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The aim of this study was to evaluate the impact of exercise order in multicomponent training (MCT) on the maximum voluntary contraction (MVC) of older women. A total of 91 older women, ranging in age from 60 to 85 years, were randomly assigned to either Group A or Group B. Group A performed a warm up followed by aerobic training and resistance training, whereas Group B followed a warm up followed by resistance training and aerobic training. A control group (CG) did not engage in any exercise interventions. Statistical analysis was conducted using one-way ANOVA for between-group comparisons, and ANOVA was used for repeated measures. The results revealed that Group A demonstrated significant increases in MVC for knee extensors (KEs) between M1 and M3 (p < 0.001) and between M2 and M3 (p < 0.001). Similarly, Group A exhibited significant increases in MVC for knee flexors (KFs) between M1 and M3 (p = 0.001) and between M2 and M3 (p < 0.001). Both Group A and Group B demonstrated significant increases in MVC for elbow flexors (EFs) between M1 and M3 (p < 0.001). Furthermore, Group B showed a significant increase in hand grip strength (HGS) between M1 and M3 (p < 0.001). Overall, the findings suggest that initiating MCT with aerobic training followed by resistance training is the most effective approach for improving muscle strength in older women.

The artistic community is increasingly relying on automatic computational analysis for authentication and classification of artistic paintings. In this paper, we identify hidden patterns and relationships present in artistic paintings by analysing their complexity, a measure that quantifies the sum of characteristics of an object. Specifically, we apply Normalized Compression (NC) and the Block Decomposition Method (BDM) to a dataset of 4,266 paintings from 91 authors and examine the potential of these information-based measures as descriptors of artistic paintings. Both measures consistently described the equivalent types of paintings, authors, and artistic movements. Moreover, combining the NC with a measure of the roughness of the paintings creates an efficient stylistic descriptor. Furthermore, by quantifying the local information of each painting, we define a fingerprint that describes critical information regarding the artists' style, their artistic influences, and shared techniques. More fundamentally, this information describes how each author typically composes and distributes the elements across the canvas and, therefore, how their work is perceived. Finally, we demonstrate that regional complexity and two-point height difference correlation function are useful auxiliary features that improve current methodologies in style and author classification of artistic paintings. The whole study is supported by an extensive website (http://panther.web.ua.pt) for fast author characterization and authentication., Comment: Website: http://panther.web.ua.pt 24 Pages; 19 pages article; 5 pages supplementary material

The identification of chemicals in articles has attracted a large interest in the biomedical scientific community, given its importance in drug development research. Most of previous research have focused on PubMed abstracts, and further investigation using full-text documents is required because these contain additional valuable information that must be explored. The manual expert task of indexing Medical Subject Headings (MeSH) terms to these articles later helps researchers find the most relevant publications for their ongoing work. The BioCreative VII NLM-Chem track fostered the development of systems for chemical identification and indexing in PubMed full-text articles. Chemical identification consisted in identifying the chemical mentions and linking these to unique MeSH identifiers. This manuscript describes our participation system and the post-challenge improvements we made. We propose a three-stage pipeline that individually performs chemical mention detection, entity normalization and indexing. Regarding chemical identification, we adopted a deep-learning solution that utilizes the PubMedBERT contextualized embeddings followed by a multilayer perceptron and a conditional random field tagging layer. For the normalization approach, we use a sieve-based dictionary filtering followed by a deep-learning similarity search strategy. Finally, for the indexing we developed rules for identifying the more relevant MeSH codes for each article. During the challenge, our system obtained the best official results in the normalization and indexing tasks despite the lower performance in the chemical mention recognition task. In a post-contest phase we boosted our results by improving our named entity recognition model with additional techniques. The final system achieved 0.8731, 0.8275 and 0.4849 in the chemical identification, normalization and indexing tasks, respectively. The code to reproduce our experiments and run the pipeline is publicly available.Database URLhttps://github.com/bioinformatics-ua/biocreativeVII_track2

ObjectiveTo review and frequently update the available evidence on injury risk factors and epidemiology of injury in trail running.DesignLiving systematic review. Updated searches will be done every 6 months for a minimum period of 5 years.Data sourcesEight electronic databases were searched from inception to 18 March 2021.Eligibility criteriaStudies that investigated injury risk factors and/or reported the epidemiology of injury in trail running.ResultsNineteen eligible studies were included, of which 10 studies investigated injury risk factors among 2 785 participants. Significant intrinsic factors associated with injury are: more running experience, level A runner and higher total propensity to sports accident questionnaire (PAD-22) score. Previous history of cramping and postrace biomarkers of muscle damage is associated with cramping. Younger age and low skin phototypes are associated with sunburn. Significant extrinsic factors associated with injury are neglecting warm-up, no specialised running plan, training on asphalt, double training sessions per day and physical labour occupations. A slower race finishing time is associated with cramping, while more than 3 hours of training per day, shade as the primary mode of sun protection and being single are associated with sunburn. An injury incidence range 0.7–61.2 injuries/1000 hours of running and prevalence range 1.3% to 90% were reported. The lower limb was the most reported region of injury, specifically involving blisters of the foot/toe.ConclusionLimited studies investigated injury risk factors in trail running. Our review found eight intrinsic and nine extrinsic injury risk factors. This review highlighted areas for future research that may aid in designing injury risk management strategies for safer trail running participation.PROSPERO registration numberCRD42021240832.

Study aim: To assess changes in physical fitness of amateur soccer players after a pre-season training period and baseline fitness dependencies. Material and methods: Twenty-one amateur soccer players were assessed during the pre-season. The following physical variables were assessed before and after a two-month pre-season training period: (i) cardiorespiratory fitness, (ii) strength and power, and (iii) change of direction (COD). Results: Significant decreases were found for countermovement jump (CMJ) (p < 0.001; d = 1.161), drop jump (DJ) (p = 0.014; d = 0.958), and horizontal jump (HJ) (p = 0.042; d = 0.640), while no significant changes were found for the overall variables from the beginning to the end of pre-season. Fit players revealed significant decreases for CMJ (p = 0.002; d = –2.495), DJ (p = 0.004; d = –1.760), HJ (p = 0.028; d = –1.005), COD deficit (p = 0.034; d = 1.013), and maximal aerobic speed (MAS) (p = 0.026; d = –4.053). No significant changes were found for unfit players. Conclusions: Amateur soccer coaches should consider assessing physical qualities at the beginning of pre-season and use the free-of-charge monitoring tools such as session-rate of perceived exertion (s-RPE) during the training process.

Background Viruses are among the shortest yet highly abundant species that harbor minimal instructions to infect cells, adapt, multiply, and exist. However, with the current substantial availability of viral genome sequences, the scientific repertory lacks a complexity landscape that automatically enlights viral genomes’ organization, relation, and fundamental characteristics. Results This work provides a comprehensive landscape of the viral genome’s complexity (or quantity of information), identifying the most redundant and complex groups regarding their genome sequence while providing their distribution and characteristics at a large and local scale. Moreover, we identify and quantify inverted repeats abundance in viral genomes. For this purpose, we measure the sequence complexity of each available viral genome using data compression, demonstrating that adequate data compressors can efficiently quantify the complexity of viral genome sequences, including subsequences better represented by algorithmic sources (e.g., inverted repeats). Using a state-of-the-art genomic compressor on an extensive viral genomes database, we show that double-stranded DNA viruses are, on average, the most redundant viruses while single-stranded DNA viruses are the least. Contrarily, double-stranded RNA viruses show a lower redundancy relative to single-stranded RNA. Furthermore, we extend the ability of data compressors to quantify local complexity (or information content) in viral genomes using complexity profiles, unprecedently providing a direct complexity analysis of human herpesviruses. We also conceive a features-based classification methodology that can accurately distinguish viral genomes at different taxonomic levels without direct comparisons between sequences. This methodology combines data compression with simple measures such as GC-content percentage and sequence length, followed by machine learning classifiers. Conclusions This article presents methodologies and findings that are highly relevant for understanding the patterns of similarity and singularity between viral groups, opening new frontiers for studying viral genomes’ organization while depicting the complexity trends and classification components of these genomes at different taxonomic levels. The whole study is supported by an extensive website (https://asilab.github.io/canvas/) for comprehending the viral genome characterization using dynamic and interactive approaches.

Considering that cognitive and motor dimensions of human beings grow together, and that primary school age is one of the most important stages of children’s cognitive and motor development, the aim of this study was to investigate the relationship between executive functions and gross motor skills in rural children aged 8–10 years. This descriptive and correlational research was conducted with 93 Iranian rural primary school children aged 8 to 10 years. A Behavior Rating Inventory of Executive Function (BRIEF) questionnaire and the Test of Gross Motor Development, second edition (TGMD-2) were used to collect data on executive functions and gross motor skills, respectively. The results showed that most of the correlations between criterion and predictor variables were moderate. In the regression results we observed that among the components of executive functions, inhibition, working memory, planning/organizing, and organization had a significant relationship with gross motor skills, but no relationship was found between other components and motor skills. As a result, it can be said that in predicting cognitive development and specifically mentioned executive functions, gross motor skills are an important and effective factor among rural children and, given the importance of cognitive development and executive functions in childhood, it seems that by helping to develop their gross motor skills, executive functions will also be strengthened. Finally, possible future studies are addressed, which could investigate the effect of different aspects of motor skill classifications on executive functions.

Electronic Health Records (EHRs) aggregate diverse information at the patient level, holding a trajectory representative of the evolution of the patient health status throughout time. Although this information provides context and can be leveraged by physicians to monitor patient health and make more accurate prognoses/diagnoses, patient records can contain information from very long time spans, which combined with the rapid generation rate of medical data makes clinical decision making more complex. Patient trajectory modelling can assist by exploring existing information in a scalable manner, and can contribute in augmenting health care quality by fostering preventive medicine practices (e.g. earlier disease diagnosis).We propose a solution to model patient trajectories that combines different types of information (e.g. clinical text, standard codes) and considers the temporal aspect of clinical data. This solution leverages two different architectures: one supporting flexible sets of input features, to convert patient admissions into dense representations; and a second exploring extracted admission representations in a recurrent-based architecture, where patient trajectories are processed in sub-sequences using a sliding window mechanism.The developed solution was evaluated on two different clinical outcomes, unexpected patient readmission and disease progression, using the publicly available Medical Information Mart for Intensive Care (MIMIC)-III clinical database. The results obtained demonstrate the potential of the first architecture to model readmission and diagnoses prediction using single patient admissions. While information from clinical text did not show the discriminative power observed in other existing works, this may be explained by the need to fine-tune the clinicalBERT model. Finally, we demonstrate the potential of the sequence-based architecture using a sliding window mechanism to represent the input data, attaining comparable performances to other existing solutions.Herein, we explored DL-based techniques to model patient trajectories and propose two flexible architectures that explore patient admissions on an individual and sequence basis. The combination of clinical text with other types of information led to positive results, which can be further improved by including a fine-tuned version of clinicalBERT in the architectures. The proposed solution can be publicly accessed at https://github.com/bioinformatics-ua/PatientTM.

The purpose of this study was to compare the external load, internal load, and technical efficacy between the first and the second matches (M1 and M2) occurring in congested fixtures (two matches in two days) using the number of sets as a moderating factor. An observational analytic research design was adopted. Data from official volleyball matches were collected during the first competitive period of the championship, comprising 14 competitive games within 10 weeks. Ten male elite volleyball athletes (age: 21.7 ± 4.19 years of age, experience: 6.2 ± 3.8 years, body mass: 85.7 ± 8.69 kg, height: 192.4 ± 6.25 cm, BMI: 23.1 ± 1.40 kg/m2) participated in this study. Players were monitored for external load (number of jumps and height of jumps) and internal load (using the rate of perceived exertion—RPE). Additionally, notational analysis collected information about attack efficacy and receptions made during matches. The mixed ANOVA revealed no significant interaction between time (M1 vs. M2) and number of sets for number of jumps per minute (p = 0.235, ηp2 = 0.114), mean jump height (p = 0.076, ηp2 = 0.193), RPE (p = 0.261, ηp2 = 0.106), attack efficacy (p = 0.346, ηp2 = 0.085), Positive reception (p = 0.980, ηp2 = 0.002) and Perfect reception (p = 0.762, ηp2 = 0.022). In conclusion, congested fixtures do not seem to affect the performance of volleyball players negatively.

The process of refining the research question in a medical study depends greatly on the current background of the investigated subject. The information found in prior works can directly impact several stages of the study, namely the cohort definition stage. Besides previous published methods, researchers could also leverage on other materials, such as the output of cohort selection tools, to enrich and to accelerate their own work. However, this kind of information is not always captured by search engines. In this paper, we present a methodology, based on a combination of content-based retrieval and text annotation techniques, to identify relevant scientific publications related to a research question and to the selected data sources.

The purpose of the study was to test the within-week variations of the internal and external training intensity outcomes organized by days of the week. An 8-month observational period was conducted during the 2020–2021 season. The training sessions and matches of an elite volleyball team were monitored daily. The data comes from 14 players (two setters, five middle blockers, five outside hitters, and two opposites) of an elite team from the Portuguese 1st League (age: 21.7 ± 4.19 years of age; experience: 6.2 ± 3.8 years; body mass: 85.7 ± 8.69 kg; height: 192.4 ± 6.25 cm; BMI: 23.1 ± 1.40 kg/m2). The CR10 Borg scale was applied daily to measure the training intensity. The rate of perceived exertion (RPE) and the session-RPE were extracted as the internal outcomes. The external intensity was measured using an inertial measurement unit (IMU). The number of jumps, height average of jumps (JHA), minimum jump (MJ), maximal jump (MXJ), range jump (RJ), number of jumps (NJ), and training session density (D) were extracted as external intensity outcomes. The results showed that there was a difference between RPE and S-RPE (F (1.98) = 6.31, p = 0.01, η2 = 0.36, and F (1.73) = 28.30, p = 0.001, η2 = 0.72), as well as JHA and NJ (F (2.14) = 4.76, p = 0.02, η2 = 0.30, and F (1.77) = 4.77, p = 0.02, η2 = 0.30) within the microcycle. When analyzing the correlations between internal and external intensity, it was observed that there was a negative correlation between the Maximum Jump (4, 3, and 1 days before the Match day) (r2 = 0.34, r2 = 0.40, r2 = 0.41, respectively) and the Range Jump (3 and 1 days before the Match day (r2 = 0.33, r2 = 0.38, respectively) with the RPE (4 days before the Match day) and Maximum Jump (5, 4, 3, and 1 days before the Match day (r2 = 0.35, r2 = 0.39, r2 = 0.44, r2 = 0.34, respectively) and Range Jump (5, 4, 3, and 1 days before the Match day) (r2 = 0.34, r2 = 0.35, r2 = 0.40 and r2 = 0.36, respectively) with S_RPE (4 days before the Match day). Such findings show that higher internal intensities are correlated with lower external intensities in sessions further away from the game day. Such results could be an important tool for coaches to reflect, plan, monitor, and execute the training unit according to the temporal distance to the competition.

Trail running involves off-road running over different surfaces of positive and negative unevenness. Given these particularities and the associated physical demands, it is essential to understand this relationship and how fitness levels influence performance. This study aimed to analyze fitness level variations during different times of the season and establish a relationship between changes in fitness levels and accumulated load. Twenty-five trail running athletes (age: 36.23 ± 8.30 years) were monitored over 52 weeks. Three periods of assessment were implemented, while load between those periods was calculated. Athletes were monitored daily by global positioning systems. The collected data included distance covered, duration, and rate of perceived exertion (RPE), which were used to obtain session-RPE. Additionally, maximal aerobic speed, vertical jump, and dynamic balance were tested periodically. Moderate inverse correlations were found between assessment 1 and 2 for total sRPE and vertical jump: countermovement jump (VJ: CMJ) (r = −0.349), and Y balance test: left posterolateral (YBT: LPL) (r = −0.494). Similar correlations were found between assessment 2 and 3 for total sRPE and VJ: CMJ (r = −0.397), and vertical jump: drop jump (VJ: DJ) (r = −0.395). The results suggest that trail running coaches should monitor and assess dose–response relationships and possible anterior asymmetries of dynamic balance performance.

This paper presents preliminary work on the topic of emotion analysis on Twitter, in the context of the coronavirus pandemic in Portugal We collected, curated and analyzed covid-related tweets of users in Portugal in order to understand the evolution of the six basic emotions reflected in these tweets We analyzed tweets written in both English and Portuguese In this first step of our work we correlate this information with key events of the evolution of the pandemic in Portugal during March, which was the most critical period in Portugal We do so in an attempt to estimate the online manifestation of the psychological toll that this pandemic has on the overall well-being status of the general population Our findings show that the sentiment analysis of covid-related tweets is consistent with our hypothesis that negative emotions would intensify as the pandemic progressed The preliminary results obtained stand as proof of concept that the analysis of real-time tweets or other social media messages through sentiment analysis can be an important tool for behavioural and well-being tracking Copyright © 2021 by SCITEPRESS – Science and Technology Publications, Lda All rights reserved

Transformer-based “behemoths” have grown in popularity, as well as structurally, shattering multiple NLP benchmarks along the way. However, their real-world usability remains a question. In this work, we empirically assess the feasibility of applying transformer-based models in real-world ad-hoc retrieval applications by comparison to a “greener and more sustainable” alternative, comprising only 620 trainable parameters. We present an analysis of their efficacy and efficiency and show that considering limited computational resources, the lighter model running on the CPU achieves a 3 to 20 times speedup in training and 7 to 47 times in inference while maintaining a comparable retrieval performance. Code to reproduce the efficiency experiments is available on “https://github.com/bioinformatics-ua/EACL2021-reproducibility/“.

BACKGROUND Electronic health records store large amounts of patient clinical data. Despite efforts to structure patient data, clinical notes containing rich patient information remain stored as free text, greatly limiting its exploitation. This includes family history, which is highly relevant for applications such as diagnosis and prognosis. OBJECTIVE This study aims to develop automatic strategies for annotating family history information in clinical notes, focusing not only on the extraction of relevant entities such as family members and disease mentions but also on the extraction of relations between the identified entities. METHODS This study extends a previous contribution for the 2019 track on family history extraction from national natural language processing clinical challenges by improving a previously developed rule-based engine, using deep learning (DL) approaches for the extraction of entities from clinical notes, and combining both approaches in a hybrid end-to-end system capable of successfully extracting family member and observation entities and the relations between those entities. Furthermore, this study analyzes the impact of factors such as the use of external resources and different types of embeddings in the performance of DL models. RESULTS The approaches developed were evaluated in a first task regarding entity extraction and in a second task concerning relation extraction. The proposed DL approach improved observation extraction, obtaining F1 scores of 0.8688 and 0.7907 in the training and test sets, respectively. However, DL approaches have limitations in the extraction of family members. The rule-based engine was adjusted to have higher generalizing capability and achieved family member extraction F1 scores of 0.8823 and 0.8092 in the training and test sets, respectively. The resulting hybrid system obtained F1 scores of 0.8743 and 0.7979 in the training and test sets, respectively. For the second task, the original evaluator was adjusted to perform a more exact evaluation than the original one, and the hybrid system obtained F1 scores of 0.6480 and 0.5082 in the training and test sets, respectively. CONCLUSIONS We evaluated the impact of several factors on the performance of DL models, and we present an end-to-end system for extracting family history information from clinical notes, which can help in the structuring and reuse of this type of information. The final hybrid solution is provided in a publicly available code repository.

The purpose of this study was to compare the variations of weekly workload indices of internal and external load measures across the three weeks prior to injury occurrences in trail runners. Twenty-five trail runners (age: 36.23 &plusmn, 8.30 years old, body mass: 67.24 &plusmn, 5.97 kg, height: 172.12 &plusmn, 5.12 cm) were monitored daily for 52 weeks using global positioning systems (GPSs) to determine the total distance covered. Additionally, a rate of perceived exertion (RPE) scale was applied to determine session-RPE (sRPE: RPE multiplied by training time). The accumulated load (AL), acute: chronic workload ratio (ACWR), training monotony (TM), and training strain (TS) indices were calculated weekly for each runner. During the period of analysis, the injury occurrences were recorded. The differences were observed in AL and ACWR for sRPE and training time were significantly greater during the injury week when compared to the previous weeks. Similar evidence was found in TM and TS indices for sRPE, training time, and total distance. Furthermore, no meaningful differences were observed in AL and ACWR for total distance in the weeks prior to injury occurrence. Nevertheless, significant between-subjects variability was found, and this should be carefully considered. For that reason, an individualized analysis of the workload dynamics is recommended, avoiding greater spikes in load by aiming to keep a progressive increment of load without consequences for injury risk.

The wide adoption of electronic health records (EHRs) has fostered an improvement in healthcare quality, with EHRs currently representing a major source of medical information. Nevertheless, this process has also brought new challenges to the medical environment since the facilitated replication of information (e.g. using copy-paste) has resulted in less concise and sometimes incorrect information, which hinders the understandability of this data and can compromise the quality of medical decisions drawn from it. Due to the high volume and redundancy in medical data, it is imperative to develop solutions that can condense information whilst retaining its value, with a possible methodology involving the assessment of the semantic similarity between clinical text excerpts. In this paper we present an approach that explores neural networks and different types of text preprocessing pipelines, and that evaluates the impact of using word embeddings or sentence embeddings. We present the results following our participation in the n2c2 shared-task on clinical semantic textual similarity, perform an error analysis and discuss obtained results along with possible future improvements. published

Endurance sports like trail running constitute an extensive individual modality causing numerous physiological changes to occur in the athlete. In this sense, an adequate monitoring of training load appears to be essential to improve competition performance. The aim of this study was two-fold: (i) to analyze trail runners&rsquo, weekly load variations in the four weeks leading up to a trail running competition, and (ii) to determine the relationship between the runners&rsquo, pacing in competitions and their physical fitness and workload parameters. Twenty-five amateur male trail runners (age: 36.23 &plusmn, 8.30 years old, minimum International Trail Running Association performance index: 600) were monitored daily for the duration of a season (52 weeks). External load (distance covered, pace) and internal load (rate of perceived exertion) were measured daily. Additionally, weekly workload measures of acute:chronic workload ratio (ACWR), training monotony, and training strain were calculated. The runners were also assessed for maximal aerobic speed (MAS) every four months. No significant differences in workload measures (p &gt, 0.05) were observed in the four weeks leading up to each short trail competition, however, leading up to the long trail, ultra-trail medium, and ultra-trail long/extra-long competitions, the differences in the runners&rsquo, workload measures were significant (p &lt, 0.05). In the short trail, pace was found to be moderately correlated with the ACWR of total distance (r = &minus, 0.334) and with training monotony of rate of perceived exertion (RPE) (r = &minus, 0.303). In the ultra-trail, a large correlation was observed between pace and elevation accumulated (r = 0.677). We concluded that significant workload differences from one week to the next only occurred in preparation for longer-distance competitions, with sudden acute load decreases and very low ACWR values reported mainly in weeks 1 and 2 of the taper. Meaningful relationships were found between performance (pace) and MAS for longer trails and between pace and MAS for ultra-trail competitions.

Electronic health records contain valuable information on patients’ clinical history in the form of free text. Manually analyzing millions of these documents is unfeasible and automatic natural language processing methods are essential for efficiently exploiting these data. Within this, normalization of clinical entities, where the aim is to link entity mentions to reference vocabularies, is of utmost importance to successfully extract knowledge from clinical narratives. In this paper we present sieve-based models combined with heuristics and word embeddings and present results of our participation in the 2019 n2c2 (National NLP Clinical Challenges) shared-task on clinical concept normalization.

The World Health Organization reports that half of all mental illnesses begin by the age of 14. Most of these cases go undetected and untreated. The expanding use of social media has the potential to leverage the early identification of mental health diseases. As data gathered via social media are already digital, they have the ability to power up faster automatic analysis. In this article we evaluate the impact that psycholinguistic patterns can have on a standard machine learning approach for classifying depressed users based on their writings in an online public forum. We combine psycholinguistic features in a rule-based estimator and we evaluate their impact on this classification problem, along with three other standard classifiers. Our results on the Reddit Self-reported Depression Diagnosis dataset outperform some previously reported works on the same dataset. They stand for the importance of extracting psychologically motivated features when processing social media texts with the purpose of studying mental health.

[Abstract] Hospitalised patients suffering from secondary illnesses that require daily medication typically need personalised treatment. Although clinical guidelines were designed considering those circumstances, existing decision-support features fail in assimilating detailed relevant patient information, which opens up opportunities for systems capable of performing a real-time evaluation of such data against existing knowledge and providing recommendations during clinical treatments. In this paper, we present a proposal for a new feature to integrate with electronic health record (EHR) systems that enriches the health treatment process by automatically extracting information from patient medical notes and aggregating it in clinical protocols. Our goal is to leverage the historical component of the patient trajectory to improve clinical decision support systems performance. EU/EFPIA Innovative Medicines Initiative 2 Joint Undertaking; 806968 NETDIAMOND project; POCI-01-0145-FEDER-016385 Foundation for Science and Technology; PD/BD/142878/2018 Foundation for Science and Technology; SFRH/BD/147837/2019

Sources that generate symbolic sequences with algorithmic nature may differ in statistical complexity because they create structures that follow algorithmic schemes, rather than generating symbols from a probabilistic function assuming independence. In the case of Turing machines, this means that machines with the same algorithmic complexity can create tapes with different statistical complexity. In this paper, we use a compression-based approach to measure global and local statistical complexity of specific Turing machine tapes with the same number of states and alphabet. Both measures are estimated using the best-order Markov model. For the global measure, we use the Normalized Compression (NC), while, for the local measures, we define and use normal and dynamic complexity profiles to quantify and localize lower and higher regions of statistical complexity. We assessed the validity of our methodology on synthetic and real genomic data showing that it is tolerant to increasing rates of editions and block permutations. Regarding the analysis of the tapes, we localize patterns of higher statistical complexity in two regions, for a different number of machine states. We show that these patterns are generated by a decrease of the tape&rsquo, s amplitude, given the setting of small rule cycles. Additionally, we performed a comparison with a measure that uses both algorithmic and statistical approaches (BDM) for analysis of the tapes. Naturally, BDM is efficient given the algorithmic nature of the tapes. However, for a higher number of states, BDM is progressively approximated by our methodology. Finally, we provide a simple algorithm to increase the statistical complexity of a Turing machine tape while retaining the same algorithmic complexity. We supply a publicly available implementation of the algorithm in C++ language under the GPLv3 license. All results can be reproduced in full with scripts provided at the repository.

Question answering can be described as retrieving relevant information for questions expressed in natural language, possibly also generating a natural language answer. This paper presents a pipeline for document and passage retrieval for biomedical question answering built around a new variant of the DeepRank network model in which the recursive layer is replaced by a self-attention layer combined with a weighting mechanism. This adaptation halves the total number of parameters and makes the network more suited for identifying the relevant passages in each document. The overall retrieval system was evaluated on the BioASQ tasks 6 and 7, achieving similar retrieval performance when compared to more complex network architectures.

In the present study, mono- and multi-material laser textured Ti6Al4V implants are manufactured and characterized in vivo to explore their applicability in orthopedic implants. Laser surface texturing is used for manufacturing grooved Ti6Al4V implants while a pressure-assisted sintering technique is employed to impregnate beta-tricalcium phosphate into grooves for an improved bioactivity. After implantation into Sprague Dawley rat's femur for 4 and 12 weeks, bone-implant fixation and osseointegration are assessed, by performing push-out tests and histological characterization. Histological characterization showed bone formation around all implants, characterized by immature bone at 4 weeks of implantation and a more mature bone after 12 weeks. The maximum push-out forces are higher for the textured and multi-material solution, when compared to non-textured implants right after 4 weeks of implantation (p < 0.05). After 12 weeks, multi-material implant displayed higher fracture energy when compared to non-textured implants (p < 0.05). Results revealed that laser surface texturing and bioactive multi-material solutions are highly effective to promote bone regeneration and enhance bone-implant fixation for further application in orthopedic implants. published

Word sense disambiguation (WSD) is an important step in biomedical text mining, which is responsible for assigning an unequivocal concept to an ambiguous term, improving the accuracy of biomedical information extraction systems. In this work we followed supervised and knowledge-based disambiguation approaches, with the best results obtained by supervised means. In the supervised method we used bag-of-words as local features, and word embeddings as global features. In the knowledge-based method we combined word embeddings, concept textual definitions extracted from the UMLS database, and concept association values calculated from the MeSH co-occurrence counts from MEDLINE articles. Also, in the knowledge-based method, we tested different word embedding averaging functions to calculate the surrounding context vectors, with the goal to give more importance to closest words of the ambiguous term. The MSH WSD dataset, the most common dataset used for evaluating biomedical concept disambiguation, was used to evaluate our methods. We obtained a top accuracy of 95.6 % by supervised means, while the best knowledge-based accuracy was 87.4 %. Our results show that word embedding models improved the disambiguation accuracy, proving to be a powerful resource in the WSD task.

A industria textil e um setor de grande importância no cenario economico mundial, entretanto e responsavel pela geracao de uma grande quantidade de efluentes liquidos ricos em substâncias corantes, surfactantes e outras especies danosas ao meio ambiente e a saude humana. Dentre as alternativas tecnologicas no tratamento das aguas residuarias texteis a reacao Fenton heterogenea se destaca como um processo de boa eficiencia e relativo baixo custo-beneficio. Neste contexto, este estudo teve como objetivo avaliar a eficiencia de degradacao do azo corante Ponceau BS em meio aquoso via catalise heterogenea sobre nanoparticulas de oxidos de ferro. Os experimentos foram conduzidos a temperatura ambiente (25 oC) sob uma concentracao fixa de 100 mg L -1 de H 2 O 2 segundo uma metodologia de otimizacao via planejamento fatorial usando como variaveis: a dosagem de catalisador, o pH inicial e a concentracao de corante. Os resultados para 80 mg L -1 do corante mostraram uma degradacao maxima de 57.1% a pH = 3.5 e de 46.1 % a pH = 6.5 usando a dosagem de 3.2 g L -1 do catalisador oxido. A analise estatistica confirmou um efeito positivo e significante da quantidade de catalisador e um efeito negativo do pH e da concentracao de corante na eficiencia de conversao da molecula. Estes valores mostram o carater promissor da aplicacao das nanoparticulas magneticas de ferro na descontaminacao de aguas residuarias coloridas.

Introduction Cough in bronchiectasis is associated with significant impairment in health status. This study aimed to quantify cough frequency objectively with a cough monitor and investigate its relationship with health status. A secondary aim was to identify clinical predictors of cough frequency. Methods Fifty-four patients with bronchiectasis were compared with thirty-five healthy controls. Objective 24-h cough, health status (cough-specific: Leicester Cough Questionnaire LCQ and bronchiectasis specific: Bronchiectasis Health Questionnaire BHQ), cough severity and lung function were measured. The clinical predictors of cough frequency in bronchiectasis were determined in a multivariate analysis. Results Objective cough frequency was significantly raised in patients with bronchiectasis compared to healthy controls [geometric mean (standard deviation)] 184.5 (4.0) vs. 20.6 (3.2) coughs/24-h; mean fold-difference (95% confidence interval) 8.9 (5.2, 15.2); p

The scientific literature contains large amounts of information on genes, proteins, chemicals and their interactions. Extraction and integration of this information in curated knowledge bases help researchers support their experimental results, leading to new hypotheses and discoveries. This is especially relevant for precision medicine, which aims to understand the individual variability across patient groups in order to select the most appropriate treatments. Methods for improved retrieval and automatic relation extraction from biomedical literature are therefore required for collecting structured information from the growing number of published works. In this paper, we follow a deep learning approach for extracting mentions of chemical–protein interactions from biomedical articles, based on various enhancements over our participation in the BioCreative VI CHEMPROT task. A significant aspect of our best method is the use of a simple deep learning model together with a very narrow representation of the relation instances, using only up to 10 words from the shortest dependency path and the respective dependency edges. Bidirectional long short-term memory recurrent networks or convolutional neural networks are used to build the deep learning models. We report the results of several experiments and show that our best model is competitive with more complex sentence representations or network structures, achieving an F1-score of 0.6306 on the test set. The source code of our work, along with detailed statistics, is publicly available.

The Precision Medicine Initiative is a multicenter effort aiming at formulating personalized treatments leveraging on individual patient data (clinical, genome sequence and functional genomic data) together with the information in large knowledge bases (KBs) that integrate genome annotation, disease association studies, electronic health records and other data types. The biomedical literature provides a rich foundation for populating these KBs, reporting genetic and molecular interactions that provide the scaffold for the cellular regulatory systems and detailing the influence of genetic variants in these interactions. The goal of BioCreative VI Precision Medicine Track was to extract this particular type of information and was organized in two tasks: (i) document triage task, focused on identifying scientific literature containing experimentally verified protein-protein interactions (PPIs) affected by genetic mutations and (ii) relation extraction task, focused on extracting the affected interactions (protein pairs). To assist system developers and task participants, a large-scale corpus of PubMed documents was manually annotated for this task. Ten teams worldwide contributed 22 distinct textmining models for the document triage task, and six teams worldwide contributed 14 different text-mining systems for the relation extraction task. When comparing the textmining system predictions with human annotations, for the triage task, the best F-score was 69.06%, the best precision was 62.89%, the best recall was 98.0% and the best average precision was 72.5%. For the relation extraction task, when taking homologous genes into account, the best F-score was 37.73%, the best precision was 46.5% and the best recall was 54.1%. Submitted systems explored a wide range of methods, from traditional rule-based, statistical and machine learning systems to state-of-the-art deep learning methods. Given the level of participation and the individual team results we find the precision medicine track to be successful in engaging the text-mining research community. In the meantime, the track produced a manually annotated corpus of 5509 PubMed documents developed by BioGRID curators and relevant for precision medicine. The data set is freely available to the community, and the specific interactions have been integrated into the BioGRID data set. In addition, this challenge provided the first results of automatically identifying PubMed articles that describe PPI affected by mutations, as well as extracting the affected relations from those articles. Still, much progress is needed for computer-assisted precision medicine text mining to become mainstream. Future work should focus on addressing the remaining technical challenges and incorporating the practical benefits of text-mining tools into real-world precision medicine information-related curation. Database URL: https://biocreative.bioinformatics.udel.edu/tasks/biocreative-vi/track-4/

Clinical trials play a critical role in medical studies. However, identifying and selecting cohorts for such trials can be a troublesome task since patients must match a set of complex pre-determined criteria. Patient selection requires a manual analysis of clinical narratives in patients’ records, which is a time-consuming task for medical researchers. In this work, natural language processing (NLP) techniques were used to perform automatic patient cohort selection. The approach herein presented was developed and tested on the 2018 n2c2 Track 1 Shared-Task dataset where each patient record is annotated with 13 selection criteria. The resulting hybrid approach is based on heuristics and machine learning and attained a micro-average and macro-average F1-score of 0.8844 and 0.7271, respectively, in the n2c2 test set. Part of the source code resultant from this work is available at https://github.com/ruiantunes/2018-n2c2-track-1/.