Universidad de Valparaíso

Industry 4.0 is a new industrial paradigm that brings new challenges for workers as they have to actively collaborate with robots in an interconnected environment. The main purpose of this paper is to give a brief overview of how collaborative robots can be used to support human workers in Industry 4.0 manufacturing environments. The use of collaborative robots certainly brings many advantages as these machines enable more efficient product systems by supporting workers with both physical and cognitive tasks, as is the case of exoskeletons. On the other hand, human–robot interaction might also have some risks if human factors considerations are not well thought through throughout the process. Moreover, it becomes clear that the role that humans have been playing so far in a manufacturing environment is rapidly changing. Human workers will have to adapt to these new systems by acquiring and improving a set of skills that have sometime been neglected until nowadays.

Background: Few implementation science (IS) measures have been evaluated for validity, reliability and utility – the latter referring to whether a measure captures meaningful aspects of implementation contexts. We present a real-world case study of rigorous measure development in IS that assesses Barriers and Facilitators in Implementation of Task-Sharing in Mental Health services (BeFITS-MH), with the objective of offering lessons-learned and a framework to enhance measurement utility. Methods: We summarize conceptual and empirical work that informed the development of the BeFITS-MH measure, including a description of the Delphi process, detailed translation and local adaptation procedures, and concurrent pilot testing. As validity and reliability are key aspects of measure development, we also report on our process of assessing the measure’s construct validity and utility for the implementation outcomes of acceptability, appropriateness, and feasibility. Results: Continuous stakeholder involvement and concurrent pilot testing resulted in several adaptations of the BeFITS-MH measure’s structure, scaling, and format to enhance contextual relevance and utility. Adaptations of broad terms such as “program,” “provider type,” and “type of service” were necessary due to the heterogeneous nature of interventions, type of task-sharing providers employed, and clients served across the three global sites. Item selection benefited from the iterative process, enabling identification of relevance of key aspects of identified barriers and facilitators, and what aspects were common across sites. Program implementers’ conceptions of utility regarding the measure’s acceptability, appropriateness, and feasibility clustered across several common categories. Conclusions: This case study provides a rigorous, multi-step process for developing a pragmatic IS measure. The process and lessons learned will aid in the teaching, practice and research of IS measurement development. The importance of including experiences and knowledge from different types of stakeholders in different global settings was reinforced and resulted in a more globally useful measure while allowing for locally-relevant adaptation. To increase the relevance of the measure it is important to target actionable domains that predict markers of utility (e.g., successful uptake) per program implementers’ preferences. With this case study, we provide a detailed roadmap for others seeking to develop and validate IS measures that maximize local utility and impact.

Abstract Aims Dynamin‐2 is a large GTPase, a member of the dynamin superfamily that regulates membrane remodelling and cytoskeleton dynamics. Mutations in the dynamin‐2 gene ( DNM2 ) cause autosomal dominant centronuclear myopathy (CNM), a congenital neuromuscular disorder characterised by progressive weakness and atrophy of the skeletal muscles. Cognitive defects have been reported in some DNM2 ‐linked CNM patients suggesting that these mutations can also affect the central nervous system (CNS). Here we studied how a dynamin‐2 CNM‐causing mutation influences the CNS function. Methods Heterozygous mice harbouring the p.R465W mutation in the dynamin–2 gene (HTZ), the most common causing autosomal dominant CNM, were used as disease model. We evaluated dendritic arborisation and spine density in hippocampal cultured neurons, analysed excitatory synaptic transmission by electrophysiological field recordings in hippocampal slices, and evaluated cognitive function by performing behavioural tests. Results HTZ hippocampal neurons exhibited reduced dendritic arborisation and lower spine density than WT neurons, which was reversed by transfecting an interference RNA against the dynamin‐2 mutant allele. Additionally, HTZ mice showed defective hippocampal excitatory synaptic transmission and reduced recognition memory compared to the WT condition. Conclusion Our findings suggest that the dynamin‐2 p.R465W mutation perturbs the synaptic and cognitive function in a CNM mouse model and support the idea that this GTPase plays a key role in regulating neuronal morphology and excitatory synaptic transmission in the hippocampus.

81 Background: Although the proportion of elderly cancer patients (pts) increases, few randomized trials provide separate results on this group. Here, we present a sub-analysis of the CRITICS trial, comparing elderly with non-elderly pts. Methods: Preoperative (preop) chemotherapy (CT) included three cycles of epirubicin, cisplatin/oxaliplatin and capecitabine (ECC/EOC); pts were upfront randomized between postoperative (postop) CT (3x ECC/EOC) and chemoradiotherapy (CRT; 45Gy + cisplatin/capecitabine). Elderly pts were defined as age ≥ 70 years at the time of randomization. We present tolerability and outcomes for elderly versus non-elderly pts. Results: Details on baseline characteristics, preop treatment, surgery, postop treatment and survival are shown in Table 1. Tumor type and localization did not differ between both groups. Conclusions: Age had a significant impact on toxicity and tolerability of preop CT, but did not affect surgical resection rates and complications. Although less elderly pts started postop treatment and elderly pts received lower dose in de CT arm, there were no differences in treatment related toxicities. Survival was not significantly different. Clinical trial information: NCT00407186. [Table: see text]

BACKGROUND:Collecting anthropometric data for real-life applications demands a high degree of precision and reliability. It is important to test new equipment that will be used for data collection. OBJECTIVE: Compare two anthropometric data gathering techniques - manual methods and a Kinect-based 3D body scanner - to understand which of them gives more precise and reliable results. METHODS: The data was collected using a measuring tape and a Kinect-based 3D body scanner. It was evaluated in terms of precision by considering the regular and relative Technical Error of Measurement and in terms of reliability by using the Intraclass Correlation Coefficient, Reliability Coefficient, Standard Error of Measurement and Coefficient of Variation. RESULTS: The results obtained showed that both methods presented better results for reliability than for precision. Both methods showed relatively good results for these two variables, however, manual methods had better results for some body measurements. CONCLUSION: Despite being considered sufficiently precise and reliable for certain applications (e.g. apparel industry), the 3D scanner tested showed, for almost every anthropometric measurement, a different result than the manual technique. Many companies design their products based on data obtained from 3D scanners, hence, understanding the precision and reliability of the equipment used is essential to obtain feasible results.

Ejection fraction (EF) is defined by the ratio of end-systolic volume (ESV) and end-diastolic volume (EDV). The resulting fraction is a dimensionless number whose interpretation is ambiguous and most likely misleading. Despite this limitation, EF is widely accepted as a clinical marker of cardiac function. In this article we analyze the role of ESV, a fundamental variable of ventricular mechanics, compared with the popular EF. Common physiology-based mathematics can explain a simple association between EF and ESV. This concept is illustrated by a detailed analysis of the information obtained from angiocardiography, echocardiography and cardiac magnetic resonance studies. EF versus ESV produces a non-linear curve. For a small ESV, the EF approaches 100%, while for a large ESV, the EF gradually decreases toward zero. This elemental relationship is commonly observed in innervated natural hearts. Thus, the popularity of EF mostly derives from a fortuitous connection with the pivotal variable ESV. Alongside this finding, we unfold historical events that facilitated the emergence of EF as a result of serendipity. Our physiology-based approach denounces the circumstantial theories invoked to justify the importance of EF as an index of cardiac function, which are critically discussed. EF appears to be nothing more than a blessing in disguise. For this reason, we propose the ESV as a more logical metric for the analysis of ventricular function.

The patient profile is one of low socioeconomic status, severe psychopathology, maladaptive behavior, family dysfunction, and parental psychopathology. Substance use disorder is also associated with readmission.

Mammography is known as one of the best forms to screen possible breast cancer in women, and recently deep learning models have been developed to assist the radiologist in the diagnosis. However, their lack of interpretability has become a significant drawback to their extended use in clinical practice. This paper introduces a novel approach for detecting and localising pathological findings in mammography exams through the use of a EfficientNet-based deep learning model. The model is trained using cropped segments of labelled pathological findings from Vindr Mammography Dataset. Achieving an average F1-score of 72.7 %, and reaching on mass and suspicious calcifications an F1-Score of 79.9 % and 84.5 % respectively. Using this classifier we propose a method to visualise from local information the regions of interest where pathological findings could be present on the complete image. Plus, we describe the limitations regarding area coverage of these patches on the model's capability of generalization and certainty on its predictions, explaining its functionality.

BACKGROUND: The dimensional seat design process should consider both the users’ tasks and their physical characteristics. OBJECTIVE: To use an approach for the design and evaluation of seat dimensions based on the anthropometric characteristics of the Iranian population and the requirements of tractor operators. METHODS: Some existing equations relating the seat dimensions to anthropometric characteristics were modified according to logical justifications and international standards. A new mathematical-statistical method was used to extract the equations estimating the constant seat dimensions based on the theoretical maximizing of the accommodation level. In addition, an Overall Seat Accommodation Score (OSAS) was developed to represent the mean of seat dimensions accommodation level and dimensional accommodation equality, simultaneously. RESULTS: The dimensional seat design can be affected under different conditions of adjustability, esthetic, and space limitations. However, it was shown that it is possible to improve the design of tractor seats without any significant increase in the final cost and complexity. CONCLUSION: A new approach was used for tractor seats for a sample of Iranian operators and can be used for the design and evaluation of tractor seats for other target populations.

The 2019 Coronavirus disease (COVID-19) pandemic is a current challenge for the world’s health systems aiming to control this disease. From an epidemiological point of view, the control of the incidence of this disease requires an understanding of the influence of the variables describing a population. This research aims to predict the COVID-19 incidence in three risk categories using two types of machine learning models, together with an analysis of the relative importance of the available features in predicting the COVID-19 incidence in the Chilean urban commune of Concepción. The classification results indicate that the ConvLSTM (Convolutional Long Short-Term Memory) classifier performed better than the SVM (Support Vector Machine), with results between 93% and 96% in terms of accuracy (ACC) and F-measure (F1) metrics. In addition, when considering each one of the regional and national features as well as the communal features (DEATHS and MOBILITY), it was observed that at the regional level the CRITICAL BED OCCUPANCY and PATIENTS IN ICU features positively contributed to the performance of the classifiers, while at the national level the features that most impacted the performance of the SVM and ConvLSTM were those related to the type of hospitalization of patients and the use of mechanical ventilators.

Insufficient oxygen supply represents a relevant issue in several fields of human physiology and medicine. It has been suggested that the implantation of photosynthetic cells can provide oxygen to tissues in the absence of a vascular supply. This approach has been demonstrated to be successful in several in vitro and in vivo models; however, no data is available about their safety in human patients. Here, an early phase-1 clinical trial ( ClinicalTrials.gov identifier: NCT03960164, https://clinicaltrials.gov/ct2/show/NCT03960164 ) is presented to evaluate the safety and feasibility of implanting photosynthetic scaffolds for dermal regeneration in eight patients with full-thickness skin wounds. Overall, this trial shows that the presence of the photosynthetic microalgae Chlamydomonas reinhardtii in the implanted scaffolds did not trigger any deleterious local or systemic immune responses in a 90 days follow-up, allowing full tissue regeneration in humans. The results presented here represent the first attempt to treat patients with photosynthetic cells, supporting the translation of photosynthetic therapies into clinics. Clinical Trial Registration: www.clinicaltrials.gov/ct2/show/NCT03960164 , identifier: NCT03960164.

Establishing a node importance ranking is a problem that has attracted the attention of many researchers in recent decades. For unweighted networks where the edges do not have any attached weight, many proposals have been presented, considering local or global information of the networks. On the contrary, it occurs in undirected edge-weighted networks, where the proposals to address this problem have been more scarce. In this paper, a ranking method of node importance for undirected and edge-weighted is provided, generalizing the measure of line importance (DIL) based on the centrality degree proposed by Opsahl. The experimentation was done on five real networks and the results illustrate the benefits of our proposal.

Background: Despite offering many benefits, direct manual anthropometric measurement method can be problematic due to their vulnerability to measurement errors. OBJECTIVE: The purpose of this literature review was to determine, whether or not the currently published anthropometric studies of school children, related to ergonomics, mentioned or evaluated the variables precision, reliability or accuracy in the direct manual measurement method. METHODS: Two bibliographic databases, and the bibliographic references of all the selected papers were used for finding relevant published papers in the fields considered in this study. RESULTS: Forty-six (46) studies met the criteria previously defined for this literature review. However, only ten (10) studies mentioned at least one of the analyzed variables, and none has evaluated all of them. Only reliability was assessed by three papers. Moreover, in what regards the factors that affect precision, reliability and accuracy, the reviewed papers presented large differences. This was particularly clear in the instruments used for the measurements, which were not consistent throughout the studies. Additionally, it was also clear that there was a lack of information regarding the evaluators' training and procedures for anthropometric data collection, which are assumed to be the most important issues that affect precision, reliability and accuracy. CONCLUSIONS: Based on the review of the literature, it was possible to conclude that the considered anthropometric studies had not focused their attention to the analysis of precision, reliability and accuracy of the manual measurement methods. Hence, and with the aim of avoiding measurement errors and misleading data, anthropometric studies should put more efforts and care on testing measurement error and defining the procedures used to collect anthropometric data.

Schizophrenia is a chronic neurobiological disorder whose early detection has attracted significant attention from the clinical, psychiatric, and also artificial intelligence communities. This latter approach has been mainly focused on the analysis of neuroimaging and genetic data. A less explored strategy consists in exploiting the power of natural language processing (NLP) algorithms applied over narrative texts produced by schizophrenic subjects. In this paper, a novel dataset collected from a proper field study is presented. Also, grammatical traits discovered in narrative documents are used to build computational representations of texts, allowing an automatic classification of discourses generated by schizophrenic and non-schizophrenic subjects. The attained results showed that the use of the proposed computational representations along with machine learning techniques enables a novel and precise strategy to automatically detect texts produced by schizophrenic subjects.

Objectives: Overlap of primary studies is a key methodological challenge for overviews. There are limited reports of methods used to address overlap, and there is no detailed assessment of the corrected covered area (CCA) of a representative sample of overviews. To describe the approaches used to address overlap, and to estimate the overall and pairwise CCA. Methods: We searched PubMed for overviews published in 2018. Two authors conducted the screening process. We described the strategy used for assessing overlap, and calculated overall and pairwise CCA for each overview. Results: We analyzed a random sample of 30 out of 89 eligible articles. Eleven did not address the overlap. Of the remainder, most frequent strategies were visual assessment and discussion of overlap as a limitation. Median overall CCA among the included overviews was 6.7%. The pairwise analysis showed that 52.8% of SR pairs had slight overlap, while 28.3% had very high overlap. Conclusion: Reported strategies for addressing overlap vary considerably among overview authors. The pairwise approach for assessing the CCA revealed highly overlapped pairs of SRs in overviews with overall slight overlap and vice versa. We encourage authors to complement the overall CCA assessment with a pairwise approach.

Objectives: The study aimed at analyzing the prevalence of five psychological outcomes (depression, anxiety, stress, post-traumatic stress disorder (PTSD), and suicidal ideation) among Chinese healthcare workers (HCWs), and measured the total possible negative psychological impact 1 year after the COVID-19 initial outbreak. Methods: A cross-sectional nationwide multi-center study was performed between November 2020 and March 2021 in China. A self-report questionnaire was applied, and three psychological scales were used. Binary logistic regression was performed to analyze the risk factors associated with each psychological outcome. Results: The findings demonstrated that the COVID-19 pandemic had a negative psychological impact on HCWs, which was still evident 1 year after the initial outbreak. Nurses showed higher depression and anxiety than other HCWs. Female gender, passive coping, long working hours, having a chronic disease, and experiencing violence, among other factors, were all risk factors for psychological impairment. Conclusion: Developing and promoting programs to improve mental health among HCWs, and identifying those who might need psychological support is still relevant 1 year after the initial outbreak.

Neuromodulation can profoundly impact the gain and polarity of postsynaptic changes in Hebbian synaptic plasticity. An emerging pattern observed in multiple central synapses is a pull–push type of control in which activation of receptors coupled to the G-protein Gs promote long-term potentiation (LTP) at the expense of long-term depression (LTD), whereas receptors coupled to Gq promote LTD at the expense of LTP. Notably, coactivation of both Gs- and Gq-coupled receptors enhances the gain of both LTP and LTD. To account for these observations, we propose a simple kinetic model in which AMPA receptors (AMPARs) are trafficked between multiple subcompartments in and around the postsynaptic spine. In the model AMPARs in the postsynaptic density compartment (PSD) are the primary contributors to synaptic conductance. During LTP induction, AMPARs are trafficked to the PSD primarily from a relatively small perisynaptic (peri-PSD) compartment. Gs-coupled receptors promote LTP by replenishing peri-PSD through increased AMPAR exocytosis from a pool of endocytic AMPAR. During LTD induction AMPARs are trafficked in the reverse direction, from the PSD to the peri-PSD compartment, and Gq-coupled receptors promote LTD by clearing the peri-PSD compartment through increased AMPAR endocytosis. We claim that the model not only captures essential features of the pull–push neuromodulation of synaptic plasticity, but it is also consistent with other actions of neuromodulators observed in slice experiments and is compatible with the current understanding of AMPAR trafficking.

Detecting and extracting findings in a radiological report is crucial for text mining tasks in several applications. In this case, a labeled process for the image associated with the radiological report in mammography and Spanish context for a computer vision model is required. This paper shows the methodology and process generated for this goal. This paper presents a Named Entity Recognition (NER) approach based on a transformer deep learning model, using a labeled corpus and fine-tuning process to find three concepts that compose a typical finding in a mammographic radiological report: laterality, location, and the finding. We add another concept in the labeled process, the negation, necessary to identify falses positive inside the text that writes the radiologist. Our model achieves an F1 score of 88.24% classifying the three principal concepts for a finding, product of the labeled and fine-tuning process. The results presented here will be used as input for future training work on a computer vision model.