dataesr/scientific-paragraphs-categorization huggingface dataset:
765 nT. onto a plot of the time series of the IDV(1d) composite. Also shown, as a grey band, is the combined uncertainty arising from the two regressions used to join the data sets together into a composite. The exceptional agreement between the IDV(1d) composite and IDV after 1880 is apparent. Note that this means that the scaled HLS data of our composite agrees very well with IDV for most of two solar cycles  (cycles 12 and 13, 1880-1897). However, before 1880 the agreement is quite poor. In particular, IDV shows some large amplitude cycles that are not seen in the scaled HLS data (particularly cycle 11). The annual means of the IDV(1d) composite and its uncertainty are given in Table  1  of Part 2  (Lockwood et al. , 2013a).
Comparison with other data Figure  14  is a detailed view of Fig. 13  for before 1930. It also shows annual mean IDV(1d) data from the other observatories listed in Table  1. In each case, the data are normalised to Eskdalemuir in 2000 using Eqs. (  1 ) and (  2 ) so as to remove the effects of the secular change in the field. All the data agree well with the IDV(1d) composite, which is significant as none of them contributed to the composite and so form independent tests. The Ekaterinburg data IDV(1d) EKA cover both joins in the composite and agree well over solar cycles 13, 14 and 15. Similarly, the Parc St Maur data IDV(1d) PSM agree well with both the new composite and the IDV variation. The Greenwich data, IDV(1d) GRW , require some explanation and are the least reliable of the data shown.
The measurement of the H component requires a temperature correction and for many of the Greenwich Observatory yearbooks this means that both the uncorrected values and the equipment temperature data must be digitised and the temperature correction correctly implemented. In addition, for many of the years mean values are not given for disturbed days (instead plates of the magnetogram traces are given). Fortunately, for 1882-1911 the yearbooks give both the corrected and uncorrected values in the same table and using these data the effect of the temperature and of the missing days on the annual mean IDV(1d) GRW values can be evaluated. Of these years, data for 1892 and after are not usable because of the data stability issues seen in Fig. 1. From the remaining 10 yr we find that the corrected values give annual means of IDV(1d) GRW that are, on average, 0.
This error is not eliminated until the new instrumentation became fully operational at Greenwich in 1915. We found intervals of concern could be identified using the standard deviation over a year of the H values, σ H , in that IDV(1d) GRW begins to deviate from the other values if σ H exceeded a threshold of 30 nT. In Fig. 14 , IDV(1d) GRW values for σ H < 30 nT are shown by white circles whereas those for σ H ≥ 30 nT (even after cleaning the data to try to remove stability problems) are shown by small black dots. The St Petersburg data IDV(1d) SPE are much closer to the IDV(1d) composite than SC10's IDV in solar cycles 9 and 10 (before 1865). Very similar results are obtained using data from the other early Russian stations Nertchinsk (NER) and Barnaul (BAR)  (Nevanlinna and Häkkinen, 2010).
There are also data at this time from Ekaterinburg. However, as for the Greenwich data for 1893-1914 there are stability problems in the H data series in that σ H ≥ 30 nT for several of the years and the IDV(1d) EKA values are higher than IDV(1d) SPE , IDV(1d) BAR and IDV(1d) NER. Within the uncertainty error bars, the Greenwich data agree with the IDV(1d) composite but not with the IDV series: however given the several concerns about the Greenwich data, we consider this comparison indicative rather than definitive.
Correlations of monthly data From the last section, the IDV(1d) composite derived here is very similar indeed to the IDV index, compiled from many stations, after about 1880. However, before 1880 there are considerable differences. At this time the composite is derived from the Helsinki magnetometer data. On the other hand, at this time IDV is the linearly regressed u index which, before the introduction of data from Batavia in 1884, was derived from data from two stations: Greenwich and Bombay. Before 1872, u is constructed from proxy relationships to diurnal variation in declination observed at Greenwich at fixed hours of the day (termed E) and from the summed deviation from its mean of the diurnal variation of H detected at the Colaba Observatory, Bombay (termed s)  (Bartels, 1932; Svalgaard and Cliver, 2010).
We cannot apply the same test to u as we have only monthly values. It is illuminating to look at the coherence of modern data at different sites for monthly averaging timescales and compare it with the corresponding behaviour of the early data as we go back in time. The lower panel in Fig. 15  shows monthly means of the IDV(1d) from Eskdalemuir, Nurmijärvi and Hartland over a 27 yr interval. The Nurmijärvi Observatory has been chosen here as it is the modern station closest to the Helsinki Observatory which is used to compile the composite. The degree of correspondence between the three time series is extremely high, with an overall cor- relation of 0. 931 between NUR and ESK and 0. 974 between HAD and ESK for these 27 d means. Taking correlations over 3 yr intervals, and incrementing the start of those intervals by 1 yr, yields 25 correlation coefficients between the NUR and ESK data, r EN.
To quantify this, we here take correlation coefficients between the data series over 3 yr intervals (incrementing the start dates by a year) for the pairs of observatories, using the same procedure described above to derive r EN for the modern ESK and NUR data. The results are shown in the upper panel in Fig. 16. For comparison, the horizontal black line shows <r EN > from the NUR and ESK correlations described above and the grey band around it shows <r EN > ± 2σ EN. The  pairs of colours of the dashed lines match the colour coding used in Figs. 1 and 2 (green for HLS, mauve for PSM, yellow for WLH) while blue is used for u. Hence the mauve and yellow dashed line is the correlation between PSM and WLH, the green/yellow dashed line is for the pairing of HLS and WHL, and so on. The lower panel in Fig.
16  shows 3 yr running means of the daily data, also using the same colour coding scheme. The PSM and WLH correlation (mauve/yellow line) remains within, or very close to, the <r EN > ± 2σ EN grey band at all times. Hence these two stations show a coherence in their data series that matches that of modern data and implying that they match modern standards of accuracy on the monthly timescales studied. Between 1886 and 1896, the same is true for the correlations between WLH and u (yellow/blue dashed line) and WLH and HLS (yellow/green dashed line). Hence we can have great confidence in both u and the HLS data at this time. (Figure  16  confirms that the quality of the HLS data falls after 1896 due to the increased noise at the site as the correlations of HLS with both PSM and WLH fall. ) This agrees with the behaviour of the annual means shown in Fig.
11 , in that IDV (based on u) and the composite (based on IDV(1d) HLS ) agree very closely after about 1886. However, before 1886 the correlations of IDV(1d) WLH with u and IDV(1d) HLS both decline as we go back in time, such that they are notably lower than the range for the modern data. Given that the correlation between IDV(1d) WLH and IDV(1d) PSM remains at high levels, this strongly implies that both the u index and HLS data decline in accuracy as we move back in time from 1886. The similarity of the declines in the correlations of both PSM and WLH with both u and HLS suggests that both u and HLS data are similarly affected. The plot indicates that the data meet modern standards in terms of their coherence after about 1886, but there is a detectable decline as one goes back in time before then.
If the plot is taken at face value it implies that the magnetometry techniques improved rather considerably over a rather short interval (about 7 yr between 1887 and 1894). Note the need to use annual correction factors on the Greenwich data mean that we cannot apply the same tests to monthly data. Also shown is the correlation between u and IDV(1d) HLS (black/green line). The decay in quality of both u and IDV(1d) HLS around 1883, as inferred by comparison with the WLH and PSM data, is seen as a deeper minimum in the variation of this correlation. Earlier still, this correlation shows considerable variability, reaching almost the modern levels around 1880 but with another deep minimum around 1877. There is no categorical way of knowing if this difference arises from u or the Helsinki data or both.
Discussion and conclusions We have presented a new composite of geomagnetic activity based on interdiurnal variability in the observed horizontal component of the geomagnetic field. The aim is that the composite is as close to homogeneous in its construction as it possibly can be: it uses data from Helsinki for 1846-1890 (inclusive) and 1893-1897 and from Eskdalemuir from 1911 to the present day. The gaps are filled using data from the Potsdam  (1891-1892 and 1898-1907)  and the nearby Seddin observatories  (1908) (1909) (1910)  and intercalibration achieved using the Potsdam-Seddin sequence. We revert to using the interdiurnal variation between daily means (as employed by  Bartels, 1932) , rather than just the midnight value used in IDV, and we name the index derived as IDV(1d) to avoid potential confusion.
Allowance has been made for the effects of secular changes in the geomagnetic field on the geomagnetic latitude of the stations used. The uncertainties in the composite due to the necessary intercalibrations of the stations have been computed. In all cases, we have ensured that regression fits used are homoskedastic and unbiased and that the fit residuals follow a normal distribution, as required for least-squares fitting. The composite has been tested against independent early data from Greenwich, St Petersburg, Parc St Maur, Ekaterinburg and the composite u index, compiled from 1-6 stations by  Bartels (1932). For the 20th century it is tested against the IDV index of SC10. In order to do this we have digitised the data from the Greenwich yearbooks and applied the temperature correction to annual values, rather than to each hourly mean.
Agreement between these other data and the IDV(1d) composite was found to be extremely good in all cases, with two exceptions. Firstly, the Greenwich data are shown to have degraded in quality, even in annual means, after about 1890 until new instrumentation was installed in 1915. We note that  Bartels (1932)  had similar misgivings about the Greenwich data. Secondly, we find that the Bartels u index does not agree at all well with the Helsinki data before about 1880, with the solar cycle amplitudes and the average level geomagnetic activity before then considerably greater in u. Before 1872, u is based on proxy data rather than interdiurnal variability and, as previously discussed, Bartels himself did not regard these data as satisfactory. After 1886, u agrees very well the Helsinki data (until the noise problem at the Helsinki site degrades the quality of the data after 1896).
We here find the u data between 1872 and 1886 also shows solar cycles greater in amplitude than is seen in the Helsinki data (see the lower panel in Fig. 13 ). This would also explain the larger cycles in u before 1872 because the s and E proxies used before 1872 have been fitted to solar cycles in u that are too large in amplitude. The one firm conclusion we can make is that the u index (and hence IDV) and the Helsinki data are not in such good agreement before 1886. There is no definitive test that allows us to state, with total certainty, the relative contributions that their respective errors make to the discrepancy. However there are a number of reasons to place much greater faith in the Helsinki data. The Helsinki data are a homogeneous set of continuous measurements made for 70 yr from the same site with the same equipment and using the same procedures  (Nevanlinna, 2004; Nevanlinna and Häkkinen, 2010).
On the other hand, u is compiled from a mixture of sources and relies on proxies for the interdiurnal variation before 1872. Before the start of the Batavia data in 1884, the actual interdiurnal variation data between 1872 and 1884 is taken from Greenwich and Bombay. We here find the stability of the Greenwich data to be poor and variable, as did Bartels and he ascribed Greenwich lower weighting in u as a result. We also note Bartels expressed concerns about the effects of many data gaps on the monthly means of some of the early Bombay data and ascribes that station half weighting also at such times. Thus for much of the early interval, u is based on Bombay and Greenwich data with weightings of 1 and 0. 5, respectively. A point to note is that u uses whole-day means (as does IDV(1d) but unlike IDV) and the analysis for lowlatitude modern data shown in Fig.
3  reveals that the Bombay u data will have had a peak response of about BV -0. 4 whereas the Greenwich data will have had a peak response close to BV -0. 2. This means that fast speed flow streams which raise annual means of V will cause u to be lower than an index with a peak response of B. This fits with the differences between u (and hence IDV) and IDV(1d) (and the Russian stations) in the declining phases of the solar cycles. The u index also mixes midlatitude and equatorial stations and so the range of latitude correction factors is large. Using the values from geomagnetic field models for the stations and the empirical dependence of station responses in modern times we make allowance for the secular drift in the station locations. The one direct test that we have been able to make is that the earliest Helsinki data agree very well with interdiurnal variation data from the Russian observatories, St Petersburg, Nertchinsk and Barnaul  (Nevanlinna and Häkkinen, 2010) , whereas the u index data (at this time based on the proxies) do not.
Hence we think that Bartels' warnings that the pre-1872 data should be regarded as unsatisfactory and illustrative are both well-founded and wise. This paper is the first of a series of three. In Part 2 (Lockwood et al. , 2013a) we employ the composite described here to reconstruct the interplanetary magnetic field (IMF) from 1845 to the present day. In Part 3  (Lockwood et al. , 2013b) , we construct a range index composite that is as homogeneous as it can be and apply the method of  Lockwood et al. (1999)  (as adapted by  Lockwood and Owens (2011)  to yield the near-Earth IMF rather than the open solar flux) and compare the results with those of Part 2.
Acknowledgements. The authors are grateful to by the staff of the  British Geological Survey, Edinburgh  and the  World Data Centre (WDC) for Geomagnetism, Edinburgh  for digital data and for scanning the Greenwich yearbooks. In particular, we thank  Ellen Clarke  and  Sarah Reay  for assistance and valuable discussions. The Niemegk data composite was supplied by  Hans-Joachim Linthe of GeoForschungs Zentrum, Potsdam, Germany. The Helsinki data were digitised from yearbooks and paper records by  Anneli Ketola  with the help of several staff of the  Finnish Meteorological Institute  and pupils of the Hankasalmi upper secondary school. Other data were obtained via the  WDC for Solar Terrestrial Physics, Chilton  and from  UCLA 's Virtual Magnetospheric Observatory Data Repository, in particular the hourly means deposited there by  L.
Precision. The precision (also called reproducibility or repeatability) is the degree to which repeated measurements are able to show the same results under unchanged conditions. Thus, precision quantifies the variability of an analytical result as a function of operator, method manipulations, and day-to-day environment. Statistical analysis of data generated are essential to demonstrate assay precision. Repeatability, in flow cytometry, consists to analyse the peak distribution in only one experimentation. The coefficient of variation (CV) obtained from the 10 experiments with each blocker has been determined using the cell lab quanta software (Figure  4(a) ). In the control conditions (concentration = 0), the CV values are scattered. This means that the efflux capacity of the P-gp is heterogeneous in resistant cell population.
Cell Viability Study (MTT Assay). In order to take the number and viability of cells into account, an MTT assay was systematically performed after reading the calcein fluorescence. 10% of MTT reagent was added to each well to a final concentration of 500 g/mL, and the cells were incubated for 4 hours at 37 ∘ C. The medium has been then replaced with 200 L of DMSO to dissolve the reaction product. The optical density was read at 570 nm versus 630 with an Elx 808 microplate reader (Bio-tek Instruments, Winooski, VE). Normalized calcein accumulation was then expressed as the ratio of the well fluorescence to the MTT absorbance. 4. 6. Statistics. For statistical analysis and graphical representation, Excel (Microsoft Corporation) and SigmaPlot (SysStat, Erkrath, Germany) softwares were used. Numerical results are given as means ± SEM ( = sample size).
Acknowledgments This work was supported by  European Regional Development Fund  as part of the  Interreg IVA Project Admin (Transchannel Advanced Microscopy network ). Jennifer Pasquier  and  Damien Rioult  were recipients for a fellowship from the  Conseil Regional de Haute-Normandie. The authors are indebted to  Pr. Jean-Pierre Marie  ( Hôtel Dieu, Paris, France ) for providing MCF-7/Doxo and  PSC833. This paper was made possible by Grants from the  Qatar National Research Fund  under its  National Priorities Research Program Award  nos. NPRP 09-1099-3-279 ,  NPRP 09-1174-3-291 , and  NPRP 4-640-1-096. Its contents are solely the responsibility of the authors and do not necessarily represent the views of the  Qatar National Research Fund.
[32]. This solution addresses the problem of poor TCP performance due to contention on the wireless channel. It uses two techniques. The first is disjoint routing and the reversed routes, which consist to choose disjoint routes for TCP data packets and TCP ACK packets. The second is the dynamic contention balancing, which consist to update dynamically the disjoint routes. Once the route controversy exceeds a given threshold, called the backoff threshold, a new and less restraint route is selected to replace the higher contention route. In this proposal, the contention on the wireless channel is measured by the number of times which a node performs a backoff during each time interval. Also at any time a route is broken, in addition to the initialization of the routes recovery procedure, COPAS redirects TCP packets with the second alternative route.
However, in this solution, the additional backoff time is based on the packet size. Thus the existence of different sizes of data packet in the network should be inspected. Neighborhood RED  [35]. This proposal aims to increase the fairness of TCP in MANET. In contrast to wired networks, the authors show that RED does not solve the TCP unfairness in MANET, because the congestion does not occur in one node, but in an entire sector involving multiple nodes. The packets local queue within each individual node can not completely reflect the state of network congestion. For this reason, the authors define a new distributed queue, called neighborhood queue. At a node, the neighborhood queue should contain all the packets which transmissions affect its own transmission in addition to its packets.
Using simulations, they check the effectiveness of their proposal and the improving of TCP fairness. [36]. The purpose of this proposal is to improve the fairness between TCP flows in ad hoc wireless networks and wired networks. The authors adopt the policy of "non work conserves scheduling" for ad hoc networks instead "work conserving scheduling". The queue of the link layer sets up a timer each time it sends a data packet to MAC. Only after the timer expires, the queue product another packet to MAC. The duration of the timer is updated according to the value of output rate of the queue. Specifically, the timer duration is the sum of three sides of D1, D2, and D3. D1 is equal to the length of the data packet divided by the channel bandwidth. D2 is a time whose value is determined by the recent output effeciency of the queue.
The parameters of the evaluation Important metrics were used, the throughput of TCP, the goodput, the average time from start to finish and routing traffic. The goodput (%): The number of bits of the received data, excluding the number of data bits transmitted, by the simulation time. The end-to-end delay (second): the ratio between the time for receipt of data minus the data transmission time and number of data packets received. The routing traffic (normalized routing overhead): the ratio between the number of routed packets and the number of packets received. The throughput (%): is given by the received data ratio taking into account all data sent.
Data warehousing, data analysis and statistics Collection and warehousing of omic data were performed using BASE (Emergentec Biodevelopment GmbH, www. emergentec. com). Identification of deregulated TCX probes was carried out using a moderated one-way ANOVA to reduce the set of relevant probes to those that exhibit a significant change under any condition with a Benjamini-Hochberg corrected p-value below or equal to 0. 05. On the remaining probes, a moderated two-sided t-test was calculated and corrected via Benjamini-Hochberg. For both steps, the R package "limma" was used. Pathway and transcription factor enrichment was conducted with Ingenuity Pathway Analysis (IPA®, www. ingenuity. com/), using a fold cutoff of 1. 5 and p-value of 0. 001. Heat maps were generated with Treeview (rana. lbl.
gov/EisenSoftware). Principal component analysis (PCA) plots were generated using prcomp in the R package "stats". Protein identification and quantification workflow were implemented in OpenMS (version 1. 9)  [15]  and TOPPAS  [16]. Statistical analysis was performed using the isobar package  [17]  and custom R code. For additional details see the Supplementary Methods. The software package BRB-ArrayTools was used to identify significantly altered features in the supernatant MTX data  [18]. A class comparison with less than 0. 001 p-value and 2 fold alteration from treated to time matched control was used.
Discussion Integrating omic approaches is becoming more important in cellular and molecular biology and will most likely change the way we approach biological investigations  [4]. However, attempts thus far to combine omic data streams have been met with limited success  [2, 25]. In the present study, we have brought together experts from the various fields of TCX, PTX, MTX, epithelial cell culture and pharmacokinetic modeling. The major aim was to investigate the benefit of integrating omic data streams and pharmacokinetics for application in drug safety, utilizing an in vitro system treated with CsA in repeat dose. The discovery of the potent immunosuppressive properties of CsA revolutionized transplant medicine. Three decades later, CsA is still one of the leading immunosuppressive agents in clinical use.
Protein phosphatase 1 induction is a key negative feedback mechanism and acts to reactivate translation, by dephosphorylating eIF2α  [44]. The regulatory subunit of protein phosphatase 1 (PPP1R15A) was increased in the TCX data and the catalytic subunit PPP1CA was increased in the PTX data. In addition, many of the aminoacyl-tRNA synthesis genes and proteins were induced. Asparagine synthetase (ASNS) was heavily induced in both TCX and PTX data sets. ASNS catalyzes the glutamine-and ATP-dependent conversion of aspartic acid to asparagine  [43] , producing in the process glutamate, a precursor for glutathione synthesis. Both glutamate and glutathione metabolic pathways were severely affected. A number of amino acid transporters and intracellular amino acids were induced here. Interestingly, Nrf2 increases ATF4 transcription  [21]  and thus acts as a primer for UPR.