Physical Phenomena Governing Mineral Morphogenesis in Molluscan Nacre.

Mollusks, as well as many other living organisms, have the ability to shape mineral crystals into unconventional morphologies and to assemble them into complex functional mineral-organic structures, an observation that inspired tremendous research efforts in scientific and technological domains. Despite these, a biochemical toolkit that accounts for the formation of the vast variety of the observed mineral morphologies cannot be identified yet. Herein, phase-field modeling of molluscan nacre formation, an intensively studied biomineralization process, is used to identify key physical parameters that govern mineral morphogenesis. Manipulating such parameters, various nacre properties ranging from the morphology of a single mineral building block to that of the entire nacreous assembly are reproduced. The results support the hypothesis that the control over mineral morphogenesis in mineralized tissues happens via regulating the physico-chemical environment, in which biomineralization occurs: the organic content manipulates the geometric and thermodynamic boundary conditions, which in turn, determine the process of growth and the form of the biomineral phase. The approach developed here has the potential of providing explicit guidelines for the morphogenetic control of synthetically formed composite materials.

A Review of IoT Firmware Vulnerabilities and Auditing Techniques.

In recent years, the Internet of Things (IoT) paradigm has been widely applied across a variety of industrial and consumer areas to facilitate greater automation and increase productivity. Higher dependability on connected devices led to a growing range of cyber security threats targeting IoT-enabled platforms, specifically device firmware vulnerabilities, often overlooked during development and deployment. A comprehensive security strategy aiming to mitigate IoT firmware vulnerabilities would entail auditing the IoT device firmware environment, from software components, storage, and configuration, to delivery, maintenance, and updating, as well as understanding the efficacy of tools and techniques available for this purpose. To this effect, this paper reviews the state-of-the-art technology in IoT firmware vulnerability assessment from a holistic perspective. To help with the process, the IoT ecosystem is divided into eight categories: system properties, access controls, hardware and software re-use, network interfacing, image management, user awareness, regulatory compliance, and adversarial vectors. Following the review of individual areas, the paper further investigates the efficiency and scalability of auditing techniques for detecting firmware vulnerabilities. Beyond the technical aspects, state-of-the-art IoT firmware architectures and respective evaluation platforms are also reviewed according to their technical, regulatory, and standardization challenges. The discussion is accompanied also by a review of the existing auditing tools, the vulnerabilities addressed, the analysis method used, and their abilities to scale and detect unknown attacks. The review also proposes a taxonomy of vulnerabilities and maps them with their exploitation vectors and with the auditing tools that could help in identifying them. Given the current interest in analysis automation, the paper explores the feasibility and impact of evolving machine learning and blockchain applications in securing IoT firmware. The paper concludes with a summary of ongoing and future research challenges in IoT firmware to facilitate and support secure IoT development.

UnSafengine64: A Safengine Unpacker for 64-Bit Windows Environments and Detailed Analysis Results on Safengine 2.4.0.

Despite recent remarkable advances in binary code analysis, malware developers still use complex anti-reversing techniques that make analysis difficult. Packers are used to protect malware, which are (commercial) tools that contain diverse anti-reversing techniques, including code encryption, anti-debugging, and code virtualization. In this study, we present UnSafengine64: a Safengine unpacker for 64-bit Windows. UnSafengine64 can correctly unpack packed executables using Safengine, which is considered one of the most complex commercial packers in Windows environments; to the best of our knowledge, there have been no published analysis results. UnSafengine64 was developed as a plug-in for Pin, which is one of the most widely used dynamic analysis tools for Microsoft Windows. In addition, we utilized Detect It Easy (DIE), IDA Pro, x64Dbg, and x64Unpack as auxiliary tools for deep analysis. Using UnSafengine64, we can analyze obfuscated calls for major application programming interface (API) functions or conduct fine-grained analyses at the instruction level. Furthermore, UnSafengine64 detects anti-debugging code chunks, captures a memory dump of the target process, and unpacks packed files. To verify the effectiveness of our scheme, experiments were conducted using Safengine 2.4.0. The experimental results show that UnSafengine64 correctly executes packed executable files and successfully produces an unpacked version. Based on this, we provided detailed analysis results for the obfuscated executable file generated using Safengine 2.4.0.

Stimulation-mediated reverse engineering of silent neural networks.

Reconstructing connectivity of neuronal networks from single-cell activity is essential to understanding brain function, but the challenge of deciphering connections from populations of silent neurons has been largely unmet. We demonstrate a protocol for deriving connectivity of simulated silent neuronal networks using stimulation combined with a supervised learning algorithm, which enables inferring connection weights with high fidelity and predicting spike trains at the single-spike and single-cell levels with high accuracy. We apply our method on rat cortical recordings fed through a circuit of heterogeneously connected leaky integrate-and-fire neurons firing at typical lognormal distributions and demonstrate improved performance during stimulation for multiple subpopulations. These testable predictions about the number and protocol of the required stimulations are expected to enhance future efforts for deriving neuronal connectivity and drive new experiments to better understand brain function.NEW & NOTEWORTHY We introduce a new concept for reverse engineering silent neuronal networks using a supervised learning algorithm combined with stimulation. We quantify the performance of the algorithm and the precision of deriving synaptic weights in inhibitory and excitatory subpopulations. We then show that stimulation enables deciphering connectivity of heterogeneous circuits fed with real electrode array recordings, which could extend in the future to deciphering connectivity in broad biological and artificial neural networks.

Serial co-expression analysis of host factors from SARS-CoV viruses highly converges with former high-throughput screenings and proposes key regulators.

The current genomics era is bringing an unprecedented growth in the amount of gene expression data, only comparable to the exponential growth of sequences in databases during the last decades. This data allow the design of secondary analyses that take advantage of this information to create new knowledge. One of these feasible analyses is the evaluation of the expression level for a gene through a series of different conditions or cell types. Based on this idea, we have developed Automatic and Serial Analysis of CO-expression, which performs expression profiles for a given gene along hundreds of heterogeneous and normalized transcriptomics experiments and discover other genes that show either a similar or an inverse behavior. It might help to discover co-regulated genes, and common transcriptional regulators in any biological model. The present severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic is an opportunity to test this novel approach due to the wealth of data that are being generated, which could be used for validating results. Thus, we have identified 35 host factors in the literature putatively involved in the infectious cycle of SARS-CoV viruses and searched for genes tightly co-expressed with them. We have found 1899 co-expressed genes whose assigned functions are strongly related to viral cycles. Moreover, this set of genes heavily overlaps with those identified by former laboratory high-throughput screenings (with P-value near 0). Our results reveal a series of common regulators, involved in immune and inflammatory responses that might be key virus targets to induce the coordinated expression of SARS-CoV-2 host factors.

P2P Cloud Manufacturing Based on a Customized Business Model: An Exploratory Study.

To overcome the problems of long production cycle and high cost in the product manufacturing process, a P2P (platform to platform) cloud manufacturing method based on a personalized custom business model has been proposed in this paper by integrating different technologies such as deep learning and additive manufacturing (AM). This paper focuses on the manufacturing process from a photo containing an entity to the production of that entity. Essentially, this is an object-to-object fabrication. Moreover, based on the YOLOv4 algorithm and DVR technology, an object detection extractor and a 3D data generator are constructed, and a case study is carried out for a 3D printing service scenario. The case study selects online sofa photos and real car photos. The recognition rates of sofa and car were 59% and 100%, respectively. Retrograde conversion from 2D data to 3D data takes approximately 60 s. We also carry out personalized transformation design on the generated sofa digital 3D model. The results show that the proposed method has been validated, and three unindividualized models and one individualized design model have been manufactured, and the original shape is basically maintained.

A Machine Learning Approach to Simulate Gene Expression and Infer Gene Regulatory Networks.

The ability to simulate gene expression and infer gene regulatory networks has vast potential applications in various fields, including medicine, agriculture, and environmental science. In recent years, machine learning approaches to simulate gene expression and infer gene regulatory networks have gained significant attention as a promising area of research. By simulating gene expression, we can gain insights into the complex mechanisms that control gene expression and how they are affected by various environmental factors. This knowledge can be used to develop new treatments for genetic diseases, improve crop yields, and better understand the evolution of species. In this article, we address this issue by focusing on a novel method capable of simulating the gene expression regulation of a group of genes and their mutual interactions. Our framework enables us to simulate the regulation of gene expression in response to alterations or perturbations that can affect the expression of a gene. We use both artificial and real benchmarks to empirically evaluate the effectiveness of our methodology. Furthermore, we compare our method with existing ones to understand its advantages and disadvantages. We also present future ideas for improvement to enhance the effectiveness of our method. Overall, our approach has the potential to greatly improve the field of gene expression simulation and gene regulatory network inference, possibly leading to significant advancements in genetics.

Characterization of Highly Ferulate-Tolerant Acinetobacter baylyi ADP1 Isolates by a Rapid Reverse Engineering Method.

Adaptive laboratory evolution (ALE) is a powerful approach for improving phenotypes of microbial hosts. Evolved strains typically contain numerous mutations that can be revealed by whole-genome sequencing. However, determining the contribution of specific mutations to new phenotypes is typically challenging and laborious. This task is complicated by factors such as the mutation type, the genomic context, and the interplay between different mutations. Here, a novel approach was developed to identify the significance of mutations in strains evolved from Acinetobacter baylyi ADP1. This method, termed rapid advantageous mutation screening and selection (RAMSES), was used to analyze mutants that emerged from stepwise adaptation to and consumption of high levels of ferulate, a common lignin-derived aromatic compound. After whole-genome sequence analysis, RAMSES allowed rapid determination of effective mutations and seamless introduction of the beneficial mutations into the chromosomes of new strains with different genetic backgrounds. This simple approach to reverse engineering exploits the natural competence and high recombination efficiency of ADP1. Mutated DNA, added directly to growing cells, replaces homologous chromosomal regions to generate transformants that will become enriched if there is a selective benefit. Thus, advantageous mutations can be rapidly identified. Here, the growth advantage of transformants under selective pressure revealed key mutations in genes related to aromatic transport, including hcaE, hcaK, and vanK, and a gene, ACIAD0482, which is associated with lipopolysaccharide synthesis. This study provided insights into the enhanced utilization of industrially relevant aromatic substrates and demonstrated the use of A. baylyi ADP1 as a convenient platform for strain development and evolution studies. IMPORTANCE Microbial conversion of lignin-enriched streams is a promising approach for lignin valorization. However, the lignin-derived aromatic compounds are toxic to cells at relevant concentrations. Although adaptive laboratory evolution (ALE) is a powerful approach to develop more tolerant strains, it is typically laborious to identify the mechanisms underlying phenotypic improvement. We employed Acinetobacter baylyi ADP1, an aromatic-compound-degrading strain that may be useful for biotechnology. The natural competence and high recombination efficiency of this strain can be exploited for critical applications, such as the breakdown of lignin and plastics and abundant polymers composed of aromatic subunits. The natural transformability of this bacterium enabled us to develop a novel approach for rapid screening of advantageous mutations from ALE-derived, aromatic-tolerant, ADP1-derived strains. We clarified the mechanisms and genetic targets for improved tolerance toward common lignin-derived aromatic compounds. This study facilitates metabolic engineering for lignin valorization.

Comprehensive Characterization of Shredded Lithium-Ion Battery Recycling Material.

Herein we report on an analytical study of dry-shredded lithium-ion battery (LIB) materials with unknown composition. Samples from an industrial recycling process were analyzed concerning the elemental composition and (organic) compound speciation. Deep understanding of the base material for LIB recycling was obtained by identification and analysis of transition metal stoichiometry, current collector metals, base electrolyte and electrolyte additive residues, aging marker molecules and polymer binder fingerprints. For reversed engineering purposes, the main electrode and electrolyte chemistries were traced back to pristine materials. Furthermore, possible lifetime application and accompanied aging was evaluated based on target analysis on characteristic molecules described in literature. With this, the reported analytics provided precious information for value estimation of the undefined spent batteries and enabled tailored recycling process deliberations. The comprehensive feedstock characterization shown in this work paves the way for targeted process control in LIB recycling processes.

Effective non-adiabatic holonomic quantum computation of cavity modes via invariant-based reverse engineering.

In this paper, we propose a protocol to realize non-adiabatic holonomic quantum computation (NHQC) of cavity modes via invariant-based reverse engineering. Coupling cavity modes with an auxiliary atom trapped in a cavity, we derive effective Hamiltonians with the help of laser pulses. Based on the derived Hamiltonians, invariant-based reverse engineering is used to find proper evolution paths for NHQC. Moreover, the systematic-error-sensitivity nullified optimal control method is considered in the parameter selections, making the protocol insensitive to the influence of systematic errors of pulses. We also estimate the imperfections induced by random noise and decoherence. Numerical results show that the protocol holds robustness against these imperfections. Therefore, the protocol may provide useful perspectives to quantum computation with optical qubits in cavity quantum electrodynamics systems. This article is part of the theme issue 'Shortcuts to adiabaticity: theoretical, experimental and interdisciplinary perspectives'.

Evolutionary and reverse engineering to increase Saccharomyces cerevisiae tolerance to acetic acid, acidic pH, and high temperature.

Saccharomyces cerevisiae scarcely grows on minimal media with acetic acid, acidic pH, and high temperatures. In this study, the adaptive laboratory evolution (ALE), whole-genome analysis, and reverse engineering approaches were used to generate strains tolerant to these conditions. The thermotolerant strain TTY23 and its parental S288C were evolved through 1 year, in increasing concentrations of acetic acid up to 12 g/L, keeping the pH ≤ 4. Of the 18 isolated strains, 9 from each ancestor, we selected the thermo-acid tolerant TAT12, derived from TTY23, and the acid tolerant AT22, derived from S288C. Both grew in minimal media with 12 g/L of acetic acid, pH 4, and 30 °C, and produced ethanol up to 29.25 ± 6 mmol/gDCW/h-neither of the ancestors thrived in these conditions. Furthermore, only the TAT12 grew on 2 g/L of acetic acid, pH 3, and 37 °C, and accumulated 16.5 ± 0.5 mmol/gDCW/h of ethanol. Whole-genome sequencing and transcriptomic analysis of this strain showed changes in the genetic sequence and transcription of key genes involved in the RAS-cAMP-PKA signaling pathway (RAS2, GPA2, and IRA2), the heat shock transcription factor (HSF1), and the positive regulator of replication initiation (SUM1), among others. By reverse engineering, the relevance of the combined mutations in the genes RAS2, HSF1, and SUM1 to the tolerance for acetic acid, low pH, and high temperature was confirmed. Alone, the RAS2 mutation yielded acid tolerance and HSF1 nutation thermotolerance. Increasing the thermo-acidic niche and acetic acid tolerance of S. cerevisiae can contribute to improve economic ethanol production. KEY POINTS: • Thermo-acid tolerant (TAT) yeast strains were generated by adaptive laboratory evolution. • The strain TAT12 thrived on non-native, thermo-acidic harmful conditions. • Mutations in RAS2, HSF1, and SUM1 genes rendered yeast thermo and acid tolerant.

Reverse engineering field-derived vertical distribution profiles to infer larval swimming behaviors.

Biophysical models are well-used tools for predicting the dispersal of marine larvae. Larval behavior has been shown to influence dispersal, but how to incorporate behavior effectively within dispersal models remains a challenge. Mechanisms of behavior are often derived from laboratory-based studies and therefore, may not reflect behavior in situ. Here, using state-of-the-art models, we explore the movements that larvae must undertake to achieve the vertical distribution patterns observed in nature. Results suggest that behaviors are not consistent with those described under the tidally synchronized vertical migration (TVM) hypothesis. Instead, we show (i) a need for swimming speed and direction to vary over the tidal cycle and (ii) that, in some instances, larval swimming cannot explain observed vertical patterns. We argue that current methods of behavioral parameterization are limited in their capacity to replicate in situ observations of vertical distribution, which may cause dispersal error to propagate over time, due to advective differences over depth and demonstrate an alternative to laboratory-based behavioral parameterization that encompasses the range of environmental cues that may be acting on planktic organisms.

A Reverse Modification Method for Binary Code and Data.

This paper reveals the hidden dangers of reverse data modifications on distributed software with network synchronization, during the era of 5G, which may occur in more important domains, such as telemedicine and automatic driving. We used pseudo-codes to formally elaborate the distributed software architectures and design patterns. It is necessary to deal with three challenges for the modification of binary code and data in the distributed software architectures: (1) the base virtual addresses of software objects are changed frequently for safety; (2) prior knowledge of the reverse is not considered; (3) system memory values of some target objects are changed with extreme speed. For this purpose, a novel reverse modification method for binary code and data is proposed. According to the knowledge-based rules, our method can manipulate physical data, sight data, animation data, etc., while the game synchronization mechanism cannot detect the changes. The implementation details of our method are presented using high-level programming languages (C++) and low-level programming languages (assembly), based on multiple snippets, so that readers can understand both the overall distributed software developments and the corresponding reverse processes. In particular, two network games are used for the demonstrations in this paper. The demonstration results show that our proposed methodology is efficient (as proved by formulas and practices) to manipulate the codes and data of distributed software using a synchronization mechanism.

Comparison of Three 3D Segmentation Software Tools for Hip Surgical Planning.

In hip arthroplasty, preoperative planning is fundamental to reaching a successful surgery. Nowadays, several software tools for computed tomography (CT) image processing are available. However, research studies comparing segmentation tools for hip surgery planning for patients affected by osteoarthritic diseases or osteoporotic fractures are still lacking. The present work compares three different software from the geometric, dimensional, and usability perspectives to identify the best three-dimensional (3D) modelling tool for the reconstruction of pathological femoral heads. Syngo.via Frontier (by Siemens Healthcare) is a medical image reading and post-processing software that allows low-skilled operators to produce prototypes. Materialise (by Mimics) is a commercial medical modelling software. 3D Slicer (by slicer.org) is an open-source development platform used in medical and biomedical fields. The 3D models reconstructed starting from the in vivo CT images of the pathological femoral head are compared with the geometries obtained from the laser scan of the in vitro bony specimens. The results show that Mimics and 3D Slicer are better for dimensional and geometric accuracy in the 3D reconstruction, while syngo.via Frontier is the easiest to use in the hospital setting.

Metrology Benchmarking of 3D Scanning Sensors Using a Ceramic GD&T-Based Artefact.

The use of non-contact scanning equipment in metrology and in dimensional and geometric inspection applications is increasing due to its ease of use, the speed and density of scans, and the current costs. In fact, these technologies are becoming increasingly dominant in the industrial environment, thus moving from reverse engineering applications to metrological applications. However, this planned transfer requires actions to ensure the achievable accuracy by providing traceability of measurements. In the present study, a comparison between the devices is carried out and a specific standard artefact is designed, equipped with multiple ceramic optically friendly entities, and allowing a wide variety of geometric dimensioning and tolerancing (GD&T). Four different 3D scanning sensors are used in the experimentation. Three of them are based on laser triangulation, and the fourth is a structured blue light sensor (fringe pattern projection). The standard artefact is calibrated with a high accuracy, using a coordinate measuring machine (CMM) and probing sensors. With this CMM, reference values of multiple predefined GD&T are obtained. The evaluation methodology maximises the accuracy of each device in measuring the dimensions of the artefact due to the good dimensional (milling and turning), surface (control of machining variables), and the dimensional and spatial distribution characteristics. The procedure also includes the same treatment of the captured point clouds (trimming, filtering, and best-fit algorithm, etc.) in each of the four 3D scanning sensors considered. From this process, very reliable measurements of the maximum achievable accuracy of each device (deviations from the CMM measurements) are finally obtained, and a multi-characteristic comparison between the four sensors is performed, also with high reliability.

Disordered-Layer-Mediated Reverse Metal-Oxide Interactions for Enhanced Photocatalytic Water Splitting.

In heterogeneous catalysts, metal-oxide interactions occur spontaneously but often in an undesired way leading to the oxidation of metal nanoparticles. Manipulating such interactions to produce highly active surface of metal nanoparticles can warrant the optimal catalytic activity but has not been established to date. Here we report that a prior reduced TiO2 support can reverse the interaction with Pt nanoparticles and augment the metallic state of Pt, exhibiting a 3-fold increase in hydrogen production rate compared to that of conventional Pt/TiO2. Spatially resolved electron energy loss spectroscopy of the Ti valence state and the electron density distribution within Pt nanoparticles provide direct evidence supporting that the Pt/TiO2/H2O triple junctions are the most active catalytic sites for water reduction. Our reverse metal-oxide interaction scheme provides a breakthrough in the stagnated hydrogen production efficiency and can be applied to other heterogeneous catalyst systems composed of metal nanoparticles with reducible oxide supports.

Ninth-grade students' perceptions on the design-thinking mindset in the context of reverse engineering.

Recently, design thinking has become recognized as a necessity for every student, especially when they engage in design-based learning, as a pedagogical approach to science, technology, engineering, and mathematics education. However, design-based learning is mostly based on forward engineering, in which students' design thinking can be nurtured by designing unknown solutions. Little is known about whether design thinking can be facilitated in the context of reverse engineering, when students learn from already designed products. This study therefore seeks to explore the perceptions of 38 ninth-grade students on the characteristics of design thinking before and after a four-week reverse engineering project, using Likert scales to measure six aspects of design thinking, namely (a) being comfortable with uncertainty and risks, (b) human-centeredness, (c) mindfulness to the process and impacts on others, (d) collaboratively working with diversity, (e) orientation to learning by making and testing, and (f) being confident and optimistic to use creativity. The data were analyzed using descriptive and inferential statistics, including means, standard deviations, paired-samples t-tests, and Wilcoxon signed-rank tests. The results indicate that two aspects, human-centeredness and being confident and optimistic to use creativity, were significant (p = 0.008 and p = 0.043, respectively), with size effects of 0.43 and 0.34, respectively. Based on this potential, reverse engineering can be a design-based learning approach to facilitate students' design thinking. It is recommended that instructional activities involving reverse engineering maintain some degree of ambiguity and risk to prevent design fixation among students.

BtsT/ BtsS is involved in glyoxylate transport in E. coli and its mutations facilitated glyoxylate utilization.

Glyoxylate is an important chemical and is also an intermediate involved in metabolic pathways of living microorganisms. However, it cannot be rapidly utilized by many microbes. We observed a very long lag phase (up to 120 h) when E. coli is growing in a mineral medium supplemented with 50 mM glyoxylate. To better understand this strange growth pattern on glyoxylate and accelerate glyoxylate utilization, a random genomic library of E. coli was transformed into E. coli BW25113, and mutants that showed significantly shortened lag phase on glyoxylate were obtained. Interestingly, mutations in BtsT/BtsS, a two component system that is involved in pyruvate transport, were found to be a common feature in all mutants retrieved. We further demonstrated, through reverse engineering, that the mutations in BtsT/BtsS can indeed increase glyoxylate uptake. Growth experiments with different concentration of glyoxylate also showed the higher the concentration of glyoxylate, the shorter the lag phase. These new findings thus increased our understanding on microbial utilization of glyoxylate.

Harnessing the Full Potential of Extracellular Vesicles as Drug Carriers.

Extracellular vesicles are natural delivery systems widely implicated in cellular communication. However, to fully utilize these vehicles as nanocarriers, we must explore various methods to modify their applicability as drug delivery vehicles. In this review, we outline and discuss techniques to engineer extracellular vehicles for enhanced loading, targeting, circulation, and tracking. We highlight cutting-edge methods to amplify extracellular vesicle secretion and production and optimize storage conditions to improve their clinical suitability. Moreover, we focus on reverse engineering as an important step in controlling their biological function. By taking a reductionist approach to characterize and understand the individual components of these carriers, we can not only elucidate complex mechanisms of action but also advance the field through the creation of synthetic drug delivery vehicles. Finally, we propose current challenges and future directions of the field.

Evolutionary engineering reveals amino acid substitutions in Ato2 and Ato3 that allow improved growth of Saccharomyces cerevisiae on lactic acid.

In Saccharomyces cerevisiae, the complete set of proteins involved in transport of lactic acid across the cell membrane has not been determined. In this study, we aimed to identify transport proteins not previously described to be involved in lactic acid transport via a combination of directed evolution, whole-genome resequencing and reverse engineering. Evolution of a strain lacking all known lactic acid transporters on lactate led to the discovery of mutated Ato2 and Ato3 as two novel lactic acid transport proteins. When compared to previously identified S. cerevisiae genes involved in lactic acid transport, expression of ATO3T284C was able to facilitate the highest growth rate (0.15 ± 0.01 h-1) on this carbon source. A comparison between (evolved) sequences and 3D models of the transport proteins showed that most of the identified mutations resulted in a widening of the narrowest hydrophobic constriction of the anion channel. We hypothesize that this observation, sometimes in combination with an increased binding affinity of lactic acid to the sites adjacent to this constriction, are responsible for the improved lactic acid transport in the evolved proteins.