dataesr/scientific-paragraphs-categorization huggingface dataset:
The transition from bulk measures to more comprehensive data, such as tissue displacements and strains, has enabled more elaborate approaches for estimating a larger number of parameters  12, 35  and heterogeneous parameter distributions. 25, 38 As recent advances in medical imaging offer increasingly more detail on the heart anatomy and regional kinematics, rich datasets for model personalisation and characterisation of passive parameters are becoming more accessible. 2, 14, 39 espite the wealth of clinical data available and the continuous enhancements in the complexity and accuracy of personalised models, significant work is still required to enable the translation of model-based stiffness assessment to the clinic. A fundamental prerequisite is the reliable quantification of myocardial stiffness, an issue tightly coupled to the unique and accurate estimation of model parameters which was analysed in our previous study.
MATERIALS AND METHODS In this section we present the process followed for assessing stiffness using personalised diastolic left ventricular (LV) models of cardiac mechanics. The model personalisation pipeline, summarised in Fig. 1  1 , is pre-1 Images were created using Eidolon (https://github. com/ericspod/ Eidolon), developed by Dr Eric Kerfoot at King's College London. sented in ''Data Processing Pipeline'' section. The mechanics model is then reviewed along with the process for estimating model parameters (''Personalised Cardiac Mechanics Model'' section). To assess the reliability of the obtained parameters we examine model uncertainties that can influence and improve parameter identifiability and model accuracy (''Model Fidelity and Parameter Identifiability Study'' section).
Data Processing Pipeline Clinical data was collected from 5 volunteers with healthy heart function (V1-V5) and 3 DCM patients (P1-P3), recruited at St Thomas' hospital. The study included male and female participants, with ages ranging from 28 to 55 (Table  1 ). Cardiac MRI scans (Table  2 ) were performed on a 1. 5T Philips Achieva system. Written informed consent was obtained from all participants prior to scanning, and the protocol (study number 12/LO/1456) was approved by the London Bridge NRES committee. In order to enable consistent use of the available data, spatial registration of images was performed using the Image Registration Toolkit (IRTK)  2. Image registration was essential to minimise misalignment between images caused by changes in patient's position and breathing pattern.
E was measured from 4D PCMRI using GyroTools GTFlow 4  and E a 0. 81 ± 0. 10 0. 83 ± 0. 09 0. 73 ± 0. 14 0. 74 ± 0. 12 0. 86 ± 0. 09 0. 75 ± 0. 12 0. 82 ± 0. 10 0. 78 ± 0. 13 Element quality q was computed as q = 3r, r being the ratio of inradius to circumradius. The mesh size was computed as h ¼ detðSÞ 1=3 , S being the affine mapping between elements in mesh and the unit right tetrahedron. velocity was estimated at the lateral basal region using the displacements extracted from TMRI. The presented data processing steps were essential for model personalisation, but also enabled the derivation of important clinical metrics (Table  1 ), allowing for additional comparisons between patients and volunteers.
2, 3  A data-derived constraint was also applied on the base plane, which was set to follow the TMRI extracted motion. Following Asner et al. ,  2, 3  a relaxed basal boundary condition was employed to restrict the influence of noisy tracked data on the model and avoid pressure and stress peaks arising with strict enforcement of displacement constraints. A common practice in cardiac patient-specific applications is to assume that the epicardial energy is negligible. This approach was also examined in this work and will be referred to as the ''No-traction boundary condition'' (NT BC). The right ventricle (RV) is likely, however, to exert a substantial force on the septal wall of the LV during filling. To account for this effect, a relaxed boundary condition on the epicardial boundary was also examined, which incorporated data-derived motion on the region of attachment to the RV wall (RV BC).
(  7 ) over the parameter space (c). The values of J , the relative displacement error, at the estimated parameter were used to assess model accuracy, while the overall behaviour of J provided a characterisation of parameter identifiability. Three different fibre distributions were examined, as discussed in ''Personalised Cardiac Mechanics Model'' section, with the maximum angle h ¼ f50 ; 60 ; 70 g. The effect of the epicardial boundary condition on model fidelity and parameter identifiability was also assessed, by considering both NT BC and RV BC, as discussed in ''Boundary Conditions'' section. Additionally, different data frames were employed as reference state, and compared with respect to their influence on both accuracy and identifiability.
CONCLUSIONS In this work we have collected comprehensive non-invasive clinical data from a group of healthy volunteers and DCM patients. Following data processing, metrics reported in the clinical literature were compared between the two groups, demonstrating significant deterioration in function in diseased hearts. Additional insight into cardiac function in DCM was obtained through patient-specific modelling, which enabled comparisons to transition from image-derived metrics to a model-based tissue stiffness. Acknowledging the need for reliable estimation of model stiffness, personalised models were developed with the objective of achieving sufficient model accuracy and unique parametrisation. Accordingly, key model uncertainties were systematically examined, elucidating that accounting for the mechanical influence of the RV and using a fibre angle of h ¼ 50 produced consistently lower model errors and resulted in better identifiability characteristics.
ACKNOWLEDGMENTS The authors would like to acknowledge the funding from the  BHF   New Horizons program  ( NH/11/5/ 29058 ) and by  Engineering and Physical Sciences Research Council  ( EP/H046410/1  and  EP/N011554/1 ). The research was in part supported by the  NIHR Biomedical Research Centre (BRC) based at Guy's and St Thomas' NHS Foundation Trust and King's College London  and by the  Wellcome Trust-EPSRC Centre of Excellence in Medical Engineering  ( WT 088641/Z/09/Z ). The Image Registration Toolkit (IRTK) was used under Licence from Ixico Ltd. The views expressed are those of the authors and not necessarily those of the  NHS , the  NIHR , or the DoH.
Elastomeric material presents different behavior according to amplitude of angular displacement (rotation) and conforming to the imposed static preload as illustrated in works of  Saad et al. (2006)  and  Rendek and Lion (2010). Tests are performed for different levels of static preload imposed at the juncture of clamping area P =[45, 100, 150, 500] N. Tangential accelerations and forces signals are transferred to a computer via the data acquisition card with a sampling frequency f s = 2kHz. The dynamic stiffness is obtained, for each frequency of excitation by the Frequency Response Function: Twisting moment (torque)/Angular displacement (rotation) identified using Lagrange's formalism.
Acknowledgments Funding for this work was partially provided by the  Spanish MINECO  and  UE FEDER  under projects:  FPA2010-22056-C06-02 ,  FPA2010-22056-C06-01 ,  FPA2015-69210-C6-2-R , and MDM-2014-0369 of ICCUB; SEV-2012-0249 of IFAE;  FPA2010-22056-C06-06 ,  FPA2013-48381-C06-2-P , and  FPA2014-55819-C4-3-P  of  UCM ; and finally  FPA2010-22056-C06-03 ,  FPA2013-48381-C6-3-P , and  FPA2014-55819-C4-2-P  of  CIEMAT. This paper has gone through internal review by the  CTA Consortium. We would like to thank the reviewers and committee ( J. F. Glicenstein ,  M. Daniel ,  R. Paoletti  and  J. Rico ) for their useful suggestions.
1 ). The parameterization and synchronization of the two cameras was carried out using the free software digiCamControl (v1. 1. 0). The resolution of the images was dependent on the distance between the camera and the root. The resolution was therefore 140 px mm -1 for the vertical camera (cam2) and 220 px mm -1 for the horizontal camera (cam1). We combined this experimental set-up (hydroponic tank and cameras) with a sensor dedicated to the mechanical characterization of the root.
In contrast, the above fastest path needs three connections (5→F→E→J). We compute the duration of this path using travel times from the Metropolitan Transportation Authority (MTA) Data Feeds (see Materials and Methods). We neglect walking and waiting times. (B) In the dual space, nodes represent routes [where ACE, BDFM, and NQR are service names  (49) ], and edges represent connections. A "simplest path" in the primal space is defined as a shortest path with the minimal number of edges in the dual space (light-blue arrow). It has a length of C = 1 and occurs along the direct connection between line 5 (dark-green node) and line J (brown node). The above fastest path in the primal space has a length of C = 3 (gray arrows) in the dual space, as one has to change lines three times. [We extracted the schematic of the NYC metropolitan system from a map that is publicly available on Wikimedia Commons (45).
Effects of multimodal couplings on the information In our discussion above, we estimated the cognitive threshold for the most complex paths in the 15 largest metropolitan networks. We now consider the effects of including other transportation modes (for ex-ample, buses and trams). The effects of intermodal coupling are significant  (2, 33) , and the natural framework is a multilayer network  (34, 35) , which associates each transportation mode with a different "layer" in a network and where interchanges (that is, connection points) between different modes are represented by interlayer edges. As we discussed in Materials and Methods, a major difficulty is obtaining data for different modes for a given city, as this type of data is not easily available. However, we were able to combine multiple data sources for large cities on three different continents: NYC, Paris, and Tokyo (see table  S2 ).
Paris The data that we used to construct the Paris multilayer transportation network  (34, 35)  were obtained from the Régie Autonome des Transports Parisiens (RATP) (41) and the Société Nationale des Chemins de fer Français (SNCF)  (42). Both data sets are in GTFS (General Transit Feed Specification) format. We extracted the bus layer from RATP data. To reproduce all of the information available in the Paris Metro map (31), we merged the metro and tramway data from the RATP with the light rail and tramway data from the SNCF. This aggregation yielded what we call the "MRT" layer, which we used as a single metro layer. We distinguished services that went in opposite directions or toward different branches, and we were able to identify them as a particular route because they shared the same short name.
We used the services that were available on 26 May 2014 (a Monday), and we excluded any bus route that was completely subsumed by another route. The RATP data provide extremely detailed transfer times between stops. We used this information to reconstruct connections between routes. With the exception of connections in the central station Châtelet-Les Halles, we ignored transfers that take longer than 8 min and considered the two corresponding nodes as different entities. By studying the cumulative distribution for the walking distances in these connections data, we found that the 99th percentile corresponds roughly to d w = 250 m. More precisely, F(250 m) ≈ 0. 986. Motivated by this calculation, we allowed a maximum walking distance of d w = 250 m for the coarse-graining procedure that we needed to construct the multilayer networks in NYC and Tokyo.
The transfer data for Paris allow a more accurate method than the data for NYC and Tokyo. We constructed a transfer network in which (i) a transfer is defined between two stops and (ii) two stops are located in the same geographical position. We associated a single node with each connected component of the transfer network, and each isolated stop constitutes a single node. We weighted the edges using travel times. Relatively large connection areas emerged from our choices. They correctly reflect possible choices on the Paris Metro map that are suggested to travelers (31). Intrastation walking paths are represented as white edges. Finally, we also coupled nodes if they were closer than d w = 250 m to each other and either (i) they both came from the RATP data and were labeled with the same name or (ii) at least one node came from the SNCF data (where transfer times between lines were not provided).
New York City We constructed the NYC multilayer network with data from the Metropolitan Transportation Authority (MTA) Data Feeds (43), which provide a snapshot of their services on different days using the GTFS format  (44). We used the services that were available on 1 December 2014 (a Monday). For the metro layer, we excluded six routes [which correspond to services (45)] that represent shuttles or other special services. For the bus layer, we integrated the data from the five NYC boroughs with the multiborough data from the subsidiary MTA Bus Company. We performed a coarse-graining procedure in which (i) we coupled bus stops to metro locations if they lie within a walking distance of d w = 250 m and then (ii) combined bus stops into a single entity if the distance between them is less than d w.
Tokyo The Tokyo metro data set, which we extracted from Wikipedia and can be found online  (39) , allowed us to study another of the world's largest metropolitan networks. The bus data for Tokyo (for 2010) are provided freely by the Japanese Ministry of Land, Infrastructure, Transport, and Tourism  (47). We used only the Toei bus lines  (48) , which serve central Tokyo. This data set associates stops with bus routes but gives no information on the topology of the line. We reconstructed an approximate topology by generating a minimal spanning tree (49) among all stops of a route. We defined the intraroute connections using the same coarsegraining procedure as for NYC. We again combined nodes that are within a walking distance of d w = 250 m from each other. We had no information on the travel times along the edges, so we estimated them from the trip length and used typical transportation speeds from the bus and metropolitan systems in the Paris data.
Acknowledgments: We thank  M. Kivelä  for helpful comments on an earlier draft of this paper. We thank  P. Brodersen ,  F. Corradi ,  A. Solé Ribalta , and the anonymous reviewers for useful comments. R. G. thanks  L. Alessandretti  for suggesting the use of SNCF data. M. B. thanks  Y. Shibata  for helping to find and process the Tokyo data. Funding: All authors were supported by the  European Commission FET-Proactive  project  PLEXMATH  (grant  317614 ). Author contributions: R. G. , M. A. P. , and M. B. performed and designed the research. R. G. wrote the codes for data analysis and prepared the figures. R. G. , M. A. P. , and M. B. wrote the paper. Competing interests: The authors declare that they have no competing interests. Data and materials availability: All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials.
Between 80 and 95% is collected by the ESP depending on aerosol size confirming previous result from Forsyth  (1998). Aerosol concentration is tuned by dilution. The aerosol mobility distributions were measured in both positive and negative polarities with a DMA and a CPC (condensation particle counter) for both the post-DBD neutralizer and a radioactive ( 85 Kr of 74 MBq) neutralizer. The TSI SMPS software (Aerosol Instrument Manager, Version 8. 0) was used to calculate particle size distribution from the mobility distributions measured with the DMA-CPC system. For monodisperse aerosol, the charge distribution can be estimated from the measured mobility distribution using the height of each mobility peak related to each particle charge level. This method has been confirmed on one diameter by comparison of charge distribution measured downstream a 85 Kr neutralizer and calculated from theoretical laws (cf.
Δl tip (i; x, y) denotes the extension length of tip i when it reaches (x, y) in the interval of time Δt tip. hR tip i is the average tip extension rate and N atip is the number of active tips. The distribution of tip extension rate for each image was calculated by Eq (3): f ðx i Þ ¼ n i n t Dx ð3Þ where x is the data to be classified (in this case the tip extension rate, R tip , was the sample data); n i the number of data in the i th interval [x i -Δx/2, x i + Δx/2); n t the sample size and Δx the width of the interval; f(x i ) the probability density of x falling within the i th interval. Moreover, the quantities of new emerged branches per hypha, as well as, the ratio of active tips from the measured tips were quantified for each image. Due to the linear and radial growth of hyphae, the biomass of mycelium depends on the total length of hyphae not the area of colony.
The location of branch development is denoted as one branch site, differing from a cross that is the intersection of two hyphae at different depths (Fig  1(b)-(ii) ). Moreover, the branching length is defined as the hyphal length between the two adjacent branch sites. Next, the two types of branch angles, the number of AB sites and LB sites and the branching length were quantified and measured in each region from color projection images using the proprietary Zeiss software (ZEN 2012, black edition). The distributions of two branch angles, as well as, branching lengths were calculated using Eq (3) where the measured branch angle θ and branching length l were the sample data. Due to the projection in 2D of the real 3D structure, an error is possible for some of the measured branch angles and branching lengths.
This work was financially supported by the  LabeX LaSIPS  ( ANR-10-LABX-0040-LaSIPS ), managed by the  French National Research Agency  (ANR -http://www. agence-nationale-recherche. fr/)under the " Investissements d'avenir" program  (n  °ANR-11-IDEX-0003-02 ). Grant holder was PP. This work was also financially supported by the  China Scholarship Council (CSC). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Also, many instruments are not portable or need a larger current-potential range. Facing these problems and with the need of miniaturization and the portability of the electrochemical platform, we managed to design, construct and validate a LabView-PC based system in order to control both cyclic voltammetry (CV) and chronoamperometry (CA). As we can see in figure  2 , the whole system is composed of both software and hardware parts. The interfacing between the computer and the micro-sensor is assured by the DAQ NI-USB 6009 and an external electric control board. Concerning the software part, it presents a graphical interface that has the advantage to be considered as a virtual instrument that controls the NI-DAQ. It is divided into two blocks: The first one, its purpose is to generate the necessary signal either a ramp (CV) or a DC potential (CA) depending on the desired application.
Distance-specific NO x emissions of Euro VI heavy-duty vehicles Heavy-duty vehicles are responsible for roughly half of the NO x emissions originating from road transport (TREMOVE database,  Breemersch et al. , 2010). Since recent measurements show that Euro VI heavy-duty vehicles generally comply with the Euro VI limit  (Vermeulen et al. , 2012) , we uniformly apply here an emission factor of 0. 507 g NO x /km as stated in COPERT 4  (Ntziachristos and Samaras, 2014; Ntziachristos et al. , 2011). This is a big reduction compared to the average emission factor of heavy duty vehicles in 2010 (5. 6 g NO x /km for Belgium). However, Euro VI heavy duty vehicles seem to comply, two scenarios are considered with an emission factor of 1. 0 g NO x /km.
The level of ctfAB expression was 3-fold higher in the adhE1 knockout compared to the control strain. This indicates that the lower flux of acetone production is the result of a control at the enzyme level due to a lower acetoacetyl-CoA concentration and/or higher acetyl-CoA/ butyryl-CoA concentrations. The remaining ability of the ΔadhE1 strain to produce butanol under solventogenesis is explained by the higher adhE2 expression (~127-fold higher than the control strain, but only 25 mRNA molecules/cell) (Table  1 , Additional file 2: Dataset S1). For the ΔadhE1 mutant, the butyrate flux increased by 5-fold compared to the control strain (Additional file 1: Fig. S3 ), although neither ptb-buk (CA_C3076-CA_C3075) nor buk2 (CA_C1660) experienced a significant transcriptional increase (Additional file 2: Dataset S1).
S2 ). The ΔadhE2 mutant was not analyzed because it does not produce butanol. Under solventogenesis, AdhE2 replaced AdhE1 for the conversion of butyryl-CoA to butyraldehyde in the ΔadhE1 mutant, while in the ΔadhE2 mutant, which behaved the same as the control strain, AdhE1 was responsible for all the conversion. The two main contributors to the conversion of butyraldehyde to butanol under these conditions were AdhE2 (67 % of the flux) and BdhB (30 % of the flux) in the ΔadhE1 mutant, while in the ΔadhE2 mutant, which behaved the same as the control strain, BdhB (75 % of the flux) and BdhA (16 % of the flux) were the main contributors (Additional file 1: Fig. S2 ). Under alcohologenesis, adhE1 was not expressed (Table  1 , Additional file 2: Dataset S1), and thus, AdhE1 could not replace AdhE2 for the conversion of butyryl-CoA to butyraldehyde in the ΔadhE2 mutant.
A long gene cluster linked to iron/sulfur/molybdenum metabolism (CA_C1988-CA_C2019) exhibited significantly decreased expression (except for CA_C1988, CA_C1990, CA_C1992 and CA_C1995, for which some values were below the significance criterion of 4-fold but were higher than 3-fold) (Table  3 , Additional file 2: Dataset S1). A part of this cluster, CA_C1988-CA_C1996, was previously reported to be down-regulated under oxygen-exposed conditions  [29]. Moreover, this cluster was shown by Schwarz et al. [30]  to be repressed by butanol stress in an acidogenic chemostat.
In ΔadhE1, the CA_C0102-CA_C0110 operon which was shown to be up-regulated in acidogenesis and belongs to the CymR regulon, was also up-regulated by >18-fold under solventogenesis (Additional file 1: Table  S4 ). However, the up-regulation of this operon (under alcohologenesis in the control strain, acidogenesis and solventogenesis in ΔadhE1, or acidogenesis in ΔadhE2) did not have striking shared features with the main product profile. Interestingly, expression of the natAB operon (CA_ C3551-CA_C3550) (>10-fold), encoding a potential Na + -ABC transporter, and the kdp gene cluster (CA_ C3678-CA_C3682), encoding a potential K + transporter (>20-fold), was highly up-regulated under solventogenesis (Additional file 1: Table  S4 , Additional file 2: Dataset S1) in ΔadhE1. The natAB operon and the kdp gene cluster have previously been reported to be up-regulated by both acetate and butyrate stress  [27].
A : This work presents the 600 MHz clock multiplier PLL and the pseudo-LVDS driver which are two essential components of the Data Transmission Unit (DTU), a fast serial link for the 1. 2 Gb/s data transmission of the ALICE inner detector front-end chip (ALPIDE). The PLL multiplies the 40 MHz input clock in order to obtain the 600 MHz and the 200 MHz clock for a fast serializer which works in Double Data Rate mode. The outputs of the serializer feed the pseudo-LVDS driver inputs which transmits the data from the pixel chip to the patch panel with a limited number of signal lines. The driver drives a 5. 3 m-6. 5 m long differential transmission line by steering a maximum of 5 mA of current at the target speed. To overcome bandwidth limitations coming from the long cables the pre-emphasis can be applied to the output.