dataesr/scientific-paragraphs-categorization huggingface dataset:
CD-1 mice, hind limb weight bearing Animals weight distribution on the four limbs was assessed on day 13 using the dynamic weight bearing test (BioSeb, France)  [16]. This disability test consists in a continuous measurement of all pressure points applied by a freely moving animal, allowing a quantitative evaluation of the weight imbalance caused by pain. Each mouse was placed in an 11 ? 11 ? 22 cm cage with a 44 ? 44 sensor cells grid on the floor. The pressure applied on sensor cells by animal s paws was recorded at a 10 Hz frequency over a 5 min period. Pressure and surface detection thresholds were determined automatically for each animal by the Dynamic weight bearing 1. 3. 2 h software (Bioseb, France). After the manual attribution of each pressure point to the corresponding paw, the mean weight applied on each paw was calculated for surface ratio.
Morphometric analyses Composite image of the entire nerve section of each sample was obtained using an optical microscope (Nikon optiphot-2) equipped with a digital camera (Nikon DS-Fi1). Morphometric analyses were performed with digital images using Image-Pro Plus software (Media Cybernetics Inc. , USA), which computes the axonal and myelin pixel sizes after individualisation of each myelinated fibre via the grey level of toluidine blue-stained myelin sheath. These two parameters were used to calculate the number of myelinated axons and the g-ratio (ratio of the inner axonal diameter to the total outer).
Statistical analyses Statistical tests were two-tailed and conducted at a 5% significance level. Sample sizes used for behavioural analyses were defined on the basis of previous experience on assay variability  [9, 13]. Data distribution and withingroup variation were preliminary assessed in order to guide our methodological choices. Statistical analyses were performed with Prism (http://www. graphpad. com/ scientific-software/prism) and R (http://cran. r-project. org). We applied an Analysis of Variance (ANOVA) with Dunnett s test for comparison of more than one experimental group to a reference and a Welch s t-test for comparison of two experimental groups. These tests are described to be robust enough to overcome changes in distribution and variation  [18, 19]. We performed a combined analysis of all cultures and replicates of in vitro experimental data to assess myelination in DRG cocultures after 10 11 days of treatment, and to study gene expression in RT4 schwannoma cells.
In each in vitro experimental system (DRG and RT4), data were normalised to the non-treated vehicle control. In DRG co-cultures, synergy was assessed by drug combination analysis with isobologram and calculation of Combination Index (CI) that compares dosages for a given combination to those expected to obtain the same combination effect under a simple additive assumption, and offers a quantitative definition for additive effect (CI = 1), synergism (CI < 1), or antagonism (CI > 1)  [20] [21] [22] [23]. To study the superiority of PXT3003 to its single drugs in RT4 cells, an ANOVA model was fitted and the difference between the effect of PXT3003 and the effect of the most efficient drug (i. e. the drug having the largest effect when compared to control) was tested using a post-hoc test of contrast.
We confirmed by RTqPCR analysis the ~1. 6-fold up-regulation of normalised Pmp22 mRNA relative to Mpz mRNA in the TG group when compared to WT littermates (Figure  4A ). In order to examine the ability of PXT3003 to lower Pmp22 gene levels in vivo, we determined the expression of this gene in sciatic nerves at the end of 9 weeks of PXT3003 treatment. Importantly, we observed its significant but limited (-12%) down-regulation (Figure  4A ) in line with the in vitro data obtained with Schwannoma cells (Figure  3C ). Efficacy of 9 weeks of oral daily PXT3003 treatment given to CMT1A TG rats was assessed by (i) measuring muscle strength in the bar test, (ii) performance on an inclined plane and (iii) thermal sensitivity in the hot plate test. We observed a substantial heterogeneity of TG rats with respect to muscular strength, as previously described  [13, 42].
Although designed from a series of mechanistic hypotheses, our preclinical data remains for the moment mostly observational. More efforts must be performed for a better understanding of exact mechanism of action and the combinational synergy observed for PXT3003. It will be particularly relevant to verify experimentally the involvement of GABA B , opioid or muscarinic receptors in the therapeutic effect reported here. Investigating the mechanism of synergy could be even more challenging. Launching these sophisticated and laborious mechanistic experiments was out of scope of this study also because of limited correlations generally observed for efficacy when translating data in animal models to the human clinical studies  [50]. Finally, as the profile of PXT3003 seemed rather safe, taking into account the nature and low doses of the 3 compounds, we decided to concentrate our efforts on its testing in an exploratory phase 2.
Acknowledgements We would like to thank  Prof Eric R. Kandel  for his critical reviewing and editorial suggestions on the manuscript. We thank  Dr. Oxana Guerassimenko  for her input in the preliminary experiments. We thank  Dr. No? lle Callizot  and  Dr R? my Steinschneider  for the scientific advice on in vitro studies. We would also like to thank  Dr. Fabrice Trovero ,  Dr. Jean-Charles Bizot ,  Dr. Fabienne Mass ? ,  Sabrina David  and  Nicolas Cogrel  for the scientific and technical support on the behavioural tests performed in the CMT1A rat model. We thank  Dr. St? phanie Wagner  and  Dr. Emile Andriambeloson  for their scientific advice on electrophysiology and on the nerve crush model. We are also grateful to  Ms Linda Belo  for reviewing the English of the manuscript.
Geomorphological indices The dynamics of wetland losses and passive submergence are temporally and spatially consistent. a Denotes studies that also refer to spatial patterns to establish a relation between shoreline changes and sea-level rise. coastal zone and in the related watersheds  (Webb and Kench, 2010; Yates et al. , 2013; Romine et al. , 2013; Shearman et al. , 2013). This protocol is not necessarily always applicable in practice and the existing data may not clearly indicate why the shoreline has changed or remained stable. This first qualitative approach is therefore adapted when the causes of changes are relatively obvious to observe, and data are available. As the amount of coastal data to be analyzed is growing, the qualitative approach becomes difficult to apply. To extract information from larger coastal datasets, several methods can be applied, from looking for correlations between variables  (Garcin et al.
, 2013)  to more advanced statistical approaches:  Gutierrez et al. (2011)  proposed to model the relations between shoreline mobility and its causative factors using Bayesian networks. They used coastal observations to calculate the parameters of the Bayesian network (i. e. the conditional probabilities linking variables such as the probability of erosion given the geomorphology, the wave and tidal climate, sea-level rise, etc. ), and used the developed network to perform retrospective predictions of observed shoreline changes. They finally interpreted performance scores of the Bayesian network as indicators of the relative importance of each variable in the model  (Hapke and Plant, 2010). This approach highlights the existing statistical links between variables (including variables representing the spatial variability of sea-level rise and shoreline evolution) and provides a way to extract synthetic statistical information from very large coastal datasets, such as those collected by  Quelennec et al.
Romine et al. (2013)  provide the same type of argument to conclude that the most likely cause of two Hawaiian islands being affected differently by coastal erosion is that they are affected by different relative sea-level rise rates. In both cases, the role of sea-level rise is demonstrated by the absence of evidence that other processes caused shoreline changes. The East Coast of the USA is also an example where the regions affected by the highest sea-level rise rates are also those affected by the fastest shoreline retreat rates  (Zhang et al. , 2004; Gutierrez et al. , 2011). Similarly, the Eurosion coastal dataset (http://www. eurosion. org) indicates that the uplifting coasts of Scandinavia are also mostly showing shoreline advance  (Yates and Le Cozannet, 2012). Many of these examples have suggested that relatively moderate changes of sea level (on the order of a few millimeters per year) can cause significant shoreline changes.
Data based approach Analysis of spatial patterns of sea-level rise and of shoreline changes Qualitative analysis of shoreline change observations and likely causative factors -Any homogeneous set of similar coastal systems (e. g. Shearman et al. ,2013)  -Or information aggregated from multiple word's surveys  (Bird, 1985)  5 Statistical analysis over large datasets -Homogeneous set of similar coastal systems (e. g. Zhang et al. , 2004)  -Or database of heterogeneous coastal systems, used within a statistical approach that allows to consider the relative importance of other factors causing shoreline changes (e. g. Gutierrez et al. , 2011)  4 Analysis of temporal dynamics of sea-level rise and of shoreline changes -Any single coastal site 5.
(2011) ): they require shoreline change data collected locally by means of different techniques and then aggregated at several scales, which raises concerns about the consistency. To summarize, both methods discussed in parts 2 and 3 require shoreline data that are sufficiently accurate, but also temporally and spatially consistent. Most studies are currently limited by the short timescale of shoreline change observations, which should ideally cover multiple decades to adequately identify the impacts of sea-level rise on shoreline position. Improvements can be expected here with the increase in the availability of existing datasets, the creation of sustained coastal observatories, and the accumulation of very high resolution satellite imagery.
Fig. 4  provides the reconstruction of  Meyssignac et al. (2012)  and the associated uncertainties (estimated as the root mean square error of three different reconstructions). This figure highlights that the errors are spatially variable and are higher in locations affected by more rapid sea level changes from 1960 to 2010. A cautious use of these datasets can thus provide part of the necessary information for data-based studies (part 2). However, it is necessary to take into account the uncertainties, particularly where there is high regional variability in sea-level rise, where several reconstructions disagree, or where few or less reliable tide gauge data constrain the reconstruction. In the most general case, studies attempting to explore the relation between sea-level rise and shoreline mobility face the difficult task of separating the different components of relative sea-level rise at the coast.
However, identifying similar coastal sites is difficult in general due to the number of aspects to consider (e. g. , tidal range, exposure to waves, coastal geomorphology, lithology). We note that the analysis based on Bayesian networks attempts to undertake this categorization task. Repeated careful applications of this approach with large coastal databases may help to identify appropriate typologies of coastal systems with respect to their vulnerability to sea level rise. Future model-based approaches can continue to be improved upon with a more careful analysis of the model assumptions and selected study sites. For example, the Bruun rule assumes that cross-shore transport processes dominate shoreline changes on beaches, and this model should not be applied in other coastal environments or at sites with complex sediment transport fluxes.
Conclusion: results and recommended methods Collectively, the reviewed studies do not provide observational evidence that contemporary sea-level rise has been a major driver of shoreline retreat over the last few decades. Instead, the results are different from site to site, suggesting the significance of the local coastal setting. The results of these studies suggest that no general conclusions can be drawn relating sea-level and shoreline changes on a global scale without taking into account the characteristics of each site. Hence, three   (Zhang et al. , 2004; Gutierrez et al. , 2011). Relative sea level change at the coast along the shoreline  major conclusions can be drawn from this review: (1) even with the recent acceleration in global sea-level rise, local variability in sea-level change rates, coastal geomorphology, and forcing factors limits making broad conclusions about the impacts of sea-level changes on shoreline position in different coastal environments; (2) future studies can benefit from expanding coastal sea-level and shoreline change datasets, as well as additional observations of other important coastal processes; and finally, (3) it is necessary to clearly define the assumptions and limitations associated with each approach before selecting the appropriate method to apply at a selected site.
They also have common limitations due to a lack of coastal shoreline and sea-level rise data. However, they provide the basic methodological framework for detecting the impact of sea-level rise on shoreline changes. Future studies using both approaches can benefit from extending existing coastal sea-level rise and shoreline change datasets by increasing both the length of the time series, as well as the temporal and spatial resolution. In addition, observation-based approaches can be improved upon by including a wider range of data concerning coastal processes, such as wind and wave measurements, tidal fluctuations, and sediment sources and sinks (e. g. longshore fluxes, offshore losses, inputs from rivers). While many sites may currently be in the first phase of adaptation to sea-level changes, in which other coastal processes dominate present day shoreline changes, continued observations may allow the detection of the transition to the second phase, in which the impacts of sea-level rise become dominant.
Acknowledgments This study was funded by the  French Research National Agency (ANR)  ( CECILE  project: ' Coastal Environmental Changes: Impact of sea-level rise ', under grant number  ANR-09-CEP-001-01 ). We thank  Anny Cazenave ,  Carlos Oliveros ,  Franck Lavigne , as well as  Robin Davidson-Arnott ,  Marcel Stive  and two other anonymous reviewers for their additional recommendations that significantly improved the manuscript.
Spanish Basque coast Inter and supra-tidal Chust et al. Passive flooding Shoreline and habitat changes over Tide gauge SLR accounts for smaller changes habitats (2009) 50 years obtained from a database in habitats than human impacts of aerial images, field surveys and LiDAR data. French Mediterranean Pocket beaches Brunel and Passive flooding 4 field and aerial surveys over Tide gauge SLR accounts for 60% of shoreline Provence Sabatier 103 years retreat (2007, 2009) French Mediterranean Open wave Brunel and Passive flooding 4 shoreline positions over 103 years Tide gauge SLR accounts for 10% of shoreline Camargue dominated beaches Sabatier (2009) from maps, field surveys and aerial retreat imagery Suffolk coast, UK Unconsolidated Brooks and Several cliff 4 shoreline positions over 125 years Tide gauge SLR a major marine driver soft cliffs Spencer (2012) erosion models from maps, field surveys and aerial imagery a Denotes studies that attempt to quantify sediment mass or volume changes.
Acknowledgments The publication was supported by the  European Community 's  Seventh Framework Programme  ( FP7/ 2007-2013 ) under the grant agreements  FP7-266018 (AnimalChange) ,  FP7-244983  (MultiSward), PITN-GA-2011-289377 (LegumePlus) and  FP7-245216 (Legume Futures ). This invited review is an edited and updated version of the plenary paper prepared for the 17th Symposium of the  European Grassland Federation  ' The Role of Grasslands in a Green Future: Threats and Perspectives in Less Favoured Areas '. We are grateful to J. N€ osberger,  M. Suter  and  O. Huguenin-Elie  for helpful discussions, two anonymous reviewers and the editor for his very constructive contributions, which we appreciate.
These reports have laid a nearly unanimous emphasis on the importance of reaching a critical minimum islet mass to achieve transplant success. Consistent results across the studies have been observed for some variables, such as age and BMI; however, controversial findings also have been reported  (12, 13). The lack of a consistent or sufficiently detailed reporting of variables and outcome measures limits the efficacy of meta-analysis to resolve these debates. In addition, recent investigations have begun to highlight the importance of nonyield-based indicators in predicting the ultimate goal of any islet transplantation procedure, normalization of blood sugar control and a relief from diabetic symptoms in the transplant recipients  (4, 10, 11). The current investigation represents a comprehensive review of islet characteristics, analyzing data from 1017 clinical islet isolations conducted in North American and Juvenile Diabetes Research Foundation (JDRF) European and Australian centers through the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) -and JDRF-sponsored Collaborative Islet Transplant Registry (CITR) database.
Donor steroid treatment pushed yields higher, which confirms similar results of an earlier study in which steroid use was associated with twice as many successful clinical isolations as nonsteroid treated donors  (7). On the other hand, steroid use, in this investigation, was also correlated to reduced insulin and elevated endotoxin in the final islet product. The CITR database provided information on donor insulin treatment, which has not been previously considered as a clinical islet isolation variable. Nevertheless, we found that insulin use was correlated with higher beta cell/kg recipient. This finding is supported by preclinical research suggesting that insulin treatment can promote or maintain islet beta cell mass in rodents  (19, 20). The impact of donor vasopressor treatment is unclear and has been correlated to reduced yield (2), elevated yield  (7)  or no effect (9,13).
Acknowledgments The  CITR  is funded by the  NIDDK, National Institutes of Health  and by a supplemental grant from the  JDRF International. Additional data were made available through cooperative agreements with the  U. S. United Network for Organ Sharing (UNOS), Alexandria, Virginia , the  Administrative and Bioinformatics Coordinating Center of the City of Hope, Duarte, California  ( 1999-2009 ) and the  NIDDK-sponsored CIT  (www. citisletstudy. org),coordinated by the  University of Iowa Clinical Trials  and Data Management Center (2008-present).
Acknowledgements All PPHSN partners should be acknowledged for the hard work that has contributed to this overview. In particular we wish to acknowledge: the  Pacific Island countries  and territories and their health professionals for country reports and updates on PacNet and to the Pacific Syndromic Surveillance reports; the  WHO Suva  for managing the Pacific Syndromic Surveillance reports; all international and regional partner organisations that assist PPHSN member countries in surveillance and outbreak investigations and capacity building thereof; national laboratories for primary diagnosis and sampling and laboratories serving as regional references or assisting in first diagnostic confirmation of mosquito-borne disease - Fiji Centre for Communicable Disease Control (FCDCC) Mataika House, Guam Public Health Laboratory (GPHL) ,  Institut Pasteur New Caledonia (IPNC), Institut Louis Malardé (ILM) Tahiti French Polynesia, CDC Atlanta US, CDC Puerto Rico, Hawaii State Laboratory (HSL) ,  Fort Collins Laboratory USA, Queensland University of Technology (QUT) Australia, Queensland Health & Forensic Scientific Services (QHFSS) Australia ; and the surveillance units reporting imported cases of mosquito-borne disease - La Direction des Affaires Sanitaires et Sociales de la Nouvelle-Calédonie New Caledonia, Queensland Health Australia notifiable conditions data and Institute of Environmental Science and Research (ESR) New Zealand dengue report.
Breast cancer ranks first in the causes of cancer deaths among women around the world. Early detection and diagnosis is the key for breast cancer control, and it can increase the success of treatment, save lives and reduce cost. Mammography is one of the most frequently used diagnosis tools to detect and classify abnormalities of the breast. In this aim, Digital Database for Screening Mammography (DDSM) is an invaluable resource for digital mammography research, the purpose of this resource is to provide a large set of mammograms in a digital format. DDSM has been widely used by researchers to evaluate different computer-aided algorithms such as neural networks or SVM. The Artificial Immune Systems (AIS) are adaptive systems inspired by the biological immune system, they are able of learning, memorize and perform pattern recognition.
In this context, various articles have been published in the aim of classifying breast cancer databases using artificial intelligence techniques as Neural Networks  (Marcano-Cedeno et al. ,2011) (Timmy  Manning and Paul Walsh, 2013)   (Zadehand et al. , 2012)   (Aboul Ella Hassanien et al. , 2014) , SVM  (Mahnaz and Broumandnia, 2013)   (Aboul Ella Hassanien et al. , 2013) ; Genetic Algorithms  (Jain, R. and J. Mazumdar, 2003)   (Mazurowski, M. A. , et al. , 2007)  Expert Systems  (Wan Noor et al. , 2013)  and Artificial Immune Systems  (Sharma and Sharma, 2011)   (Daoudi et al. , 2013)   (Daoudi et al. , 2013). The natural immune system is composed of diverse sets of cells and molecules that work together with other systems (like neural and endocrine) for maintaining homeostatic state. It primary function is to protect the body from foreign substances called antigens by recognizing and eliminating them.
, 2014). The negative selection is a mechanism that protects the body from self-reactive lymphocytes. It deals with the immune system's ability to detect unknown antigens while not reacting self-cells  (Daoudi et al. , 2013). The Clonal Selection algorithms are derives from the clonal selection principle, which is based on initiation of candidate solution, affinity maturation, selection, cloning, mutation and reselection. In the last decade, AIS have proven their effectiveness in different areas and especially in the medical field, they were applied to the detection of lung disease, diagnosis of diabetes, tuberculosis, heart disease and the detection of several types of cancers. etc. This paper proposes enhancements of a popular clonal selection algorithm named CLONALG, for classification of breast cancer cells into benign/malignant classes taking into account three new descriptors of the Digital Database for Screening Mammography (DDSM).