Dynamics of receptor-mediated nanoparticle internalization into endothelial cells.

International audienceNanoparticles offer a promising medical tool for targeted drug delivery, for example to treat inflamed endothelial cells during the development of atherosclerosis. To inform the design of such therapeutic strategies, we develop a computational model of nanoparticle internalization into endothelial cells, where internalization is driven by receptor-ligand binding and limited by the deformation of the cell membrane and cytoplasm. We specifically consider the case of nanoparticles targeted against ICAM-1 receptors, of relevance for treating atherosclerosis. The model computes the kinetics of the internalization process, the dynamics of binding, and the distribution of stresses exerted between the nanoparticle and the cell membrane. The model predicts the existence of an optimal nanoparticle size for fastest internalization, consistent with experimental observations, as well as the role of bond characteristics, local cell mechanical properties, and external forces in the nanoparticle internalization process

Modeling and forecasting gender-based violence through machine learning techniques

Gender-Based Violence (GBV) is a serious problem that societies and governments must address using all applicable resources. This requires adequate planning in order to optimize both resources and budget, which demands a thorough understanding of the magnitude of the problem, as well as analysis of its past impact in order to infer future incidence. On the other hand, for years, the rise of Machine Learning techniques and Big Data has led different countries to collect information on both GBV and other general social variables that in one way or another can affect violence levels. In this work, in order to forecast GBV, firstly, a database of features related to more than a decade’s worth of GBV is compiled and prepared from official sources available due to Spain’s open access. Then, secondly, a methodology is proposed that involves testing different methods of features selection so that, with each of the subsets generated, four techniques of predictive algorithms are applied and compared. The tests conducted indicate that it is possible to predict the number of GBV complaints presented to a court at a predictive horizon of six months with an accuracy (Root Median Squared Error) of 0.1686 complaints to the courts per 10,000 inhabitants—throughout the whole Spanish territory—with a Multi-Objective Evolutionary Search Strategy for the selection of variables, and with Random Forest as the predictive algorithm. The proposed methodology has also been successfully applied to three specific Spanish territories of different populations (large, medium, and small), pointing to the presented method’s possible use elsewhere in the world

Active galactic nuclei synapses: X-ray versus optical classifications using artificial neural networks

(Abridged) Many classes of active galactic nuclei (AGN) have been defined entirely throughout optical wavelengths while the X-ray spectra have been very useful to investigate their inner regions. However, optical and X-ray results show many discrepancies that have not been fully understood yet. The aim of this paper is to study the "synapses" between the X-ray and optical classifications. For the first time, the new EFLUXER task allowed us to analyse broad band X-ray spectra of emission line nuclei (ELN) without any prior spectral fitting using artificial neural networks (ANNs). Our sample comprises 162 XMM-Newton/pn spectra of 90 local ELN in the Palomar sample. It includes starbursts (SB), transition objects (T2), LINERs (L1.8 and L2), and Seyferts (S1, S1.8, and S2). The ANNs are 90% efficient at classifying the trained classes S1, S1.8, and SB. The S1 and S1.8 classes show a wide range of S1- and S1.8-like components. We suggest that this is related to a large degree of obscuration at X-rays. The S1, S1.8, S2, L1.8, L2/T2/SB-AGN (SB with indications of AGN), and SB classes have similar average X-ray spectra within each class, but these average spectra can be distinguished from class to class. The S2 (L1.8) class is linked to the S1.8 (S1) class with larger SB-like component than the S1.8 (S1) class. The L2, T2, and SB-AGN classes conform a class in the X-rays similar to the S2 class albeit with larger fractions of SB-like component. This SB-like component is the contribution of the star-formation in the host galaxy, which is large when the AGN is weak. An AGN-like component seems to be present in the vast majority of the ELN, attending to the non-negligible fraction of S1-like or S1.8-like component. This trained ANN could be used to infer optical properties from X-ray spectra in surveys like eRosita.Comment: 15 pages, 7 figures, accepted for publication in A&A. Appendix B only in the full version of the paper here: https://dl.dropboxusercontent.com/u/3484086/AGNSynapsis_OGM_online.pd

Predictive models as screening tools for DNA recovery from baked and burned porcine bones

Burnt bones and skeletal remnants continue to challenge the proficiency of forensic investigations in human individualization and identification. The various natural disasters and human inflicted crimes involving fire leave the forensic investigators with very little to work on. Thus, demand for practical studies to obtain useful facts for improvisation of current techniques and to overcome the short comings is a prerequisite. In this study Design of Experiments (DOE) as an investigative and screening tool to relate the different variables (burning temperature, time, thickness of flesh, presence of accelerants) involved in the burning process and to detect the probability of obtaining successful DNA identification from burnt bones is proposed. We show that high temperature and large base pair PCR primer have a significant effect on DNA retrieval and amplification. The baking study provides reproducible DNA identification with maximum retrieval temperature of 320°C for the smallest (106bp) amplicon. The study involving accelerants demonstrates that those with high specific heat capacity decrease DNA recovery, hence suggesting probable damage to DNA. Through this study the positive effect of presence of flesh for DNA recovery was also verified with a maximum DNA recovery temperature of 500°C. Utilizing all these information through DOE, predictive models were also created with regression equations to calculate positive DNA amplification and to predict the different variables respective to the burning process. These models created using porcine bones could be related for real scenarios and with more data procurement it could be used effectively in forensic investigations

Plasmatic E-selectin levels were decreased in young women with metabolic syndrome after exercise training

Cellular adhesion molecules (CAMs) such as E-selectin are involved in the rolling, adhesion and extravasation of monocytes into the atherosclerotic plaque. Fortunately regular exercise may improve pro-inflammatory status in individuals with metabolic syndrome. Accordingly, this study was designed to determine the influence of exercise on soluble plasmatic E-selectin levels in women with metabolic syndrome. Sixty adult women with metabolic syndrome according to the criteria reported by the National Cholesterol Education Program Adult Treatment Panel III volunteered for this study. Fourty-five were randomly included in experimental group to perform a 12-weeks aerobic training program, 3 days/week, consisting of warm up (10-min), main part (20-35-min [increasing 5 minutes each 3 weeks]) at a work intensity of 60-75% of peak heart rate (increasing 5% each 3 weeks) and cool-down(10-min). Control group included 15 age, sex and BMI-matched women with metabolic syndrome that will not perform any program. Written informed consent was obtained. Further the protocol was approved by an institutional ethic committee. Plasmatic E-Selectin levels was measured by ELISA, using a commercially available kit (Parameter, R&D Systems) twice: 72-hours before starting the program (pre-test) and after its ending (post-test).Results: When compared to baseline soluble E-Selectin concentration was significantly decreased after the 6-weeks protocol (76.4±7.2 vs 57.1±6.4 ng/ml; p\u3c0.05). No changes were reported in controls. A 12-weeks aerobic training program decreased plasmatic E-Selectin concentration in women with metabolic syndrome

Optical Particle Detection in Liquid Suspensions with a Hybrid Integrated Microsystem

AbstractA compact, robust and portable system for optical particle detection in liquid suspensions, achieved through the hybrid integration of commercial components, such as VCSELs and microlenses, in a silicon micromachined structure is presented. We demonstrate the feasibility of fabricating a device providing up to 4 collimated laser beams, with the ability of detecting and distinguishing microparticles of several diameters, even in mixed suspensions. This optical microsystem represents an alternative design for microflow cytometers based on optical fibres, and is aligned with the current tendency set by the Point-of-care devices