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Orthopantomographs taken from 308 Spanish Caucasian and 200 Venezuelan Amerindian children, aged between 2 and 18 years, were analysed following the Demirjian's method. The aims of this study were to test the applicability of the Demirjian's method to two different sample populations, and to develop age prediction models for both populations using the original French Canadian scores described by Demirjian (1976) and the new multi-ethnic dental scores proposed by Chaillet et al. (2005) when the ethnic origin is unknown. Results showed that despite the good correlation between dental and chronological age, Demirjian's method overestimates the age in the Spanish Caucasian sample using both scores, the mean overestimation being higher when the Demirjian's scores were used than when the Chaillet's scale was applied. In the Venezuelan Amerindian sample, the opposite was found: Demirjian's method underestimates the age using both scores, the underestimation being higher when the Chaillet's scale was applied than when Demirjian's scale was used. New graphs were produced to convert the maturity scores to dental age for Spanish and Venezuelan children. With these graphs, the Demirjian's scores showed to be inadequate after the age of 12 in both populations, while Chaillet's scores offered useful information until 14 years of age.

The tsscds method, recently developed in our group, discovers chemical reaction mechanisms with minimal human intervention. It employs accelerated molecular dynamics, spectral graph theory, statistical rate theory and stochastic simulations to uncover chemical reaction paths and to solve the kinetics at the experimental conditions. In the present review, its application to solve mechanistic/kinetics problems in different research areas will be presented. Examples will be given of reactions involved in photodissociation dynamics, mass spectrometry, combustion chemistry and organometallic catalysis. Some planned improvements will also be described.

BackgroundTemporomandibular disorders are the most common cause of chronic orofacial pain, but, except where they occur subsequent to trauma, their cause remains unknown. This cross-sectional study assessed chewing function (habitual chewing side) and the differences of the chewing side and condylar path and lateral anterior guidance angles in participants with chronic unilateral temporomandibular disorder. This is the preliminary report of a randomized trial that aimed to test the effect of a new occlusal adjustment therapy.MethodsThe masticatory function of 21 randomly selected completely dentate participants with chronic temporomandibular disorders (all but one with unilateral symptoms) was assessed by observing them eat almonds, inspecting the lateral horizontal movement of the jaw, with kinesiography, and by means of interview. The condylar path in the sagittal plane and the lateral anterior guidance angles with respect to the Frankfort horizontal plane in the frontal plane were measured on both sides in each individual.ResultsSixteen of 20 participants with unilateral symptoms chewed on the affected side; the concordance (Fisher’s exact test, P = .003) and the concordance-symmetry level (Kappa coefficient κ = 0.689; 95% confidence interval [CI], 0.38 to 0.99; P = .002) were significant. The mean condylar path angle was steeper (53.47(10.88) degrees versus 46.16(7.25) degrees; P = .001), and the mean lateral anterior guidance angle was flatter (41.63(13.35) degrees versus 48.32(9.53) degrees P = .036) on the symptomatic side.DiscussionThe results of this study support the use of a new term based on etiology, “habitual chewing side syndrome”, instead of the nonspecific symptom-based “temporomandibular joint disorders”; this denomination is characterized in adults by a steeper condylar path, flatter lateral anterior guidance, and habitual chewing on the symptomatic side.

It is well established in the world’s fire-prone regions that wildfires can considerably change the hydrological dynamics of freshwater catchments. Limited research, however, has focused on the potential impacts of wildfire ash toxicity on aquatic biota. Here, we assess the chemical composition and toxicity of ash generated from wildfires in six contrasting vegetation types distributed globally (UK grassland, Spanish pine forest, Spanish heathland, USA chaparral, Australian eucalypt forest and Canadian spruce forest). Acute (48h) immobilisation tests were conducted on the extensively studied aquatic macroinvertebrate Daphnia magna, a sensitive indicator of aquatic contaminants. We found significant differences between the chemical composition and toxicity of these ash types. The UK and Spanish ash had no detectable toxicity to Daphnia magna, whereas the Australian eucalypt, USA chaparral and Canadian spruce ash all caused significant toxicity (immobilisation). The principal characteristics of the latter ash types were their high pH, and NO3−, Cl− and conductivity levels. Elevated water-soluble and total concentrations of metals (e.g. Mn, Fe, Zn, Pb, Cu and As) and total polycyclic aromatic hydrocarbons (PAHs) were not linked to toxicity.

The relation between the potassium concentration in the vitreous humor, [K+], and the postmortem interval has been studied by several authors. Many formulae are available and they are based on a correlation test and linear regression using the PMI as the independent variable and [K+] as the dependent variable. The estimation of the confidence interval is based on this formulation. However, in forensic work, it is necessary to use [K+] as the independent variable to estimate the PMI. Although all authors have obtained the PMI by direct use of these formulae, it is, nevertheless, an inexact approach, which leads to false estimations. What is required is to change the variables, obtaining a new equation in which [K+] is considered as the independent variable and the PMI as the dependent. The regression line obtained from our data is [K+] = 5.35 + 0.22 PMI, by changing the variables we get PMI = 2.58[K+] − 9.30. When only nonhospital deaths are considered, the results are considerably improved. In this case, we get [K+] = 5.60 + 0.17 PMI and, consequently, PMI = 3.92[K+] − 19.04.

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