Analysis of Antibody Data Using Skew-normal and Skew-T Mixture Models

Gaussian mixture models, which assume a Normal distribution for each component, are popular in antibody (or serological) data analysis to help determining antibody-positive and antibody-negative individuals. In this work, we advocate using finite mixture models based on Skew-Normal and Skew-t distributions for serological data analysis. These flexible mixing distributions have the advantage of describing right and left asymmetry often observed in the distributions of known antibody-negative and antibody-positive individuals, respectively. We illustrate the application of these alternative mixture models in a data set on the role of human herpesviruses in the Myalgic Encephalomyelitis/Chronic Fatigue Syndrome

Early biomarkers of extracapsular extension of prostate cancer using MRI-derived semantic features

Funding This work is supported by a PhD student scholarship (Adalgisa Guerra) it was granted as a scientifc project by Hospital da Luz (ID LH.INV.F2019027). Helena Mouriño was supported by CEAUL (funded by FCT, Portugal, through the project UIDB/00006/2020).BACKGROUND: To construct a model based on magnetic resonance imaging (MRI) features and histological and clinical variables for the prediction of pathology-detected extracapsular extension (pECE) in patients with prostate cancer (PCa). METHODS: We performed a prospective 3 T MRI study comparing the clinical and MRI data on pECE obtained from patients treated using robotic-assisted radical prostatectomy (RARP) at our institution. The covariates under consideration were prostate-specific antigen (PSA) levels, the patient's age, prostate volume, and MRI interpretative features for predicting pECE based on the Prostate Imaging-Reporting and Data System (PI-RADS) version 2.0 (v2), as well as tumor capsular contact length (TCCL), length of the index lesion, and prostate biopsy Gleason score (GS). Univariable and multivariable logistic regression models were applied to explore the statistical associations and construct the model. We also recruited an additional set of participants-which included 59 patients from external institutions-to validate the model. RESULTS: The study participants included 184 patients who had undergone RARP at our institution, 26% of whom were pECE+ (i.e., pECE positive). Significant predictors of pECE+ were TCCL, capsular disruption, measurable ECE on MRI, and a GS of ≥7(4 + 3) on a prostate biopsy. The strongest predictor of pECE+ is measurable ECE on MRI, and in its absence, a combination of TCCL and prostate biopsy GS was significantly effective for detecting the patient's risk of being pECE+. Our predictive model showed a satisfactory performance at distinguishing between patients with pECE+ and patients with pECE-, with an area under the ROC curve (AUC) of 0.90 (86.0-95.8%), high sensitivity (86%), and moderate specificity (70%). CONCLUSIONS: Our predictive model, based on consistent MRI features (i.e., measurable ECE and TCCL) and a prostate biopsy GS, has satisfactory performance and sufficiently high sensitivity for predicting pECE+. Hence, the model could be a valuable tool for surgeons planning preoperative nerve sparing, as it would reduce positive surgical margins.publishersversionpublishe

Adaptive Filtering for the Maternal Respiration Signal Attenuation in the Uterine Electromyogram

Funding Information: For Arnaldo Batista and Manuel Ortigueira, this work was supported by the Portuguese National Funds, through the FCT Foundation for Science and Technology, within the scope of the CTS Research Unit, Center of Technology and Systems, UNINOVA, under the project UIDB/00066/2020 (FCT). Helena Mouriño was financed by national funds through FCT, Fundação para a Ciência e a Tecnologia, under the project UIDB/00006/2020. Publisher Copyright: © 2022 by the authors.The electrohysterogram (EHG) is the uterine muscle electromyogram recorded at the abdominal surface of pregnant or non-pregnant woman. The maternal respiration electromyographic signal (MR-EMG) is one of the most relevant interferences present in an EHG. Alvarez (Alv) waves are components of the EHG that have been indicated as having the potential for preterm and term birth prediction. The MR-EMG component in the EHG represents an issue, regarding Alv wave application for pregnancy monitoring, for instance, in preterm birth prediction, a subject of great research interest. Therefore, the Alv waves denoising method should be designed to include the interference MR-EMG attenuation, without compromising the original waves. Adaptive filter properties make them suitable for this task. However, selecting the optimal adaptive filter and its parameters is an important task for the success of the filtering operation. In this work, an algorithm is presented for the automatic adaptive filter and parameter selection using synthetic data. The filter selection pool comprised sixteen candidates, from which, the Wiener, recursive least squares (RLS), householder recursive least squares (HRLS), and QR-decomposition recursive least squares (QRD-RLS) were the best performers. The optimized parameters were L = 2 (filter length) for all of them and λ = 1 (forgetting factor) for the last three. The developed optimization algorithm may be of interest to other applications. The optimized filters were applied to real data. The result was the attenuation of the MR-EMG in Alv waves power. For the Wiener filter, power reductions for quartile 1, median, and quartile 3 were found to be −16.74%, −20.32%, and −15.78%, respectively (p-value = 1.31 × 10−12).publishersversionpublishe

Estimating group size from acoustic footprint to improve Blainville’s beaked whale abundance estimation

The numbers of animals in groups and the density of Blainville’s beaked whale Mesoplodon densirostris (Md) were estimated using passive acoustic data collected on the Atlantic Undersea Test and Evaluation Center (AUTEC). Md typically associate in groups, producing ultrasonic echolocation signals when foraging, and are routinely detected year-round on the AUTEC range. AUTEC includes a large network of hydrophones cabled to shore that can be used to detect Md echolocation signals. Using a first data set, with known group sizes, we used generalized linear models (GLMs) to model group size as a function of the acoustic footprint of a detected deep dive as perceived on the AUTEC hydrophones. The most important variable to explain group size was the detected click rate (total number of clicks detected divided by total length of vocal period duration). Using a second data set, covering 3 separate time periods in 2011 with automated group dive detections, we estimated beaked whale density using a dive counting approach. False positives were removed through manual inspection, removing dives with biologically infeasible characteristics. This led to a total of 8271 detections of beaked whale deep dives, with the average number per day in the three time periods considered being 75, 80 and 76 respectively. Using selected GLM, the mean estimated group size was 2.36 (95% CI 2.15-2.60), 2.30 (95% CI 2.08-2.56), and 2.33 (95% CI 2.19-2.58) whales/group for the 1st, 2nd and 3rd time period. Md density was estimated at 15.8 (95% CI 13.6-21.9), 16.5 (95% CI 13.8-22.4), and 15.8 (95% CI 13.2-21.2) whales/1000km2, respectively. These results support findings from previous studies, and will allow a more precise estimation of group sizes and densities for Md in future research.PostprintPeer reviewe

Food talk : 40-Hz fin whale calls are associated with prey biomass

Funding: This work was supported by Fundação para a Ciência eTecnologia (FCT) Azores 2020 Operational Programme and Fundo Regional da Ciência e Tecnologia (FRCT) through research projects TRACE (PTDC/MAR/74071/2006), MAPCET (M2.1.2/F/012/2011) and AWARENESS (PTDC/BIA-BMA/30514/2017), co-funded by FEDER, COMPETE, QREN, POPH, ERDF, ESF, the Lisbon Regional Operational Programme and the Portuguese Ministry for Science and Education. Okeanos R&D Centre is supported by FCT through the strategic fund (UIDB/05634/2020). M.R. was supported by a DRCT doctoral grant (M3.1.a/F/028/2015). S.P.J. was funded by EC funds (SUMMER H2020-EU.3.2.3.1), I.C. by FCT through AWARENESS–(PTDC/BIA-BMA/30514/2017). H.M. acknowledges support by CMAF-CIO (funded by FCT, Portugal, through the projects UID/MAT/00006/2013 and UIDB/04561/2020, respectively). A.P. was supported by AWARENESS project (PTDC/BIA-BMA/30514/2017) and UIDB/50019/2020–I.D.L. and T.A.M. by CEAUL and the LMR ACCURATE project (contract no. N3943019C2176). M.A.S. was funded by FCT and EC funds (IF/00943/2013, SUMMER H2020-EU.3.2.3.1, GA 817806).Animals use varied acoustic signals that play critical roles in their lives. Understanding the function of these signals may inform about key life-history processes relevant for conservation. In the case of fin whales (Balaenoptera physalus), that produce different call types associated with different behaviours, several hypotheses have emerged regarding call function, but the topic still remains in its infancy. Here, we investigate the potential function of two fin whale vocalizations, the song-forming 20-Hz call and the 40-Hz call, by examining their production in relation to season, year and prey biomass. Our results showed that the production of 20-Hz calls was strongly influenced by season, with a clear peak during the breeding months, and secondarily by year, likely due to changes in whale abundance. These results support the reproductive function of the 20-Hz song used as an acoustic display. Conversely, season and year had no effect on variation in 40-Hz calling rates, but prey biomass did. This is the first study linking 40-Hz call activity to prey biomass, supporting the previously suggested food-associated function of this call. Understanding the functions of animal signals can help identifying functional habitats and predict the negative effects of human activities with important implications for conservation.Publisher PDFPeer reviewe

Automatic contraction detection using uterine electromyography

UIDB/00066/2020 UID/MAT/04561/2019 PD/BDE/150312/2019Electrohysterography (EHG) is a promising technique for pregnancy monitoring and preterm risk evaluation. It allows for uterine contraction monitoring as early as the 20th gestational week, and it is a non-invasive technique based on recording the electric signal of the uterine muscle activity from electrodes located in the abdominal surface. In this work, EHG-based contraction detection methodologies are applied using signal envelope features. Automatic contraction detection is an important step for the development of unsupervised pregnancy monitoring systems based on EHG. The exploratory methodologies include wavelet energy, Teager energy, root mean square (RMS), squared RMS, and Hilbert envelope. In this work, two main features were evaluated: contraction detection and its related delineation accuracy. The squared RMS produced the best contraction (97.15 ± 4.66%) and delineation (89.43 ± 8.10%) accuracy and the lowest false positive rate (0.63%). Despite the wavelet energy method having a contraction accuracy (92.28%) below the first-rated method, its standard deviation was the second best (6.66%). The average false positive rate ranged between 0.63% and 4.74%—a remarkably low value.publishersversionpublishe

Which meteorological index is the best descriptor for winter mortality in elderly population in Lisbon district?

Background: As recognized by WHO, health is influenced climate change (WHO 2014). Several studies have already provided the association between ambient temperature and mortality, hospital admissions and affluence to urgency services, in elderly population, especially due to cardiovascular and circulatory diseases(Yang et al. 2012, Laaidi et al. 2013, Analitis et al. 2008, Hajat, Kovats, and Lachowycz 2007, Kysely et al. 2009). However, few studies have explored the association between elderly mortality in winter and extreme cold weather, considering as covariables different meteorological indices (Vaneckova et al. 2011, Kunst, Groenhof, and Mackenbach 1994). The present study aimed to assess which meteorological index is the best descriptor for winter mortality in elderly population living in Lisbon district. Methods: Mortality data was provided by Statistics Portugal (INE), meteorological data from The Portuguese Institute for Sea and Atmosphere (IPMA) and influenza-like-illness rates from Portuguese general practitioners (GP) sentinel network (Rede Médicos-Sentinela). Distributed lag linear and non-linear models (DLNM) (Gasparrini, Armstrong, and Kenward 2010, Gasparrini 2011) were applied to study the effect of cold on mortality by all causes of death, and, particularly, by circulatory and respiratory diseases, in the Lisbon district, in the winter season (from November to March) between 2002 and 2012. Based on different combinations of the meteorological variables (that is, mean temperature, mean temperature and wind speed, mean temperature and humidity, and windchill temperature), several models were fitted and their performance compared. All models were adjusted for trend and seasonality, and for the confounding effect of flu activity. As a reference for relative risk (RR) calculations, the 50th percentile of each temperature series was used. Results: The best fit was found from a linear relation between temperature (either mean or windchill) and both mortality causes under study (all causes, and circulatory and respiratory diseases). The results showed that the effect of cold appears with delay and persisted for about 23 to 30 days. The maximum effect occurs with the lowest temperature registered (Mean Temperature=-0.4ºC and windchill Temperature=-3.96ºC) and with a delay of 5 days. The highest cumulative relative risk for all causes of death was found using the windchill temperature [RR=1,8 (CI95%: 1,7; 2,0)]. For mortality by circulatory and respiratory diseases, the highest cumulative relative risk was also found using the windchill temperature [RR=2,0 (CI95%: 1,8; 2,3)]. Conclusions: Cold weather seems to be a strong predictor of mortality in Lisbon district, with the strongest association found out between cold temperature and both circulatory and respiratory mortality. Windchill temperature seems to be a better predictor of mortality than mean temperature.info:eu-repo/semantics/publishedVersio

Characterizing phytoplankton biomass seasonal cycles in two NE Atlantic coastal bays

The seasonal and interannual variability of chlorophyll a was studied between 2008 and 2016 in two coastal bays located in the northeastern limit of the Iberia/Canary upwelling ecosystem. The work aims (i) to understand if small latitudinal distances and/or coastline orientation can promote different chlorophyll a seasonal cycles; and (ii) to investigate if different meteorological and oceanographic variables can explain the differences observed on seasonal cycles. Results indicate three main biological seasons with different patterns in the two studied bays. A uni-modal pattern with a short early summer maximum and relatively low chlorophyll a concentration characterized the westernmost sector of the South coast, while a uni-modal pattern characterized by high biomass over a long period, slightly higher in spring than in summer, and high chlorophyll a concentration characterized the central West coast. Comparisons made between satellite estimates of chlorophyll a and in situ data in one of the bays revealed some important differences, namely the overestimation of concentrations and the anticipation of the beginning and end time of the productive period by satellite. Cross-correlation analyses were performed for phytoplankton biomass and different meteorological and oceanographic variables (SST, PAR, UI, MLD and precipitation) using different time lags to identify the drivers that promote the growth and the high levels of phytoplankton biomass. PAR contributed to the increase of phytoplankton biomass observed during winter/midspring, while upwelling and SST were the main explanatory drivers to the high Chl-a concentrations observed in late-spring/summer. Zonal transport was the variable that contributed most to the phytoplankton biomass during late-spring/summer in Lisbon Bay, while the meridional transport combined with SST was more important in Lagos Bay.FCT: SFRH/BD/52560/2014/ IPMA-BCC-2016-35/ UIDB/04292/2020/ UID/Multi/04326/2020/ UID/MAT/04561/2020 LISBOA-01-0145FEDER-031265 IPMA: MAR2020PO2M01-1490 Pinfo:eu-repo/semantics/publishedVersio

Herpesviruses Serology Distinguishes Different Subgroups of Patients From the United Kingdom Myalgic Encephalomyelitis/Chronic Fatigue Syndrome Biobank.

The evidence of an association between Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) and chronic herpesviruses infections remains inconclusive. Two reasons for the lack of consistent evidence are the large heterogeneity of the patients' population with different disease triggers and the use of arbitrary cutoffs for defining seropositivity. In this work we re-analyzed previously published serological data related to 7 herpesvirus antigens. Patients with ME/CFS were subdivided into four subgroups related to the disease triggers: S0-42 patients who did not know their disease trigger; S1-43 patients who reported a non-infection trigger; S2-93 patients who reported an infection trigger, but that infection was not confirmed by a lab test; and S3-48 patients who reported an infection trigger and that infection was confirmed by a lab test. In accordance with a sensitivity analysis, the data were compared to those from 99 healthy controls allowing the seropositivity cutoffs to vary within a wide range of possible values. We found a negative association between S1 and seropositivity to Epstein-Barr virus (VCA and EBNA1 antigens) and Varicella-Zoster virus using specific seropositivity cutoff. However, this association was not significant when controlling for multiple testing. We also found that S3 had a lower seroprevalence to the human cytomegalovirus when compared to healthy controls for all cutoffs used for seropositivity and after adjusting for multiple testing using the Benjamini-Hochberg procedure. However, this association did not reach statistical significance when using Benjamini-Yekutieli procedure. In summary, herpesviruses serology could distinguish subgroups of ME/CFS patients according to their disease trigger, but this finding could be eventually affected by the problem of multiple testing