Evaluation of Effectiveness of a Novel Multicomponent Mycotoxins Detoxification Agent in the Presence of AFB1 and T-2 Toxin on Broiler Chicks.

This experimental study was conducted to determine the ability of a novel mycotoxins detoxification agent (MR) at a concentration of 0.2% to reduce the toxicity of aflatoxin B1 (AFB1) or T-2 toxin, alone or in combination, and to examine its effect on performance, pathohistological changes (PH) and the residue of these toxins in the tissues of broiler chicks. A total of 96 broiler chicks were divided into eight equal groups: group C, which served as control (without any additives); group MR, which received the novel detoxification agent (supplemented with 0.2%); group E-I (0.1 mg AFB1/kg of diet); group E-II (0.1 mg AFB1/kg of diet + MR 0.2%); group E-III (0.5 mg T-2 toxin/kg of diet); group E-IV (0.5 mg T-2 toxin/kg of diet + 0.2% MR); group E-V (combination of 0.1 mg AFB1/kg, 0.5 mg T-2 toxin/kg of diet); and group E-VI (combination of 0.1 mg AFB1/kg, 0.5 mg T-2 toxin + 0.2% MR). Results indicate that feeds containing AFB1 and T-2 toxin, alone or in combination, adversely affected the health and performance of poultry. However, the addition of MR to diets containing AFB1 and T-2 toxin singly and in combination exerted a positive effect on body weight, feed intake, weight gain, feed efficiency and microscopic lesions in visceral organs. Residual concentration of AFB1 in liver samples was significantly (p < 0.05) decreased when chicks were fed diets supplemented with 0.2% of MR.

Complexing deoxyribozymes with RNA aptamers for detection of the small molecule theophylline.

Biointegrative information processing systems offer a great advantage to autonomous biodevices, as their capacity for biological computation provides the ability to sense the state of more complex environments and better integrate with downstream biological regulation systems. Deoxyribozymes (DNAzymes) and aptamers are of interest to such computational biosensing systems due to the enzymatic properties of DNAzymes and the ligand-inducible conformational structures of aptamers. Herein, we describe a novel method for providing ligand-responsive allosteric control to a DNAzyme using an RNA aptamer. We designed a NOT-logic-compliant E6 DNAzyme to be complementary to an RNA aptamer targeting theophylline, such that the aptamer competitively interacted with either theophylline or the DNAzyme, and disabled the DNAzyme only when theophylline concentration was below a given threshold. Out of our seven designed "complexing aptazymes," three demonstrated effective theophylline-responsive allosteric regulation (2.84 ± 3.75%, 4.97 ± 2.92%, and 8.91 ± 4.19% activity in the absence of theophylline; 46.29 ± 3.36%, 50.70 ± 10.15%, and 61.26 ± 6.18% activity in the presence of theophylline). Moreover, the same three complexing aptazymes also demonstrated the ability to semi-quantitatively determine the concentration of theophylline present in solution, successfully discriminating between therapeutically ineffective (<20 µM), safe (20-100 µM), and toxic (>100 µM) theophylline concentrations. Our method of using an RNA aptamer for ligand-responsive allosteric control of a DNAzyme expands the way aptamers can be configured for biosensing, and suggests a pathway for embedding DNAzymes to provide enhanced information processing and control of biological systems.

Building a community to engineer synthetic cells and organelles from the bottom-up.

Employing concepts from physics, chemistry and bioengineering, 'learning-by-building' approaches are becoming increasingly popular in the life sciences, especially with researchers who are attempting to engineer cellular life from scratch. The SynCell2020/21 conference brought together researchers from different disciplines to highlight progress in this field, including areas where synthetic cells are having socioeconomic and technological impact. Conference participants also identified the challenges involved in designing, manipulating and creating synthetic cells with hierarchical organization and function. A key conclusion is the need to build an international and interdisciplinary research community through enhanced communication, resource-sharing, and educational initiatives.

Effect of polyvalency on tethered molecular walkers on independent one-dimensional tracks.

We study the motion of random walkers with residence time bias between first and subsequent visits to a site, as a model for synthetic molecular walkers composed of coupled DNAzyme legs known as molecular spiders. The mechanism of the transient superdiffusion has been explained via the emergence of a boundary between the new and the previously visited sites, and the tendency of the multilegged walker to cling to this boundary, provided residence time for a first visit to a site is longer than for subsequent visits. Using both kinetic Monte Carlo simulation and an analytical approach, we model a system that consists of unipedal walkers, each on its own one-dimensional track, connected by a tether, i.e., a kinematic constraint that no two walkers can be more than a certain distance apart. Even though a single unipedal walker does not at all exhibit directional, superdiffusive motion, we find that a team of unipedal walkers on parallel tracks, connected by a flexible tether, does enjoy a superdiffusive transient. Furthermore, unipedal walker teams exhibit a greater expected number of steps per boundary period and are able to diffuse more quickly than bipedal walker teams, which leads to longer periods of superdiffusion.

Robust Heterochiral Strand Displacement Using Leakless Translators.

Molecular computing offers a powerful framework for in situ biosensing and signal processing at the nanoscale. However, for in vivo applications, the use of conventional DNA components can lead to false positive signals being generated due to degradation of circuit components by nuclease enzymes. Here, we use hybrid chiral molecules, consisting of both l- and d-nucleic acid domains, to implement leakless signal translators that enable d-nucleic acid signals to be detected by hybridization and then translated into a robust l-DNA signal for further analysis. We show that our system is robust to false positive signals even if the d-DNA components are degraded by nucleases, thanks to circuit-level robustness. This work thus broadens the scope and applicability of DNA-based molecular computers for practical, in vivo applications.

Repeated Reuse of Deoxyribozyme-Based Logic Gates.

Deoxyribozymes (DNAzymes) have demonstrated a significant capacity for biocomputing and hold promise for information processing within advanced biological devices if several key capabilities are developed. One required capability is reuse-having DNAzyme logic gates be cyclically, and controllably, activated and deactivated. We designed an oligonucleotide-based system for DNAzyme reuse that could (1) remove previously bound inputs by addition of complementary oligonucleotides via toe-hold mediated binding and (2) diminish output signal through the addition of quencher-labeled oligonucleotides complementary to the fluorophore-bound substrate. Our system demonstrated, for the first time, the ability for DNAzymes to have their activity toggled, with activity returning to 90-125% of original activity. This toggling could be performed multiple times with control being exerted over when the toggling occurs, with three clear cycles observed before the variability in activity became too great. Our data also demonstrated that fluorescent output of the DNAzyme activity could be actively removed and regenerated. This reuse system can increase the efficiency of DNAzyme-based logic circuits by reducing the number of redundant oligonucleotides and is critical for future development of reusable biodevices controlled by logical operations.

Microcompartments for Protection and Isolation of Nanoscale DNA Computing Elements.

Physical isolation of molecular computing elements holds the potential for increasing system complexity by enabling the reuse of standardized components and by protecting the components from environmental degradation. However, once elements have been compartmentalized, methods for communicating into these compartments are needed. We report the compartmentalization of steroid-responsive DNA aptamers within giant unilamellar vesicles (GUVs) that are permeable to steroid inputs. Monodisperse GUVs are loaded with aptamers using a microfluidic platform. We demonstrate the target-specific activation of individual aptamers within the GUVs and then load two noninterfering aptamers into the same GUV and demonstrate specific responses to all possible combinations of the two input steroids. Crucially, GUVs prevent the degradation of DNA components by nucleases, providing a potential mechanism for deploying nucleic acid components in vivo. Importantly, our compartments also prevent nonspecific cross-talk between complementary strands, thereby providing a method for parallel execution of cross-reacting molecular logic components. Thus, we provide a mechanism for spatially organizing molecular computing elements, which will increase system modularity by allowing standardized components to be reused.

Feedforward Chemical Neural Network: An In Silico Chemical System That Learns xor.

Inspired by natural biochemicals that perform complex information processing within living cells, we design and simulate a chemically implemented feedforward neural network, which learns by a novel chemical-reaction-based analogue of backpropagation. Our network is implemented in a simulated chemical system, where individual neurons are separated from each other by semipermeable cell-like membranes. Our compartmentalized, modular design allows a variety of network topologies to be constructed from the same building blocks. This brings us towards general-purpose, adaptive learning in chemico: wet machine learning in an embodied dynamical system.

A Microsphere-Supported Lipid Bilayer Platform for DNA Reactions on a Fluid Surface.

We report a versatile microsphere-supported lipid bilayer system that can serve as a general-purpose platform for implementing DNA nanotechnologies on a fluid surface. To demonstrate our platform, we implemented both toehold-mediated strand displacement (TMSD) and DNAzyme reactions, which are typically performed in solution and which are the cornerstone of DNA-based molecular logic and dynamic DNA nanotechnology, on the surface. We functionalized microspheres bearing supported lipid bilayers (µSLBs) with membrane-bound nucleic acid components. Using functionalized µSLBs, we developed TMSD and DNAzyme reactions by optimizing reaction conditions to reduce nonspecific interactions between DNA and phospholipids and to enhance bilayer stability. Additionally, the physical and optical properties of the bilayer were tuned via lipid composition and addition of fluorescently tagged lipids to create stable and multiplexable µSLBs that are easily read out by flow cytometry. Multiplexed TMSD reactions on µSLBs enabled the successful operation of a Dengue serotyping assay that correctly identified all 16 patterns of target sequences to demonstrate detection of DNA strands derived from the sequences of all four Dengue serotypes. The limit of detection for this assay was 3 nM. Furthermore, we demonstrated DNAzyme reactions on a fluid lipid surface, which benefit from free diffusion on the surface. This work provides the basis for expansion of both TMSD and DNAzyme based molecular reactions on supported lipid bilayers for use in molecular logic and DNA nanotechnology. As our system is multiplexable and results in fluid surfaces, it may be of use in compartmentalization and improved kinetics of molecular logic reactions and as a useful building block in a variety of DNA nanotechnology systems.

Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification.

The latest generation of convolutional neural networks (CNNs) has achieved impressive results in the field of image classification. This paper is concerned with a new approach to the development of plant disease recognition model, based on leaf image classification, by the use of deep convolutional networks. Novel way of training and the methodology used facilitate a quick and easy system implementation in practice. The developed model is able to recognize 13 different types of plant diseases out of healthy leaves, with the ability to distinguish plant leaves from their surroundings. According to our knowledge, this method for plant disease recognition has been proposed for the first time. All essential steps required for implementing this disease recognition model are fully described throughout the paper, starting from gathering images in order to create a database, assessed by agricultural experts. Caffe, a deep learning framework developed by Berkley Vision and Learning Centre, was used to perform the deep CNN training. The experimental results on the developed model achieved precision between 91% and 98%, for separate class tests, on average 96.3%.

Modular verification of chemical reaction network encodings via serializability analysis.

Chemical reaction networks are a powerful means of specifying the intended behaviour of synthetic biochemical systems. A high-level formal specification, expressed as a chemical reaction network, may be compiled into a lower-level encoding, which can be directly implemented in wet chemistry and may itself be expressed as a chemical reaction network. Here we present conditions under which a lower-level encoding correctly emulates the sequential dynamics of a high-level chemical reaction network. We require that encodings are transactional, such that their execution is divided by a "commit reaction" that irreversibly separates the reactant-consuming phase of the encoding from the product-generating phase. We also impose restrictions on the sharing of species between reaction encodings, based on a notion of "extra tolerance", which defines species that may be shared between encodings without enabling unwanted reactions. Our notion of correctness is serializability of interleaved reaction encodings, and if all reaction encodings satisfy our correctness properties then we can infer that the global dynamics of the system are correct. This allows us to infer correctness of any system constructed using verified encodings. As an example, we show how this approach may be used to verify two- and four-domain DNA strand displacement encodings of chemical reaction networks, and we generalize our result to the limit where the populations of helper species are unlimited.

Supervised Learning in Adaptive DNA Strand Displacement Networks.

The development of engineered biochemical circuits that exhibit adaptive behavior is a key goal of synthetic biology and molecular computing. Such circuits could be used for long-term monitoring and control of biochemical systems, for instance, to prevent disease or to enable the development of artificial life. In this article, we present a framework for developing adaptive molecular circuits using buffered DNA strand displacement networks, which extend existing DNA strand displacement circuit architectures to enable straightforward storage and modification of behavioral parameters. As a proof of concept, we use this framework to design and simulate a DNA circuit for supervised learning of a class of linear functions by stochastic gradient descent. This work highlights the potential of buffered DNA strand displacement as a powerful circuit architecture for implementing adaptive molecular systems.

Logic circuits based on molecular spider systems.

Spatial locality brings the advantages of computation speed-up and sequence reuse to molecular computing. In particular, molecular walkers that undergo localized reactions are of interest for implementing logic computations at the nanoscale. We use molecular spider walkers to implement logic circuits. We develop an extended multi-spider model with a dynamic environment wherein signal transmission is triggered via localized reactions, and use this model to implement three basic gates (AND, OR, NOT) and a cascading mechanism. We develop an algorithm to automatically generate the layout of the circuit. We use a kinetic Monte Carlo algorithm to simulate circuit computations, and we analyze circuit complexity: our design scales linearly with formula size and has a logarithmic time complexity.

A unified sensor architecture for isothermal detection of double-stranded DNA, oligonucleotides, and small molecules.

Pathogen detection is an important problem in many areas of medicine and agriculture, which can involve genomic or transcriptomic signatures or small-molecule metabolites. We report a unified, DNA-based sensor architecture capable of isothermal detection of double-stranded DNA targets, single-stranded oligonucleotides, and small molecules. Each sensor contains independent target detection and reporter modules, enabling rapid design. We detected gene variants on plasmids by using a straightforward isothermal denaturation protocol. The sensors were highly specific, even with a randomized DNA background. We achieved a limit of detection of ∼15 pM for single-stranded targets and ∼5 nM for targets on denatured plasmids. By incorporating a blocked aptamer sequence, we also detected small molecules using the same sensor architecture. This work provides a starting point for multiplexed detection of multi-strain pathogens, and disease states caused by genetic variants (e.g., sickle cell anemia).

Design of a biochemical circuit motif for learning linear functions.

Learning and adaptive behaviour are fundamental biological processes. A key goal in the field of bioengineering is to develop biochemical circuit architectures with the ability to adapt to dynamic chemical environments. Here, we present a novel design for a biomolecular circuit capable of supervised learning of linear functions, using a model based on chemical reactions catalysed by DNAzymes. To achieve this, we propose a novel mechanism of maintaining and modifying internal state in biochemical systems, thereby advancing the state of the art in biomolecular circuit architecture. We use simulations to demonstrate that the circuit is capable of learning behaviour and assess its asymptotic learning performance, scalability and robustness to noise. Such circuits show great potential for building autonomous in vivo nanomedical devices. While such a biochemical system can tell us a great deal about the fundamentals of learning in living systems and may have broad applications in biomedicine (e.g. autonomous and adaptive drugs), it also offers some intriguing challenges and surprising behaviours from a machine learning perspective.

Biophysically inspired rational design of structured chimeric substrates for DNAzyme cascade engineering.

The development of large-scale molecular computational networks is a promising approach to implementing logical decision making at the nanoscale, analogous to cellular signaling and regulatory cascades. DNA strands with catalytic activity (DNAzymes) are one means of systematically constructing molecular computation networks with inherent signal amplification. Linking multiple DNAzymes into a computational circuit requires the design of substrate molecules that allow a signal to be passed from one DNAzyme to another through programmed biochemical interactions. In this paper, we chronicle an iterative design process guided by biophysical and kinetic constraints on the desired reaction pathways and use the resulting substrate design to implement heterogeneous DNAzyme signaling cascades. A key aspect of our design process is the use of secondary structure in the substrate molecule to sequester a downstream effector sequence prior to cleavage by an upstream DNAzyme. Our goal was to develop a concrete substrate molecule design to achieve efficient signal propagation with maximal activation and minimal leakage. We have previously employed the resulting design to develop high-performance DNAzyme-based signaling systems with applications in pathogen detection and autonomous theranostics.

Breast volume estimation from systematic series of CT scans using the Cavalieri principle and 3D reconstruction.

OBJECTIVES: Preoperative breast volume estimation is very important for the success of the breast surgery. In the present study, two different breast volume determination methods, Cavalieri principle and 3D reconstruction were compared. MATERIAL AND METHODS: Consecutive sections were taken in slice thickness of 5 mm. Every 2nd breast section in a set of consecutive sections was selected. We marked breast tissue with blue line on each selected section, and so prepared CT scans used for breast volume estimation. The volumes of the 60 breasts were estimated using the Cavalieri principle and 3D reconstruction. RESULTS: The mean breast volume value was established to be 467.79 ± 188.90 cm(3) with Cavalieri method and 465.91 ± 191.41 cm(3) with 3D reconstruction. The mean CE for the estimates in this study was calculated as 0.25%. Skin-sparing volume was about 91.64% of the whole breast volume. Both methods are very accurate and have a strong linear association. CONCLUSION: Our results suggest that the calculation of breast volume or its part in vivo from systematic series of CT scans using the Cavalieri principle or 3D breast reconstruction is accurate enough to have a significant clinical benefit in planning reconstructive breast surgery. These methods can help the surgeon guide the choice of the most appropriate implant or/and flap preoperatively.

Visual displays that directly interface and provide read-outs of molecular states via molecular graphics processing units.

The monitoring of molecular systems usually requires sophisticated technologies to interpret nanoscale events into electronic-decipherable signals. We demonstrate a new method for obtaining read-outs of molecular states that uses graphics processing units made from molecular circuits. Because they are made from molecules, the units are able to directly interact with molecular systems. We developed deoxyribozyme-based graphics processing units able to monitor nucleic acids and output alphanumerical read-outs via a fluorescent display. Using this design we created a molecular 7-segment display, a molecular calculator able to add and multiply small numbers, and a molecular automaton able to diagnose Ebola and Marburg virus sequences. These molecular graphics processing units provide insight for the construction of autonomous biosensing devices, and are essential components for the development of molecular computing platforms devoid of electronics.

Signal propagation in multi-layer DNAzyme cascades using structured chimeric substrates.

Signal propagation through enzyme cascades is a critical component of information processing in cellular systems. Although such systems have potential as biomolecular computing tools, rational design of synthetic protein networks remains infeasible. DNA strands with catalytic activity (DNAzymes) are an attractive alternative, enabling rational cascade design through predictable base-pair hybridization principles. Multi-layered DNAzyme signaling and logic cascades are now reported. Signaling between DNAzymes was achieved using a structured chimeric substrate (SCS) that releases a downstream activator after cleavage by an upstream DNAzyme. The SCS can be activated by various upstream DNAzymes, can be coupled to DNA strand-displacement devices, and is highly resistant to interference from background DNA. This work enables the rational design of synthetic DNAzyme regulatory networks, with potential applications in biomolecular computing, biodetection, and autonomous theranostics.

Exercises in molecular computing.

CONSPECTUS: The successes of electronic digital logic have transformed every aspect of human life over the last half-century. The word "computer" now signifies a ubiquitous electronic device, rather than a human occupation. Yet evidently humans, large assemblies of molecules, can compute, and it has been a thrilling challenge to develop smaller, simpler, synthetic assemblies of molecules that can do useful computation. When we say that molecules compute, what we usually mean is that such molecules respond to certain inputs, for example, the presence or absence of other molecules, in a precisely defined but potentially complex fashion. The simplest way for a chemist to think about computing molecules is as sensors that can integrate the presence or absence of multiple analytes into a change in a single reporting property. Here we review several forms of molecular computing developed in our laboratories. When we began our work, combinatorial approaches to using DNA for computing were used to search for solutions to constraint satisfaction problems. We chose to work instead on logic circuits, building bottom-up from units based on catalytic nucleic acids, focusing on DNA secondary structures in the design of individual circuit elements, and reserving the combinatorial opportunities of DNA for the representation of multiple signals propagating in a large circuit. Such circuit design directly corresponds to the intuition about sensors transforming the detection of analytes into reporting properties. While this approach was unusual at the time, it has been adopted since by other groups working on biomolecular computing with different nucleic acid chemistries. We created logic gates by modularly combining deoxyribozymes (DNA-based enzymes cleaving or combining other oligonucleotides), in the role of reporting elements, with stem-loops as input detection elements. For instance, a deoxyribozyme that normally exhibits an oligonucleotide substrate recognition region is modified such that a stem-loop closes onto the substrate recognition region, making it unavailable for the substrate and thus rendering the deoxyribozyme inactive. But a conformational change can then be induced by an input oligonucleotide, complementary to the loop, to open the stem, allow the substrate to bind, and allow its cleavage to proceed, which is eventually reported via fluorescence. In this Account, several designs of this form are reviewed, along with their application in the construction of large circuits that exhibited complex logical and temporal relationships between the inputs and the outputs. Intelligent (in the sense of being capable of nontrivial information processing) theranostic (therapy + diagnostic) applications have always been the ultimate motivation for developing computing (i.e., decision-making) circuits, and we review our experiments with logic-gate elements bound to cell surfaces that evaluate the proximal presence of multiple markers on lymphocytes.