight versus √ s per experiment. The BABAR and KLOE measurements dominate over the entire energy 2 Correlations due to systematic uncertainties among the three KLOE datasets are accounted for by matching oneby-one the uncertainties impacting the measurements. For example, the uncertainties due to the luminosity, radiator function and vacuum polarisation are taken to be correlated among the first two measurements, while they are found negligible or not present in the 2012 measurement.
The final state radiation uncertainty as well as the trigger, tracking, acceptance and background uncertainties for the ππ data are taken to be correlated among the three measurements, while the background uncertainty for the µµ data only impacts the 2012 measurement. We will replace this approximate treatment by a more accurate one once it is provided by the KLOE Collaboration. range. Owing to the sharp radiator function, the event yield increases for KLOE towards the φ(1020) mass, hence outperforming BABAR above ∼0. 8 GeV. The group of experiments labelled "Other exp" in Fig. 3  corresponds to older data with incomplete radiative corrections. Their weights are small throughout the entire energy domain. The computation of the dispersion integral over the full π + π -spectrum requires to extend the available data to the region between threshold and 0.
Other channels Data on many processes with smaller cross sections, mainly from VEPP-2000, have become available and are included in the HVPTools database. This is the case for 3π + 3π -from CMD3  [29] , π 0 γ  [30] , ηγ  [31] , ηπ + π -  [32] , and ωπ 0  [33]  from SND, ηω from SND  [34]  and CMD3  [35] , nonresonant ηπ + π -π 0 from CMD3  [35] , ηπ + π -from BABAR  [36] , which extend and improve  older measurements in these channels. Except for the ηω cross section above 1. 6 GeV, results using the ISR technique at BABAR and the scan method at VEPP-2000 are in agreement notwithstanding their different systematic uncertainties. Above 1. 8 GeV the production of pp measured by BABAR  [37]  and CMD3  [38] , nn by ADONE  [39]  and SND  [40] , and ηωπ 0 by SND  [41]  are included. Figure  8  shows the available measurements and their combination of the charm resonance region above the opening of the DD channel.
Introduction and background Of the tools available to the soft matter scientist, those which offer information about the material at a buried interface are often the most challenging, yet also the most insightful and meaningful; for example, the study of biomolecules adsorbing at the solid/liquid interface  [1] , intermixing of polymer layers  [2]  or chemical transformations of thin films  [3, 4]. Neutron Reflectivity (NR) is one of the few techniques that can probe the regions below, and at, interfaces in a non-destructive manner. Since model fitting to NR data is used to deduce the structure of the interfacial region, these measurements are often complemented by ex situ experiments to provide constraints on the possible model fits. Typically, in such experiments, a similar interface is recreated (or at least approximated) and investigated under similar conditions and parameters.
As with the combination of NR with ATR or ellipsometry, the requirements of any such in situ technique are; (i) it should be easily implemented within the conventional experimental setup on NR beamlines; (ii) allow for standard surface modification chemistries, such as silanes and thiols; (iii) provide quantitative data and (iv) offer temporal resolution at, or greater than, that of the NR beamline. Currently, the in situ combination of QCM gravimetry (which would yield data concerning the amount of a known species) with NR measurements is not feasible because, typically, QCM substrates are of the order of a few hundred microns thick with lateral dimensions of 1-2 cm and sensing areas of $5 mm diameter. Whilst in theory this is thick enough for an NR substrate, especially with recent developments in data handling  [8] , the time required for NR acquisition from such small substrates would be considerably longer (>3 h) than is considered interesting for soft matter systems.
(1)  [14]. S ¼ Df f Dm A ð1Þ where Df is the frequency change observed when a mass change, Dm, occurs over an area, A. Here, we report for the first time, measurements that combine the NR and Love wave techniques in order to correlate mass changes (Dm) in an adsorbed layer, with the solvation and microstructural profile of that layer. To establish and illustrate this method, the question that we sought to answer was whether the combination of the two techniques could be used to provide complementary data that could be used to determine the void (air filled) space within a spun polymer film, before and after it is exposed to solvent vapours that lead to polymer swelling. This will give insights into the factors determining the maximum level of vapour uptake in systems such as those often employed as gas, vapour or humidity sensors.
In addition to measuring thickness data, transmission UV spectra were collected for the spun PS films on the Love wave devices. This spectroscopic data showed that the absorption bands in the range 220-260 nm scaled linearly with the thickness data, indicating that, for the range of polymer films prepared here, there is no significant change in polymer volume fraction that might influence the mass/frequency calibration data. A typical calibration curve is shown in the ESI, Fig. S4. Note, for accuracy, to analyse the Love wave data associated with the film and block that were used to collect NR data, the Love wave frequency/mass sensitivity factor (S) used was that derived from the calibration data from that specific block (when measuring calibration curves for different Love wave devices on different neutron blocks having similar thicknesses of deposited SiO 2 , it was found that they had slopes that correlated well with the actual SiO 2 thickness, as expected).
À1 ) to the background (Q = 0. 2 Å À1 ). After data reduction, fitting was performed using Parrat32 software. The combined neutron reflectivity and Love wave experiments were conducted in the following stages; (i) gas flow adjusted to establish solvent vapour composition prior to flowing over the sample; (ii) FTIR spectra recorded to monitor solvent concentration in the vapour phase and allow constant solvent concentration during the NR measurements to be maintained, typically this took 10-30 min due to the large vapour volume inside the cell and the low vapour flow rate; (iii) Love wave signal measured; (iv) NR data collection started, and (v) Love wave signal and FTIR spectra measured periodically until the end of NR data collection.
Importantly in the context of the data analysis below, this parameter was held constant when fitting data for the film exposed to different vapour concentrations (the constant value used was determined following a series of fits to each of the reflectivity profiles in which it was allowed to float). Prior to data fitting, the reflectivities were not corrected for an energy dependant absorption of neutrons by the vapour phase since calculations show this effect to be less than 0. 05% (in the 2. 0-22. 0 Å range). The film was then exposed to a series of acetone or chloroform rich vapours. The range of concentrations used was determined by adjusting the flow through the solvent reservoirs and combining that with a varying amount of a dry N 2 stream. Acetone and chloroform were chosen due to their differing solvating properties, molecular volumes, and densities (q Acetone = 0.
There are two approaches to extract / air , or the related quantity, d air , the effective thickness of the air component within the film. One approach is to use the NR model-derived film thickness, d film , as in Eq. (  7 ) (cf. Eq. (  3 )) where the bulk density of the polymer and solvent allows you to calculate the notional thickness of these components: d air ¼ d film À d d-PS À d solv ð7Þ with d film , d d-PS and d solv being calculated from the neutron and Love wave-determined surface coverages and (void free) densities. The second approach is to use the average model fit for the film Nb, hNb film i, together with Eq. (  5 ): d air ¼ ðNb d-PS d pol þ Nb solv Á d solv À Nb film Á ðd solv þ d pol ÞÞ=hNb film i ð8Þ It is noted that if the model fits to the NR data were 'perfect', then the integral equation (Eq.
(  6 )) could be used to calculate the mass uptake when exposed to each of the different vapour concentrations from the neutron data alone. However, when this is done in practice, the integrated scattering length density sometimes increases, and sometimes decreases. This is because of the relatively low contribution to the overall Nb from the absorbed solvent (see later) and small errors in model parameters inherent when using simple model fit profiles. Thus, in order to extract further information from the NR profiles, Nb models were combined with Love wave data sets. In doing this, some assumptions need to be made about the errors associated with the quantities derived from the NR and Love wave data sets. In the case of the best fit Nb model profile, these are associated with the alignment, thickness, scattering length density of the film, and the interfacial roughnesses.
6  (note, the concurrent collection of FTIR vapour spectrum during the NR measurements enabled the vapour concentration for the ex situ measurements to be set appropriately). Fig. 6  shows the shifting of the resonance peak with increasing acetone concentration in the surrounding vapour for the d-PS film used in the NR experiments. Similar shifts are seen for chloroform absorption and these can also be converted to an increase in mass per unit area (Fig. 7 ) using the Love wave device sensitivity constant, S. A final source of uncertainty in interpreting the measured Love wave data and extracted model Nb profiles comes from consideration of whether or not any solvent is present when the films are in the 'dry' state at the start of the Love wave/NR experiments. In this respect, some of the previously reported NR measurements have indicated that there may be up to 15% v/v toluene present in freshly deposited films  [21] , whereas the NR (and FTIR) measurements in other reports indicated there was no toluene present  [22].
The effect of a history of swelling in acetone vapour could have been to allow any residual toluene to diffuse out of the film and, since acetone is a 'poor' solvent for polystyrene, any remaining acetone would have been readily removed by the dry nitrogen stream, if it hadn't been lost whilst being stored. Bearing in mind the aforementioned caveats concerning the accuracy of the NR model derived parameters (discussed in detail in the ESI), to analyse the combined NR-Love wave data sets, both the thickness and model film Nb parameters have been individually combined with the mass data from the Love wave measurements. This allows the effective thicknesses of the polymer, solvent and air fractions in the spun d-PS film of Fig. 3   Love wave amplitude (dB) - [Acetone] Fig. 6. Love wave frequency response curves, at 25.
Discussion One of the aims of these experiments was to explore whether Love wave gravimetric data with information gained from model fitting of NR data could be combined to provide new information about the system being studied, and whether a single sample and sample environment could be used for both sets of measurements. The calculations employed herein have indicated that the volume fractions of both air and solvent are small and two methods of calculating these from different parameters associated with the model Nb fits gave very similar results. The measurements also demonstrated, for the first time, that a single sample and a single sample environment can be used for both the Love wave and NR measurements. However, as can be appreciated from examination of the temperature sensitivity of the Love wave resonance frequency (ESI Fig.
In other cases, when probing the dynamics of vapour induced swelling  [24] , the gravimetric data could have been used to see if there was a time lag between the solvent uptake and the polymer swelling and so provide interesting information about the slow timescale polymer dynamics in thin film interfaces. Importantly, here we have discussed the experimental precautions that need to be taken for this combination of measurements to be valid. For example, for a simple interpretation of the Love wave data it is necessary that: (i) the films must not be too thick (this thickness coincides with the upper thickness of films generally studied using NR and X-ray reflectivity); (ii) the films should have sufficiently low viscoelastic losses, and (iii) the temperature of the samples should be well controlled.
Study participants A total of 310 participants with major depressive disorder (MDD) diagnosis will be included in the study. Diagnosis will be made according DSM-IV-TR criteria and participants will be recruited from different mental health services (i. e. , Red de Salud Mental de Guipúzkoa, Hospital del Mar, Hospital de la Santa Creu i Sant Pau, Hospital Parc Taulí and Hospital Universitario de la Princesa) and primary care facilities in their catchment areas. Sample size was established in order to detect an effect size of d = 0. 2 with 95% power (SE = 0. 5) and assuming a conservative correlation coefficient between pre-post measures of 0. 5 and a drop-out of 20%. Sample size was calculated using G*Power 3. 1. 7 software. Individuals meeting the following criteria will be eligible: 1) Participants of both genders 18 years old and older, 2) MDD diagnosis according to DSM-IV-TR criteria with mild-moderate symptoms according to the CGI-severity (scores ≤4), 3) minimum knowledge of internet use and availability of an internet device (tablet, computer, smartphone) and 3) given written informed consent.
Participant's assessment will be recorded in the electronic Case Report Form (e-CRF). In this assessment visit, and regardless of the treatment arm the patient will be assigned to, the second face-to-face assessment visit (week 8) will be booked. Randomization to one of the two conditions (i. e. , iFightDepression tool + TAU or internet-based psychoeducation + TAU) will be electronically performed by the e-CRF once the patient data is introduced. The researcher who conducts assessment visits will remain blind to participant's allocation. After the first visit, participants in both arms will received an email with instructions to be followed. The intervention group will receive instructions to create an account and to work within the iFD tool. In addition, the intervention group will receive a telephone call to make sure that participants activated successfully their user accounts.
Statistical analysis plan SPSS v22. 0 will be used for statistical analyses. Primary analyses will be conducted on the Intention-To-Treat (ITT) sample. Missing data will be inspected and handled via full information maximum likelihood (FIML) or multiple imputations (MI) as appropriate. The primary outcome is change in HDRS scores from baseline to post-treatment (8 weeks). For primary and secondary outcome measures, repeated measures ANO-VAs will be computed to examine pre-post treatment differences between groups.
Competing interests: KL is funded by a NHMRC Fellowship, GNT 1084351. Unrelated to this manuscript, she has received research funding and travel support from GlaxoSmithKline. LHC is an advisor for Shoreland, Inc. and has received speaker travel support and honorarium from GSK. DHH has served as a consultant to Inovio Pharmaceuticals. This does not alter our adherence to PLOS ONE policies on sharing data and materials. Despite recent advances in knowledge and research regarding ZIKV, a general lack of disease awareness prior to the current outbreak, misdiagnoses due to the clinical similarity of ZIKV infections with other infectious diseases, and limited access to diagnostic testing have resulted in many unresolved questions regarding the distribution and transmission risks outside the Americas.
Methods GeoSentinel is a global surveillance network established in 1995 (www. istm. org/geosentinel), currently comprising 64 specialized travel and tropical medicine clinics in 29 countries, the majority being in Europe, America, Australia or New Zealand. To be eligible for inclusion in the GeoSentinel database, patients must have crossed an international border and sought medical advice at a GeoSentinel clinic for a presumed travel-related illness. Data collected include: demographic information, travel data, reason for most recent travel, inpatient or outpatient status, travel duration, and time between the onset of symptoms and presentation at a GeoSentinel clinic. Diagnoses are taken from a list of >500 standardized diagnostic codes. Clinics collect de-identified data on ill travelers for whom they have provided clinical care using a standard form, and report these data to a centralized database  [23].
Data extraction and disease diagnostic criteria Data on patients diagnosed with ZIKV infection acquired in Africa, Asia or the Pacific (regions defined as per Harvey et al  [23] ) acquired between 1 January 2012 and 31 December 2016 were extracted from the GeoSentinel database. Sites contributing patient data provided supplemental information including clinical complications and results of diagnostic testing, which was based on local clinician preferences and availability of methods (serology, plaque reduction neutralization test [PRNT], and/or molecular testing including polymerase chain reaction [PCR] and sequencing). Using a modified version of the US Council of State and Territory Epidemiologists (CSTE) case definitions  [24]  (S1 Table ), two clinicians (DHH, MPG) independently classified each patient as confirmed or probable, and discussed discrepancies to reach a final determination.
We examined demographic information, itinerary details and reason for travel for each case; country of exposure was based on travel itinerary and relevant incubation periods, with cases excluded if the exposure country could not be ascertained. Geo-Sentinel cases were considered to be sentinel events (index cases) if no other known human case had been reported in web-based or published literature for at least 2 years prior. Data were managed in Microsoft Excel, with reporting of descriptive statistics. PubMed, ProMED, International SOS reports, WHO situation reports, ECDC situation reports, the CDC website and online news reports were searched (minimum of two coauthors) using the search criteria "Zika" and "Africa or Asia or Pacific", or "Zika" and each individual country name in these three regions to identify locally acquired or exported cases.
GeoSentinel data cannot be used to infer risk of infection since there is non-uniformity in the likelihood of presenting to a specialized GeoSentinel clinic according to geographical and other factors, but as of the end of 2016, over 400 cases of ZIKV had been reported to GeoSentinel following travel to the Americas, compared to the 29 cases reported here from other regions. Comprehensive seroprevalence and strain cross-reactivity studies are lacking, but there appears to be lower epidemic potential for ZIKV in most African and Asian countries, with fewer recognized local cases and exported cases not infrequently triggering recognition of ZIKV circulation  [16]. For Africa, exported cases in travelers have been reported from Senegal  [43]  and Cape Verde  [33] , in addition to the GeoSentinel case from Cameroon  [28].
These data underpin current international guidelines recommendations for avoidance of pregnancy for at least 6 months after potential exposure for males and at least 8 weeks for females. A limitation of this descriptive report is that surveillance data captured by GeoSentinel include only cases seen at specialized clinics, mostly in developed countries; they do not capture all imported cases and may not be representative of all global travelers. Moreover, Geo-Sentinel is unable to provide denominator data or absolute risk information. However, GeoSentinel's deliberate focus to collate data from highly specialized clinics underscores the utility of assessing disease in travelers as a mechanism for identifying geographic areas where emerging diseases may be occurring. Another limitation is that we cannot entirely exclude diagnosis or (unpublished) recognition of additional earlier ZIKV cases, thereby potentially overstating the value of the GeoSentinel reports as index cases.
However, given our extensive review of published data and media sources, we nevertheless assert that GeoSentinel reports provide important additive case information. In summary, many uncertainties in ZIKV epidemiology, transmission and risk of adverse outcomes remain, and consensus on diagnostic and definition issues is needed. We have described ZIKV cases among travelers visiting Africa, Asia and the Pacific, highlighted the integral role of travelers as sentinels of ZIKV infection, shown that GeoSentinel assists in the identification of these travelers, and have contextualized GeoSentinel cases with other reported (locally acquired or exported) cases. We have also demonstrated the differential impact of travelers as sentinels in Asia, which likely relates to the lower epidemic potential due to prior flavivirus circulation among the population of this region.
Acknowledgments Douglas H. Esposito , and  Kristina M. Angelo ,  Travelers' Health Branch, Division of Global Migration and Quarantine, US Centers for Disease Control and Prevention, Atlanta, USA  for their contribution to the data extraction and manuscript review. I confirm that all individuals who contributed to the work and who are not named as authors or additional contributors have been named in the Acknowledgments. Additional members of the  GeoSentinel Surveillance Network  who contributed data but did not author this article are:  Francesco Castelli (Brescia, Italy); Eric Caumes (Paris, France); Joannes Clerinx (Antwerp, Belgium) ;  Jakob Cramer  ( Hamburg, Germany );  Albie de Frey (Johannesburg, South Africa); Alexandre Duvignaud (Bordeaux, France); Cecile Ficko (Paris-Begin, France) ;  Gu ¨nter Fro ¨schl (Munich, Germany) ;  Abraham Goorhuis  ( Amsterdam, Netherlands );  AnneMarie Hern  ( Auckland, New Zealand );  Shuzo Kanagawa  ( Tokyo, Japan );  Emilie Javelle (Marsille, France) ;  Alberto Matteelli (Brescia, Italy) ;  Israel Molina  ( Barcelona, Spain );  Alice Perignon (Paris, France) ;  Frank von Sonnenburg (Munich, Germany) ;  Phi Truong Hoang Phu  ( Ho Chi Minh, Vietnam );  Joseph Torresi  ( Melbourne, Australia ); and  Patricia Walker  ( Minnesota, USA ).