Facial biotype classification for orthodontic treatment planning using an alternative learning algorithm for tree augmented Naive Bayes.

BACKGROUND: When designing a treatment in orthodontics, especially for children and teenagers, it is crucial to be aware of the changes that occur throughout facial growth because the rate and direction of growth can greatly affect the necessity of using different treatment mechanics. This paper presents a Bayesian network approach for facial biotype classification to classify patients' biotypes into Dolichofacial (long and narrow face), Brachyfacial (short and wide face), and an intermediate kind called Mesofacial, we develop a novel learning technique for tree augmented Naive Bayes (TAN) for this purpose. RESULTS: The proposed method, on average, outperformed all the other models based on accuracy, precision, recall, [Formula: see text], and kappa, for the particular dataset analyzed. Moreover, the proposed method presented the lowest dispersion, making this model more stable and robust against different runs. CONCLUSIONS: The proposed method obtained high accuracy values compared to other competitive classifiers. When analyzing a resulting Bayesian network, many of the interactions shown in the network had an orthodontic interpretation. For orthodontists, the Bayesian network classifier can be a helpful decision-making tool.

Individual competence predominates over host nutritional status in Arabidopsis root exudate-mediated bacterial enrichment in a combination of four Burkholderiaceae species.

BACKGROUND: Rhizosphere microorganisms play a crucial role in plant health and development. Plant root exudates (PRE) are a complex mixture of organic molecules and provide nutritional and signaling information to rhizosphere microorganisms. Burkholderiaceae species are non-abundant in the rhizosphere but exhibit a wide range of plant-growth-promoting and plant-health-protection effects. Most of these plant-associated microorganisms have been studied in isolation under laboratory conditions, whereas in nature, they interact in competition or cooperation with each other. To improve our understanding of the factors driving growth dynamics of low-abundant bacterial species in the rhizosphere, we hypothesized that the growth and survival of four Burkholderiaceae strains (Paraburkholderia phytofirmans PsJN, Cupriavidus metallidurans CH34, C. pinatubonensis JMP134 and C. taiwanensis LMG19424) in Arabidopsis thaliana PRE is affected by the presence of each other. RESULTS: Differential growth abilities of each strain were found depending on plant age and whether PRE was obtained after growth on N limitation conditions. The best-adapted strain to grow in PRE was P. phytofirmans PsJN, with C. pinatubonensis JMP134 growing better than the other two Cupriavidus strains. Individual strain behavior changed when they succeeded in combinations. Clustering analysis showed that the 4-member co-culture grouped with one of the best-adapted strains, either P. phytofirmans PsJN or C. pinatubonensis JMP134, depending on the PRE used. Sequential transference experiments showed that the behavior of the 4-member co-culture relies on the type of PRE provided for growth. CONCLUSIONS: The results suggest that individual strain behavior changed when they grew in combinations of two, three, or four members, and those changes are determined first by the inherent characteristics of each strain and secondly by the environment.

Heterogeneous responses in Google Trends measures of well-being to the COVID-19 dynamic quarantines in Chile.

We study how the Chilean population's well-being responded to the strategy implemented by their health authorities, known as Dynamic Quarantine, to contain the spread of coronavirus in which municipalities periodically entered and exited lockdowns. This unique scheme, together with the population's socioeconomic heterogeneity, facilitates the estimation of changes in this well-being as differentiated by socioeconomic status. Using Google Trends to compute measures of well-being, we find strong evidence that socioeconomic status induces heterogeneity in these changes; thus, neglecting this heterogeneity may lead to misleading prescriptions for the public policy that addresses the psychological effects of lockdowns.

Color Dependence Analysis in a CNN-Based Computer-Aided Diagnosis System for Middle and External Ear Diseases.

Artificial intelligence-assisted otologic diagnosis has been of growing interest in the scientific community, where middle and external ear disorders are the most frequent diseases in daily ENT practice. There are some efforts focused on reducing medical errors and enhancing physician capabilities using conventional artificial vision systems. However, approaches with multispectral analysis have not yet been addressed. Tissues of the tympanic membrane possess optical properties that define their characteristics in specific light spectra. This work explores color wavelengths dependence in a model that classifies four middle and external ear conditions: normal, chronic otitis media, otitis media with effusion, and earwax plug. The model is constructed under a computer-aided diagnosis system that uses a convolutional neural network architecture. We trained several models using different single-channel images by taking each color wavelength separately. The results showed that a single green channel model achieves the best overall performance in terms of accuracy (92%), sensitivity (85%), specificity (95%), precision (86%), and F1-score (85%). Our findings can be a suitable alternative for artificial intelligence diagnosis systems compared to the 50% of overall misdiagnosis of a non-specialist physician.

Stock market volatility and the COVID-19 reproductive number.

The media has prominently featured the totemic reproductive number R in its COVID-19 coverage despite being an imperfect measure of the degree of infectivity of the virus. As such, it conveys information to the public regarding the state of the pandemic that affects market sentiment. We analyze how news about R affects the volatility in stock markets worldwide and find that when R is greater than one, which means the spread of the disease should soar, it has a positive and significant effect on volatility. Our results hold after controlling for government interventions and several robustness checks.

Social sentiment segregation: Evidence from Twitter and Google Trends in Chile during the COVID-19 dynamic quarantine strategy.

The Chilean health authorities have implemented a sanitary strategy known as dynamic quarantine or strategic quarantine to cope with the COVID-19 pandemic. Under this system, lockdowns were established, lifted, or prolonged according to the weekly health authorities' assessment of municipalities' epidemiological situation. The public announcements about the confinement situation of municipalities country-wide are made typically on Tuesdays or Wednesdays before noon, have received extensive media coverage, and generated sharp stock market fluctuations. Municipalities are the smallest administrative division in Chile, with each city broken down typically into several municipalities. We analyze social media behavior in response to the confinement situation of the population at the municipal level. The dynamic quarantine scheme offers a unique opportunity for our analysis, given that municipalities display a high degree of heterogeneity, both in size and in the socioeconomic status of their population. We exploit the variability over time in municipalities' confinement situations, resulting from the dynamic quarantine strategy, and the cross-sectional variability in their socioeconomic characteristics to evaluate the impact of these characteristics on social sentiment. Using event study and panel data methods, we find that proxies for social sentiment based on Twitter queries are negatively related (more pessimistic) to increases in the number of confined people, but with a statistically significant effect concentrated on people from the wealthiest cohorts of the population. For indicators of social sentiment based on Google Trends, we found that search intensity during the periods surrounding government announcements is positively related to increases in the total number of confined people. Still, this effect does not seem to be dependent on the segments of the population affected by the quarantine. Furthermore, we show that the observed heterogeneity in sentiment mirrors heterogeneity in stock market reactions to government announcements. We provide evidence that the observed stock market behavior around quarantine announcements can be explained by the number of people from the wealthiest segments of the population entering or exiting lockdown.

Prevention of doxorubicin-induced Cardiotoxicity by pharmacological non-hypoxic myocardial preconditioning based on Docosahexaenoic Acid (DHA) and carvedilol direct antioxidant effects: study protocol for a pilot, randomized, double-blind, controlled trial (CarDHA trial).

BACKGROUND: Anthracycline-induced cardiotoxicity (AIC), a condition associated with multiple mechanisms of damage, including oxidative stress, has been associated with poor clinical outcomes. Carvedilol, a ß-blocker with unique antioxidant properties, emerged as a strategy to prevent AIC, but recent trials question its effectiveness. Some evidence suggests that the antioxidant, not the ß-blocker effect, could prevent related cardiotoxicity. However, carvedilol's antioxidant effects are probably not enough to prevent cardiotoxicity manifestations in certain cases. We hypothesize that breast cancer patients taking carvedilol as well as a non-hypoxic myocardial preconditioning based on docosahexaenoic acid (DHA), an enhancer of cardiac endogenous antioxidant capacity, will develop less subclinical cardiotoxicity manifestations than patients randomized to double placebo. METHODS/DESIGN: We designed a pilot, randomized controlled, two-arm clinical trial with 32 patients to evaluate the effects of non-hypoxic cardiac preconditioning (DHA) plus carvedilol on subclinical cardiotoxicity in breast cancer patients undergoing anthracycline treatment. The trial includes four co-primary endpoints: changes in left ventricular ejection fraction (LVEF) determined by cardiac magnetic resonance (CMR); changes in global longitudinal strain (GLS) determined by two-dimensional echocardiography (ECHO); elevation in serum biomarkers (hs-cTnT and NT-ProBNP); and one electrocardiographic variable (QTc interval). Secondary endpoints include other imaging, biomarkers and the occurrence of major adverse cardiac events during follow-up. The enrollment and follow-up for clinical outcomes is ongoing. DISCUSSION: We expect a group of anthracycline-treated breast cancer patients exposed to carvedilol and non-hypoxic myocardial preconditioning with DHA to show less subclinical cardiotoxicity manifestations than a comparable group exposed to placebo. TRIAL REGISTRATION: ISRCTN registry, ID: ISRCTN69560410. Registered on 8 June 2016.