Shear bond strength of ceramic laminate veneers to finishing surfaces with different percentages of preserved enamel under a digital guided method

Background The purpose of this in vitro study was to evaluate the effect of the percentages of preserved enamel on ceramic laminate veneers’ (CLVs) shear bond strength (SBS). Methods Seventy extracted human maxillary central incisors were scanned and reconstructed into three-dimensional models. The extracted teeth were then embedded and randomly divided into seven groups (n = 10 per group). Based on digital analyses of the three-dimensional models, guided tooth preparation and bonding procedures were performed individually to form seven different percentages (100%, 80%, 60% 50%, 40%, 20% and 0%) of remaining enamel thickness on the bonding surface. Finally, the SBS test was performed, and the data were statistically analysed by one-way ANOVA with LSD post hoc test (α = 0.05). Results The complete enamel surface exhibited the highest SBS (19.93 ± 4.55 MPa), followed by 80% enamel (19.03 ± 3.66 MPa), 60% enamel (18.44 ± 3.65 MPa), 50% enamel (18.18 ± 3.41 MPa), 40% enamel (17.83 ± 3.01 MPa) and 20% enamel (11.32 ± 3.42 MPa) group. The lowest SBS (9.63 ± 3.46 MPa) was detected in 0% enamel group. No significant difference was observed among the 40–100% enamel groups, while the 20% or 0% enamel group demonstrated a significantly lower mean SBS than the 40% enamel group (p < 0.05). Conclusion The SBS value of CLVs bonded to 100% enamel on the finishing surfaces (nearly 20 MPa) was twice that which bonded to 0% enamel (nearly 10 MPa). Bonding to 100% enamel is the most reliable treatment. When dentin exposure is inevitable, enamel should be preserved as much as possible to maintain good bonding. In addition, 40% of preserved enamel on the bonding surface was the minimal acceptable value to fulfil the requirements of good bonding strength.

Prediction of the Iron–Sulfur Binding Sites in Proteins Using the Highly Accurate Three-Dimensional Models Calculated by AlphaFold and RoseTTAFold

AlphaFold and RoseTTAFold are deep learning-based approaches that predict the structure of proteins from their amino acid sequences. Remarkable success has recently been achieved in the prediction accuracy of not only the fold of the target protein but also the position of its amino acid side chains. In this article, I question the accuracy of these methods to predict iron–sulfur binding sites. I analyze three-dimensional models calculated by AlphaFold and RoseTTAFold of Fe–S–dependent enzymes, for which no structure of a homologous protein has been solved experimentally. In all cases, the amino acids that presumably coordinate the cluster were gathered together and facing each other, which led to a quite accurate model of the Fe–S cluster binding site. Yet, cysteine candidates were often involved in intramolecular disulfide bonds, and the number and identity of the protein amino acids that should ligate the cluster were not always clear. The experimental structure determination of the protein with its Fe–S cluster and in complex with substrate/inhibitor/product is still needed to unambiguously visualize the coordination state of the cluster and understand the conformational changes occurring during catalysis.

Credible School Value-Added with Undersubscribed School Lotteries

We introduce two empirical strategies harnessing the randomness in school assignment mechanisms to measure school value-added. The first estimator controls for the probability of school assignment, treating take-up as ignorable. We test this assumption using randomness in assignments. The second approach uses assignments as instrumental variables (IVs) for low-dimensional models of value-added and forms empirical Bayes posteriors from these IV estimates. Both strategies solve the underidentification challenge arising from school undersubscription. Models controlling for assignment risk and lagged achievement in Denver and New York City yield reliable value-added estimates. Estimates from models with lower-quality achievement controls are improved by IV.

Synthesis and Computer Study of Population Dynamics Controlled Models Using Methods of Numerical Optimization, Stochastization and Machine Learning

The problems of synthesis and analysis of multidimensional controlled models of population dynamics are of both theoretical and applied interest. The need to solve numerical optimization problems for such a class of models is associated with the expansion of ecosystem control requirements. The need to solve the problem of stochastization is associated with the emergence of new problems in the study of ecological systems properties under the influence of random factors. The aim of the work is to develop a new approach to studying the properties of population dynamics systems using methods of numerical optimization, stochastization and machine learning. The synthesis problems of nonlinear three-dimensional models of interconnected species number dynamics, taking into account trophic chains and competition in prey populations, are studied. Theorems on the asymptotic stability of equilibrium states are proved. A qualitative and numerical study of the models is carried out. Using computational experiments, the results of an analytical stability and permanent coexistence study are verified. The search for equilibrium states belonging to the stability and permanent coexistence region is made using the developed intelligent algorithm and evolutionary calculations. The transition is made from the model specified by the vector ordinary differential equation to the corresponding stochastic model. A comparative analysis of deterministic and stochastic models with competition and trophic chains is carried out. New effects are revealed that are characteristic of three-dimensional models, taking into account the competition in populations of prey. The formulation of the optimal control problem for a model with competition and trophic chains is proposed. To find optimal trajectories, new generalized algorithms for numerical optimization are developed. A methods for the synthesis of controllers based on the use of artificial neural networks and machine learning are developed. The results on the search for optimal trajectories and generation of control functions are presented.The obtained results can be used in modeling problems of ecological, demographic, socio-economic and chemical kinetics systems.