Creating high-quality polygonal meshes which represent the membrane surface of neurons for both visualization and numerical simulation purposes is an important yet nontrivial task, due to their irregular and complicated structures. In this paper, we develop a novel approach of constructing a watertight 3D mesh from the abstract point-and-diameter representation of the given neuronal morphology. The membrane shape of the neuron is reconstructed by progressively deforming an initial sphere with the guidance of the neuronal skeleton, which can be regarded as a digital sculpting process. To efficiently deform the surface, a local mapping is adopted to simulate the animation skinning. As a result, only the vertices within the region of influence (ROI) of the current skeletal position need to be updated. The ROI is determined based on the finite-support convolution kernel, which is convolved along the line skeleton of the neuron to generate a potential field that further smooths the overall surface at both unidirectional and bifurcating regions. Meanwhile, the mesh quality during the entire evolution is always guaranteed by a set of quasi-uniform rules, which split excessively long edges, collapse undersized ones, and adjust vertices within the tangent plane to produce regular triangles. Additionally, the local vertices density on the result mesh is decided by the radius and curvature of neurites to achieve adaptiveness. With a rapid technical development, increasing attention has been paid to biologically detailed brain visualization and simulation. As the most significant information-processing cells in the brain, neurons are made up of dendrites and axons, which are responsible for receiving and sending signals, respectively. Each neuron is an electrical entity and various methods have been proposed to simulate the electrical behavior of neurons. However, due to the complexity of neuronal structure, most of these methods simulate the conduction behavior of electrical and biochemical signals in a low-dimensional space. Recently, there is a growing requirement to simulate cellular behavior based on 3D high-fidelity models  queries of an initial model, weighted deformations and local topology reconstructions, which can be represented with vertices and triangles. The whole workflow consists of the following steps  as input. A neuronal tree typically origins from the soma node, and consists of a number of neurite segments that further bifurcate at branching points. While such segments should have a reasonable length in order to faithfully and effectively portray the geometry of the neuron, it is not unusual to find neurons with overly short segments in common data repositories such as tree see .In order to generate the neuronal membrane mesh, a skeletal mapping-based ROI strategy is proposed, which allows local deformation region queries. A list container is attached to each skeleton node for storing the indices of the vertices that are close to the current node. The mappings are created procedually, and the mesh vertex mappings are created based on the exact projection position of the current neuronal segment that deforms the vertex in question. We first query skeleton nodes in the ROI, and then query their mapping vertices on the membrane surface mesh. Compared with the ROI query algorithm based on space partition proposed in Zhu et al. , our metSkeleton-based implicit surfaces are usually introduced to create branching structures , and progressively deforms it to approximate the target shape, which is defined as a convolution surface based on embedded neuronal line skeletons. Therefore, the final neuronal membrane surface mesh achieves high-order smoothness at arbitrary branches.Vi with a low-pass filter function f. Given a skeleton segment:Convolution surfaces can be regarded as an isosurface embedded in a three-dimensional scalar field, which is calculated by convolving the geometric skeleton f:R3 → R, the contribution of the potential value of the current skeleton in question at point p is the convolution of the function f and gi:and a kernel function: Among them, the kernel function can be divided into infinite support , λi is its weight factor, and T is the threshold for extracting the iso-surface from the embedded potential scalar field.where p on the membrane mesh, the adopted local approximation scheme assumes that the closest skeleton to p is a line segment with infinite length. This assumption could not only reduce unnecessary convolution calculation beyond support radius, but also create natural blending between adjacent line segments due to their smooth thickness variations. Therefore, it is highly suitable for large amount of neuronal morphology approximation with complex graph structures. Actually, the potential contribution of an infinite skeleton to an arbitrary 3D position p can be calculated as:For a point di is the average distance between the ith skeleton and p, and T is still the global iso-value for the entire convolution surfaces; Ri is the effective radius of the kernel function corresponding to the current skeleton, which can be set to be an empirical value of 2di in our experiments; λi is the weight of the current skeleton segment, which can be derived analytically.where The solution to the above kernel function is based on the assumption that the value of the kernel function outside a certain distance is infinitesimal. Therefore, the fourth-order polynomial kernel function is usually preferred for its local support characteristics, which could reduce huge convolution computation.The implementation of approximation is to project each vertex of the remeshed surface onto the implicit convolution surface in their respective gradient directions by performing standard Newton iterations  a triangular mesh representation of implicit surfaces should approximate the underlying geometry as accurate as possible, and (2) the mesh vertex distribution and connectivity should satisfy high-quality triangles with similar edge lengths coincident to the same polygon, allowing smooth membrane surface visualization and more stable voxelization for simulation.A quasi-uniform mesh .A uniform mesh is composed of polygon elements of roughly the same size, while a non-uniform mesh has elements of different sizes. In many aspects, uniform meshes enjoy such advantages as stable structures and efficient high-level geometric algorithms over non-uniform ones. However, creating uniform meshes is non-trivial especially for surfaces with complex topology. Therefore, a relaxation on constraints on uniform meshes is used here. As defined in Stanculescu et al. , a mesh M and a threshold lT, we can ensure that all edges of M are smaller than lT by iteratively splitting the edges larger than lT, known as lT tight mesh. The split operation amounts to adding a vertex at the midpoint of an edge, and correspondingly splitting the relevant triangle face into two adjacent triangles. The iteration over the edges is performed using a simple queue, and the resulting new edges are inserted into the queue.Given a closed manifold mesh lT tight property is not the only factor to ensure high quality triangles, since edge collapses should be performed to delete too short edges to generate higher-quality triangle meshes. A edge collapse operation moves the two vertices of a short edge to its midpoint to collapse the edge, ensuring that all the edges of M will not be shorter than the collapse threshold d . Moreover, it is necessary to check the validity of the collapse operation before each implementation. If a collapse operation produces intersected triangles, the operation is illegal and will not be performed for the current edge.The lT tightness property on the mesh. Therefore, before establishing the lT tight mesh M, it is necessary to perform the collapse operation to ensure that the minimum length of the mesh edges is greater than d, and then the lT tightness property should be restored. In fact, d can be taken as an internal parameter of a quasi-uniform mesh, since it is bound to lT:d <= lT/2, ensuring that the edges greater than lT will not be divided into edges shorter than d while establishing the lT tight mesh after a collapse operation.Obviously, it is difficult to guarantee that the lengths of all the edges generated after the collapse operation are greater than the minimum length d, and the collapse operation may also destroy the Based on the quasi-uniform mesh configuration, too long or too short edges should be splitted or collapsed, respectively, which will be followed by mesh optimizations including vertex valence equalization, triangle area equalization, and implicit surface projection see .In order to optimize each triangle of a mesh as regularly as possible, an edge flip operation can be applied to achieve the vertex valence of 6. Similar to edge collapses, a legality check is also needed before each flip implementation.M, we can get a quasi-uniform triangular mesh. The edge lengths of the mesh are basically close to the target length l, which satisfies d < l < lT, and the valences of the vertices are close to 6. However, the triangle areas and the edge lengths may still vary with each other, which can be further optimized. Therefore, we carry out a vertex fine-tuning through an area-based tangential smoothing. Each vertex pj is assigned a weight that equals to its averaged area A(pj), and a tangential smoothing moves pj to its weighted barycentric positionAfter iteratively performing the above optimization operations on the mesh Following each tangential smoothing, a projection onto implicit surfaces can be performed for each smoothed vertex , otherwini is the normal vector and λ is the damping factor for oscillation avoidance. Vertices with relatively large Voronoi regions have more weight, attracting mesh vertices and thus reducing their own area. Experiments show that the mesh area of vertex-related region can be averaged without too many iterations.where To ensure robust generation of high-quality mesh models for neuronal morphologies, a mesh optimization for high-resolution levels of details is required. However, a high-resolution representation usually results in too many tiny triangles on the surface parts with low curvatures, which not only imposes high burden on memory but also wastes unnecessary computational time. On the other hand, in order to generate topologically correct surfaces at branches robustly, a relative high-resolution surface mesh is preferred. Therefore, the marching step size of the mesh evolution should be related to both the skeletal structures and mesh vertex densities so as to create neuronal morphology with adaptive edge lengths.The robustness of the surface mesh generation at branches and the corresponding mesh vertex density depend on the angles between the branches, which are used to control the marching step size along the skeletons. Actually the mesh vertex density can be determined according to the target edge length in the current region, which is indirectly related to the branching angles. It should be noted that the mesh vertices in the ROI ought to be queried as fast as possible for efficient mesh evolution, so the step sizes of both the marching along skeletons and the convolution surface approximation should not exceed the lower length limit of the mesh edges. The details for determining vertex densities at a branch are as follows:Separating point. According to the angle of a neuron branch and the support radius R of the target convolution surfaces, the separating position t at the branch can be pre-calculated. Since the distances between t and the two bifurcations are both R, the projection points of t on the skeleton segments can be deduced based on the branching angle θ, which correspond to the positions with the maximum vertex densities in dynamically adaptive mesh evolution , the Guangdong High Level Innovation Research Institute (2021B0909050004), the Natural Science Foundation of Shanghai (20ZR1420100), and the Key-Area Research and Development Program of Guangdong Province (2021B0909060002).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


This paper seeks to explore the potential function of technological innovation and clean power in mitigating the ecological footprint in the N-11 nations during the phase 1992–2015 by applying panel cointegration analysis. The outcomes of the panel cointegration test signify the occurrence of a long-run relation among the clean energy (CE) variable, the ecological footprint (EF) variable, the per capita GDP (Y) variable, the financial development (FIN) variable, and technological innovation (TI) variable. The outcomes of the VECM signify a long-run causal relation from the ecological footprint (EF) variable to the clean energy (CE) variable, the GDP per capita (Y) variable, and technological innovation (TI) variable. This implies that the environmental degradation faced by the N-11 countries leads to shifting toward clean energy sources and technological innovation in the long run. Thus, the N-11 countries are in need to design policies that enhance shifting toward environmentally friendly energy sources. Industrialization has been one of the main reasons of climate alteration during the past decades. The industrial revolution that spanned in the eighteenth century has been associated with intensive use of accumulated capital which accelerated the economic growth rates; however, it was also associated with the spurring of harmful gases that were the main causes of global warming and environmental degradation. As a result, the issue of climate change has gained great concerns on the international levels and policy makers started to search for environmentally friendly technologies that could be utilized to maintain the goodness of the environment  by applying panel cointegration analysis with the aim of providing policy makers with the appropriate policies to ameliorate the quality of the environment in those nations. This paper complements the previous literature through several contributions. The first contribution of this paper is to utilize the ecological footprint as a gauge of climate change and environmental deterioration instead of using the carbon dioxide releases or the greenhouse gas releases. The ecological footprint is a consumption-based indicator of environmental sustainability measured in global hectares (gha) that accounts for people’s demand on biological assets and the supply of nature  that accounts for people’s demand on biological assets and the supply of nature. On the demand side, the ecological footprint calculates the amount of ecological assets in terms of the total area of land and water that the population needs to produce its needed goods and to assimilate its residuals and wastes. On the supply side, the ecological footprint gauges the ability of the productive land and water in meeting the nation’s demand to produce the needed consumption goods and absorb the residuals including the emissions of harmful gases. Therefore, if a nation’s entire demand on its ecological assets needed to produce its consumption needs exceeds the nature’s capacity, it is said that the nation runs an ecological deficit. A nation that runs an ecological deficit satisfies its consumption needs either by importing the needed consumption goods that cannot be produced given the available ecological assets or by overusing its supply of natural land and water  or by polluting the atmosphere through the emissions of harmful gases. On the other hand, if a nation’s supply of ecological assets (country’s biocapacity) exceeds its demand on natural resources, the nation is said to have an ecological reserve. The ecological footprint indicator accounts for six categories of the ecological assets: cropland, fishing grounds, forest products, grazing land, built-up land, and carbon demand on land  in this study and t denotes the time frame. The time frame in this study covers the period (1992–2015). β refers to the slope coefficient of the corresponding variable. It shows that a one unit rise in the explanatory variable will increase (decrease) the ecological footprint by β units, holding other variables constant. εit denotes the estimation residual. Table The proposed model for this study is based on the study of Demir et al. . The modentclass1pt{minimaTable Based on the studies of Jin and Kim , Saidi aThis paper performed the ADF and the PP tests to test stationarity. Both tests are established upon the Chi-square  variable, the GDP per capita (Y) variable, the clean energy (CE) variable, the financial development (FIN) variable, and the technological innovation (TI) variable follow the I(1) process. The urban population (URB) variable follows the I(2) process. The human capital index (H) variable follows the I(0) process. By considering these results, we will continue with performing the cointegration tests to explore the long-run relation among the variables that pursue the I(1) process.This paper carried out the Pedroni cointegration test to evaluate the association in the long-run among the non-stationary variables that pursue the I(1) process. To prove the reliability of the Pedroni cointegration test results, this paper performed the test of Kao cointegration. The null hypothesis of the two tests is the non-existence of cointegration between variables  which reveals the absence of a long-run association between the variables. If the null hypothesis is rejected, then the residuals will be I(0) which indicates a long-run association among the variables. The Pedroni cointegration test derives seven test statistics from the estimated residuals. Four of the seven test statistics are established upon within-dimension statistics. The other three test statistics are established upon between-dimension statistics  variables will be associated with the spurious problem which will lead to misleading results through showing a significant relationship between unrelated series  coefficient . The second phase is estimating the dynamic VECM. The dynamic VECM is stated as demonstrated.The VECM enables us to assess the short- and the long-run dynamics of the cointegrated series. The significance of the variables lagged differences is examined to determine the short-run causal relations. The significance of ECT is examined to determine the long-run causal relations. A negative and statistically significant error correction term signifies the occurrence of a long-run causal relation  variable, the clean energy (CE) variable, the per capita GDP (Y) variable, the financial development (FIN) variable, and the technological innovation (TI) variable. The VECM enables us to assess both the short- and the long-run dynamics of the cointegrated series. Regarding the short-run causal relations, there exists short-run causal link from financial development to clean power. Moreover, there exists short-run bidirectional causal relation between financial development and technological innovation. Regarding the long-run dynamics, there exists a long-run causal relation from the ecological footprint, the clean energy, the financial development, and the technological innovation to the per capita GDP. Additionally, there exists a long-run causal relation from the ecological footprint, the per capita GDP, the financial development, and the technological innovation to clean energy. Moreover, a long-run causal relation exists from the ecological footprint, the GDP per capita, the clean power, and the financial development to technological innovation.The outcomes of the VECM imply that the environmental degradation  faced by those nations leads to shifting toward clean energy sources and technological innovation in the long-run. Technological innovation enhances improving the energy efficiency and shifting toward green technologies that are environmentally friendly Ibrahiem . Thus, tClean power sources and technological innovation that improves energy efficiency can be utilized as tools of fostering the economic progress in the long run. Thus, clean energy sources can replace the fossil fuel energy sources which promote the economic progress in the Next 11 nations without deteriorating the environment. This is consistent with the policies of the international organizations which give great concern to the matter of lessening the climate change with the aim of shifting to cleaner and sustainable sources of power.Finally, financial development can promote economic progress in the Next 11 nations without causing environmental contamination in the long-run. Financial development enhances the productivity of the inputs and fosters capital accumulation which promotes the economic growth. Financial development can be associated with environmental degradation if it promoted the energy intensive industries (Ibrahiem, Further research could focus on the role of nuclear power in enhancing the development and improving the environmental quality of the developing nations.


With the high penetration of photovoltaic (PV) and electric vehicle (EV) charging and replacement power stations connected to the distribution network, problems such as the increase of line loss and voltage deviation of the distribution network are becoming increasingly prominent. The application of traditional reactive power compensation devices and the change of transformer taps has struggled to meet the needs of reactive power optimization of the distribution network. It is urgent to present new reactive power regulation methods which have a vital impact on the safe operation and cost control of the power grid. Hence, the idea that applying the reactive power regulation potential of PV and EV is proposed to reduce the pressure of reactive power optimization in the distribution network. This paper establishes the reactive power regulation models of PV and EV, and their own dynamic evaluation methods of reactive power adjustable capacity are put forward. The model proposed above is optimized via five different algorithms and approximated through the deep learning when the optimization objective is only set as line loss and voltage deviation. Simulation results show that the prediction of deep learning has an incredible ability to fit the Pareto front that the intelligent algorithms obtain in practical application. Renewable energy sources (RES) such as the photovoltaic (PV) system have played an important part in reducing environmental pollution in recent years due to their ability to reduce greenhouse effects . As a kiNevertheless, with the rapid development of electric vehicles (EVs) in recent years, it is becoming increasingly possible to alleviate this negative phenomenon. Some studies show that EVs can not only carry out routine charging operation, but also profitably provide power to the power grid after the EVs with new technology called vehicle-to-grid (V2G) are connected to the power supply . TherefoSome research has shown that when distributed PV and EVs are connected to the distribution network, reactive power optimization of distribution system will be a complex discrete, nonconvex and nonlinear problem . In the The characteristics of PV and EVs show the ability to provide reactive power support for a distribution network. As a premise, the future development trend of different types of EVs was well estimated in reference . With thSome researchers have synthesized the active power generation and consumption as constraints and considered the cost of reactive power injection. The particle swarm optimization (PSO) was used to optimize the reactive power . SimilarBy contrast, as a popular technology in recent years, deep learning (DL) may replace the traditional optimization process in the aspects of fitting data and improving the efficiency. The biggest feature of this method is that it can use a large amount of data for supervised learning and predict the optimization results in other cases via the previous optimization data set ,22,23. IIn general, the reactive power optimization only considers the regulation of traditional equipment without the participation of PV systems or EVs, so that the reactive power regulation capacities of these new regulation sources are wasted. In this work, PV and EVs are simultaneously employed to participate in reactive power optimization in a distribution network, which can greatly decline the pressure of traditional reactive power regulation and improve the regulation flexibility and performance.To address the multi-objective reactive power optimization, the meta-heuristic based Pareto optimization algorithms easily result in a long computation time to acquire the high-quality Pareto optimal solutions. Besides, they easily lead to different Pareto front in different runs due to their random operators. In contrast, the proposed deep learning-based Pareto optimization algorithm can acquire the high-quality Pareto optimal solutions within a short computation time since it cannot experience multiple iterative operators. Moreover, it is a deterministic algorithm to guarantee a high optimization stability.Thus, the innovations of the proposed technique for reactive power optimization in this paper can be summarized as follows:The rest of this paper is structured as follows. In Generally, small photovoltaic equipment is usually connected to the distribution network. PV scattered at different nodes are characterized by high flexibility and can effectively improve the performance of power system ,25. WhenWith the inverter is added behind the PV system, the reactive power regulation model is closely related to it. The output active power of the PV system will affect the range of reactive power directly in the current regulation model, as follows:EVs have basically been popularized in some big cities and their surrounding areas through some surveys. Therefore, the V2G technology of EVs can be used to assist the PV system. EVs can store more electric energy in the battery at the highest rate when the working efficiency of the PV system is too high. Similarly, EVs idled in the charging station can discharge to the distribution network at the highest rate during peak power consumption. The quantitative change will produce qualitative change and provide or absorb a large amount of power when there are a certain number of EVs connected to the distribution network . The PV The input or output active power of EVs In this work, the active power of EVs is not the optimization variable in the multi-objective reactive power optimization in a distribution network. Therefore, the optimization model will be solved by assuming the fixed active power of EVs at each time period. In a practical application, the active power of EV can be acquired via the real-time data acquisition or power forecasting. During the transmission of EVs to the distribution network, the calculation of reactive power regulation range and principle is similar to that of the PV system. Its upper limits The most important thing in the process of power generation and transmission is its economy and security, both of which are indispensable. Part of the energy will lose and convert into heat due to the resistance of the conductor when the electricity is transmitted through the transmission network. Besides, the surface density of field exceeds the breakdown strength of the surrounding air after the conductor is charged, resulting in corona loss of the thin layer of air around the conductor. Thus, line loss, which includes the loss of heat and corona loss, can be regarded as an important optimization goal related to the economy . When DGThere will be some constraints when using MOEA to optimize the MOPs. They include the constraints of power flow in power system and the setting of some important parameter ranges in distribution network .The conventional power flow equality constraints can be written as follows:The generator outputs should satisfy the following constraints:The actions of reactive power compensation device and transformer tap should be limited within their bounds, as follows:To guarantee a safe operation, the node voltage and line transmission power should satisfy the following constraints:Note that the presented multi-objective reactive power optimization in Equations (5)–(9) is a NP-hard optimization with a high non-linearity and multiple local optimums . In geneThe promising-region-based evolutionary many-objective algorithm (PREA)  investigThe strength Pareto evolutionary algorithm 2 (SPEA2)  stores tA fast non-dominated sorting genetic algorithm II (NSGA-II) is proposed in  to reducNSGA-III  can be rTwo-phase framework (ToP)  is the mIn this paper, deep deconvolutional neural network (DDNN) is applied to acquire the knowledge between the reactive power regulation command and the Pareto optimal solutions based on the standardized data. DDNN, which is based on a deep neural network (DNN) and developed from a fully connected neural network, improves the fault tolerance rate and data analysis ability of data processing. For good measure, DDNN will avoid an excessive co-adaptation between different neurons to a large degree. Then, the new network that is more robust than before can be applied to handle the overfitting problem. Thus, according to the reactive power regulation command, the trained DDNN can directly generate the approximate PF, as follows (10):To obtain multiple sets of data for prediction with DL, a variety of intelligent algorithms are applied to the optimization of the problem. Both continuous variables and discrete variables will exist in the process of reactive power optimization. Continuous variables can be iterated according to normal optimization, and discrete variables need to be rounded by continuous spatial values. Besides, the fitness function of each algorithm should also add a penalty mechanism to ensure that the optimization results meet the above constraints, as follows (11):The advantages of different algorithms will be reflected in the PF. Thus, the prediction can concentrate and use the optimized data obtained by the algorithms with different characteristics. The method that calculates the Euclidean distance between the infeasible solution and the feasible solution is common in correcting the infeasible solution. Consequently, the points on the PF can be sorted and pruned before the data training. In order to better extract the characteristics of the optimized data obtained by multiple algorithms, the number of layers of the DL network will be set to five in this work. Each layer is set as a fully connected layer, and the dimension of the input features are different. The process of multi-objective reactive power optimization can be summarized as In this work, IEEE 14-bus and IEEE 33-bus distribution network will be used to test the different algorithms. The topology of the two models are shown in Generally, the main influencing factors of PV output power are current temperature and solar irradiation. Thus, different solar irradiation will be set in different PV to observe its impact on the reactive power regulation range. In addition, each EV charging and replacement station accommodates 10 EVs, and the residual power of each EV is 0–10 kW by considering actual optimal scheduling problem of EVs . The ranIt is necessary to ensure that the initialization conditions are the same when the different intelligent algorithms for optimization are used. The population number and maximum iteration of the optimization process are set to 50 in order to simplify the calculation and accelerate the experimental progress. In the process of optimization, five different algorithms will operate in the same environment. The solution differences between DL and other five meta-heuristic-based Pareto optimization algorithms are presented in In addition, the five algorithms and DL have almost the same goal of minimizing line loss in IEEE 14-bus system from Take the IEEE 33-bus system as an example to verify the prediction effect of DL in this kind of model. Two PV and three EV charging and replacement power stations are set as same as the above experiments. Every PV can output 10 kinds of active power values, and the output data of EV charging and replacement stations are set randomly. The above five algorithms are used to optimize the model respectively, and a large amount of optimized reactive power data is obtained through the above active power. The optimized results in each case will be used as samples for DL training. Offline training only takes a few minutes to get the same number of solutions as the number of data groups. Then the training results integrated comprehensively by the five algorithms will be used in the same case. Some dominated solutions will be deleted, and some virtual solutions will be supplemented, so that the PF will become more smooth than before. There is no doubt that the supplementary solution must remain within the constraints. Thus, a set of solutions with five different excellent algorithms can be used for prediction.The comparison between the prediction results of DL and the integrated results from five algorithms is shown in In this work, a novel DL based Pareto optimization method is proposed for multi-objective reactive power optimization in a distribution network with PV and EVs, which contains the following contributions:(1) By taking the participation of PV and EVs into account, the reactive regulation burden of the distribution network can be effectively reduced. As a result, the operation economy and the voltage quality of the distribution network can be further improved. Simulation results demonstrate that the line loss can be reduced by 25.2% by the proposed method compared to that without the participation of PV and EVs on the IEEE 14-bus system, and 7.7% on the IEEE 33-bus system. In addition, the voltage deviation can be reduced by 0.16% and 0.38% on the IEEE 14-bus and IEEE 33-bus systems, respectively.(2) The proposed technique for multi-objective reactive power optimization is verified on IEEE 14-bus and IEEE 33-bus systems, which is compared with five different intelligent algorithms. Simulation results show that the proposed DL method can rapidly acquire a high-quality PF, while another five algorithms can complete the optimization task well, and the Pareto optimal solutions have little difference in the degree of optimization. Particularly, the computation time of DL is only 1.26% of that by NSGA-II on the IEEE 33-bus system, while the average line loss and voltage deviation of all the Pareto optimal solutions obtained by DL are the smallest among all the algorithms.Although the proposed DL based multi-objective reactive power optimization can perform well on the test systems, it still easily faces two main challenges. Firstly, it should spend a long computation time on the data acquisition and network training. Secondly, it will easily lead to multiple infeasible solutions due to the poor generalization of DL with insufficient data. To handle these two problems, our future work will focus on the historical data utilization and the generalization for DL.


Development of computational biology methodologies has provided comprehensive understanding of the complexity of microbiomes, and the extensive ways in which they influence their environment. This has awakened a new research goal, aiming to not only understand the mechanisms in which microbiomes function, but to actively modulate and engineer them for various purposes. However, current microbiome engineering techniques are usually manually tailored for a specific system and neglect the different interactions between the new genetic information and the bacterial population, turning a blind eye to processes such as horizontal gene transfer, mutations, and other genetic alterations. In this work, we developed a generic computational method to automatically tune the expression of heterologous genes within a microbiome according to given preferences, to allow the functionality of the engineering process to propagate in longer periods of time. This goal was achieved by treating each part of the gene individually and considering long term fitness effects on the environment, providing computational and experimental evidence for this approach.Recent research in the field of bioinformatics and molecular biology has revealed the immense complexity and uniqueness of microbiomes, while also showcasing the impact of the symbiosis between a microbiome and its host or environment. A core property influencing this process is horizontal gene transfer between members of the bacterial community used to maintain genetic variation. The essential effect of this mechanism is the exposure of genetic information to a wide array of members of the community, creating an additional “layer” of information in the microbiome named the “plasmidome”. From an engineering perspective, introduction of genetic information to an environment must be facilitated into chosen species which will be able to carry out the desired effect instead of competing and inhibiting it. Moreover, this process of information transfer imposes concerns for the biosafety of genetic engineering of microbiomes as exposure of genetic information into unwanted hosts can have unprecedented ecological impacts. Current technologies are usually experimentally developed for a specific host/environment, and only deal with the transformation process itself at best, ignoring the impact of horizontal gene transfer and gene-microbiome interactions that occur over larger periods of time in uncontrolled environments. The goal of this research was to design new microbiome-specific versions of engineered genetic information, providing an additional layer of compatibility to existing engineering techniques. The engineering framework is entirely computational and is agnostic to the selected microbiome or gene by reducing the problem into the following set up: microbiome species can be defined as wanted or unwanted hosts of the modification. Then, every element related to gene expression  and regulation is individually examined and engineered by novel algorithms to provide the defined expression preferences. Additionally, the synergistic effect of the combination of engineered gene blocks facilitates robustness to random mutations that might occur over time. This method has been validated using both computational and experimental tools, stemming from the research done in the iGEM 2021 competition, by the TAU group. The word “microbiome” refers to various types of microorganisms that exist together in a certain environment. Almost every system, natural or synthetic, is colonized by a distinct and varied population of organisms that engage with one another and with their surroundings on a regular basis. Related early research has revealed that the microbiome of an animal has a significant impact on critical characteristics, such as the lifespan and the fitness of the animal . In receFecal microbiome transplants have been shown to have positive effects for various bowl diseases such as Crohn’s disease ,8 and clThe use of genetically modified bacteria in soil microbiome improves nitrogen fixation in crops and is a commercially available technology ;The gut brain axis and connections between the microbiome and mental health have been largely explored, mostly in animal models. The use of fecal matter transplant as treatment has been largely explored  althoughSome strategies that entail the introduction of new or changed bacteria into the environment, for example genomic engineering, and microbiome directed evolution, have been successful in some scenarios ,6,7,8, mBacteria share genetic information through horizontal gene transfer  regularlRather than dealing with genetic constructs as they are, this work offers a different perspective over said gene blocks in which each genetic component associated with gene expression is analyzed and artificially transformed. In the human gut microbiome, for example, certain bacteria are symbiotic while some are harmful . In thisSince horizontal gene transfer (HGT) is such a central property of microbiomes, it naturally dictates the development of microbiome engineering techniques, posing a situation in which, all members of the microbiome are exposed to the plasmidome . MoreoveFurthermore, this technique can be applied even with relatively limited genomic data and utilize different levels of characterization that might be collected for a microbiome. The present approach works with annotated genomes, but it might also work with metagenomically assembled genomes. Finally, this approach is intended to alter the microbiome over an extended time duration. Each genetic component is analyzed and handled separately, therefore the genetic information is relatively resistant to environmental harm, as seen in Genes are expressed using a combination of different mechanisms and regulations. The process of evolution is driven by the occurrence of small and random changes, due to the inherent inaccuracy of biological machinery. These changes are then subjected to the power of natural selection, dictating which ones will be passed to future generations. As they drive the process of differentiation into species and phylogenetic progression, they affect every machinery and component in the cell, including the parts related to gene expression. By creating biophysical models for gene expression and regulation processes, the cumulative effect of random alterations is highlighted and emphasized, facilitating the different species-specific preferences of the agents which carry out these processes. In this paper, we will focus on three main steps in this process, starting from the entry of foreign genetic information into the cell, through the process of transcription up until the translation into proteins see . The synFitting genetic elements to a microbiome is defined in this paper in a rather generic manner. Once the gene itself is selected, there are two sub-communities of interest; first is the community of organisms that should be able to express the modification and will be referred to as the “wanted hosts”. The second is the group of species that should not be able to express this modification, named the unwanted hosts. The goal of the optimization process is to increase expression in the set of wanted hosts, while simultaneously decreasing expression of the same sequence in the unwanted host, considering the fitness effect on both sub communities. These biophysical algorithms are then used to accumulate and combine the small differences occurring between different species, amplifying their collective effect while remaining within the limitations of noisiness of metagenomic data. The architecture and engineering process facilitated by the algorithms, along with the use of additional databases, helps fill in the missing details and characterization automatically. However, in order to utilize the full potential of the microbiome specific collected data, additional data can be submitted and analyzed flexibly.Algorithms exhibited in Genes themselves are regulated with multiple elements, but the region that specifically codes for the protein itself is named the coding sequence (CDS) or the open reading frame (ORF), as it is the frame read by the ribosome. One of the most interesting characteristics of the genetic code, aside from its universality, is the innate redundancy present in it, posed by the combinatorial number of available codons and existing amino acids. As described in the previous section, this degree of freedom has been subjected to random changes which compile into general phylogenetically dictated preferences of the translation machinery. This preference of specific synonymous codons over others for the same amino acid is usually quantified using metrics called codon usage biases (CUB) ,19.Since ribosomes are a limiting resource in cells, these so called “synonymous changes” in the ORF may have a significant in vivo effect by influencing the flow of ribosomes on the mRNA sequence  20], th, th20], Codon harmonization is used to increase translation efficiency of a sequence for a specific organism, meaning in the context of a single proteome, considering a single set of gene expression machinery ,21,22. FInstead of solely optimizing expression, facilitation of increasing expression for the set of wanted hosts should be coupled to impairment of expression in the unwanted hosts.The degree of complementarity between the sequence and its’ corresponding microbiome should be carefully modulated considering these concerns. Data for CUB calculation: various techniques and points of view can be used in order to emphasize different aspects of the CUB, usually limited by available data. This tool is equipped to deal with the two main methods to do so; the first looks at the frequencies of different codons in different types of genes, assuming highly expressed genes are likely to have optimal codons, called the codon adaptation index CAI [ndex CAI . Secondlndex CAI  takes inOnce the CUB scores of each organism are calculated, the most important features are the mean (μ) and standard deviation (σ), calculated for every considered proteome .Codon adaptation index (CAI): the underlying assumption is that highly expressed genes have a higher selective pressure to be optimally expressed, thus they are more likely to be consistent of codons that are translated efficiently. In other words, the penalty of having a non-optimal codon out of the synonymous options is much higher in terms of fitness in highly expressed genes compared to lowly expressed genes [ed genes . AccordiEach codon has a usage score e (RSCU) , that isIn order to translate the scores from single codon scores to scoring a gene with L codons, the geometric mean calculation is applied to all RSCU scores of the codons of a gene of interest (GOI).tRNA adaptation index (tAI): CAI is calculated from an evolutionary perspective, highlighting the selective pressure effects on fitness. The tAI measure takes a different approach, aiming to capture the effect of interaction strengths between components of the ribosome, and the supply of said reaction components, highlighting factors related to the physiochemical state of the cell. Each synonymous codon is characterized considering the codon-anticodon noncovalent bond strength, and the corresponding abundance of the recognizing tRNA, as each codon can be recognized by numerous tRNA molecules by wobble interactions. In order to determine the abundance of different tRNAs, the most optimal measurement would be to measure the expression of the tRNA molecules themselves. However, while this method might work very well for gene expression, tRNA molecules are highly modified RNA sequences and are also very similar to each other, making sequencing outputs inaccurate. The selected measure for this purpose is the tGCN, tRNA genomic copy number of the different tRNAs, using the correlation between the copy number of the molecule and its contribution to the tRNA pool . The reasoning behind this is similar to the reasoning of the CAI measurements. Citing fitness considerations, if a tRNA that is highly used has a gene duplication, it will allow the cells to produce more of said tRNA and reduce the expression burden of the specific gene. The calculation of translation efficiency measure Normalization of each score and calculation of the final score of each gene are done in the same manner as in the CAI score:Whole microbiome optimization: as mentioned, the main goal of this step is to maintain optimization for multiple organisms while doing so in a selective manner. In order to accomplish those requirements two designing techniques were generated. In the first method the selected model examines the total effect of synonymous changes on the whole microbiome by comparing them to each proteome they will integrate into, named the “proteome relative method”. The second method, called the “individual amino acid method”, calculates and assigns a loss to each codon, without considering the relativity of the scores to the innate score of the microbiome. While the first approach is likely to have a higher degree of complementarity to the microbiome, it creates dependencies between the different amino acids as the protein is considered as a whole, instead of examination of each amino acid individually like in the second method. As such, optimization using the proteome relative method is done using a greedy hill climbing algorithm, which can converge into a local minimum and is more computationally intensive compared to the optimization of individual amino acids. Proteome-relative method: The effect of a quantitative change in the CUB score of a heterologous gene is relative to the endogenous CUB scores of the proteins in the environment- if the CUB scores of the proteome of a species have a wider distribution and a larger standard deviation, a small change in the CUB of the engineered gene might be less significant. The neighborhood of modified sequences during the iteration are sequences based on For each modified sequence, the CUB score in every tested organism Due to the number of constraints and considerations, the algorithm was designed to function greedily in an iterative manner. The iterations were constructed to modify the selected DNA sequence  maximum or exceeding the defined number of iterations allowed.Individual amino acid method: Along with the CAI and tAI codon usage bias measurements described in B. subtilis balanced by the lowest possible expression levels for E. coli. The weights Two optimization strategies were used to select the codon that will facilitate the highest expression levels for Ratio score (R):Difference score (D):The codon with the highest score will be chosen upon synonymous options to encode for its corresponding amino acids. Abbreviations formatted as “CUB scoring method”- optimization score. CAI results were unique since the ratio and difference optimized sequences were identical.Result evaluation: a novel evaluation score is defined as the average distance between the cluster of wanted hosts and the cluster of unwanted hosts for an additional score, comparing the normalized changes between the initial and engineered sequence. The optimization score for each organism is defined as:A positive optimization score means that the sequence was optimized compared to the non-engineered version, thus for wanted hosts the results should be as positive as possible and for unwanted hosts they should be negative. The formula for the final optimization index:As shown for the scores themselves, a higher index indicates that translation is more efficient in the wanted hosts compared to the unwanted hosts, while a lower score indicates the opposite. Gene transcription is initialized in prokaryotes by the recognition of promoter sequences, which are found upstream to a gene, and the recruitment of TFs to allow RNA polymerase to initiate transcription. The core promoters are defined as the exact segment to which the sigma factor in bacterial RNA-polymerase binds . While cFor the purpose of this study, promoter sequences were defined as the first 200 bp upstream to the ORF and intergenic sequences as all sequences on the same strand that neither belong to the ORF nor to the promoter sequences A.Motif discovery: A Position-Specific Scoring Matrix (PSSM) is usually used to represent sequence motifs, as nucleotides can vary in different positions along the sequence. A PSSM of size 4 × L contains the probability of each nucleotide to appear in each position of a motif of length L. PSSM probabilities are calculated assuming motif sites are independent one from another and neglecting insertions or deletions in the motif sequence.p-value of 0.05. Two sets of enriched motifs related to transcription were searched  software tool was used to search for enriched motifs in primary set when compared to a set of control sequences. STREME uses hidden Markov model (HMM) to scan the query sequences for enriched motifs of configured length up to a certain significance threshold ,27. In tsearched B.Transcription enhancing motifs: to ensure a motif is related to transcription activation in wanted hosts, motifs were searched from the third most highly expressed (inferred from expression data or CUB measurements) promoters of each wanted host with the promoter sequences defined as the primary input and the intergenic sequences as the control. Motifs discovered in this run configuration are enriched in sequences associated with gene expression, which likely indicates their desirable regulatory role.Transcription inhibiting anti-motifs: to verify a motif will not promote transcription in unwanted hosts, motifs were searched for each unwanted host with the intergenic sequences defined as the primary input and the promoter sequences of the third most highly expressed genes as the control. The discovered motifs, termed “anti-motifs”, are common in sequences that are not associated with gene expression, which likely indicates their transcription suppressing attribute.For an input of Single motif set construction: Let ng steps C: Calculating motif similarity thresholds: The measurement used to quantitate motif similarity is spearman correlation, which is calculated between a pair of PSSMs. In order to determine the basic amount of similarity between motifs to consider, a different threshold is calculated for each organism. Let Defining an initial motif set: The initial motif set Calculating motif scores: Motif score for a single organism:Aggregated motif score, using a tuning parameter Constructing the final motif set: Let For the purpose of this study, Promoter selection and tailoring: MAST (Motif Alignment and Search Tool) [ch Tool)  is used Restriction enzymes are the first line of defense in the bacterial immune system, they have a specific ability to recognize a nucleotide sequence and digest it, thus protecting bacteria from the effects of foreign DNA entering it. One of the most variable properties of different bacteria, is their array of recognized restriction sites and footprint of restriction enzymes, as demonstrated in The cleaved product may have different forms, depending on the specific type of restriction enzyme which performed the cleavage action. In some cases, the digestion products have complementary edges that can reattach due to the bacterial DNA repair mechanisms . TherefoAs in other bio-design cases, the topological complexion and scale of effect of the genetic element is directly related to the magnitude of its effect and engineering potential of it. As result, this work focuses solely on the ORF and does not modulate other genetic elements, in fear to disrupt its action as a byproduct. Changes in the ORF sequence have the most predictable outcome, making them the best candidate for this algorithm. Restriction site detection and filtration: in this preprocessing step, each restriction site is classified as one of the following: sites uniquely recognized by the wanted hosts or unwanted hosts, and sites recognized by both. The goal of this algorithm is to avoid any site present in a wanted host, whether or not it is present in an unwanted host as well, while simultaneously adding sites recognized only by the unwanted hosts without disrupting the sequence of amino acids.Insertion of sites: overlapping sites can obviously not be inserted together, as the insertion of one site disrupts the presence of the other, thus the objective is to specifically introduce sites that maximize the number of unwanted species that can recognize and digest the sequence, as the total number of present sites is also pursued as a secondary goal.In order to increase the overall probability of digestion as defined, all appearances  are located along the sequence. The first sites to be incorporated are the conflict free sites. In the overlapping site case, as described in The order in which the conflicting sites are resolved obviously determine the final DNA sequence to some degree as well, due to the greediness of the algorithm. In order to get the most optimal result given the insertion mechanism, the sites are iterated starting from those with the least complicated conflict up to those with the highest degree of complexity and as result, largest degree of freedom in the final choice of the site.Avoidance of sites originating from wanted hosts: The sites from the first and third group should be avoided, and their presence in the engineered sequence should be disrupted and altered using synonymous changes if possible. This algorithm re-writes this requirement as constraints that can be applied to the sequence using the DnaChisel [naChisel  softwareThe Restriction enzyme database (Rebase) is a database of information about two types of enzymes: restriction enzymes and methyltransferases. The characterization of these enzymes details their origin, recognition sites, and other metadata such as the year of discovery or commercial availability. The detailed sites themselves are noted using standard abbreviations to represent sequence ambiguity, and in some cases note the exact digestion pattern and resulting ends. The database is constantly updated, as the rate of metagenomic sequencing increases, the fraction of computationally inferred restriction enzymes becomes more prominent  ,31. In version 110 (release date: 28 September 2021), there is a total of 4735 organisms , as all strains of the same species were considered altogether. They contain a total number of 5488 sites, with an average of 4.6 sites per organism. The number of sites highly varies, as the standard deviation is 21.2. Materials and plasmids: PCR master mix, DpnI, Gibson Assembly kit, PCR cleaning kit, competent E. coli and plasmid miniprep kit were purchased from NEB. E. coli k-12, B. subtilis PY79 and AEC804-ECE59-P43-synthRBS-mCherry plasmid were kindly provided by Prof. Avigdor Eldar and primers were synthesized by IDT.Solutions: Bacillus transformation (BT) solution: 80.5 mM dipotassium dihydrate, 38.5 mM potassium dihydrogen phosphate, 3 mM trisodium citrate, 45 µM ferric ammonium citrate, 2% glucose, 0.1% casein hydrolysate, 0.2% potassium glutamate and 10 mM magnesium sulfate in DDW.Trace elements solution (x100): 123 mM magnesium chloride hexahydrate, 10 mM calcium chloride, 10 mM iron chloride hexahydrate, 1 mM manganese chloride tetrahydrate, 2.4 mM zinc chloride, 0.5 mM copper chloride dihydrate, 0.5 mM cobalt chloride hexahydrate and 0.5 mM sodium molybdate.Minimal medium: 1X M9 solution, 1X trace elements solution, 0.1 mM calcium chloride, 1 mM magnesium sulfate, 0.5% glucose, and chloramphenicol (5 µg/mL).Plasmid construction: software-designed mCherry gene variants were synthesized by IDT and cloned into AEC804-ECE59-P43-synthRBS-mCherry plasmid, to replace the original mCherry gene via Gibson assembly method. Briefly, the original mCherry gene was excluded from the vector by PCR, with primers containing complementary tails to each of the software-designed mCherry variants. PCR products were treated with DpnI to degrade the remains of the original vector and cleaned with PCR cleaning kit. Next, each software-designed mCherry gene was cloned into the vector by Gibson assembly with 1:2 molar ratio (vector: insert) and transformed into competent E. coli. Positive colonies were confirmed by colony PCR and sequencing, and the new plasmids were extracted with miniprep kit.Bacterial transformation: all plasmids harboring the modified mCherry genes were separately transformed into competent E. coli k-12 following the standard protocol, and into B. subtilis PY79. For the latter, one bacterial colony was suspended in BT solution (see solutions) and grew at 37 °C for 3.5 h. Then, the plasmid was added to the bacterial solution (1 ng/1 uL), and following 3 h incubation, bacteria was spread over pre-warmed agar plates.Fluorescence measurement assay: for each tested mCherry variant, a single colony containing the modified plasmid was grown overnight in LB medium. Then, bacterial suspension was centrifuged and resuspended in PBSx1 twice. Following the second wash, the bacterial suspension was centrifuged again, and the pellet was resuspended in minimal medium (see solutions). The bacterial suspension was allowed to grow for 4 h. Then, bacteria were diluted with minimal medium to obtain an OD600 nm of 0.2, loaded into a 96-well plate and grew for 17 h at 37 °C with continuous shaking. Fluorescence (ex/em: 587/610 nm) and bacterial turbidity (at OD600 nm) were measured every 20 min. Each sample was tested in triplicates at three independent experiments. Computational log-phase detection: growth curves (OD600 nm) were plotted over time, and linearity that represents this phase was detected by sequential removal of the last point in the linear phase. Then, a linear trendline was fitted to the curve, and if the removal of the point increased the slope of the curve, that point was considered not part of the log phase. These iterations were conducted continuously until ⅛ of the graph is left or if two iterations did not change the calculated slope. Statistical analysis: We calculated p-values with a permutation test. Briefly, for every optimization, the three experiments from the same organism were averaged and a difference between E. coli and B. subtilis was calculated. Then, splitting, averaging, and distance calculations were performed, to assess if the separation between E. coli and B. subtilis is significant. The P-value is defined as the percent of splits in which the difference between the two is larger than the difference between the original split. Evaluation of the results was carried out through both in silico and in vitro means, each shedding light on different aspects of the engineering process. The in-silico examination tested the resolution of differentiation between wanted and unwanted hosts, and the sensitivity to the community size and complexity, while the in vivo experiment was able to quantify the facilitated change in gene expression, and the effect it had on bacterial fitness.The two main characteristics to be tested for this algorithm are the phylogenetic resolution of optimization, and the ability of this engineering approach to scale up.A. thaliana soil microbiome [The selected microbiome for model analysis is a sample of the crobiome , which ccrobiome  . The clear correlation (0.737 spearman correlation) between the optimization index and estimated phylogenetic distance sided with our expectations given the underlying assumptions on which the algorithm was constructed. Moreover, the resolution of selectivity than can be achieved by this engineering method, as tested in In order to probe the resolution question a bit deeper, the 10% phylogenetically closest pairs were further examined. The mean optimization index is 1.215 ± 0.8, which is a relatively significant optimization in respect to the low degree of phylogenetic diversity presented. In conclusion, these results point out the likelihood of this optimization to be able to effect realistic sizes and microbiome complexities. As previously mentioned, promoters have a complex topology, thus the characterization of the effect of any engineering process is less complete compared to other engineered elements. This was taken into account both in the transcription algorithm design and in the analysis, using light selection and modulation in a less direct approach and trying to conserve the innate promoters’ structure as much as possible. The evaluation of the designed algorithm was done in two steps; first the ability to differentiate motifs between wanted and unwanted hosts was closely inspected, and only then was the scale up of the algorithm investigated in a similar manner to the translation efficiency model . Examination of the differentiation capacity of the algorithm between wanted and unwanted hosts  was doneAlthough the two species in The dataset chosen for examination of the scale up of the algorithm was the MGnify genome dataset , which hThe overall trend observed in all three microbiomes is the decrease in E-values scores for increasing microbiome sizes. This result is rather counter-intuitive, as larger microbiomes are more complex and thus are expected to be more difficult to optimize. However, since the algorithm uses a single motif set to fit all wanted hosts, in case of a large phylogenetic variance in the wanted hosts group, as may be the case when using randomized samples of species from different phyla, the performance of the algorithm per each host in the group is sub-optimal. When using larger microbiomes, since the dataset used contains species from a finite set of phyla, it is more likely to select wanted hosts with a closer phylogenetic distance, thus improving the fitness of the motifs in the final set for a larger number of wanted hosts.Additionally, it should be noted that the cow rumen microbiome has overall lower E-value scores for both wanted and unwanted hosts in comparison with the human oral and marine microbiomes, with less differentiation between wanted and unwanted groups. In terms of the number of species B, the huIn a rich microbiome that has phylogenetically distant species, such as the human oral and marine microbiomes, small sub-microbiomes are likely to have higher degree of distinction between wanted and unwanted hosts since the overlap between the groups is relatively small. When the size of the sub-microbiome increases, it is more likely to include wanted and unwanted hosts from overlapping phyla, thus minimizing the differentiation between the groups. When examining microbiomes that are less diverse, such as the cow rumen microbiome, randomly selected species of wanted and unwanted groups are likely to be more similar even for small sub-microbiomes, thus reducing the observed effect of microbiome size, as increasing the sub-microbiome size does not incur a proportional increase in the phylogenetic diversity of the wanted and unwanted hosts which isn’t already captured for smaller sub-microbiomes. In conclusion, while the analysis exhibits the ability of the transcription optimization model to differentiate between the group of wanted and unwanted hosts, it also highlights the effect of the phylogenetic richness of the microbiome, adding an additional consideration for the selection process of a sub-microbiome of interest for future applications of the model. In order to capture the full effect of the diversity of known sites and species, the characterized species were used as a pool to select sub microbiomes from, and asses the scale up of our model along with other properties. The optimized sequence is the same one used for ORF optimization, of the ZorA phage resistance gene. To examine the performance of our algorithm for different sizes of microbiomes, 10 random microbiomes of the tested sizes were optimized and evaluated.An important remark to be made prior to more in depth explanations, is to clarify the reason why the engineered sequence even has restriction sites recognized by enzymes present in the wanted group of hosts. A natural assumption could be that this is a byproduct of the insertion of sited from unwanted species, however this is not the case- the last optimization process aims to eliminate the part of this effect that occurs due to that reason. The problem is, restriction sites are innately ambiguous, so in many cases these degrees of freedom are larger than those posed by the redundancy of codons, thus synonymous codon swaps cannot remove the presence of these restriction sites. In all cases the new version has a larger amount of restriction sites recognized by unwanted hosts compared to wanted hosts , for percentiles ranging from 5–95%. Continuing the trend presented in the scale-up examination, for all tested ratios the algorithm was able to create a degree of differentiation between the percent of the group that has a restriction site between the wanted and the unwanted host, remaining largely indifferent to the presented condition. The main conclusion is that not only is the algorithm able to create a degree of distinction between the wanted and unwanted groups of hosts, but that it is able to do so in a way that fully expresses and accommodated the specific customizations and requirements of the engineering process. E. coli and B. subtilis. The software generated five variants of the gene’s ORF that were predicted to preferentially express in B. subtilis (wanted bacteria), but not in E. coli (unwanted bacteria). Each version was cloned into a plasmid  was plotted against time  of B. subtilis were divided by those of E. coli. The mCherry variants TDR-D, and more robustly tAI-D, clearly demonstrated selectivity toward B. subtilis, with regard to growth rates  while also decreasing for the unwanted host (E. coli). Furthermore, when examining the corresponding fitness effect reflected in the bacterial growth rate, the engineered sequence does not impose a major burden in the wanted host as compared to the initial sequence, the unwanted hosts experienced a significant reduction in fitness. These findings can be enhanced and investigated further by performing a co-culture experiment aimed at studying the dynamics of a population.We used both in silico and in vitro studies to validate this engineering process. In vitro testing revealed that the optimization method had a positive influence on the expression rate of a chosen protein , which wThe visible decreasing trend in fitness of the unwanted hosts caused by the optimization process may not have a major impact in lab conditions, but the significance of this change is that when dealing with an actual microbiome especially considering the relevant time periods of the modification. There is a stronger evolutionary pressure against the plasmid’s presence in unwanted hosts, therefore further propagating the designed expression differentiation in the microbiome. The in-silico investigation provided interesting insights on each of the computational approaches separately. The significance of the various tests is determined by the scale-up process- from two species to a complete microbiome.Origin of replication (ORI): The origin of replication has the greatest impact on the long-term microbiome-plasmid interactions, since it is the genetic factor that stimulates replication of the genetic data. Thus, tailoring the ORI to a specific subset of the microbiome species should considerably increase the efficiency of our technology (as shown in Clustered regulatory interspaced repeats (CRISPR) memory: CRISPR memory is another possibly effective method for distinguishing between distinct species. The modification plasmid is then engineered to contain target sub-sequences that match the unwanted hosts CRISPR system  and the translation initiation. This separate model should consider the location of the RBS relatively to the translation start site (TSS), as well as the folding of the mRNA around it (round it .Multiple optimizations: The next stage in this suggested method would be to create a set of alternative sequences instead of a single optimized solution. Each sequence in the set will be engineered to have poor expression in the unwanted hosts organisms while having greater expression levels in the wanted hosts organisms (as seen in Validations: The OIL-PCR method by Peter J. Diebold et al. [d et al.  enables The goal of this paper is to highlight this innovative technique, which suggests that microbiome engineering can be accomplished by adjusting individually genetic components that are related to gene-expression. The following elements should be included in the model in order improve and strengthen the method proposed in this article: Bio-sensing: microbiomes are continually reacting to environment changes, which means they have the ability to detect and respond to them [ to them . This me to them ,42,43,44Bio production/Bio degradation: the microbiome’s functional effect on the ecosystem is assisted through biomolecule production and destruction. This natural ability might be used in a variety of situations (some examples are exhibited in Microbiome specific therapy and enhancement: providing selective expression of heterologous genes within a microbiome can be used in order to repair and add capabilities while improving biosafety and functionality. Soil microbiome therapy [ therapy ,53,54 an therapy ,56, are The capability of this method to work automatically for a defined microbiome and specified gene is one of its key features. Thus, it might be used in a variety of fields, which have been categorized into three sections:


Nasopharyngeal swab sample collection is the first-line testing method for diagnosing COVID-19 infection and other respiratory infections. Current information on how to properly perform nasopharyngeal swabbing in children is largely defective. This study aimed at collecting nostril to nasopharynx distance measurements on lateral skull radiographs of children and adolescents to design a nasopharyngeal swab meant to standardize and facilitate the sample collection procedure. A total of 323 cephalograms of 152 male and 171 female children aged 4–14 years taken for orthodontic reasons were selected. On each cephalogram, the shortest distance between the most anterosuperior point of the nostril contour and the nasopharynx outline was measured in mm parallel to the palatal plane. Descriptive statistics of the measurements were calculated for each age group. The lower limit of the 95% confidence intervals of the measurements was taken as a reference to design a swab shaft with marks that, at each age, delimitate a safety boundary for swab progression up to the posterior nasopharyngeal wall. The simplification of the procedure enabled by the newly designed nasopharyngeal swab is valuable to help healthcare providers perform specimen collection on children in a safe and effective way, perhaps under the less-than-ideal conditions possibly occurring in ‘point-of-need’ contexts. Currently, the global strategy to contain the spread of the COVID-19 pandemic is based on the early detection of suspected cases, early diagnosis of symptomatic patients, and the isolation of infected patients. For the confirmation of COVID-19 infection cases, the World Health Organization recommends performing nucleic acid amplification tests (NAAT test) with the reverse polymerase chain reaction technique (RT-PCR) on respiratory secretion samples collected with nasopharyngeal or oropharyngeal swab ,3,4.Nasopharyngeal swab has been routinely used in the past for the diagnosis of viral upper respiratory tract infections in adults and children and has generally been performed in hospitalized symptomatic patients by trained personnel. However, the need for large-scale diagnostic tests, imposed by the pandemic spread of the COVID-19 infection, has required the rapid training of health personnel assigned to perform sample collection not only in hospital or ‘point-of-care’ settings but also, for screening purposes, in ‘point-of-need’ contexts, such as schools, ports, airports, and parking lots ,5.It has been reported that the training of the sample collection personnel was sometimes hasty and limited to just viewing educational videos ,10,11,12Based on the collected data, a swab shaft can be designed with marks that signpost the advisable depth of insertion of the swab within the inferior nasal meatus at the different ages of childhood and adolescence to more predictably reach the posterior nasopharynx for proper sampling, without any trauma or excessive discomfort for the young patient.The research was a retrospective cross-sectional study approved by the Pediatric Ethics Committee of Tuscany, Italy (No. 81/2021 of 23/03/2021).The study sample included 323 lateral skull radiographs taken for orthodontic purposes in 152 male and 171 female children of Caucasian race, aged between 4 years and 2 months and 14 years. The radiographs were derived from the database of the orthodontic private practice of one of the authors (CG), as well as from the Orthodontic Clinic of the University Hospital of Careggi in Florence. The subjects’ parents had given their informed consent to the use of the radiographs for the study. Subjects with severe systemic diseases and craniofacial deformities were excluded from the sample. Different skeletal or dental malocclusions, breathing patterns, habits, adenoid hypertrophy, enlarged tonsils, or snoring were not considered exclusion criteria. All the radiographs had been taken with the use of a cephalostat, and the subject was positioned with the Frankfort horizontal plane parallel to the floor and the teeth in centric occlusion. Selected cephalograms were of good quality, with the airway clearly visible and the subject is not swallowing. Measurements were calibrated by means of the ‘Calibration’ tool of the DeltaDent 2.2.0 software . The ‘Free measurement’ tool of the same software was utilized to measure, in mm, the shortest distance between the most anterosuperior point of the nostril contour and the nasopharynx outline parallel to the palatal plane  .In subjects exhibiting adenoid hypertrophy, the posterior landmark was taken on the anterior outline of adenoid tissue ,22 Figu.One investigator (AVo) made all the measurements. Forty randomly selected cephalograms were traced a second time two weeks apart, 20 by the same investigator and 20 by a different investigator (EP), in order to determine intra- and inter-observer reproducibility through the calculation of intra-class correlation coefficients (ICCs).t-test for independent samples was applied within each age group to verify whether a statistically significant difference existed in the measured distance between males and females. Only in the age-4 group, as the data distribution was found to be non-normal, was the Mann-Whitney U test applied for between-gender comparison. In all the analyses, the level of significance was set at p < 0.05. Statistical calculations were handled by the PASW Statistics 18.0.0. software .Descriptive statistics of the nostril to nasopharynx distance data were calculated for each age group separately, and within each age group, the data were stratified by gender and presence of adenoid hypertrophy . The staThe ICCs were 0.99 and 0.98 for intra- and inter-examiner repeatability, respectively.p = 0.644). The correlation was statistically significant (p < 0.001). No statistically significant difference emerged between males and females in any age range except 8–10 years. In this age interval, males had a longer nostril to nasopharynx distance than females. The occurrence of adenoid hypertrophy was more common between 7 and 9 years of age. Based on the 95% confidence intervals of the nostril to nasopharynx distance data collected for the different age groups, a swab shaft was designed with marks indicating the proper depth of penetration at different ages of childhood and adolescence (lescence . Specifilescence . TherebyThe marks are painted with colored ink along the molded plastic shaft that ends with a tip coated with nylon fibers  23]..23].The authors CG, AVi, and LF are the inventors of several embodiments of the aforementioned swab shaft, currently patent pending.Nasopharyngeal swab testing is currently the most commonly performed and most largely validated diagnostic method for COVID-19 infection ,9. An acInadequate sampling can yield false negative results, which bring about a relevant risk of disease spread. Particularly, it is crucial to properly collect upper airway secretions by reaching the nasopharyngeal region since the expression of ACE2 receptors is higher in this area than in the proximal part of the nose ,24. PropIt can additionally be predicted that the need for COVID-19 diagnostic testing of children will remain in the near future, although vaccination campaigns are ongoing in several countries, as the pediatric age will be the last age category to be targeted by this preventive measure.Furthermore, COVID-19 large-scale testing of children will still long be requested for their admittance to school, sport, and hobby activities, as well as to travel across countries. Such screening procedures will oftentimes be performed in ‘point-of-need’ contexts, perhaps under less-than-ideal sample collection conditions.Even after the COVID-19 pandemic emergency is overcome, healthcare providers will still resort to nasopharyngeal swabbing as the appropriate test for differential diagnosis of several respiratory infections, which are particularly common in the pediatric age.The current study stemmed from the discernment that the information available to healthcare workers on how to properly perform nasopharyngeal swabbing in children was largely defective. Of particular concern is the lack of guidelines on the proper depth of swab penetration along the nasal floor in children of different ages.By using lateral skull radiographs acquired for orthodontic purposes, the present study collected a database of linear measurements of the nostril to nasopharynx distance in a large sample of growing subjects that was stratified by age and gender.The statistical analysis confirmed that age-related differences in the nostril to nasopharynx distance exist in children, and they should be given due consideration by operators. Since measurements were statistically comparable between the two genders in the great majority of age intervals, male and female data were pooled together for the calculation of descriptive statistics within each age group. The calculated descriptive statistics directed the design of an intuitive, user-friendly swab shaft, helping the operators perform nasopharyngeal swabs in children in a more standardized, controlled, and harmless way . KaufmanConcerning the landmarks that were selected for the linear measurements on the lateral skull radiographs, the most antero-superior point of the nostril contour was chosen as an easily traceable point identifying the emergence of the swab shaft from the nose at the skin level. As for the posterior landmark, in the presence of adenoid hypertrophy, the measurement stopped at the adenoid anterior outline. In this regard, it is worth mentioning that whether SARS-CoV-2 can replicate the adenoid tissue remains an unanswered question . It has The investigation also pointed out that the nostril to nasopharynx distance stabilized after 11 years of age at around a value of 80 mm .The performed linear measurements of the nostril to nasopharynx distance demonstrated high intra- and inter-examiner repeatability. It was preferred to measure the shortest distance between the nostril and nasopharynx rather than the curvilinear path along the sagittal view of the nasal floor because the latter method revealed lower reliability in a preliminary pilot study, mainly due to the superimposition of the permanent teeth buds with the anatomic structures of interest.Although the sample was large overall, the 4–4.11 age group had a relatively small size. This may have reflected a low power of the statistical test assessing the between-gender difference. The latter lacked statistical significance, although being quite remarkable. The low numerosity may also have accounted for the relatively large standard deviation and confidence interval that as calculated in this age group and was related to the reduced number of patients undergoing orthodontic treatment at such an early age. Despite the relatively large 95% confidence interval, by referring to its lower limit for the placement of the age-related mark on the shaft, a safety boundary for swab progression was provided, implying that when the mark is aligned with the nostril, the tip is most likely about to reach the nasopharyngeal posterior wall. This observation warns the operator to further advance very cautiously in order to avoid any discomfort or harm to the small child.As a further development of the research, it is planned to perform the measurements also on radiographs of children belonging to ethnic groups other than the Caucasian race, which was investigated in the present study.Additionally, a clinical study for the validation of the newly designed nasopharyngeal swab is in progress.Based on the nostril to nasopharynx distance measurements taken on lateral skull radiographs, a nasopharyngeal swab prototype was designed, featuring marks along the shaft that pinpoint the proper depth of penetration along the nasal floor at the different ages of childhood and adolescence. The new swab is meant to standardize and ease the nasopharyngeal swabbing procedure for the benefit of healthcare providers, who may have to perform the maneuver on poorly cooperating children or under less-than-ideal sample collection conditions in parking lots, airports, stations, schools, and other ‘point-of-need’ contexts. The newly designed nasopharyngeal swab is valuable for child specimen collection and will still be useful even after the COVID-19 pandemic emergency is overcome since healthcare providers will still resort to nasopharyngeal swabbing as the appropriate test for differential diagnosis of several respiratory infections, which are particularly common in the pediatric age.The authors CG, AVi, and LF are the inventors of several embodiments of the aforementioned swab shaft, currently patent pending.