dataesr/scientific-paragraphs-categorization huggingface dataset:
Residue-residue contact prediction with the Hopfield-Potts model The most important feature of DCA is its ability to predict pairs of residues, which are distantly positioned in the sequence, but which form native contacts in the protein's tertiary structure, cf. the right panel of Fig. 4. Here, our contact prediction is based on the sampling-corrected Frobenius norm of the (q -1)dimensional statistical coupling matrices e ij , cf. Methods, which in  [49]  has been shown to outperform the direct-information measure used in  [17]. This measure assigns a single scalar value for the strength of the direct coupling between two residue positions. The contact map predicted from the 50 strongest direct couplings for the PF00014 family is compared to the native contact map in Fig. 5. In accordance with  [19] , a residue pairs is considered to be a true positive prediction if its minimal atom distance is below 8 Å in the before mentioned exemplary protein crystal structures.
It is, however, beyond the scope of this paper to shed light onto the details of the biological interpretation of the principal components of Γ. In which cases does the dimensional reduction achieved by selecting only a relatively small number of patterns provide an actual advantage over the standard mean-field DCA approach with p = L(q -1) patterns? We have seen that for relatively large MSAs, where DCA gives very accurate results, the approach presented here achieves a strong dimensional reduction almost without loss in predictive power, but it did not improve the contact map prediction, cf. Figs. 5  and 6. However, when we reduce the number of sequences in the MSA, DCA undergoes a strong reduction in accuracy of prediction, see the full lines in Fig. 8  where DCA is applied to sub-alignments of the PF00014 domain family.
Repeating the same experiment with a finite number of patterns (p = 16 in Fig. 8 ), the MSA-size dependence is strongly reduced. For very small alignments of only 10-30 sequences, the Hopfield-Potts model is still able to extract contacts with an astonishing TP rate of 70-80%, whereas DCA produces almost random results (TP rate ca. 30%). The success of the Hopfield-Potts approach for small MSA is not specific to the PF00014 domain, and holds for other protein families, see Fig. S15  in Text S1. Hopfield-Potts patterns are therefore an efficient means to reduce overfitting effects found in DCA, and to improve the signal-to-noise ratio.
In this context it would also be interesting to find a threshold for these contact scores, which separates a signal-rich from a noise-dominated region. And last but not least, it would be interesting to integrate prior knowledge about proteins, like e. g. amino-acid properties or predicted secondary structure, into the purely statistical inference approach presented here. The MATLAB program necessary for the analysis of the data, the computation of the patterns, and the contact prediction is available as part of the Supporting Information. Users of the program are kindly requested to cite the present work.
Supporting Information Files Text S1. Supporting Information for From principal component to direct coupling analysis of coevolution in proteins: Low-eigenvalue modes are needed for structure prediction. Code S1. Matlab code for the Hopfield-Potts inference. The most attractive patterns (corresponding to the three largest eigenvalues); the top pattern is extended, with inverse participation ratio 0. 003, while the second and third patterns,with inverse participation ratios 0. 033, 0. 045 respectively, have essentially non-zero components over the gap symbols only which accumulate on the edges of the sequence. Note the x-coordinates i + a/(q -1); its integer part is the site index, i, and the fractional part multiplied by q -1 is the residue value, a.
After positioning the hand-held device extremity on the skin area under study, the practitioner presses a wireless remote control located in the handle. The pressure triggers the acquisition which is performed in less than two seconds. If the patient is properly installed during the acquisition process, we consider the scene completely static. The system yields ten monoband images which compose one multispectral image. The system is operated using a personal computer running on Windows XP operating system (Microsoft, Seattle, WA, USA). An in-house software, developed using Visual C++ (Microsoft, Seattle, Washington), controls the filter wheel rotation, the image acquisitions, the reflectance cube reconstruction, and the database.
The acquisition of flat-field multispectral image is obtained by averaging multispectral image of a spectralon gray-scale standard target with 99% reflectance across the visible and is denoted  and stands for the flat-field image. The gain correction image is composed of specific coefficient for each pixel of each monoband image. The coefficients are obtained using the following formula: [𝑆] = [𝐹] -[𝑂] DR , ( 2 ) where  is the flat-field monoband image and DR is the dynamic range of the camera. In the case of ASCLEPIOS, DR is equal to 4. 096, corresponding to 12 bits images. The final correction of the raw image is applied using (2): [𝑈] = [𝑅] -[𝑂] [𝑆]. (3) The cube reconstruction aims to extract only the skin reflectance spectra in each pixel of the data acquired. In order to reconstruct spectra linked to the physical properties of the skin element, the proposed method takes into account a model of light propagation in which the spectral response of all the elements involved in the acquisition process is employed (Figure  4 ) where () is the spectral radiance of the illuminant, Φ  () is the spectral transmittance of the th filter, () is the spectral reflectance of the surface, () is the spectral transmittance of the optical system, () is the spectral sensitivity of the camera and   is the acquired image by the th filter.
A study involving 150 healthy participants covering five of the six Skin PhotoTypes (SPT) (according to the Fitzpatrick scale) was performed using ASCLEPIOS  [27]. Three acquisitions are performed at three different body locations, two facultative skin colour areas and a constitutive skin colour one. Constitutive skin colour is the natural, genetically determined colour of the skin whereas facultative skin colour is skin area affected by the environment (sun, hormones, etc. ) resulting in modification of its original appearances over time. The reconstruction validation is performed by comparing data acquired using a commercial spectrophotometer with reflectance cube reconstructed by ASCLEPIOS. The ASCLEPIOS data used for comparison is of the same size and at similar location to that the data acquired by the spectrophotometer.
[47]  and a hybrid version of it was developed by Choi  [48]. Both prove the usefulness of the GA, but these techniques also retrieve unrealistic values mainly due to the nonconstraint of the search space. In our version, the boundary condition of each element (minimum and maximum parameter values) is restricted to their physical limit. This restriction limits the search space to realistic values. The values are not body-location-dependent to avoid user interaction. This nonrestriction aims to be applied on any data taken wherever on the body, however; this might potentially influence the results. The evolution of the population is roughly based on an iterative three-step process: selection, crossover, and mutation (see Figure  5 ). The selection process is determined by a fitness function.
Melasma and Vitiligo Data Analysis. We applied our method to quantify and compare data acquired from two populations with skin diseases that have characteristic effect on the melanin composition. Melasma (also called chloasma) is a hyperpigmentation skin disease that is characterized by an increase level of melanin concentration released by melanocytes. The symptoms are characterized by dark, irregular, and welldemarcated skin lesions. Vitiligo (also called leukoderma) is a common genetic autoimmune skin disease caused by the disappearance of melanocytes in the epidermis resulting in hypopigmentation. The symptom of vitiligo is the depigmentation of patches of skin with irregular shape. All multispectral images acquired contain both healthy and hypo/hyperpigmented skin area. Every reflectance cube was analyzed using our method, generating five skin parameter maps.
The data were acquired on four healthy volunteers. The data are processed by the algorithm and the average parameter values are obtained from identical areas manually selected on each map. Table  8  presents the average retrieved skin parameters for the four healthy volunteers. The values of melanin concentration, epidermis thickness, and dermis thickness do not show significant variation between the three acquisitions. The occlusion does not have effect on these parameters, which is the result expected. The percentage of difference between the baseline and acquisition 2 and acquisition 3 is calculated by the following formula: 𝑃 diff = (Val Acq -Val Baseline ) Val Baseline , (17) where Val Acq is the retrieved skin parameter value for acquisition (either number 2 or 3) and Val Baseline is the retrieved baseline skin parameter value.
Conclusion We presented a multispectral camera which has the capacity to reconstruct reflectance cubes only linked to the skin characteristics. The reflectance cube allows quantitative measure of cutaneous tissue based on a Kubelka-Munk model combined with evolutionary algorithm. Using this approach, quantification maps of five skin parameters (melanin concentration, epidermis/dermis thickness, haemoglobin concentration, and the oxygenated haemoglobin) are obtained for each multispectral image acquired. The developed algorithm was tested on a set of healthy skin data acquired using our system. The results retrieved by the algorithm are in good agreement with the data from the literature. Finally, the usefulness of the developed technique was tested during a clinical study based on healthy skin, vitiligo, and melasma skin lesions.
Acknowledgments The authors would like to thank  Professor M. H. A. Fadzil  from  UTP  for his collaboration on this research. The authors also thank the financial support provided by  Conseil Régional de Bourgogne, France ,  Fond Européen de Développement Régional (FEDER) ,  France , the  ministères des Affaires Étrangèeres (MAE) et de l'Enseignement Supérieur et de la Recherche (MESR), France , the  Office of Higher Education Commission, Thailand , the  Thailand Research Fund, Thailand , the National Science and  Technology Development Agency, Thailand, and laboratoire Bioderma, France.
The baseline for both the Super Beam and Beta Beam facilities is the MEMPHYS detector  [8] , a 500 kT fiducial mass water Cherenkov detector. This would be located in the Laboratoire Souterrain de Modane in the Fréjus tunnel in France, at a distance of 130 km from CERN. The current plan is to build the detector from two modules, 65 m in diameter and 103 m in height (see Figure  21 ), in two separate caverns. Based on a large experience from the SuperKamiokande experiment  [32] , light will be detected using 12000 8" or 10" PMTs in each module. To reduce costs, it is planned to group readout electronics  [33]. To test this and other aspects of the detector, a prototype called MEMPHYNO  [34]  has been built at Université ParisVII and is being tested (see Figure  22 ). As for MIND, a simulation has been developed to determine the detector performance, also using GENIE for event generation and GEANT 4 for modeling the detector response.
2005) ; Vanuatu and Papua New Guinea (gray diamonds,  Cutler et al. 2004 ); Barbados (light blue triangles); Tahiti and Mururoa (green circles,  Bard et al. 1998 ). These data have been corrected from the reservoir age adopted by the IntCal09  (Reimer et al. 2009 ). (B) Comparison of the new results from Tahiti with the marine sediment records. The foraminifera record from Cariaco Basin varved sediments (orange crosses,  Hughen et al. 2000 Hughen et al. , 2004a,b),b) , the foraminifera record from Cariaco Basin non-varved sediments (brown crosses, Cariaco Basin-Hulu timescale,  Hughen et al. 2006) , and the foraminifera record from Iberian Margin non-varved sediments (black crosses, Iberian Margin-Hulu timescale,  Bard et al. 2004a,b). The error bars for 14 C ages and U-Th ages are also given at the 2 level.
(C) IntCal04 (purple) and IntCal09 (black) calibration curves  (Reimer et al. 2004 (Reimer et al. ,2009)). (iii) from 15. 0 to 16 cal kyr BP, where our results shed new light on the large discrepancy between the coral records and the Cariaco Basin sediment record; and (iv) from 29 to 37 cal kyr BP, where the Tahiti record provides only a few new data sets but are especially important in terms of calibration for this time window. i) The time interval from 10 to 14. 25 cal kyr BP was already well documented by 115 samples from previous coral records (see  Reimer et al. 2009 and references therein). We add 20 new data for this period in very good agreement with all these previous records (Figure  7A ). The new Tahiti results are also in good agreement with the Cariaco Basin sediment record within analytical uncertainty, except for 2 samples which are respectively slightly older (23 A 12.
1 140-144) and younger (20 A 16. 1 55-66) than the general trend depicted by the Cariaco record by about 1 century (Figure  7B ). The good agreement between the Tahiti record and the previous coral records validates the reservoir correction used for this time period. ii) The period from 14. 25 to 15. 0 cal kyr BP is characterized by a pronounced age plateau in the IntCal04 and IntCal09 calibration curves. As discussed in detail later, the 31 new Tahiti data sets we add to this interval enable us to refine the timing of this age plateau. We consider the broader interval, defined previously, in order to precisely compare the Tahiti results with: the 14 previous coral data available for this period (Figure  8A ); with the Cariaco record (Figure  8B ); with the Bahamas and Hulu cave speleothems  (Hoffmann et al.
By contrast, the agreement is excellent with the Bahamas speleothem data (Figure  8C ; 12. 572 ± 0. 132, n = 16). Nevertheless, in spite of these second-order differences, the main feature is that the average values of all these different records agree with each other within the 1 uncertainty for the period from 14. 25 to 15. 0 cal kyr BP. As recorded by the Tahiti samples, the end of the plateau is clearly registered at 14. 25 cal kyr BP (Figures  8  and 9 ). By contrast, the beginning of the plateau is much less defined due to the scarcity of coral samples: it starts between 14. 6 and 14. 7 cal kyr BP. Indeed, 5 samples dated between 14. 5 and 14. 7 cal kyr BP define a clear dip in the middle of the plateau, with 14 C ages 150 yr younger than others. As discussed later, these samples that could mark the beginning of the plateau, corresponding exactly to the occurrence of MWP-1A in the Tahiti sea-level record.
6 cal kyr BP (Figure  7B ). The Hulu Cave data set  (Southon et al. 2012)  fits in an intermediate position between the Cariaco and coral data set (~200 yr younger than the latter all along that period; Figure  8C ). (iv) For the 29 to 37 cal kyr BP time period, the data provided by the Tahiti record are in good agreement with the corals from Barbados, except for the sample around 31 kyr BP (Figure  10 ). This last sample passed all our screening criteria but is in clear disagreement with the closest Tahiti data and with the Barbados data, so it can probably be considered an outlier. There are only a few new Tahiti data sets, but as the other coral data sets contain numerous gaps, each new measurement is crucial, as it contributes to improving the precision and the accuracy of the calibration curve for this time window.
2006) , which exhibits a sharp 150‰ decrease between 16. 5 and 15 kyr BP  (Figures 9, 11). The new Hulu Cave speleothem record also shows a significant discrepancy (up to 60‰  500 yr) with the Cariaco record, but it is also somewhat higher than several individual data from Tahiti corals and Iberian sediments  (Figures 9, 11). The residual discrepancy of ~300 yr between the Hulu and Tahiti data would disappear by assuming a larger reservoir age correction for Tahiti and/or a lower dead carbon fraction for the Hulu speleothems during the 17. 5 to 15 kyr BP time window. At a low-latitude site like Tahiti, the only ways to increase the surface reservoir age are through upwelling of old water at the surface or by changing wind speed that controls the CO 2 exchange with the atmosphere  (Bard 1988).
The tropical Pacific surface reservoir age exhibits a general increase by ~300 yr for glacial age conditions. However, freshwater injection experiments do not seem to produce large reservoir age changes, although  Butzin et al. (2005)  mention that regionalscale effects may not be fully captured by their model. As stressed by these authors, fully coupled ocean-atmosphere models are still needed to simulate in a realistic way the changes of the ocean surface 14 C ages. Such modeling work, beyond the scope of our paper, should help determine if the various effects mentioned above compensate each other or if a residual variation existed in Tahiti. Variations of the reservoir age may have also affected the Cariaco record over the period corresponding to the H1 event, which could explain its discrepancy with other independent data (Tahiti corals, Iberian Margin sediments, and Hulu Cave speleothems).
2001). Although they admit that "the exceptional stability of the H82 DCF remains a puzzle," Southon et al. have assumed that it remained constant for older periods between 14 and 26 kyr BP. Besides the residual discrepancy with Tahiti data during the H1 event (see above), the Hulu data are also systematically younger (~300 yr) in 14 C than other available data between 20 and 26 kyr BP, corresponding to the last glacial maximum (corals from various sites and Lake Suigetsu varved sediments; cf. Figures  6  and 7  of  Southon et al. 2012). One could invoke large changes of surface ocean reservoir ages at all sites, at the same time as missing varves in the lacustrine record  (Kitagawa and van der Plicht 2000). A time-variable DCF would be a more parsimonious explanation, but it would imply a strongly reduced value during drier periods such as H1 and the LGM.
CONCLUSION This work, based on the comparison of 14 C and U-Th ages in Tahiti coral samples, provides a new data set to the 14 C calibration for the last deglaciation period, including a higher resolution for the 14-16 cal kyr BP time interval. These new results are in good agreement with previous coral data sets and they extend the previous Tahiti records beyond 13. 9 cal kyr BP, which was the oldest U-Th age obtained on cores drilled onshore in the modern Tahiti barrier reef. The numerous data obtained for the 14-15 cal kyr BP time period enable us to refine the 14 C age plateau. This age plateau is associated with a period characterized by an abrupt decrease of atmospheric  14 C that might reflect changes in the global carbon cycle due to variations in the rates of exchange between the different carbon pools.
It is difficult to directly evaluate the exact sequence of perturbations of the global carbon cycle, as well as their relationships with the abrupt events that punctuated the deglaciation, i. e. the Bølling warming, the MWP-1A event, and the increase of the atmospheric CO 2. The changes in strength or configuration of the deep-ocean circulation may have been the main causes of the observed variation of the  14 C during this time interval. For the end of H1 period, the Tahiti record agrees with other corals and the Iberian Margin data sets but disagrees with the Cariaco Basin record. The observed discrepancies between the coral data set and the Cariaco Basin record could be related to changes in the western subtropical Atlantic reservoir ages during the mystery interval period, as was the case during the Younger Dryas event.
ACKNOWLEDGMENTS We thank the  IODP and ECORD (European Consortium for Ocean Research Drilling)  for drilling offshore from  Tahiti , and the  Bremen Core Repository  members for organizing the onshore sampling party. The CEREGE group thanks  W Barthelemy  for maintaining the mass spectrometers. Paleoclimate work at CEREGE is supported by the  Comer Science and Education Foundation , the  European Science Foundation (EuroMARC) , the  European Community  (Project  Past4Future ), the  College de France , the  Institut de Recherche pour le Développement (IRD) , and the  EQUIPEX ASTER-CEREGE. Radiocarbon dating has been supported through the  French CNRS-INSU Radiocarbon program  and performed at the  LMC14  laboratory with the ARTEMIS facility in Saclay. The  Oxford University  team is supported by  UK Natural Environment Research Council  grant  NE/ D001250/1  and the  Comer Science and Education Foundation.
According to Jun  [37]  closed-loop PLM focuses on tracking and managing the information on the whole product lifecycle, with information feedback. Pratt  [38]  and Newman  [39]  define the exchanges CAD-Simulation allowed through STEP and STEP-NC standard. Biahmou  [40]  develops a translator called CAMAT (CATIA-MATLAB Translator) insuring the interoperability between Design and Simulation. According to Tan  [41]  Product Service System which places the simulation for design is a new organizational interoperability.
Design / Manufacturing Link The capitalization of knowledge from the machine tool enables an optimized design for manufacturing. Moreover, if a problem is corrected directly on the CNC interoperability between design and manufacturing can propagate upstream changes. Harik  [42]  develop a tool for the Usiquick project which enriches the CAD model in order to define the machining program and tool path. Valilai  [35]  with the INFELT platform allows the interoperability between CAD and CAM systems. Delplace  [43]  developed an automated cell to promote the casting process full integration. Newman  [39]  defines the CAD-CAM exchanges through STEP-NC standard. Nassehi  [36]  has developed interfaces to connect CAD and CAM systems on the "universal platform". Martin  [44]  developed a tool based on the Visual Basic language to create a "mediator" for all the APIs to ensure the link between design and foundry manufacturing.
Three gels from three independent experiments were scanned with a Typhoon Variable Mode Imager 9. 400 (Amersham Biosciences AB). The digitalized 2DE images were standardized and compared with the DeCyder Differential Analysis Software program, version 6. 0 (Amersham Biosciences AB) according to the manufacturer's recommendation using the DeCyder biological variation analysis module. An average of 1780 spots were visualized in the 2DE from three independent experiments. Of these, 821 spots were further analyzed to assess the differential expression of proteins between parental cells (231) and bone metastatic variant BO2 cells, and simultaneously to match all 56 protein spot maps from three experiments. Student's t test was used for statistical analysis of the protein spot data and a p value of Ͻ0.
For matrix assisted laser desorption ionization-time of flight (MALDI-TOF) MS, 2. 5 l of tryptic digest was deposited onto Mass Spec Turbo 192 type 1 peptide chips prespotted with CHCA (Qiagen, Venlo, Netherlands). Each spot was then washed with finishing solution (Qiagen). Peptide mass fingerprinting spectra were recorded on a Voyager short tandem repeat MALDI-TOF (Applied Biosystems) using a 337 nm nitrogen laser operated in the reflector mode, with an accelerating voltage of 20 kV. Samples were analyzed in the m/z 800 -3000 range and were calibrated externally with the Sequazyme Peptide Mass Standards kit (Applied Biosystems). Peptides from trypsin autolysis were used for the internal calibration. Peak lists were generated with the MoverZ software (Knexus edition, Genomic Solutions, Ann Arbor, MI, USA).