Aprender a enseñar ciencias vinculando el museo como recurso didáctico para la enseñanza del sistema circulatorio humano

Esta investigación se centró en describir cómo aprende a enseñar ciencias una profesora de secundaria en formación continua, incorporando el Museo de Ciencias en la enseñanza del sistema circulatorio. A través de la clínica didáctica, se buscaron evidencias sobre las modificaciones en el estatus de sus ideas respecto a este recurso en el marco de su PCK, las cuales se interpretaron desde el cambio conceptual en relación con el papel regulador de la metacognición. Los resultados indican que la idea es inteligible y plausible en su discurso, destacando el potencial del museo para la enseñanza y el aprendizaje, pero la fructibilidad se alcanza cuando la profesora reflexiona sobre sus acciones, luego de ejecutar la unidad didáctica

Organización lateral de monocapas mixtas

III Encuentro sobre Nanociencia y Nanotecnología de Investigadores y Tecnólogos Andaluce

Gait recognition and fall detection with inertial sensors

In contrast to visual information that is recorded by cameras placed somewhere, inertial information can be obtained from mobile phones that are commonly used in daily life. We present in this talk a general deep learning approach for gait and soft biometrics (age and gender) recognition. Moreover, we also study the use of gait information to detect actions during walking, specifically, fall detection. We perform a thorough experimental evaluation of the proposed approach on different datasets: OU-ISIR Biometric Database, DFNAPAS, SisFall, UniMiB-SHAR and ASLH. The experimental results show that inertial information can be used for gait recognition and fall detection with state-of-the-art results.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

A weakly-supervised approach for discovering common objects in airport video surveillance footage

Object detection in video is a relevant task in computer vision. Standard and current detectors are typically trained in a strongly supervised way, what requires a huge amount of labelled data. In contrast, in this paper we focus on object discovery in video sequences by using sets of unlabelled data. Thus, we present an approach based on the use of two region proposal algorithms (a pretrained Region Proposal Network and an Optical Flow Proposal) to produce regions of interest that will be grouped using a clustering algorithm. Therefore, our system does not require the collaboration of a human except for assigning human understandable labels to the discovered clusters. We evaluate our approach in a set of videos recorded at the outdoor area of an airport where the aeroplanes park to load passengers and luggage (apron area). Our experimental results suggest that the use of an unsupervised approach is valid for automatic object discovery in video sequences, obtaining a CorLoc of 86.8 and a mAP of 0.374 compared to a CorLoc of 70.4 and mAP of 0.683 achieved by a supervised Faster R-CNN trained and tested on the same dataset.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Preferential Solvation of the Antioxidant Agent Daidzein in some Aqueous Co-Solvent Mixtures according to IKBI and QLQC Methods

The preferential solvation parameters by ethanol (EtOH) or propylene glycol (PG) of daidzein were derived from their solution thermodynamic properties by means of the inverse Kirkwood-Buff integrals and the quasi-lattice-quasi-chemical (QLQC) methods. According to IKBI method, the preferential solvation parameter by the co-solvent, δx1,3, is negative in water-rich mixtures but positive in co-solvent-rich mixtures in both kinds of systems. This could demonstrate the relevant role of hydrophobic hydration around the aromatic rings in the drug solvation in water-rich mixtures. Furthermore, the more solvation by co-solvent in co-solvent-rich mixtures could be due mainly to polarity effects and acidic behavior of the hydroxyl groups of the compound in front to the more basic solvents present in the mixtures, i.e. EtOH or PG. Otherwise, according to QLQC method, this drug is preferentially solvated by the co-solvents in all the mixtures in both kind of systems

Maximum-confidence discrimination among symmetric qudit states

We study the maximum-confidence (MC) measurement strategy for discriminating among nonorthogonal symmetric qudit states. Restricting to linearly dependent and equally likely pure states, we find the optimal positive operator valued measure (POVM) that maximizes our confidence in identifying each state in the set and minimizes the probability of obtaining inconclusive results. The physical realization of this POVM is completely determined and it is shown that after an inconclusive outcome, the input states may be mapped into a new set of equiprobable symmetric states, restricted, however, to a subspace of the original qudit Hilbert space. By applying the MC measurement again onto this new set, we can still gain some information about the input states, although with less confidence than before. This leads us to introduce the concept of "sequential maximum-confidence" (SMC) measurements, where the optimized MC strategy is iterated in as many stages as allowed by the input set, until no further information can be extracted from an inconclusive result. Within each stage of this measurement our confidence in identifying the input states is the highest possible, although it decreases from one stage to the next. In addition, the more stages we accomplish within the maximum allowed, the higher will be the probability of correct identification. We will discuss an explicit example of the optimal SMC measurement applied in the discrimination among four symmetric qutrit states and propose an optical network to implement it.Comment: 14 pages, 4 figures. Published versio

Selective C-13-Labels on Repeating Glycan Oligomers to Reveal Protein Binding Epitopes through NMR: Polylactosamine Binding to Galectins

A combined chemo-enzymatic synthesis/NMR-based methodology is presented to identify, in unambiguous manner, the distinctive binding epitope within repeating sugar oligomers when binding to protein receptors. The concept is based on the incorporation of C-13-labels at specific monosaccharide units, selected within a repeating glycan oligomeric structure. No new chemical tags are added, and thus the chemical entity remains the same, while the presence of the C-13-labeled monosaccharide breaks the NMR chemical shift degeneracy that occurs in the non-labeled compound and allows the unique identification of the different components of the oligomer. The approach is demonstrated by a proof-of-concept study dealing with the interaction of a polylactosamine hexasaccharide with five different galectins that display distinct preferences for these entities.This research was funded by European Research Council for financial support (ERC-2017-AdG, project number 788143-RECGLYCANMR). We also thank Agencia Estatal de Investigacion (Spain) for project RTI2018-094751-B-C21 and the Severo Ochoa Excellence Accreditation (SEV-2016-0644

Selective C-13-Labels on Repeating Glycan Oligomers to Reveal Protein Binding Epitopes through NMR: Polylactosamine Binding to Galectins

A combined chemo-enzymatic synthesis/NMR-based methodology is presented to identify, in unambiguous manner, the distinctive binding epitope within repeating sugar oligomers when binding to protein receptors. The concept is based on the incorporation of C-13-labels at specific monosaccharide units, selected within a repeating glycan oligomeric structure. No new chemical tags are added, and thus the chemical entity remains the same, while the presence of the C-13-labeled monosaccharide breaks the NMR chemical shift degeneracy that occurs in the non-labeled compound and allows the unique identification of the different components of the oligomer. The approach is demonstrated by a proof-of-concept study dealing with the interaction of a polylactosamine hexasaccharide with five different galectins that display distinct preferences for these entities.This research was funded by European Research Council for financial support (ERC-2017-AdG, project number 788143-RECGLYCANMR). We also thank Agencia Estatal de Investigacion (Spain) for project RTI2018-094751-B-C21 and the Severo Ochoa Excellence Accreditation (SEV-2016-0644

Pilularia minuta Durieu, a new fern for Córboba province (Andalusia, Spain).

Pilularia minuta Durieu, un nuevo helecho para la provincia de Córdoba (Andalucía, España) Key words. Pilularia, Marsileaceae, Córdoba, Western Andalusia, Iberian Peninsula. Palabras clave. Pilularia, Marsileaceae, Córdoba, Andalucía Occidental, Península Ibérica