Mosquito (Diptera: Culicidae) Fauna of a Zoological Park in an Urban Setting: Analysis of Culex pipiens s.l. and Their Biotypes.

Mosquito-borne diseases (MBDs) are important emerging diseases that affect humans and animals. Zoological parks can work as early warning systems for the occurrence of MBDs. In this study, we characterized the mosquito fauna captured inside Lisbon Zoo from May 2018 to November 2019. An average of 2.4 mosquitos per trap/night were captured. Five mosquito species potentially causing MBDs, including Culex pipiens biotypes, were found in the zoo. The sympatric occurrence of Culex pipiens biotypes represents a risk factor for the epizootic transmission of West Nile virus and Usutu virus. The mosquito occurrence followed the expected seasonality, with the maximum densities during summer months. However, mosquito activity was detected in winter months in low numbers. The minimum temperature and the relative humidity (RH) on the day of capture showed a positive effect on Culex pipiens abundance. Contrary, the RH the week before capture and the average precipitation the week of capture had a negative effect. No invasive species were identified, nor have flaviviruses been detected in the mosquitoes. The implementation of biosecurity measures regarding the hygiene of the premises and the strict control of all the animals entering the zoo can justify the low prevalence of mosquitoes and the absence of flavivirus-infected mosquitoes.

G-bic: generating synthetic benchmarks for biclustering.

BACKGROUND: Biclustering is increasingly used in biomedical data analysis, recommendation tasks, and text mining domains, with hundreds of biclustering algorithms proposed. When assessing the performance of these algorithms, more than real datasets are required as they do not offer a solid ground truth. Synthetic data surpass this limitation by producing reference solutions to be compared with the found patterns. However, generating synthetic datasets is challenging since the generated data must ensure reproducibility, pattern representativity, and real data resemblance. RESULTS: We propose G-Bic, a dataset generator conceived to produce synthetic benchmarks for the normative assessment of biclustering algorithms. Beyond expanding on aspects of pattern coherence, data quality, and positioning properties, it further handles specificities related to mixed-type datasets and time-series data.G-Bic has the flexibility to replicate real data regularities from diverse domains. We provide the default configurations to generate reproducible benchmarks to evaluate and compare diverse aspects of biclustering algorithms. Additionally, we discuss empirical strategies to simulate the properties of real data. CONCLUSION: G-Bic is a parametrizable generator for biclustering analysis, offering a solid means to assess biclustering solutions according to internal and external metrics robustly.

Artificial intelligence and statistical methods for stratification and prediction of progression in amyotrophic lateral sclerosis: A systematic review.

BACKGROUND: Amyotrophic Lateral Sclerosis (ALS) is a fatal neurodegenerative disorder characterised by the progressive loss of motor neurons in the brain and spinal cord. The fact that ALS's disease course is highly heterogeneous, and its determinants not fully known, combined with ALS's relatively low prevalence, renders the successful application of artificial intelligence (AI) techniques particularly arduous. OBJECTIVE: This systematic review aims at identifying areas of agreement and unanswered questions regarding two notable applications of AI in ALS, namely the automatic, data-driven stratification of patients according to their phenotype, and the prediction of ALS progression. Differently from previous works, this review is focused on the methodological landscape of AI in ALS. METHODS: We conducted a systematic search of the Scopus and PubMed databases, looking for studies on data-driven stratification methods based on unsupervised techniques resulting in (A) automatic group discovery or (B) a transformation of the feature space allowing patient subgroups to be identified; and for studies on internally or externally validated methods for the prediction of ALS progression. We described the selected studies according to the following characteristics, when applicable: variables used, methodology, splitting criteria and number of groups, prediction outcomes, validation schemes, and metrics. RESULTS: Of the starting 1604 unique reports (2837 combined hits between Scopus and PubMed), 239 were selected for thorough screening, leading to the inclusion of 15 studies on patient stratification, 28 on prediction of ALS progression, and 6 on both stratification and prediction. In terms of variables used, most stratification and prediction studies included demographics and features derived from the ALSFRS or ALSFRS-R scores, which were also the main prediction targets. The most represented stratification methods were K-means, and hierarchical and expectation-maximisation clustering; while random forests, logistic regression, the Cox proportional hazard model, and various flavours of deep learning were the most widely used prediction methods. Predictive model validation was, albeit unexpectedly, quite rarely performed in absolute terms (leading to the exclusion of 78 eligible studies), with the overwhelming majority of included studies resorting to internal validation only. CONCLUSION: This systematic review highlighted a general agreement in terms of input variable selection for both stratification and prediction of ALS progression, and in terms of prediction targets. A striking lack of validated models emerged, as well as a general difficulty in reproducing many published studies, mainly due to the absence of the corresponding parameter lists. While deep learning seems promising for prediction applications, its superiority with respect to traditional methods has not been established; there is, instead, ample room for its application in the subfield of patient stratification. Finally, an open question remains on the role of new environmental and behavioural variables collected via novel, real-time sensors.

Triclustering-based classification of longitudinal data for prognostic prediction: targeting relevant clinical endpoints in amyotrophic lateral sclerosis.

This work proposes a new class of explainable prognostic models for longitudinal data classification using triclusters. A new temporally constrained triclustering algorithm, termed TCtriCluster, is proposed to comprehensively find informative temporal patterns common to a subset of patients in a subset of features (triclusters), and use them as discriminative features within a state-of-the-art classifier with guarantees of interpretability. The proposed approach further enhances prediction with the potentialities of model explainability by revealing clinically relevant disease progression patterns underlying prognostics, describing features used for classification. The proposed methodology is used in the Amyotrophic Lateral Sclerosis (ALS) Portuguese cohort (N = 1321), providing the first comprehensive assessment of the prognostic limits of five notable clinical endpoints: need for non-invasive ventilation (NIV); need for an auxiliary communication device; need for percutaneous endoscopic gastrostomy (PEG); need for a caregiver; and need for a wheelchair. Triclustering-based predictors outperform state-of-the-art alternatives, being able to predict the need for auxiliary communication device (within 180 days) and the need for PEG (within 90 days) with an AUC above 90%. The approach was validated in clinical practice, supporting healthcare professionals in understanding the link between the highly heterogeneous patterns of ALS disease progression and the prognosis.

Tribological Behavior of TiO2 PEEK Composite and Stainless Steel for Pediatric Crowns.

Dental decay still presents a major health problem among children. Its treatment usually requires the use of stainless steel crowns. This study compares the wear behavior of 316 L stainless steel and polyetheretherketone (PEEK) composite under identical test conditions. The wear tests were conducted in a reciprocating ball-on-plate tribometer (Plint TE67/R) using alumina balls as a counterface and artificial saliva as a lubricant at 37 °C to faithfully mimic oral conditions. The coefficient of friction (COF) and specific wear rate (k) values were determined and SEM/EDS examinations were performed to identify the predominant wear mechanisms. Results showed that PEEK exhibited a significantly lower coefficient of friction (COF = 0.094 ± 0.004) and thus lower wear volume (ΔV = 0.0078 ± 0.0125 mm3) and higher wear resistance, with an average value of specific wear rate of k = 9.07 × 10-6 mm3N-1m-1 when compared to stainless steel (COF = 0.32 ± 0.03, ΔV = 0.0125 ± 0.0029 mm3, k = 1.45 × 10-5 mm3N-1m-1). PEEK was revealed to be a potential material for use in pediatric crowns due to its high wear resistance while overcoming the disadvantages associated with steel at both an aesthetic and biological level.

Learning prognostic models using a mixture of biclustering and triclustering: Predicting the need for non-invasive ventilation in Amyotrophic Lateral Sclerosis.

Longitudinal cohort studies to study disease progression generally combine temporal features produced under periodic assessments (clinical follow-up) with static features associated with single-time assessments, genetic, psychophysiological, and demographic profiles. Subspace clustering, including biclustering and triclustering stances, enables the discovery of local and discriminative patterns from such multidimensional cohort data. These patterns, highly interpretable, are relevant to identifying groups of patients with similar traits or progression patterns. Despite their potential, their use for improving predictive tasks in clinical domains remains unexplored. In this work, we propose to learn predictive models from static and temporal data using discriminative patterns, obtained via biclustering and triclustering, as features within a state-of-the-art classifier, thus enhancing model interpretation. triCluster is extended to find time-contiguous triclusters in temporal data (temporal patterns) and a biclustering algorithm to discover coherent patterns in static data. The transformed data space, composed of bicluster and tricluster features, capture local and cross-variable associations with discriminative power, yielding unique statistical properties of interest. As a case study, we applied our methodology to follow-up data from Portuguese patients with Amyotrophic Lateral Sclerosis (ALS) to predict the need for non-invasive ventilation (NIV) since the last appointment. The results showed that, in general, our methodology outperformed baseline results using the original features. Furthermore, the bicluster/tricluster-based patterns used by the classifier can be used by clinicians to understand the models by highlighting relevant prognostic patterns.

Modification of Zirconia Implant Surfaces by Nd:YAG Laser Grooves: Does It Change Cell Behavior?

The aim of this study was to evaluate gingival fibroblasts and human osteoblasts' response to textured Nd:YAG laser microgrooves, with different dimensions, on zirconia implant surfaces. A total of 60 zirconia disks (8 mm in diameter and 2 mm in thickness) were produced and divided between four study groups (N = 15): three laser-textured (widths between 125.07 ± 5.29 µm and 45.36 ± 2.37 µm and depth values from 50.54 ± 2.48 µm to 23.01 ± 3.79 µm) and a control group without laser treatment. Human osteoblasts and gingival fibroblasts were cultured on these surfaces for 14 days. FEG-SEM (Field Emission Gun-Scanning Electron Microscope) images showed cellular adhesion at 24 h, with comparable morphology in all samples for both cell types. A similar cell spreading within the grooves and in the space between them was observed. Cell viability increased over time in all study groups; however, no differences were found between them. Additionally, proliferation, ALP (Alkaline phosphatase) activity, collagen type I, osteopontin and interleukin levels were not significantly different between any of the study groups for any of the cell types. Analysis of variance to compare parameters effect did not reveal statistically significant differences when comparing all groups in the different tests performed. The results obtained revealed similar cell behavior based on cell viability and differentiation on different microtopographic laser grooves, compared to a microtopography only established by sandblasting and acid-etching protocol, the reference surface treatment on zirconia dental implants.

Biclustering fMRI time series: a comparative study.

BACKGROUND: The effectiveness of biclustering, simultaneous clustering of rows and columns in a data matrix, was shown in gene expression data analysis. Several researchers recognize its potentialities in other research areas. Nevertheless, the last two decades have witnessed the development of a significant number of biclustering algorithms targeting gene expression data analysis and a lack of consistent studies exploring the capacities of biclustering outside this traditional application domain. RESULTS: This work evaluates the potential use of biclustering in fMRI time series data, targeting the Region × Time dimensions by comparing seven state-in-the-art biclustering and three traditional clustering algorithms on artificial and real data. It further proposes a methodology for biclustering evaluation beyond gene expression data analysis. The results discuss the use of different search strategies in both artificial and real fMRI time series showed the superiority of exhaustive biclustering approaches, obtaining the most homogeneous biclusters. However, their high computational costs are a challenge, and further work is needed for the efficient use of biclustering in fMRI data analysis. CONCLUSIONS: This work pinpoints avenues for the use of biclustering in spatio-temporal data analysis, in particular neurosciences applications. The proposed evaluation methodology showed evidence of the effectiveness of biclustering in finding local patterns in fMRI time series data. Further work is needed regarding scalability to promote the application in real scenarios.

Dynamic Bayesian networks for stratification of disease progression in amyotrophic lateral sclerosis.

BACKGROUND AND PURPOSE: Progression rate is quite variable in amyotrophic lateral sclerosis (ALS); thus, tools for profiling disease progression are essential for timely interventions. The objective was to apply dynamic Bayesian networks (DBNs) to establish the influence of clinical and demographic variables on disease progression rate. METHODS: In all, 664 ALS patients from our database were included stratified into slow (SP), average (AP) and fast (FP) progressors, according to the Amyotrophic Lateral Sclerosis Functional Rating Scale Revised (ALSFRS-R) rate of decay. The sdtDBN framework was used, a machine learning model which learnt optimal DBNs with both static (gender, age at onset, onset region, body mass index, disease duration at entry, familial history, revised El Escorial criteria and C9orf72) and dynamic (ALSFRS-R scores and sub-scores, forced vital capacity, maximum inspiratory pressure, maximum expiratory pressure and phrenic amplitude) variables. RESULTS: Disease duration and body mass index at diagnosis are the foremost influences amongst static variables. Disease duration is the variable that better discriminates the three groups. Maximum expiratory pressure is the respiratory test with prevalent influence on all groups. ALSFRS score has a higher influence on FP, but lower on AP and SP. The bulbar sub-score has considerable influence on FP but limited on SP. Limb function has a more decisive influence on AP and SP. The respiratory sub-score has little influence in all groups. ALSFRS-R questions 1 (speech) and 9 (climbing stairs) are the most influential in FP and SP, respectively. CONCLUSIONS: The sdtDBN analysis identified five variables, easily obtained during clinical evaluation, which are the most influential for each progression group. This insightful information may help to improve prognosis and care.

Human Gingival Fibroblast and Osteoblast Behavior on Groove-Milled Zirconia Implant Surfaces.

Two type of cells representing periodontal hard tissues (osteoblasts) and soft tissues (fibroblasts) were evaluated in response to microgroove-milled zirconia surfaces. A total of 90 zirconia discs were randomly assigned to four width-standardized milling microgroove-textured groups and a control group without grooves (UT). The sandblast and acid-etch protocol were applied to all samples. Both cell lines were cultured on zirconia discs from 1 day up to 14 days. Cell morphology and adhesion were evaluated after 1 day of culturing. Cell viability and proliferation of the cells were measured. Alkaline phosphatase activity, collagen I, osteopontin, interleukin 1ß and interleukin 8 secretions were assessed at predefined times. The results obtained were presented in the form of bar graphs as means and standard deviations. Multi comparisons between groups were evaluated using two-away ANOVA or Mann−Whitney tests, and a p-value < 0.05 was established. Group comparisons with regard to cell viability, proliferation and secretion of collagen I, interleukin-1ß and interleukin 8 revealed no statistically significant differences. The alkaline phosphatase activity and osteopontin secretion were significantly higher in the group with a large groove compared to the small one and the control group. Nevertheless, the viability of gingival and bone cells did not appear to be affected by the milled microgroove texture compared to the conventional sandblasted and acid-etched texture, but they seem to influence osteoblasts' cellular differentiation.

Peri-implant cell response on groove and pore-textured zirconia surfaces.

OBJECTIVES: This study aimed to assess the independent influence of grooves and pores texturized by milling on gold-standard zirconia implant surfaces. METHODS: Milled groove and pore textured with equivalent width, depth, and spacing on zirconia discs were produced using press and sintering techniques. All samples were sandblasted and acid-etched (SBAE), and untextured discs were used as controls. Osteoblasts and fibroblasts were cultured on discs for 14 days. Field emission gun-scanning electron microscopy (FEG-SEM) was used to observe cellular adhesion and morphology. Cell viability and proliferation assays were performed. Additionally, alkaline phosphatase activity, collagen type I, and osteopontin were evaluated at pre-defined time points. Results are presented as mean and standard deviation (SD), group comparisons were tested using one-way ANOVA (Tukey's post-hoc), and significance was set at P < 0.05. RESULTS: FEG-SEM images revealed cellular adhesion at 24 h in all samples with differences in distribution. Although both cell lines showed increased cell viability and differentiation cell markers such as collagen and osteopontin over time, statistically significant differences between groups were found in none of the quantitative study variables (P > 0.05). CONCLUSION: The results suggest similar cellular behavior between different patterns with similar dimensions and between them and microtopography by SBAE protocol currently used as the gold-standard for zirconia dental implants. The addition of pore and groove microtextures to the gold-standard zirconia dental implant surfaces treated with SBAE does not seem to be an asset in the cellular behavior of the hard and soft tissue cells.

Design and surface characterization of micropatterned silica coatings for zirconia dental implants.

The use of zirconia as an alternative biomaterial for titanium implants has been increasing due to its biocompatibility, favorable aesthetic features, less potential for early plaque accumulation and mechanical properties. Despite the developed efforts, strategies to promote an effective osseointegration are still enough. In this sense and combining the silica properties to improve bone formation with the micropatterning guidance characteristics, silica coatings with micropatterns were designed and evaluated regarding their hydrophilicity and integrity through resistance to scratch and friction tests against femoral bone plates (simulating implant insertion). A combined sol-gel and soft-lithography techniques were used to produce silica coatings onto zirconia substrates and different techniques were used to characterize the micropatterned silica coatings. The results revealed that the production of lines and pillars micropatterns increases the surface roughness (Ra values) and improves the surface strength adhesion. Through the scratch test, it was possible to verify that the integrity and topography characteristics of all micropatterned coatings were not significantly affected after the friction test meaning that their function is not compromised after implant insertion. Additionally, the lines micropattern was the one that presented the highest hydrophilicity for distilled water, thus being a promising surface to promote improved osseointegration. The combined use of different surface micropatterns could potentially be used to guide bone apposition and avoiding peri-implantitis.

Laser surface treatment on Yttria-stabilized zirconia dental implants: Influence on cell behavior.

Yttria-stabilized zirconia (YSZ) is being proposed as an alternative material to Titanium for dental implants due to its aesthetic and biocompatibility properties. However, is it yet to define the optimal surface treatment to improve YSZ bioactivy. Texturization is a promising approach, but the biological role of patterned YSZ surfaces in cell cultures is yet to be determined. Thus, cellular behavior of osteoblasts and fibroblasts in contact with groove-texturized YSZ surfaces was investigated. YSZ discs were groove-textured by conventional milling and Nd:YAG laser. All samples including control were sandblasted and acid-etched. Human osteoblasts and fibroblasts were cultured on discs for 14 days. Morphology and cellular adhesion were observed. Cell viability, interleukin-1ß, osteopontin, collagen type I prodution, alkaline phosphatase activity, and interleukin-8 were measured. YSZ texturization by conventional milling improved osteoblasts viability and differentiation when compared to laser texturization. Fibroblasts behavior did not seem to be influenced by the texturing technique. Compared to sandblasting and acid etching currently used as gold standard for zirconia dental implants no superiority of macrotexturization was found.

Learning Prognostic Models Using Disease Progression Patterns: Predicting the Need for Non-Invasive Ventilation in Amyotrophic Lateral Sclerosis.

Amyotrophic Lateral Sclerosis is a devastating neurodegenerative disease causing rapid degeneration of motor neurons and usually leading to death by respiratory failure. Since there is no cure, treatment's goal is to improve symptoms and prolong survival. Non-invasive Ventilation (NIV) is an effective treatment, leading to extended life expectancy and improved quality of life. In this scenario, it is paramount to predict its need in order to allow preventive or timely administration. In this work, we propose to use itemset mining together with sequential pattern mining to unravel disease presentation patterns together with disease progression patterns by analysing, respectively, static data collected at diagnosis and longitudinal data from patient follow-up. The goal is to use these static and temporal patterns as features in prognostic models, enabling to take disease progression into account in predictions and promoting model interpretability. As case study, we predict the need for NIV within 90, 180 and 365 days (short, mid and long-term predictions). The learnt prognostic models are promising. Pattern evaluation through growth rate suggests bulbar function and phrenic nerve response amplitude, additionally to respiratory function, are significant features towards determining patient evolution. This confirms clinical knowledge regarding relevant biomarkers of disease progression towards respiratory insufficiency.

The digital alveolar cast: A revised approach to an old concept.

An alveolar cast is recommended for the fabrication of specific fixed dental prostheses. The analog workflow for such casts is labor-intensive, time-consuming, and highly skill dependent. Advancements in digital technologies are bringing new, efficient, and streamlined protocols for dental practice. This article presents a digital workflow for the fabrication of an alveolar cast by using computer-aided design and computer-aided manufacturing (CAD-CAM)technologies.

Stone verification device for verifying the definitive cast of an implant-supported restoration: A dental technique.

To reduce the risk of implant and prosthetic complications, implant-supported prostheses should be passive. A verification device is used to verify the abutment analog position on the definitive cast, and hence its accuracy. This article describes the fabrication of a Type IV stone implant verification device for verifying the accuracy of the definitive cast.

Bacteria co-culture adhesion on different texturized zirconia surfaces.

Zirconia is becoming reckoned as a promising solution for different applications, in particular those within the dental implant investigation field. It has been proved to successfully overcome important limitations of the commonly used titanium implants. The adhesion of microorganisms to the implants, in particular of bacteria, may govern the success or the failure of a dental implant, as the accumulation of bacteria on the peri-implant bone may rapidly evolve into periodontitis. However, bacterial adhesion on different zirconia architectures is still considerably unknown. Therefore, the adhesion of Escherichia coli, Staphylococcus aureus and Pseudomonas aeruginosa to zirconia surfaces with different finishings was evaluated and compared to a titanium surface. The adhesion interaction between S. aureus and P. aeruginosa was also evaluated using a co-culture since these bacteria are infamous due to their common presence in chronic wound infections. Results showed that different bacterium species possess different properties which influence their propensity to adhere to different roughness levels and architectures. E. coli revealed a higher propensity to adhere to zirconia channelled surfaces (7.15 × 106 CFU/mL), whereas S. aureus and P. aeruginosa adhered more to the titanium control group (1.07 × 105 CFU/mL and 8.43 × 106 CFU/mL, respectively). Moreover, the co-culture denoted significant differences on the adhesion behaviour of bacteria. Despite not having shown an especially better behaviour regarding bacterial adhesion, zirconia surfaces with micro-channels are expected to improve the vascularization around the implants and ultimately enhance osseointegration, thus being a promising solution for dental implants.

A DNA barcode reference library of Portuguese mosquitoes.

Mosquitoes are important biological vectors of pathogens and species identification in all life stages is the first step for effective monitoring and control of mosquito-borne diseases. Molecular methods for species identification have been developed over the last years to overcome the limitations of the taxonomic identification based on morphology. DNA barcoding, using a fragment of the mitochondrial cytochrome oxidase I (COI) gene, can be used for species identification but a reliable and comprehensive reference database of verified sequences is required. In this study, we aimed to generate a DNA barcode reference library for the identification of mosquito species from Portuguese mosquito fauna, including most relevant vector species. Mosquitoes captured under the National Vector Surveillance Program (REVIVE) were processed for DNA extraction, COI gene fragment amplification and sequencing. Nighty-eight barcode sequences were obtained, representing 26 species and 6 genera. Sequences were submitted to GenBank and BOLD and were used for validation of phenetic classification. Barcode Index Number (BIN) assignment and Automatic Barcode Gap Discovery (ABGD) were used and clustered COI sequences into twenty-five molecular operational taxonomic units (MOTUs). This is the first comprehensive study that combines morphological and molecular identification of most mosquito species present in Portugal aiming to offer a reliable framework for mosquito species identification.