New synthesis method of HA/P(D,L)LA composites: study of fibronectin adsorption and their effects in osteoblastic behavior for bone tissue engineering

A novel synthetic method to synthesize hydroxyapatite/poly (D,L) lactic acid biocomposite is presented in this study by mixing only the precursors hydroxyapatite and (D,L) LA monomer without adding neither solvent nor catalyst. Three compositions were successfully synthesized with the weight ratios of 1/1, 1/3, and 3/5 (hydroxyapatite/(D,L) lactic acid), and the grafting efficiency of poly (D,L) lactic acid on hydroxyapatite surface reaches up to 84?%. Scanning electron microscopy and Fourier transform infrared spectroscopy showed that the hydroxyapatite particles were successfully incorporated into the poly (D,L) lactic acid polymer and X ray diffraction analysis showed that hydroxyapatite preserved its crystallinity after poly (D,L) lactic acid grafting. Differential scanning calorimetry shows that Tg of hydroxyapatite/poly (D,L) lactic acid composite is less than Tg of pure poly (D,L) lactic acid, which facilitates the shaping of the composite obtained. The addition of poly (D,L) lactic acid improves the adsorption properties of hydroxyapatite for fibronectin extracellular matrix protein. Furthermore, the presence of poly (D,L) lactic acid on hydroxyapatite surface coated with fibronectin enhanced pre-osteoblast STRO-1 adhesion and cell spreading. These results show the promising potential of hydroxyapatite/poly (D,L) lactic acid composite as a bone substitute material for orthopedic applications and bone tissue engineering.     

A Perspective on Emerging and Future Sintering Technologies of Ceramic Materials

Novel sintering methods have emerged in the recent past years, which have raised great interest in the scientific community. Relying on electric field effects, high heating rates, the use of mechanical pressure, or hydrothermal conditions, they offer fundamental advantages compared to conventional sintering routes like minimizing the energy consumption and enhancing the process efficiency. This perspective aims at explaining these effects in a general way and presenting the status quo of using them for the processing of high-performing ceramic materials. In detail, this work focuses on flash sintering, ultrafast high-temperature sintering, spark plasma sintering, cold sintering, and photonic sintering methods based on different light sources. The specificities, potentials, and limitations of each method are compared, especially in the light of a possible industrialization.     

An improved algorithm for disaggregating microwave-derived soil moisture based on red, near-infrared and thermal-infrared data

Accurate high-resolution soil moisture data are needed for a range of agricultural and hydrologic activities. To improve the spatial resolution of ∼ 40 km resolution passive microwave-derived soil moisture, a methodology based on 1 km resolution MODIS (MODerate resolution Imaging Spectroradiometer) red, near-infrared and thermal-infrared data has been implemented at 4 km resolution. The three components of that method are (i) fractional vegetation cover, (ii) soil evaporative efficiency (defined as the ratio of actual to potential evaporation) and (iii) a downscaling relationship. In this paper, 36 different disaggregation algorithms are built from 3 fractional vegetation cover formulations, 3 soil evaporative efficiency models, and 4 downscaling relationships. All algorithms differ with regard to the representation of the nonlinear relationship between microwave-derived soil moisture and optical-derived soil evaporative efficiency. Airborne L-band data collected over an Australian agricultural area are used to both generate ∼ 40 km resolution microwave pixels and verify disaggregation results at 4 km resolution. Among the 36 disaggregation algorithms, one is identified as being more robust (insensitive to soil, vegetation and atmospheric variables) than the others with a mean slope between MODIS-disaggregated and L-band derived soil moisture of 0.94. The robustness of that algorithm is notably assessed by comparing the disaggregation results obtained using composited (averaged) Terra and Aqua MODIS data, and using data from Terra and Aqua separately. The error on disaggregated soil moisture is systematically reduced by compositing daily Terra and Aqua data with an error of 0.012 vol./vol.     

A survey on fraud and service misuse in voice over IP (VoIP) networks

The migration from circuit-switched networks to packet-switched networks necessitates the investigation of related issues such as service delivery, QoS, security, and service fraud and misuse. The latter can be seen as a combination of accounting and security aspects. In traditional telecommunication networks, fraud accounts for annual losses at an average of 3%–5% of the operators’ revenue and still increasing at a rate of more than 10% yearly. It is also expected that in VoIP networks, the situation will be worse due to the lack of strong built-in security mechanisms, and the use of open standards. This paper discusses the fraud problem in VoIP networks and evaluates the related available solutions.     

Deliberate word access: an intuition,a roadmap and some preliminary empirical results

No doubt, words play a major role in language production, hence finding them is of vital importance, be it for writing or for speaking (spontaneous discourse production, simultaneous translation). Words are stored in a dictionary, and the general belief holds, the more entries the better. Yet, to be truly useful the resource should contain not only many entries and a lot of information concerning each one of them, but also adequate navigational means to reveal the stored information. Information access depends crucially on the organization of the data (words) and the access keys (meaning/form), two factors largely overlooked. We will present here some ideas of how an existing electronic dictionary could be enhanced to support a speaker/writer to find the word s/he is looking for. To this end we suggest to add to an existing electronic dictionary an index based on the notion of association, i.e. words co-occurring in a well balanced corpus, the latter being supposed to represent the average citizen’s knowledge of the world. Before describing our approach, we will briefly take a critical look at the work being done by colleagues working on automatic, spontaneous or deliberate language production,—that is, computer-generated language, simulation of the mental lexicon, or WordNet (WN),—to see how adequate they are with regard to our goal.     

Boosted multi-task learning

In this paper we propose a novel algorithm for multi-task learning with boosted decision trees. We learn several different learning tasks with a joint model, explicitly addressing their commonalities through shared parameters and their differences with task-specific ones. This enables implicit data sharing and regularization. Our algorithm is derived using the relationship between ? ₁-regularization and boosting. We evaluate our learning method on web-search ranking data sets from several countries. Here, multi-task learning is particularly helpful as data sets from different countries vary largely in size because of the cost of editorial judgments. Further, the proposed method obtains state-of-the-art results on a publicly available multi-task dataset. Our experiments validate that learning various tasks jointly can lead to significant improvements in performance with surprising reliability.     

Neural mass activity, bifurcations, and epilepsy

In this letter, we propose a general framework for studying neural mass models defined by ordinary differential equations. By studying the bifurcations of the solutions to these equations and their sensitivity to noise, we establish an important relation, similar to a dictionary, between their behaviors and normal and pathological, especially epileptic, cortical patterns of activity. We then apply this framework to the analysis of two models that feature most phenomena of interest, the Jansen and Rit model, and the slightly more complex model recently proposed by Wendling and Chauvel. This model-based approach allows us to test various neurophysiological hypotheses on the origin of pathological cortical behaviors and investigate the effect of medication. We also study the effects of the stochastic nature of the inputs, which gives us clues about the origins of such important phenomena as interictal spikes, interictal bursts, and fast onset activity that are of particular relevance in epilepsy.     

Digital Elevation Model generation for historical landscape analysis based on LiDAR data,a case study in Flanders (Belgium)

This paper discusses the generation of a high precision DEM (Digital Elevation Model) based on high density airborne LiDAR (Light Detection and Ranging) data for an interdisciplinary landscape archaeological study concerning the settlement history and environment in Sandy Flanders, a region to the north of Ghent (Belgium). The objective was to create a detailed topographical surface free of artificial features and topographical artefacts, in the form of a DEM, visualizing the natural and current topography through the implementation of true ground points only. The semi-automatical removal of these features and artefacts was based on topographical vector data, visual interpretations and slope analysis. Ultimately two DEM’s were constructed (1) a TIN (Triangulated Irregular Network) model, whereby the inherent large file format restricts the usability to large scale and (2) a grid model which can be used for small-, medium- and large-scale applications. Both datasets were used as an image that is interpreted using ancillary data from historical sources. Its usefulness is illustrated in a case of field pattern and microfield topography. Starting from this DEM, the approach of this landscape historical study is mainly retrogressive, i.e. starting from the landscape structures and elements that are still present in the contemporary landscape and moving into the past.     

Characterization of renal tumours based on Raman spectra classification

In this study, we propose to evaluate the potential of Raman spectroscopy (RS) to assess renal tumours at surgery. Different classes of Raman renal spectra acquired during a clinical protocol are discriminated using support vector machines classifiers. The influence on the classification scores of various preprocessing steps generally involved in RS are also investigated and evaluated in the particular context of renal tumour characterization. Encouraging results show the interest of RS to evaluate kidney cancer and suggest the potential of this technique as a surgical assistance during partial nephrectomy.