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Alzheimer's disease is characterized by a progressive and irreversible cognitive deterioration. In a previous stage, the so-called Mild Cognitive Impairment or cognitive loss appears. Nevertheless, this previous stage does not seem sufficiently severe to interfere in independent abilities of daily life, so it is usually diagnosed inappropriately. Thus, its detection is a crucial challenge to be addressed by medical specialists. This paper presents a novel proposal for such early diagnosis based on automatic analysis of speech and disfluencies, and Deep Learning methodologies. The proposed tools could be useful for supporting Mild Cognitive Impairment diagnosis. The Deep Learning approach includes Convolutional Neural Networks and nonlinear multifeature modeling. Additionally, an automatic hybrid methodology is used in order to select the most relevant features by means of nonparametric Mann-Whitney U test and Support Vector Machine Attribute evaluation. This work has been supported by FEDER and MICINN, TEC2016-77,791-C4-2-R, and UPV/EHU-Basque Research Groups IT11156 and Basque Country EleKin Research Group

Community opposition is one of the key obstacles to the expansion of wind turbine developments. The scholarly literature has focused on public opinion on wind farms (WFs) and the level of community acceptance. The efforts of Original Equipment Manufacturers (OEMs), to gain community acceptance has been under-researched. To fill this gap, the present study analyzes the evolution of interventions by a leading OEM to secure the acceptance of local communities for wind energy projects and the outcomes of those efforts. The fieldwork consisted of a longitudinal case study carried out from 2014 to 2017 by a leading international OEM. In depth personal interviews were also conducted with six experts in the field. The main efforts of the OEM focused on the visual impact of the developments, health and safety issues, community involvement and social investment in the community. In selecting the location for developing a WF, economic criteria usually prevail over social criteria. Although the company makes social investments in the community, different groups point out that those local communities should be taken more seriously, as they can serve as facilitators in the development of projects. Implications for stakeholders such as developers and policy makers are discussed.

Purpose: A videofluoroscopic analysis is the gold standard approach to determine whether a dysphagia problem exists. This procedure is invasive as it involves radiation but also provides th...

This paper proposes a multilingual audio information management system based on semantic knowledge in complex environments. The complex environment is defined by the limited resources (financial, material, human, and audio resources); the poor quality of the audio signal taken from an internet radio channel; the multilingual context (Spanish, French, and Basque that is in under-resourced situation in some areas); and the regular appearance of cross-lingual elements between the three languages. In addition to this, the system is also constrained by the requirements of the local multilingual industrial sector. We present the first evolutionary system based on a scalable architecture that is able to fulfill these specifications with automatic adaptation based on automatic semantic speech recognition, folksonomies, automatic configuration selection, machine learning, neural computing methodologies, and collaborative networks. As a result, it can be said that the initial goals have been accomplished and the usability of the final application has been tested successfully, even with non-experienced users. This work is being funded by Grants: TEC201677791-C4 from Plan Nacional de I + D + i, Ministry of Economic Affairs and Competitiveness of Spain and from the DomusVi Foundation Kms para recorder, the Basque Government (ELKARTEK KK-2018/00114, GEJ IT1189-19, the Government of Gipuzkoa (DG18/14 DG17/16), UPV/EHU (GIU19/090), COST ACTION (CA18106, CA15225).

Environmental enrichment (EE) paradigms are designed to enhance laboratory animals surroundings to encourage natural behaviors. Some enrichment paradigms also include a social component, based on the social interactions typical of the genus and species. Novel automatic methodologies based on image are becoming useful tools to improve laboratory works. This paper present a first approach to the automatic image analysis of laboratory rats in EE: behaviour, drug effects and pathology. The new methodology is based on image and Machine Learning paradigms and will become a useful tool for Neuroscience issues.

Dementia, and specially Alzheimer’s disease (AD) and Mild Cognitive Impairment (MCI) are one of the most important diseases suffered by elderly population. Music therapy is one of the most widely used non-pharmacological treatment in the field of cognitive impairments, given that music influences their mood, behavior, the decrease of anxiety, as well as facilitating reminiscence, emotional expressions and movement. In this work we present HAIDA, a multi-platform support system for Musical Therapy oriented to cognitive impairment, which includes not only therapy tools but also non-invasive biometric analysis, speech, activity and hand activity. At this moment the system is on use and recording the first sets of data. Results obtained using HAIDA will be presented in a near future after the analysis.

Among neural disorders related to movement, essential tremor has the highest prevalence, in fact, it is twenty times more common than Parkinson&rsquo, s disease. The drawing of the Archimedes&rsquo, spiral is the gold standard test to distinguish between both pathologies. The aim of this paper is to select non-linear biomarkers based on the analysis of digital drawings. It belongs to a larger cross study for early diagnosis of essential tremor that also includes genetic information. The proposed automatic analysis system consists in a hybrid solution: Machine Learning paradigms and automatic selection of features based on statistical tests using medical criteria. Moreover, the selected biomarkers comprise not only commonly used linear features (static and dynamic), but also other non-linear ones: Shannon entropy and Fractal Dimension. The results are hopeful, and the developed tool can easily be adapted to users, and taking into account social and economic points of view, it could be very helpful in real complex environments.

Essential tremor (ET) is the most common movement disorder. In fact, its prevalence is about 20 times higher than that of Parkinson's disease. In addition, studies have shown that a high percentage of cases, between 50 and 70%, are estimated to be of genetic origin. The gold standard test for diagnosis, monitoring and to differentiate between both pathologies is based on the drawing of the Archimedes' spiral. Our major challenge is to develop the simplest system able to correctly classify Archimedes' spirals, therefore we will exclusively use the information of the x and y coordinates. This is the minimum information provided by any digitizing device. We explore the use of features from drawings related to the Discrete Cosine Transform as part of a wider cross-study for the diagnosis of essential tremor held at Biodonostia. We compare the performance of these features against other classic and already analyzed ones. We outperform previous results using a very simple system and a reduced set of features. Because the system is simple, it will be possible to implement it in a portable device (microcontroller), which will receive the x and y coordinates and will issue the classification result. This can be done in real time, and therefore without needing any extra job from the medical team. In future works these new drawing-biomarkers will be integrated with the ones obtained in the previous Biodonostia study. Undoubtedly, the use of this technology and user-friendly tools based on indirect measures could provide remarkable social and economic benefits. We thank the Ministry of Business and Knowledge of the Government of Catalonia that partially supported this study through the Industrial Doctorates Plan to IA-E. We also thank the grant of Domus Vi Foundation "Kms para recordar," the programs of Basque Government, ETORTEK and IT115-16, the Gipuzkoa Goverment, Red Guipuzcoana de Ciencia, Tecnologia e Innovacion, and the Ministry of Science and Innovation for the TEC2016-77791-C04-R grant, which partially supported the study. Finally we would like to thank reviewers for their detailed and helpful comments to the manuscript.

The so-called Mild Cognitive Impairment (MCI) or cognitive loss appears in a previous stage before Alzheimer's Disease (AD), but it does not seem sufficiently severe to interfere in independent abilities of daily life, so it usually does not receive an appropriate diagnosis. Its detection is a challenging issue to be addressed by medical specialists. This work presents a novel proposal based on automatic analysis of speech and disfluencies aimed at supporting MCI diagnosis. The approach includes deep learning by means of Convolutional Neural Networks (CNN) and non-linear multifeature modelling. Moreover, to select the most relevant features non-parametric Mann-Whitney U-testt and Support Vector Machine Attribute (SVM) evaluation are used., Comment: 5 pages, published in 2017 International Conference and Workshop on Bioinspired Intelligence (IWOBI), 2017, pp. 1-4, 10-12 July Funchal (Portugal)

Objective: Nowadays proper detection of cognitive impairment has become a challenge for the scientific community. Alzheimer's Disease (AD), the most common cause of dementia, has a high prevalence that is increasing at a fast pace towards epidemic level. In the not-so-distant future this fact could have a dramatic social and economic impact. In this scenario, an early and accurate diagnosis of AD could help to decrease its effects on patients, relatives and society. Over the last decades there have been useful advances not only in classic assessment techniques, but also in novel non-invasive screening methodologies. Methods: Among these methods, automatic analysis of speech -one of the first damaged skills in AD patients- is a natural and useful low cost tool for diagnosis. Results: In this paper a non-linear multi-task approach based on automatic speech analysis is presented. Three tasks with different language complexity levels are analyzed, and promising results that encourage a deeper assessment are obtained. Automatic classification was carried out by using classic Multilayer Perceptron (MLP) and Deep Learning by means of Convolutional Neural Networks (CNN) (biologically- inspired variants of MLPs) over the tasks with classic linear features, perceptual features, Castiglioni fractal dimension and Multiscale Permutation Entropy. Conclusion: Finally, the most relevant features are selected by means of the non-parametric Mann- Whitney U-test.

In this paper some of novel and low-cost methodologies are analysed for most common cognitive impairments: Alzheimer disease, Parkinson and Essential Tremor. These methodologies allow us to obtain information from the brain in an indirect form, without needing to do neither invasive nor expensive or long-time tests. We will present handwriting and speech as interesting biomarkers for the early detection and monitoring of cognitive impairments and detail possible fields of application, with real experiments carried-out at the Hospital Donostia (Spain) in several past and current projects.

This paper proposes an automatic and adaptive phonetic transcriber (PT) for Basque. In order to develop this transcriber, the sociocultural situation of the Basque Country has to be taken into account. Basque is an minority language that has many dialects, so it shows a wide range of language varieties spread across very small geographical zones. Thus, words can be pronounced in different ways depending of the origin of the speaker, or if she/he is non-native, which is very common due to the sociolinguistic characteristics of the Basque region. In this context, there are phonetic realizations out of transcription standards for Basque, so we present an automatic PT that is able to achieve transcriptions in standard Basque and adapt to real particular realizations. The phonetic transcription of spoken discourse is the foundation on which any system based on voice recognition is built, so this point is crucial not only to enrich the knowledge of a minority language, but also to develop other related speech recognition applications.

Computer vision algorithms that use color information require color constant images to operate correctly. Color constancy of the images is usually achieved in two steps: first the illuminant is detected and then image is transformed with the chromatic adaptation transform ( CAT). Existing CAT methods use a single transformation matrix for all the colors of the input image. The method proposed in this paper requires multiple corresponding color pairs between source and target illuminants given by patches of the Macbeth color checker. It uses Delaunay triangulation to divide the color gamut of the input image into small triangles. Each color of the input image is associated with the triangle containing the color point and transformed with a full linear model associated with the triangle. Full linear model is used because diagonal models are known to be inaccurate if channel color matching functions do not have narrow peaks. Objective evaluation showed that the proposed method outperforms existing CAT methods by more than 21%; that is, it performs statistically significantly better than other existing methods. This work was supported by the Ministry of Science of Republic of Slovenia, Program Computer Vision.

7549 Background: CChRT is recommended as the evidence-based approach for the management of patients (p) with locally advanced stage III NSCLC and a good performance status, although a clearly superior regimen has not been identified. The aim of our study was to evaluate the effectiveness and toxicities of CChRT with bi-weekly docetaxel (D) and cisplatin (C) and thoracic radiotherapy. Methods: 50 p with histologically confirmed inoperable locally advanced NSCLC, stage IIIAN2/IIIB (no pleural T4), PS 0-1 and adequate lung function (FEV1 > 1.1, V20 < 25%) were included: one cycle of D 75 mg/m2 on day 1 and C 40 mg/m2 days 1-2 followed at 21 days by CChRT with bi-weekly D 40 mg/m2 and C 40 mg/m2 for four courses, during conformal thoracic radiotherapy (66 Gys, 180 cGy/day). The primary objective was overall survival (OS); secondary objectives were progression free survival (PFS), response rate (RR) and toxicity. Median follow-up: 14,5 months. Results: The p characteristics were: mean age 59,1 years (34-75); male/female 44/6; squamous/adeno/large cell carcinoma: 52%/34%/14%; stage IIIAN2 14 p (28%) and stage IIIB 36 p (72%). All p were evaluable for response and toxicity. RR: 4 CR, 36 PR (RR 80%; 95% CI:69-91), 4 SD (8%) and 6 PD (12%). The median PFS was 13 months (95% CI:8-18) and median OS was 19 months (95% CI:14-24). The PFS and OS at 1/2 years were 52%/30% and 79%/40% respectively. A total of 50 cycles of D-C induction chemotherapy were given; main toxicities (NCI-CTC 3.0) per p Grade (g) 1-2/3-4 (%) were as follows: neutropenia 2/16; anemia 12/0; nausea/vomiting 28/2; diarrhea 22/4; there were two episodes of febrile neutropenia. Main toxicities per p in CChRT (D-C doses: 192, 3.8 per p; mean doses RT: 64,6 Gys) were g1-2/3 (%): neutropenia 28/6; anemia 60/0; esophagitis 52/4 and pneumonitis 34/0; there were four episodes of hospitalization: febrile neutropenia, 2 p and g3 esophagitis, 2 p. Conclusions: CChRT with bi-weekly docetaxel and cisplatin and thoracic radiotherapy is a feasible treatment option for inoperable locally advanced stage III NSCLC, showing good clinical efficacy and tolerability with acceptable long-term survival.

20531 Background: Rectal cancer incidence increase with age. Neoadyuvant radiochemotherapy is the standard treatment for locally advanced rectal cancer. Due to the fact that management of cancer in elderly is difficult because of age related conditions, we investigated the efficacy and safety profile of this treatment for stages II-III rectal cancer among elderly. Methods: Between January 1999 and July 2007,134 patients older than 70 years out of a total of 300 consecutive cases with locally advanced rectal adenocarcinoma (stage II: 25%; stage III: 75%) were treated with neoadyuvant conformational radiochemotherapy. Median age was 75 years (range 70–87) and they were in acceptable clinical condition (ECOG 0–1). The treatment consisted in radiation therapy up to a median total dose of 50 Gy (range 4,500–5,940 cGy) delivered using a conformational technique concomitant with 5-FU by continuous i.v. infusion (54 %), 5-FU bolus according to Mayo Clinic (20%), UFT oral (25%) and capecitabine (1%). Toxicity was ...

This paper is an extended version of one paper published in the 4th IEEE International Work Conference on Bioinspired Intelligence, Donostia, Spain, 9–12 June 2015 Biomedical systems produce biosignals that arise from interaction mechanisms. In a general form, those mechanisms occur across multiple scales, both spatial and temporal, and contain linear and non-linear information. In this framework, entropy measures are good candidates in order provide useful evidence about disorder in the system, lack of information in time-series and/or irregularity of the signals. The most common movement disorder is essential tremor (ET), which occurs 20 times more than Parkinson's disease. Interestingly, about 50%-70% of the cases of ET have a genetic origin. One of the most used standard tests for clinical diagnosis of ET is Archimedes' spiral drawing. This work focuses on the selection of non-linear biomarkers from such drawings and handwriting, and it is part of a wider cross study on the diagnosis of essential tremor, where our piece of research presents the selection of entropy features for early ET diagnosis. Classic entropy features are compared with features based on permutation entropy. Automatic analysis system settled on several Machine Learning paradigms is performed, while automatic features selection is implemented by means of ANOVA (analysis of variance) test. The obtained results for early detection are promising and appear applicable to real environments. This work has been partially supported by the University of the Basque Country under project ref. UPV/EHU-58/14, SAIOTEK program and others from the Basque Government, the Spanish Ministerio de Ciencia e Innovacion TEC2012-38630-C04-03, the University of Vic-Central University of Catalonia under the research grant R0904, INNPACTO program from the Spanish Government, and UPV/EHU Summer Courses Foundation.

In this paper, which is part of a wide-ranging cross study for the diagnosis of Essential Tremor, we present a selection of nonlinear biomarkers from drawings and handwriting. These include Fractal Dimension and Entropy. The presence of integrated features of other diseases such as stress will be also analyzed. Then these new biomarkers will be integrated in future works with those obtained in the Biodonostia study. It should be highlighted that the use of this technology could provide undoubted benefits towards the development of more sustainable, low cost, high quality, non-invasive technologies. These systems are easily adaptable to the user and environment, and from a social and economic point of view can be very useful in real complex environments.