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Prosthetic rehabilitation of missing teeth with dental implant-supported restorations has recently become a predictable treatment option in contemporary dentistry with a highly successful rate. Due to different factors, vertical and horizontal bone loss could present, and the available alveolar bone may not be sufficient for optimum implant position. Ridge augmentation procedures could be applied to increase the volume of the deficient sites for accurate prosthetic implant placement, which assures functional and esthetic stability of tissues around the implants, essential for long-term success.

Results from a metallographic study of the microstructure of 1.2080 tool steel with and without modifying additions of silicon nitride (Si3N4), after various applied head treatments are obtained. Distribution of the phases (ledebur eutectic, alloyed martensite, retained austenite and dispersed carbides) and hardness in samples from 1.2080 steel after applied 35% plastic deformation and modifying with silicon nitride have been observed. [ABSTRACT FROM AUTHOR]

In this work results for steel type Y210Crl2 alloyed with nanodispersed siliceous nitride (Si3N4, after different heat treatment are presented. The obtained results are compared with heat treatment of the same steel but without modification of the structure. The microstructure of the presented phases after heat treatment and hardness measurement of the used steel are shown. [ABSTRACT FROM AUTHOR]

In this work results for steel type Y210Crl2 alloyed with nanodispersed siliceous nitride (Si3N4, after different heat treatment are presented. The obtained results are compared with heat treatment of the same steel but without modification of the structure. The microstructure of the presented phases after heat treatment and hardness measurement of the used steel are shown. [ABSTRACT FROM AUTHOR]

The liquid-membrane (pertraction) separation technique was applied for the recovery and preconcentration of the indole alkaloid vincamine from native acidic extracts of Vinca minor L. On the basis of the distribution coefficients of vincamine determined for eight couples of feed solution/membrane and membrane/strip solution, the most suitable pertraction three-phase system was found to be: native extract (acetate buffer)–trichloroethylene–hydrochloric acid solution. This system, applied in a glass laboratory pertractor, proved the possibility of the direct recovery and preconcentration of the alkaloid from the plant extract without its alkalization. This permitted to integrate the processes of plant extraction and solute separation and preconcentration. [ABSTRACT FROM AUTHOR]

We present a framework called the Reasoning Engine, which implements Satisfiability Modulo Theories (SMT)-based methods within a unified computational environment to address diverse biological analysis problems. The Reasoning Engine was used to reproduce results from key scientific studies, as well as supporting new research in stem cell biology. The framework utilizes an intermediate language for encoding partially specified discrete dynamical systems, which bridges the gap between high-level domain-specific languages and low-level SMT solvers. We provide this framework as open source together with various biological case studies, illustrating the synthesis, enumeration, optimization, and reasoning over models consistent with experimental observations to reveal novel biological insights.

Computational modelling of metabolic processes has proven to be a useful approach to formulate our knowledge and improve our understanding of core biochemical systems that are crucial to maintaining cellular functions. Towards understanding the broader role of metabolism on cellular decision-making in health and disease conditions, it is important to integrate the study of metabolism with other core regulatory systems and omics within the cell, including gene expression patterns. After quantitatively integrating gene expression profiles with a genome-scale reconstruction of human metabolism, we propose a set of combinatorial methods to reverse engineer gene expression profiles and to find pairs and higher-order combinations of genetic modifications that simultaneously optimize multi-objective cellular goals. This enables us to suggest classes of transcriptomic profiles that are most suitable to achieve given metabolic phenotypes. We demonstrate how our techniques are able to compute beneficial, neutral or "toxic" combinations of gene expression levels. We test our methods on nine tissue-specific cancer models, comparing our outcomes with the corresponding normal cells, identifying genes as targets for potential therapies. Our methods open the way to a broad class of applications that require an understanding of the interplay among genotype, metabolism, and cellular behaviour, at scale.

Targeted high-throughput DNA sequencing is a primary approach for genomics and molecular diagnostics, and more recently as a readout for DNA information storage. Oligonucleotide probes used to enrich gene loci of interest have different hybridization kinetics, resulting in non-uniform coverage that increases sequencing costs and decreases sequencing sensitivities. Here, we present a deep learning model (DLM) for predicting Next-Generation Sequencing (NGS) depth from DNA probe sequences. Our DLM includes a bidirectional recurrent neural network that takes as input both DNA nucleotide identities as well as the calculated probability of the nucleotide being unpaired. We apply our DLM to three different NGS panels: a 39,145-plex panel for human single nucleotide polymorphisms (SNP), a 2000-plex panel for human long non-coding RNA (lncRNA), and a 7373-plex panel targeting non-human sequences for DNA information storage. In cross-validation, our DLM predicts sequencing depth to within a factor of 3 with 93% accuracy for the SNP panel, and 99% accuracy for the non-human panel. In independent testing, the DLM predicts the lncRNA panel with 89% accuracy when trained on the SNP panel. The same model is also effective at predicting the measured single-plex kinetic rate constants of DNA hybridization and strand displacement., (© 2021. The Author(s).)

Background: Blood centers (BCs) rely on schools and businesses. Shelter-in-place orders closed them. This study determined how COVID-19 affected donation habits., Study Design and Methods: Two periods were reviewed (May 1 through June 30, 2018 vs 2019 [control] and 2019 vs 2020 [study-COVID period]). These donations were reviewed: first-time, repeat (donation ≤ 2 years), and lapsed (no donation > 2 years); sex; age; ethnicity; and ABO blood groups at high school and college drives. Testing all donors for SARS-CoV-2 antibodies started May 18, 2020., Results: In the study period donations significantly increased (control P = .683, study P ≤ .0001) and comparing sex (control male P = .716, female P = .657; study male P = .004, female P ≤ .0001). In the study period there was a significant decrease in Hispanic (P = .001) and African American (P < .0001) donations also seen among high school and college drives and an increase in Caucasian (P < .0001) donations. There was a significant increase in first-time (P < .0001) and lapsed donors (P < .0001) in the study period vs control (first-time P = .087, lapsed P = .308) and a significant decrease in donors not more than 30 years (study 16-20 P < .0001, 21-30 P < .0001). There was a significant increase in all blood types in the study period (P < .0001) and in donations after implementation of SARS-CoV-2 antibody testing (P = .001)., Conclusions: Significant changes occurred in donation habits in the study vs the control periods. These included increased total donations, comparing sexes, first-time and lapsed donors, all blood types, and Caucasian donations. Significant decreases were seen in Hispanic and African American donations and those not more than 30 years old., (© 2021 AABB.)

A recurring set of small sub-networks have been identified as the building blocks of biological networks across diverse organisms. These network motifs are associated with certain dynamic behaviors and define key modules that are important for understanding complex biological programs. Besides studying the properties of motifs in isolation, current algorithms typically evaluate the occurrence frequency of a specific motif in a given biological network compared to that in random networks of similar structure. However, it remains challenging to relate the structure of motifs to the observed and expected behavior of the larger, more complex network they are contained within. This problem is compounded as even the precise structure of most biological networks remains largely unknown. Previously, we developed a formal reasoning approach enabling the synthesis of biological networks capable of reproducing some experimentally observed behavior. Here, we extend this approach to allow reasoning over the requirement for specific network motifs as a way of explaining how these behaviors arise. We illustrate the approach by analyzing the motifs involved in sign-sensitive delay and pulse generation. We demonstrate the scalability and biological relevance of the approach by studying the previously defined networks governing myeloid differentiation, the yeast cell cycle, and naïve pluripotency in mouse embryonic stem cells, revealing the requirement for certain motifs in these systems.

Methods from stochastic dynamical systems theory have been instrumental in understanding the behaviours of chemical reaction networks (CRNs) arising in natural systems. However, considerably less attention has been given to the inverse problem of synthesizing CRNs with a specified behaviour, which is important for the forward engineering of biological systems. Here, we present a method for generating discrete-state stochastic CRNs from functional specifications, which combines synthesis of reactions using satisfiability modulo theories and parameter optimization using Markov chain Monte Carlo. First, we identify candidate CRNs that have the possibility to produce correct computations for a given finite set of inputs. We then optimize the parameters of each CRN, using a combination of stochastic search techniques applied to the chemical master equation, to improve the probability of correct behaviour and rule out spurious solutions. In addition, we use techniques from continuous-time Markov chain theory to analyse the expected termination time for each CRN. We illustrate our approach by synthesizing CRNs for probabilistically computing majority, maximum and division, producing both known and previously unknown networks, including a novel CRN for probabilistically computing the maximum of two species. In future, synthesis techniques such as these could be used to automate the design of engineered biological circuits and chemical systems., (© 2018 The Author(s).)

Hybridization is a key molecular process in biology and biotechnology, but so far there is no predictive model for accurately determining hybridization rate constants based on sequence information. Here, we report a weighted neighbour voting (WNV) prediction algorithm, in which the hybridization rate constant of an unknown sequence is predicted based on similarity reactions with known rate constants. To construct this algorithm we first performed 210 fluorescence kinetics experiments to observe the hybridization kinetics of 100 different DNA target and probe pairs (36 nt sub-sequences of the CYCS and VEGF genes) at temperatures ranging from 28 to 55 °C. Automated feature selection and weighting optimization resulted in a final six-feature WNV model, which can predict hybridization rate constants of new sequences to within a factor of 3 with ∼91% accuracy, based on leave-one-out cross-validation. Accurate prediction of hybridization kinetics allows the design of efficient probe sequences for genomics research.

Studying the gene regulatory networks (GRNs) that govern how cells change into specific cell types with unique roles throughout development is an active area of experimental research. The fate specification process can be viewed as a biological program prescribing the system dynamics, governed by a network of genetic interactions. To investigate the possibility that GRNs are not fixed but rather change their topology, for example as cells progress through commitment, we introduce the concept of Switching Gene Regulatory Networks (SGRNs) to enable the modelling and analysis of network reconfiguration. We define the synthesis problem of constructing SGRNs that are guaranteed to satisfy a set of constraints representing experimental observations of cell behaviour. We propose a solution to this problem that employs methods based upon Satisfiability Modulo Theories (SMT) solvers, and evaluate the feasibility and scalability of our approach by considering a set of synthetic benchmarks exhibiting possible biological behaviour of cell development. We outline how our approach is applied to a more realistic biological system, by considering a simplified network involved in the processes of neuron maturation and fate specification in the mammalian cortex., (Copyright © 2016 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.)

Predictive biology is elusive because rigorous, data-constrained, mechanistic models of complex biological systems are difficult to derive and validate. Current approaches tend to construct and examine static interaction network models, which are descriptively rich but often lack explanatory and predictive power, or dynamic models that can be simulated to reproduce known behavior. However, in such approaches implicit assumptions are introduced as typically only one mechanism is considered, and exhaustively investigating all scenarios is impractical using simulation. To address these limitations, we present a methodology based on automated formal reasoning, which permits the synthesis and analysis of the complete set of logical models consistent with experimental observations. We test hypotheses against all candidate models, and remove the need for simulation by characterizing and simultaneously analyzing all mechanistic explanations of observed behavior. Our methodology transforms knowledge of complex biological processes from sets of possible interactions and experimental observations to precise, predictive biological programs governing cell function.

Bidirectional intercellular signaling is an essential feature of multicellular organisms, and the engineering of complex biological systems will require multiple pathways for intercellular signaling with minimal crosstalk. Natural quorum-sensing systems provide components for cell communication, but their use is often constrained by signal crosstalk. We have established new orthogonal systems for cell-cell communication using acyl homoserine lactone signaling systems. Quantitative measurements in contexts of differing receiver protein expression allowed us to separate different types of crosstalk between 3-oxo-C6- and 3-oxo-C12-homoserine lactones, cognate receiver proteins, and DNA promoters. Mutating promoter sequences minimized interactions with heterologous receiver proteins. We used experimental data to parameterize a computational model for signal crosstalk and to estimate the effect of receiver protein levels on signal crosstalk. We used this model to predict optimal expression levels for receiver proteins, to create an effective two-channel cell communication device. Establishment of a novel spatial assay allowed measurement of interactions between geometrically constrained cell populations via these diffusible signals. We built relay devices capable of long-range signal propagation mediated by cycles of signal induction, communication and response by discrete cell populations. This work demonstrates the ability to systematically reduce crosstalk within intercellular signaling systems and to use these systems to engineer complex spatiotemporal patterning in cell populations., (© 2016 The Authors. Published under the terms of the CC BY 4.0 license.)

This chapter provides an overview of a programming language for Genetic Engineering of Cells (GEC). A GEC program specifies a genetic circuit at a high level of abstraction through constraints on otherwise unspecified DNA parts. The GEC compiler then selects parts which satisfy the constraints from a given parts database. GEC further provides more conventional programming language constructs for abstraction, e.g., through modularity. The GEC language and compiler is available through a Web tool which also provides functionality, e.g., for simulation of designed circuits.

The ability to design and construct synthetic biological systems with predictable behavior could enable significant advances in medical treatment, agricultural sustainability, and bioenergy production. However, to reach a stage where such systems can be reliably designed from biological components, integrated experimental and computational techniques that enable robust component characterization are needed. In this paper we present a computational method for the automated characterization of genetic components. Our method exploits a recently developed multichannel experimental protocol and integrates bacterial growth modeling, Bayesian parameter estimation, and model selection, together with data processing steps that are amenable to automation. We implement the method within the Genetic Engineering of Cells modeling and design environment, which enables both characterization and design to be integrated within a common software framework. To demonstrate the application of the method, we quantitatively characterize a synthetic receiver device that responds to the 3-oxohexanoyl-homoserine lactone signal, across a range of experimental conditions.