AUTOMATIC IDENTIFICATION OF DYSPHONIAS USING MACHINE LEARNING ALGORITHMS

Dysphonia is a prevalent symptom of some respiratory diseases that affects voice quality, even for prolonged periods. For its diagnosis, speech-language pathologists make use of different acoustic parameters to perform objective evaluations on patients and determine the type of dysphonia that affects them, such as hyperfunctional and hypofunctional dysphonia, which is important because each type requires a different treatment. In the field of artificial intelligence this problem has been addressed through the use of acoustic parameters that are used as input data to train machine learning and deep learning models. However, its purpose is usually to identify whether a patient is ill or not, making binary classifications between healthy voices and voices with dysphonia, but not between dysphonias. In this paper, harmonic-to-noise ratio, cepstral peak prominence-smoothed, zero crossing rate and the means of the Mel frequency cepstral coefficients (2-19) are used to make multiclass classification of voices with euphony, hyperfunction and hypofunction by means of six machine learning algorithms, which are: Random Forest, K nearest neighbors, Logistic regression, Decision trees, Support vector machines and Naive Bayes. In order to evaluate which of them presents a better performance to identify the three voice classes, bootstrap.632 was used. It is concluded that the best confidence interval ranges from 87% to 92%, in terms of accuracy for the K Nearest Neighbors model. Results can be implemented in the development of a complementary application for the clinical diagnosis or monitoring of a patient under the supervision of a specialist

MOS Meets NEMS: The Born of Hybrid Devices

Nowadays, the semiconductor industry is reaching an impasse due to the scaling-down process according to Moore’s Law, initiated back in 1960s, for the Metal-Oxide-Technology in use. To overcome such issue, the semiconductor industry started to foresee novel materials that allow the development of nanodevices with a broad variety of characteristics such as high switching speed, low power consumption, robust, among others; that can overcome the inherent issues for Silicon. A few “exotic materials” appear such as Graphene, MoS2, BN-h, among others. However, the time for the novel technology to be mature is a few decades in the future. To allow the “exotic materials” to mature, the semiconductor industry requires of novel nano-structures that can overcome a few of the issues that Silicon-based technology is facing today. A key alternative is based on hybrid structures. Hybrid structures encompass two dissimilar technologies nano-electromechanical systems with the well known Metal-Oxide-Technology. The hybrid nano-structure provides a broad variety of options to be used in such as transistors, memories and sensors. These hybrid devices can give enough time for the technology based on “exotic materials” to be reliable as Silicon based is

Cost-effectiveness of multicomponent interventions in type 2 diabetes mellitus in a cluster randomised controlled trial: the INDICA study

Objective: To analyse the cost-effectiveness of multicomponent interventions designed to improve outcomes in type 2 diabetes mellitus (T2DM) in primary care in the Canary Islands, Spain, within the INDICA randomised clinical trial, from the public health system perspective. Design: An economic evaluation was conducted for the within-trial period (2 years) comparing the four arms of the INDICA study. Setting: Primary care in the Canary Islands, Spain. Participants: 2334 patients with T2DM without complications were included. Interventions: Interventions for patients (PTI), for primary care professionals (PFI), for both (combined intervention arm for patients and professionals, CBI) and usual care (UC) as a control group. Outcomes: The main outcome was the incremental cost per quality-adjusted life-years (QALY). Only the intervention and the healthcare costs were included. Analysis: Multilevel models were used to estimate results, and to measure the size and significance of incremental changes. Missed values were treated by means of multiple imputations procedure. Results: There were no differences between arms in terms of costs (p=0.093), while some differences were observed in terms of QALYs after 2 years of follow-up (p=0.028). PFI and CBI arms were dominated by the other two arms, PTI and UC. The differences between the PTI and the UC arms were very small in terms of QALYs, but significant in terms of healthcare costs (p=0.045). The total cost of the PTI arm (€2571, 95% CI €2317 to €2826) was lower than the cost in the UC arm (€2750, 95% CI €2506 to €2995), but this difference did not reach statistical significance. Base case estimates of the incremental cost per QALY indicate that the PTI strategy was the cost-effective option. Conclusions: The INDICA intervention designed for patients with T2DM and families is likely to be cost-effective from the public healthcare perspective. A cost-effectiveness model should explore this in the long term.This study received financial support from the Spanish Ministry of Economy, Industry and Competitiveness (Instituto de Salud Carlos III), grants: ADE10/00032 and PI16/00769, jointly funded by the European Regional Development Fund (FEDER) ‘A way to make Europe’. The sponsor did not play any role in study design, collection, analysis and interpretation of data, drawing up of the report or the decision to submit the article for publication.S

Metabolic Impairments Caused by Pesticides in Mammals and Their Interactions with Other Pollutants

The biological systems are exposed to a complex environment in which the contaminants can interact in a synergistic/antagonistic fashion and for this reason, the study of “chemical cocktails” is of great interest to fully understand the final biological effect. To evaluate the final biological response of a pollutant, it is essential to have an adequate analytical methodology that allows the correct monitoring of environmental systems in order to establish their quality, and, when appropriate, the application of corrective measures. Undoubtedly, massive methods “the omics” are among the most efficient current tools. To this end, transcriptomics, proteomics, metabolomics and chemical speciation can provide very useful information, mainly when they are combined. However, the combination of proteomics with metabolomics has some drawbacks as the temporal space is different (i.e. metabolomics gives information about what happens right now, but it can be related with numerous post-translational modifications happened previously). In this sense, it seems that the combination of genomics with metabolomics is easier. Thus, when metabolomics data are interpreted in combination with genomic, transcriptomic and proteomic results, in the so-called systems biology approach, a holistic knowledge of the organism/process under investigation can be achieved

Patient-reported outcome measures for knowledge transfer and behaviour modification interventions in type 2 diabetes-the INDICA study: a multiarm cluster randomised controlled trial

Objective: This study assesses the effectiveness of different interventions of knowledge transfer and behaviour modification to improve type 2 diabetes mellitus patients' (T2DM) reported outcomes measures (PROMs) in the long-term. Design: open, community-based pragmatic, multicentre, controlled trial with random allocation by clusters to usual care (UC) or to one of the three interventions. Participants: A total of 2334 patients with uncomplicated T2DM and 211 healthcare professionals were included of 32 primary care centres. Setting: Primary Care Centers in Canary Islands (Spain). Intervention: The intervention for patients (PTI) included an educational group programme, logs and a web-based platform for monitoring and automated short message service (SMS). The intervention for professionals (PFI) included an educational programme, a decision support tool embedded into the electronic clinical record and periodic feedback about patients' results. A third group received both PTI and PFI (combined intervention, CBI). Outcome measure: Cognitive-attitudinal, behavioural, affective and health-related quality of life (HQoL) variables. Results: Compared with UC at 24 months, the PTI group significantly improved knowledge (p=0.005), self-empowerment (p=0.002), adherence to dietary recommendations (p<0.001) and distress (p=0.01). The PFI group improved at 24 months in distress (p=0.03) and at 12 months there were improvements in depression (p=0.003), anxiety (p=0.05), HQoL (p=0.005) and self-empowerment (p<0.001). The CBI group improved at 24 months in self-empowerment (p=0.008) and adherence to dietary recommendations (p=0.004) and at 12 months in knowledge (p=0.008), depression (p=0.006), anxiety (p=0.003), distress (p=0.01), HQoL (p<0.001) and neuropathic symptoms (p=0.02). Statistically significant improvements were also observed at 24 months in the proportion of patients who quit smoking for PTI and CBI (41.5% in PTI and 42.3% in CBI vs 21.2% in the UC group). Conclusions: Assessed interventions to improve PROMs in T2DM attain effectiveness for knowledge, self-empowerment, distress, diet adherence and tobacco cessation. PTI produced the most lasting benefits. Trial registration number: ClinicalTrials.gov NCT01657227 (6 August 2012) https://clinicaltrials.gov/ct2/show/NCT01657227.This work was supported by the Spanish Ministry of Economy, Industry and Competitiveness (Instituto de Salud Carlos III), grant number: ADE10/00032 and PI16/00769 co-funded by the European Regional Development Fund (ERDF) “A way to make Europe”.S

Improvement on social representation of climate change through a knowledge-based MOOC in spanish

Climate Change is the most important threat to our society and all species on Earth. Large alterations in the climate are affecting every aspect of our society and in order to limit this impact we must decarbonize the economy before 2050. Although science presents solid evidence on the magnitude of the problem and outlines precisely the consequences, people do not act accordingly and do not consider this issue a priority for their survival. The reason behind this paradox might be a non-appropriate Social Representation of Climate Change in society as the Social Representation conditions and forms the response of the society. In this paper, we extend previous investigations of how this Social Representation is formed in order to find ways to improve it through a Massive Online Open Course on the Science of Climate Change. Using a validated questionnaire, we investigated the knowledge dimension of the Social Representation of Climate Change in a group of students of a MOOC on Climate Change. A pre- and posttest revealed general improvements in all the categories that were considered in this study. A detailed analysis showed different degrees of improvement for different groups, providing new insights in the efficiency of knowledge-based online courses. Well designed Massive Online Open Courses, based on scientific evidence, targeted to the general public might improve the Social Representation of Climate Change, which may in turn trigger awareness and an effective mobilization to address this important and urgent topicThis research was funded by the project 2017/00287/001 of the Fundación Biodiversidad del Ministerio de Agricultura, Pesca, Alimentación y Medio Ambiente (España)S

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Centrality evolution of the charged-particle pseudorapidity density over a broad pseudorapidity range in Pb-Pb collisions at root s(NN)=2.76TeV

Peer reviewe

Design, Fabrication, and Characterisation of a Label-Free Nanosensor for Bioapplications

In this paper, we present a hybrid semiconductor structure for biosensing applications that features the co-integration of nanoelectromechanical systems with the well-known metal oxide semiconductor technology. The proposed structure features an MOSFET as a readout element, and a doubly clamped beam that is isolated from the substrate by a thin air gap, as well as by a tunnel oxide layer. The beam structure is functionalised by a thin layer of biotargets, and the main aim is to detect a particular set of biomolecules, such as enzymes, bacteria, viruses, and DNA/RNA chains, among others. In here, a three-dimensional finite element analysis is performed in order to study the behaviour of the functionalised, doubly clamped beam. Preliminary results for the fabrication and characterisation processes show good agreement between the simulated and measured characteristics