dataesr/scientific-paragraphs-categorization huggingface dataset:
Frequency offset was calculated as the difference between each transient's observed water signal frequency and the frequency of the first transient. The median drift is the median across the 95 datasets, of the mean offset throughout each measurement. Drift rate was measured pre-fMRI and over the same time period (5:20 min) of post-fMRI. The average drift rate is a mean across the 95 datasets which was calculated for each from the difference between the first and the last frequency offsets in Hz divided by the duration of the scan in minutes. Pearson's correlation coefficients ( r ) and two-sided paired t-tests were calculated between the mean absolute frequency offsets before and after fMRI for correlation and distribution analyses. Spearman's rank correlation coefficients ( ) were measured for the correlation of operating hours and years of usage with frequency offsets.
Repeated protocol Sites were encouraged to repeat the acquisition protocol on a different day to investigate consistency and reproducibility of frequency drift characteristics. Pearson's correlation coefficients and intraclass correlation coefficients (ICC) were calculated for the mean absolute frequency offsets between the two runs. ICC calculation was based on a two-way mixed model using absolute agreement. The median values and interquartile ranges of the mean absolute frequency offsets across all datasets and within vendors are shown in violin plots with p-values from the paired t-tests. Datasets from the first and second day were plotted against each other to allow visualized observation for consistency between the two runs. All statistical analysis was performed using R (RStudio: Integrated Development for R.
The simulated spectra with maximum and minimum drifts across all datasets were plotted to observe how extreme frequency drift affect signal intensities. The maximum amplitude of the 2. 01 ppm NAA singlet was determined to compare the change of signal intensity of the synthetic 'drift-affected' spectra before and after fMRI. Additionally, the impact of linear drift (from 0 to 50 Hz range) was demonstrated for two scenarios. First, these synthetic drift time-courses were convolved into the same simulated short-TE spectrum, and the NAA peak height reported. Second, the offset dependence of GABA editing efficiency was simulated in FID-A using 15 ms editing pulses and TE 68 ms, and the change of relative GABA signal integral was reported as a function of linear drift.
Results Of the data collected from 99 scanners, 4 were excluded from the final analysis due to unexpected artifacts or corruption of data that were unable to be processed. Thus, data from 95 scanners were ultimately analyzed. No data were excluded from the repeated scans. Table  1  summarizes the numbers of sites and datasets. Table  2  summarizes scanner details. The average estimated scanner operation time per day was 7. 2 ± 2. 9 hours (no differences across vendors:  2 = 0. 071; p = 0. 965), and the average age of scanner was 5. 5 ± 3. 8 years (no differences across vendors:  2 = 4. 458; p = 0. 108). Low correlations were observed between operating time ( r = 0. 17/0. 13) and age of scanner ( r = 0. 026/0. 19) with pre-/post-fMRI frequency drift respectively, all p > 0. 05. The median (IQR) FWHM of the water signal was 2.
Second acquisition from repeated protocol Sixty scanners repeated the protocol on a different day. Frequency offsets recorded on two separate days before fMRI are correlated in Fig. 3  a. The same plot is shown for post-fMRI data in Fig. 3 b. Within these sixty scanners, the median (IQR) frequency drift before fMRI was 0. 31 (1. 09) Hz and 0. 33 (1. 14) Hz, for the first and repeated datasets, respectively. The mean absolute frequency offsets for each session were compared pairwise within-site for repeatability and correlation. ICC and Pearson's coefficients were 0. 85 and 0. 75, respectively, indicating good agreement and correlation between sessions for pre-fMRI datasets. Post-fMRI, median frequency drifts on the two days were 2. 55 (3. 06) Hz and 2. 88 (2. 98) Hz, with ICC and Pearson's coefficients being 0.
Limitations One disadvantage of the data visualizations in Figs. 1-3 is that they over-emphasize the outlier sites with large drift and tend to conceal the sites displaying very little drift. Frequency drift induced by gradient heating is a known issue, however, readings from sensors for gradient temperature monitoring have not been widely reported, while the number and locations of them also vary across vendors. Temperature readings are archived in log files, which can be retrieved retrospectively after the scan. However, the files consist of a lot of other information for the scan that make temperature retrieval more complicated. Furthermore, gradient temperatures are stored in time series and users would have to match the time of the scan with the data in log files. Real-time gradient temperature monitoring would be helpful, but it is not an available option for all scanners.
Acknowledgements This work was supported by  NIH  grants  R01 EB016089  ,  R01 EB023963  ,  R21 AG060245  ,  S10 OD021726  ,  K01 AA025306  ,  K99 AG062230  ,  K99 DA051315  ,  K99 EB028828  ,  R01 MH110270  ,  R01-DC008871  ,  S10 OD012336  ,  S10 OD021648  ,  P41 EB031771  ,  Taiwan MOST  grant  108-2410-H-038-008-MY2  ,  JST COI  grant  JPMJCE1311  ,  ERC  grant # 249516 ,  DLR 01ZX1909A   SySMedSUDs, Natural Sciences and Engineering Research Council of Canada   RGPIN/03875-2017  ,  CONACYT  grant  CF-2019-6390  , The  European Research Council  under the European Union's  Horizon 2020 Research and Innovation program  ( ERC StG 802998  ),  Ariane Fillmer  has received funding from the  18HLT09 NeuroMET2  project within the EMPIR programme co-financed by the Participating States and from the  European Union 's  Horizon 2020 research and innovation programme.
Data and code availability statements All data were collected prospectively using MRI phantom. They will be available on the NITRC portal in the "Big Drift " project repository ( https://www. nitrc. org/projects/bigdrift/ ). See appendix in manuscript for more details. Data analysis was performed using MATLAB (R2020b, MathWorks, Natick, USA), and statistical analysis using R (RStudio: Integrated Development for R. RStudio, PBC, Boston, MA). All functions and packages are freely available on MATHWORKS and GITHUB.
Background: The Group-based trajectory modelling (GBTM) method is increasingly used in pharmacoepidemiologic studies to describe medication adherence trajectories over time. However, assessing the effects of these medication adherence trajectories on health-related outcomes remains challenging. The purpose of this review is to describe studies assessing the effects of medication adherence trajectories estimated by the GBTM method on health-related outcomes. Methods: We will conduct a systematic review according to the recommendations of the Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA) guidelines. We will search in the following databases: PubMed, Embase, PsycINFO, Web of Science, CINAHL, and Cochrane database up to April 1st, 2021. Two reviewers will independently select articles and extract data.
Background Medication adherence is a real challenge for healthcare professionals and patients. Sub-optimal adherence may be associated with poorer healthrelated outcomes and higher healthcare costs, depending on the disease. [1] [2] [3]  Numerous adherence studies have been conducted to identify patients' characteristics with suboptimal adherence and the impact of non-adherence on health-related outcomes. [4]  Several measures of adherence exist. When using medico-administrative databases as the source of drug information, researchers often use the proportion of days covered (PDC) or medication possession ratio (MPR), usually dichotomized with an 80% cut-off to distinguish adherent from non-adherent individuals. [4] [5] [6]  Other adherence measures include self-reported questionnaires (e.
Extensions of the classical GBTM model have been made, such as the possibility to account for risk and protective factors (e. g. , age, sex, gender). [17]  These analyses are relatively widespread as they are well described in the literature and bene t from software macros to help and guide researchers. [25, 26]  Medication adherence trajectories issued from GBTM could be bene cial for studying the associations between adherence and health-related outcomes, such as hospitalizations, death, or any critical clinical event. However, the GBTM literature does not provide speci c instructions for assessing these associations. To our knowledge, there is no software macro developed to perform analyses combining trajectories and the outcome in a joint model. Researchers generally proceed in two steps; identifying the adherence trajectories with GBTM and then using these trajectories as an observed variable in any suitable model to infer health-related outcomes.
Information sources We will search for relevant article references in the following databases: PubMed, Embase, PsycINFO, Web of Science, CINAHL, and Cochrane database up to April 1st, 2021. The search strategy has been developed and adapted for each database using the most sensible approach validated by a specialized librarian at Laval University. A complete description of the applied search strategies is described in Table  2  in Appendix B. Duplicate citations will be removed using EndNote and Covidence Solution software. [35]  A nal manual revision of the database will be conducted to check for remaining duplicates. We will consider and include any additional articles not identi ed by our search strategy and brought to our attention by screening references of selected articles or relevant systematic reviews if they meet the inclusion criteria.
All identi ed studies will be compiled and kept with the full text (when needed) in a shared reference management software (i. e. , EndNote)[36] and with Covidence, a web-based solution for systematic reviews. [35]  Study selection Article selection will be performed using Covidence solution. [35]  First, two reviewers will screen titles and abstracts independently, and articles will either be included, excluded, or categorized as unsure. Articles excluded by both reviewers will not be selected. Second, reviewers will discuss discrepancies to reach a consensus for every disagreed article. A pilot screening test will be conducted on a sample of a randomly selected 10% of the articles. Likewise, the full text of selected articles will be reviewed independently by two reviewers, and articles will be included or excluded.
Studies will also be classi ed according to the source of data for medication adherence, medication adherence measure, the parameters used in GBTM, including the rationale behind parameter choice (e. g. , statistics, clinical characteristics, the minimal number of patients included, number of groups). In the third phase, we will perform classi cation and narrative synthesis. We will review the choice and modalities used to estimate the effect of adherence trajectories on health-related outcomes, including the source of data, nature, and de nition of the health-related outcomes studied, the statistical model used, the effect measure yield, and how medication adherence measure has been incorporated in the model. We will also summarize limitations and biases reported by the authors and their attempt to mitigate them.
Moreover, we will classify studies according to the quality of reporting and the study's overall quality in the synthesis. As the review does not aim to estimate a measured effect, we will not conduct a metaanalysis and assess studies' heterogeneity. Discussion method has grown in popularity in adherence research over the last 20 years. [17]  The method and its applications, the macroimplementations in the software, are well established and developed in many disciplines, such as pharmacoepidemiology. [42]  Most of the studies on GBTM in this eld have primarily used trajectories to describe membership groups over time and associated factors. [43, 44]  To our knowledge, there are two systematic reviews on Latent Class Modelling approaches, including the GBTM method. [24, 45]  Nevertheless, they did not focus on the GBTM method to assess the association between adherence trajectories and health-related outcomes and related challenges.
Acknowledgments: We would like to thank  Sue-Ling Chang , who reviewed the manuscript for English. Authors' information:  Victoria Memoli*. Aix Marseille Univ ,  INSERM, IRD, SESSTIM, ISSPAM, Cancer, Biomedicine & Society group, Equipe Labellisée Ligue Contre le Cancer, Marseille, France victoria. memoli@inserm. frGiraud Ekanmian*. Population Health and  Optimal Health Practices Research Axis ,  CHU de Québec-Université Laval Research Center, CISSS de Chaudière-Appalaches Research Center. codjo-giraud-ulrich. ekanmian. 1@ulaval. caCarlotta  Lunghi. Université du Québec à Rimouski ;  CISSS de Chaudière-Appalaches Research Center ;  Population Health and Optimal Health Practices Research Axis ,  CHU de Québec-Université Laval Research Center carlotta_lunghi@uqar. ca Anne-Déborah Bouhnik. Aix Marseille Univ, INSERM, IRD, SESSTIM, ISSPAM, Cancer, Biomedicine & Society group ,  Equipe Labellisée Ligue Contre le Cancer ,  Marseille , France anne-deborah.
As a result, the vSwitch can hardly guarantee all tenants' network SLA performance in all situations. Existing network Quality-of-Service (QoS) strategies in software vSwitch are inherited from the interface-based solutions of hardware switches, and do not consider the issues of CPU resources competition among tenants, as well as the variable vSwitch forwarding capacity. As a result, they cannot ensure VMs' SLA performance targets. Our experimental results on a multi-tenant cloud platform in Section 2, show that the innocent VM bandwidth can be decreased by up to 20% due to the competition of IO-dedicated CPU usage. In terms of latency, all VM's latencies increase hundreds of times, since all VMs' forwarding tasks compete for the limited CPU cores in terms of timing and are not processed in a differentiated manner.
Some works  [11, 12]  have noticed the resources competition issue, and they added a module for CPU resources isolation before the QoS module. The effects of these works were limited because they are still interface-based and the variable vSwitch forwarding capacity is still ignored. Different from existing solutions, in this paper we propose a new CPU-cycle based QoS strategy (C2QoS) to completely solve this issue. As the ''virtual'' network resources are realized by the IO-dedicated CPU cores, we guarantee VM's network SLA performance by directly apportioning these CPU cycles to VMs. The challenges of achieving this goal include:  (1)  How to establish a correspondence between VM's bandwidth and CPU usage. (2)  How to assign CPU cycles to VM to strictly guarantee its bandwidth. (3) How to ensure the SLA latency, especially for the delay-sensitive applications (e.
The main reasons are argued in  [21] : the overhead of processing each packet is fixed; the token buckets and queues are implemented by hardware and they can complete the corresponding functions without compromising the performance; high-precision clock and hardware feedback support  [22, 23]. Unfortunately, none of the above advantages exist in software vSwitch. As mentioned in Section 1, the CPU cores left for vSwitch are limited, and meanwhile their processing capacity is variable when being used to forward traffic with different characteristics. For example, in Google's experiments, forwarding a flow with 64-byte packets at a speed of 512 Mbps will consume more CPU cycles than forwarding a flow with 1518-byte packets at a speed of 2. 4 Gbps  [11]. On the other hand, as a software based process, the vSwitch has particular bottleneck and resource competition points, which are completely different from hardware switch.
Latency issue In addition to the bandwidth issue, the existing traffic scheduling mechanisms inherited from the hardware switch also cause latency issues in vSwitch. As processing engine and logic are very different between the hardware switch and the vSwitch, the resource competition occurs at different stages. Simply applying the previous strategy cannot solve the competition problem on a different platform. As shown in Fig. 2 , we present the abstract of packet processing logic in the hardware switch and the software vSwitch to analyze the different requirements of scheduling in the two architectures. In the hardware switch, the circuits with powerful processing capabilities make the resource contentions mainly occur at the egress stage, where the traffic of multiple in_ports is gathered for sending out on a particular port (see port P6 in Fig.
2(a) ). The existing traffic scheduling mechanisms working at this stage  [27] [28] [29]  can queue up packets of different in_ports so as to guarantee differentiated latencies. However, in the software vSwitch, the packet processing capacity of CPU cores is far inferior to the hardware circuits that can achieve line speed. With the common practice in the cloud platform that very limited CPU cores are used for vSwitch, concurrent VMs compete for these CPU cores to execute the expensive batch I/O processing 1  in the ingress stage (see Fig. 2(b) ). Due to the absence of task scheduling at the ingress stage, VMs indiscriminately queue up for batch I/O tasks to be completed, which causes mutual influence and high latency. In the worst case, on a server running  VMs, each VM will suffer from the additional latency caused by waiting for ( -1) times of batch I/O processing to complete.
We also use experiments to demonstrate this issue. To simulate the multi-tenant scenario on the cloud, we increase the number of VMs to 16 on one server and measure the TCP latency. One CPU core is used to forward traffic in the OVS-DPDK throughout. To measure TCP latency, we run qperf  [30]  as a client-side program in all VMs simultaneously, while the server-side program is run in another directly connected physical server. We measure 20 sets of data for each experiment to avoid accidents. The results are shown in Fig. 3 , where the latency in each case is shown in the form of a box diagram. It can be seen that when there is only 1 VM running on the server, its TCP latency is stable and maintains at 26-27 s. But with the number of VMs grows, the TCP latency becomes unstable and all increases exponentially.
Motivation The reason that VM's bandwidth and latency cannot be guaranteed is that the existing QoS strategies ignore the IO-dedicated CPU resources competition inside the vSwitch. The lack of management and apportionment of CPU resources brings a series of flaws including bandwidth isolation and undifferentiated high latency. Some previous works have mentioned this issue, and some solutions have been proposed, e. g. , Addanki et al. [12]  considered separately apportioning IO-dedicated CPU resources and bandwidth on the software router, and Kumar et al. [11]  proposed a method by using a CPU-based weighted fair queue to isolate CPU competition among VMs. But all of these works have limited effects because they only add a CPU isolation module before or after the existing interface-based QoS mechanisms, but fail to consider the variable vSwitch forwarding capacity and the different resource competition points in the software vSwitch process.
On average, the deviation of C2TB under mixed packet size is between (-2%, 3%). In Fig. 11 (b), we increase the number of VMs and deploy them on every NUMA node to compare their real bandwidth with their purchased bandwidth. The increase in the number of VMs did not have large impacts on the deviation. But it can be concluded that the more variables introduced in the modeling, the greater the deviations are. When using only one CPU core for forwarding in vSwitch, the deviation is between (-2%, 4%). But in the case of using two cores for forwarding, resource competition becomes even more unpredictable, so the deviation has almost doubled to (-3%, 6%). Although the accuracy of the C2TB is incomparable to the traditional precise rate limiting methods, the CSPs believe the rate limiting under software forwarding does not need to be so precise and occupy much resources  [21].
The C2QoS fails in such a condition, because it cannot solve the congestion and competition on the NIC. This situation is not common on the public cloud, so we can adopt a simple handler to deal with it. We use the original monitoring module that is inside the vSwitch to detect the forwarding rate of the vSwitch. If the throughput of the vSwitch reaches the upper limit of the NIC bandwidth, the vSwitch will be triggered to use the BPS/PPSbased rate limiting mechanism to assist C2QoS strategy for ensuring the performance of tenant networks. We carry out an experiment to show this extreme situation. As shown in Fig. 13 , the traffic characteristic preference of VM1 includes the factors of 2 Gbps, 128 bytes packet size, and the single flow, while Fig. 13. The scenario where C2QoS may fail, and the effectiveness of our handler.
Acknowledgment We thank the reviewers for their insightful feedback. This work was supported in part by the  National Key Research and Development Program of China  (Grant No. 2019YFB1804500 ), the  National Natural Science Foundation of China  (Grant No. U20A20180 ),  Chinese Academy of Sciences -Austria Project  (Grant No. GJHZ202114 ) and  Beijing Municipal Natural Science Foundation  (Grant No. JQ20024 ).
A period of 6 h was chosen since pilot trials indicated that both maximum swelling and de-swelling occurred by 6 h submersion at room temperature. Changes in cross-sectional area of entire and notched stems and their main tissue areas were then calculated using the Image analysis software Optimas v. 6. 5. 172, Media Cybernetics, Inc. , Rockville, MD, USA (electronic supplementary material, table  S1 ).
Samples were compressed in two directions: axial compression, where the direction of the force is along the length of the sample and radial compression in a direction normal to the axis length (electronic supplementary material, figure  S4e ). Medium grade (120) sandpaper was used to prevent slipping during the test (electronic supplementary material, figure  S4d ). Force/displacement data were obtained from compression tests as overall/effective stiffness. In addition, force at failure (N) and rigidity (N mm) were used to study the compressive behaviour of the plant using the force-displacement response.
Data handling and statistics Sampling of stem segments aimed to extract branches from different plant individuals, but this could not be guaranteed because of the clonal life history of the plant. Representative circular, triangular and star-shaped branch segments can be accepted as different branches from mostly different individuals. All primary fieldwork and laboratory data and measurements are available at the figshare link. Statistical tests included non-parametric Kruskal-Wallace ANOVA followed by post hoc multiple comparisons tests to assess the significance of values between specific treatments. Nonparametric tests were chosen for field mechanical tests and swelling tests because of skewed and highly variable values particularly for rigidity, and variably positively skewed percentage datasets of swelling and de-swelling tests.
Full datasets of field and swelling/de-swelling tests can be found at the figshare link. Parametric ANOVA were carried out on results from rheology and compression laboratory tests on the stem and mucilaginous tissue. Details of the tests can be found in the electronic supplementary material and full datasets on figshare. Each group sample (base, triangle and star) was drawn from a normally distributed population. Statistical tests were carried out with Microsoft Excel, Statistica (StatSoft, Inc. , 2013) and using R-studio (v. 3. 6. 1; R Foundation for Statistical Computing, Vienna, Austria; www. r-project. org) (see electronic supplementary material, tables S2-S6).