Modular Tool Research Articles

Evacuation Scenario Simulator with Location-Based Social Network Data Support

Disaster management systems apply different evacuation models to provide transportation responses to disaster situations. The main goal of the evacuation models is to reduce evacuation times to save lives.The significant increase in the use of location-based social networks (LBSN) as a major media factor can provide data for inferring the Origin-Destination matrix that represents the demands for trips during routine times. To achieve the goal of reducing overall evacuation travel times, an innovative methodology was developed that supports LBSN data for both demand prediction and decentralized personal destination recommendation.A modular multi-dimensional tool for evacuation scenario simulation (MMDT-ESS) was developed for supporting this methodology. This tool can handle multiple scenarios and compute performance evaluation values of VhT (Vehicle-Hours Traveled). MMDT-ESS can be applied using both conventional evacuation models (i.e., evacuation to predefined shelters) or decentralized personalized evacuation models and can provide different recommendations: either self-evacuation to nearest family or best friend that is situated outside the affected area or to a predefined shelter area.A test case using data from a U.S. metropolitan agency showed significant reduction in VhT when choosing evacuation scenarios with LBSN data for decentralized evacuation. Sensitivity analysis on main model parameters illustrate the robustness of the results.

Harnessing synthetic biology to engineer organoids and tissues.

The development of an organism depends on intrinsic genetic programs of progenitor cells and their spatiotemporally complex extrinsic environment. Exvivo generation of organoids from progenitor cells provides a platform for recapitulating and exploring development. Current approaches rely largely on soluble morphogens or engineered biomaterials to manipulate the physical environment, but the emerging field of synthetic biology provides a powerful toolbox to genetically manipulate cell communication, adhesion, and even cell fate. Applying these modular tools to organoids should lead to a deeper understanding of developmental principles, improved organoid models, and an enhanced capability to design tissues for regenerative purposes.

Screenwerk: A modular tool for the design and analysis of drug combination screens.

There is a rapidly growing interest in high-throughput drug combination screening to identify synergizing drug interactions for treatment of various maladies, such as cancer and infectious disease. This creates the need for pipelines that can be used to design such screens, perform quality control on the data, and generate data files that can be analyzed by synergy-finding bioinformatics applications. screenwerk is an open source, end-to-end modular tool available as an R-package for the design and analysis of drug combination screens. The tool allows for a customized build of pipelines through its modularity and provides a flexible approach to quality control and data analysis. screenwerk is adaptable to various experimental requirements with an emphasis on precision medicine. It can be coupled to other R packages, such as bayesynergy, to identify synergistic and antagonistic drug interactions in cell lines or patient samples. screenwerk is scalable and provides a complete solution for setting up drug sensitivity screens, read raw measurements and consolidate different datasets, perform various types of quality control, and analyze, report and visualize the results of drug sensitivity screens. The R-package and technical documentation is available at https://github.com/Enserink-lab/screenwerk; the R source code is publicly available at https://github.com/Enserink-lab/screenwerk under GNU General Public License v3.0; bayesynergy is accessible at https://github.com/ocbe-uio/bayesynergy; Selected modules are available through Galaxy, an open-source platform for FAIR data analysis at https://oncotools.elixir.no. Supplementary data are available at Bioinformatics online.

Worn cutter picks as a secondary resource for manufacturing modular cutting tools for shearers

Upon agreement with a Kuzbass mine’s management, the worn picks of shearer JOY 4LS-20 were collected to manufacture load-bearing members of modular cutter picks. Four innovative picks were set on the cutting drum of the shearer and their performance was observed. The mine trials proved the theoretical hypothesis on potential recycling of the worn traditional picks as a secondary scrap metal for manufacturing modular tools. Three out of four experimental picks were run for 35 days on the shearer which produced 55 Kt of broken rock mass within that space of time. Due to the inconsistency of the working coal seam thickness, the overcut of the shearer was to 0.2 m deep in the middle of the longwall floor composed of grey sandstone having the hardness factor of 5–6 on the Protodyakonov scale. The mine managers’ fears concerning attachment unreliability of the cutting modules, their falling out and expanding of screws remained unconfirmed. Only one pick was damaged with tearing-off of the attaching ring. Inspection of the load-bearing members of the modular picks after trials exhibited their insignificant operational wear at the level of 0.5–2.5% of their initial mass. The length and the attaching diameter of the members remained nearly the same, which proves their long-term usability in a set of the modular cutter picks in underground coal cutting. The mass of the load-bearing module is 80% of the whole tool, which allows considerable reduction (to 5–7 times) in metal consumption of coal shearing with redesign and recycling of the worn tools on the ground of modularity.

Facile Assembly of Modular-Type Phosphines for Tackling Modern Arylation Processes.

ConspectusThis Account presents an overview of a promising collection of phosphine ligands simply made from the modular Fischer indolization process and their applications in modern arylation processes. Using one easily accessible 2-arylindole scaffold, three major phosphino-moiety-positioned ligand series can be readily generated. We have attempted to explore challenging electrophilic and nucleophilic partners for the coupling reaction using the modular ligand tool. For the electrophilic partner study, CM-phos-type ligands, where the phosphino group is located at the 2-arene position of 2-arylindole, allow the successful cross-coupling of aryl mesylates. The CM-phos ligand forms a palladacycle before entering the cross-coupling catalytic cycle. For the nucleophilic partner investigation, the indole C3-positioned phosphines show the first accomplishment of Pd-catalyzed organotitanium nucleophile arylation. Indeed, the aryl-titanium nucleophile undergoes cross-coupling more efficiently than does the organoboron coupling partner in particular cases. Moreover, in the indole C3-positioned phosphine series, the -PPh2-containing ligands perform better in the highly sterically hindered cross-coupling of aryl chlorides than do ligands containing the -PCy2 moiety. The catalyst loading can even be reduced to 0.2 mol % Pd for tetra-ortho-substituted biaryl synthesis. This finding offers a new perspective on the next-generation design of phosphine ligands in which the sterically bulky and electron-rich -PR2 group (R = alkyl) may not be necessary for the cross-coupling of aryl chlorides. In general, we hypothesize that a good balance of steric and electronic properties for entertaining the oxidative addition and reductive elimination steps is crucial to the success of the reaction. For the steric factor, the highly sterically congested -PR2 group normally favors the reductive elimination, yet we conjecture that this sterically bulky group would serve as an obstacle for the incoming aryl halides. For the electronic factor, the electron rich -PR2 group is believed to support the oxidative cleavage of the C(Ar)-Cl bond by donating more electron density to the corresponding σ* orbital. Nevertheless, the high electron richness of the -PR2 group may disfavor the reductive elimination electronically. Overall, an appropriate balance of both electron density and steric bulkiness is suggested to allow the sterically hindered cross-coupling to proceed smoothly. We have found that the -PPh2-containing ligand is a good starting point for this investigation. The formation of aromatic carbon-carbon (C-C) and carbon-heteroatom (C-X) bonds from aryl chlorides was successfully realized using our proprietary phosphines.In addition to the indole-core-bearing ligand skeleton, we also explored the relevant imidazolyl and carbazolyl phosphines for their unique applications. Interestingly, the carbazolyl ligand, having more flexible C-N axial chirality, displays particular interchangeable Pd-N and Pd-arene coordination, which facilitates both oxidative addition and reductive elimination processes. Moreover, this C-N axially chiral ligand allows the successful asymmetric Suzuki-Miyaura coupling for attaining the most sterically hindered tetra-ortho-substituted biaryls with excellent enantioselectivity. The rationale behind these scientifically interesting findings is presented in detail.

NLoed: A Python Package for Nonlinear Optimal Experimental Design in Systems Biology.

Modeling in systems and synthetic biology relies on accurate parameter estimates and predictions. Accurate model calibration relies, in turn, on data and on how well suited the available data are to a particular modeling task. Optimal experimental design (OED) techniques can be used to identify experiments and data collection procedures that will most efficiently contribute to a given modeling objective. However, implementation of OED is limited by currently available software tools that are not well suited for the diversity of nonlinear models and non-normal data commonly encountered in biological research. Moreover, existing OED tools do not make use of the state-of-the-art numerical tools, resulting in inefficient computation. Here, we present the NLoed software package and demonstrate its use with in vivo data from an optogenetic system in Escherichia coli. NLoed is an open-source Python library providing convenient access to OED methods, with particular emphasis on experimental design for systems biology research. NLoed supports a wide variety of nonlinear, multi-input/output, and dynamic models and facilitates modeling and design of experiments over a wide variety of data types. To support OED investigations, the NLoed package implements maximum likelihood fitting and diagnostic tools, providing a comprehensive modeling workflow. NLoed offers an accessible, modular, and flexible OED tool set suited to the wide variety of experimental scenarios encountered in systems biology research. We demonstrate NLoed's capabilities by applying it to experimental design for characterization of a bacterial optogenetic system.

XAPI Made Easy: A Learning Analytics Infrastructure for Interdisciplinary Projects

Learning Analytics provides plenty of pedagogical uses. However, the integration of learning analytics must be accompanied by different perspectives: technical, organizational, and pedagogical. At this point, there are still gaps, e.g., the need to connect the various stakeholders and support the systematic, structured, and sustainable process. This paper presents different approaches to making the learning data standard xAPI for interdisciplinary projects easier by working on other starting points. Starting with a basic infrastructure to support the interdisciplinary collection of definitions for the standardized data format, it continues with a graphical user interface supporting different stakeholders. A modular tool for quickly connecting programming IDEs with the vocabulary is also presented. Last, a connector is shown for easier multi-modal data management using virtual reality as an example.

Feedforward growth rate control mitigates gene activation burden

Heterologous gene activation causes non-physiological burden on cellular resources that cells are unable to adjust to. Here, we introduce a feedforward controller that actuates growth rate upon activation of a gene of interest (GOI) to compensate for such a burden. The controller achieves this by activating a modified SpoT enzyme (SpoTH) with sole hydrolysis activity, which lowers ppGpp level and thus increases growth rate. An inducible RelA+ expression cassette further allows to precisely set the basal level of ppGpp, and thus nominal growth rate, in any bacterial strain. Without the controller, activation of the GOI decreased growth rate by more than 50%. With the controller, we could activate the GOI to the same level without growth rate defect. A cell strain armed with the controller in co-culture enabled persistent population-level activation of a GOI, which could not be achieved by a strain devoid of the controller. The feedforward controller is a tunable, modular, and portable tool that allows dynamic gene activation without growth rate defects for bacterial synthetic biology applications.

SDNSandbox — Enabling learning-based innovation in provider networks

Provider networks are looking to follow the footsteps of cloud-based networks/data centers and incorporate Software-Defined Networking (SDN) technology. This move is problematic for various reasons, such as the networks’ size and the providers’ inability to control users’ activity. Additionally, research into these networks is handicapped by the lack of information stemming from the confidentiality of these complex networks. To that end, we have created SDNSandbox — an SDN-based provider network simulator prototype. SDNSandbox is an open-source, easy-to-use, provider-network in-a-laptop simulator. It aims to facilitate the creation of reproducible experiments and large-scale synthetic datasets. In its current prototype form, it uses a basic traffic generator module alongside real-world provider topologies. SDNSandbox allows users to simulate provider networks, enabling them to conduct research in the field and examine practical applications. To demonstrate SDNSandbox, we use the prototype to simulate basic traffic conditions over several topologies. We then feed the generated datasets to DCRNN, a Convolutional Neural Network (CNN) traffic patterns prediction module. We adapt DCRNN to accept SDNSandbox output and show that it can predict traffic conditions at various points within the network tens of seconds into the future. We further compare its performance with other baseline algorithms. Our results demonstrate that SDNSandbox can also be used as a testbed for a digital twin, creating datasets that are hard to replicate in production networks. It also serves as a demonstration of the framework’s power and versatility as a modular research tool.

A Study of the Effect of a Kinematic Pair Containing Clearance on the Dynamic Characteristics of a Tool-Changing Robot

The clearance of a kinematic pair will lead to the contact collision between the joints of the mechanism, which will have a great influence on the dynamic characteristics of a mechanical system with clearance. In order to study the influence of a rotating motion pair with clearance on the dynamic characteristics of a tool changing robot, a contact wear dynamics model of a modular tool changing robot was established based on the three-state model of “free-contact-collision”. Different from analysis of a light linkage structure, this paper takes a solid structure with large mass as the analysis object. Based on the impact contact force model and the improved Coulomb friction model, the effects of clearance size, rotational speed and friction coefficient on the dynamic characteristics of the tool changing robot were analyzed. The Archard wear model was used to predict the wear between the motion pairs with clearance. The analysis results show that, with the increase of clearance size and actuating speed, the fluctuation range of velocity and acceleration increases, and the fluctuation frequency decreases. Under the action of friction, the contact force between components will be reduced due to energy loss so that the kinematic reliability of the mechanism is improved. The wear of the moving pair with clearance is non-uniform. Through the research of this paper, the motion characteristics of the tool-changing robot at low speed and heavy load are clarified. The results show that the established method can realize the dynamic characteristics analysis of low-speed heavy-duty mechanisms with joint gaps, which can be used to guide the design of tool-changing robots, and also has important reference significance for the design of mechanisms containing joint gaps in general.

The 4CAT Capture and Analysis Toolkit: A Modular Tool for Transparent and Traceable Social Media Research

This paper introduces the 4CAT Capture and Analysis Toolkit (4CAT), an open-source Web-based research tool. 4CAT can capture data from a variety of online sources (including Twitter, Telegram, Reddit, 4chan, 8kun, BitChute, Douban and Parler) and analyze them through analytical processors. 4CAT seeks to make robust data capture and analysis available to researchers not familiar with computer programming, without ‘black-boxing’ the implemented research methods. Before outlining the practical use of 4CAT, we discuss three ‘affordances’ that inform its design: modularity, transparency, and traceability. 4CAT is modular because new data sources and analytical processors can be easily added and changed; transparent because it aims to render legible its inner workings; and traceable because of automatic and shareable documentation of intermediate analysis steps. We then show how 4CAT operationalizes these features through a description of its general setup and a short walkthrough. Finally, we discuss how 4CAT strives for an ‘ethics by design’ development philosophy that enables ethically sound data-driven research. 4CAT is then positioned as both an answer to and a further call for ‘tool criticism’ in computational social research.

Engineered Protein Nanocages for Concurrent RNA and Protein Packaging In Vivo.

Protein nanocages have emerged as an important engineering platform for biotechnological and biomedical applications. Among naturally occurring protein cages, encapsulin nanocompartments have recently gained prominence due to their favorable physico-chemical properties, ease of shell modification, and highly efficient and selective intrinsic protein packaging capabilities. Here, we expand encapsulin function by designing and characterizing encapsulins for concurrent RNA and protein encapsulation in vivo. Our strategy is based on modifying encapsulin shells with nucleic acid-binding peptides without disrupting the native protein packaging mechanism. We show that our engineered encapsulins reliably self-assemble in vivo, are capable of efficient size-selective in vivo RNA packaging, can simultaneously load multiple functional RNAs, and can be used for concurrent in vivo packaging of RNA and protein. Our engineered encapsulation platform has potential for codelivery of therapeutic RNAs and proteins to elicit synergistic effects and as a modular tool for other biotechnological applications.

Late-Stage Functionalization with Cysteine Staples Generates Potent and Selective Melanocortin Receptor-1 Agonists.

In this work, cysteine staples were used as a late-stage functionalization strategy to diversify peptides and build conjugates targeting the melanocortin G-protein-coupled receptors [melanocortin receptor-1 (MC1R) and MC3R-MC5R]. Monocyclic and bicyclic agonists based on sunflower trypsin inhibitor-1 were used to generate a selection of stapled peptides that were evaluated for binding (pKi) and functional activation (pEC50) of the melanocortin receptor subtypes. Stapled peptides generally had improved activity, with aromatic stapled peptides yielding selective MC1R agonists, including a xylene-stapled peptide (2) with an EC50 of 1.9 nM for MC1R and >150-fold selectivity for MC3R and MC4R. Selected stapled peptides were further functionalized with linkers and payloads, generating a series of conjugated peptides with potent MC1R activity, including one pyridazine-functionalized peptide (21) with picomolar activity at MC1R (Ki 58 pM; EC50 < 9 pM). This work demonstrates that staples can be used as modular synthetic tools to tune potency and selectivity in peptide-based drug design.

Application of deep eutectic solvents in protein extraction and purification.

Deep eutectic solvents (DESs) are a mixture of hydrogen bond donor (HBD) and hydrogen bond acceptor (HBA) molecules that can consist, respectively, of natural plant metabolites such as sugars, carboxylic acids, amino acids, and ionic molecules, which are for the vast majority ammonium salts. Media such as DESs are modular tools of sustainability that can be pointed toward the extraction of bioactive molecules due to their excellent physicochemical properties, their relatively low price, and accessibility. The present review focuses on the application of DESs for protein extraction and purification. The in-depth effects and principles that apply to DES-mediated extraction using various renewable biomasses will be discussed as well. One of the most important observations being made is that DESs have a clear ability to maintain the biological and/or functional activity of the extracted proteins, as well as increase their stability compared to traditional solvents. They demonstrate true potential for a reproducible but more importantly, scalable protein extraction and purification compared to traditional methods while enabling waste valorization in some particular cases.

Engineered endosymbionts that alter mammalian cell surface marker, cytokine and chemokine expression

Developing modular tools that direct mammalian cell function and activity through controlled delivery of essential regulators would improve methods of guiding tissue regeneration, enhancing cellular-based therapeutics and modulating immune responses. To address this challenge, Bacillus subtilis was developed as a chassis organism for engineered endosymbionts (EES) that escape phagosome destruction, reside in the cytoplasm of mammalian cells, and secrete proteins that are transported to the nucleus to impact host cell response and function. Two synthetic operons encoding either the mammalian transcription factors Stat-1 and Klf6 or Klf4 and Gata-3 were recombined into the genome of B. subtilis expressing listeriolysin O (LLO) from Listeria monocytogenes and expressed from regulated promoters. Controlled expression of the mammalian proteins from B. subtilis LLO in the cytoplasm of J774A.1 macrophage/monocyte cells altered surface marker, cytokine and chemokine expression. Modulation of host cell fates displayed some expected patterns towards anti- or pro-inflammatory phenotypes by each of the distinct transcription factor pairs with further demonstration of complex regulation caused by a combination of the EES interaction and transcription factors. Expressing mammalian transcription factors from engineered intracellular B. subtilis as engineered endosymbionts comprises a new tool for directing host cell gene expression for therapeutic and research purposes.

KaIDA: a modular tool for assisting image annotation in deep learning.

Deep learning models achieve high-quality results in image processing. However, to robustly optimize parameters of deep neural networks, large annotated datasets are needed. Image annotation is often performed manually by experts without a comprehensive tool for assistance which is time- consuming, burdensome, and not intuitive. Using the here presented modular Karlsruhe Image Data Annotation (KaIDA) tool, for the first time assisted annotation in various image processing tasks is possible to support users during this process. It aims to simplify annotation, increase user efficiency, enhance annotation quality, and provide additional useful annotation-related functionalities. KaIDA is available open-source at https://git.scc.kit.edu/sc1357/kaida.

Protein Condensate Formation via Controlled Multimerization of Intrinsically Disordered Sequences.

Many proteins harboring low complexity or intrinsically disordered sequences (IDRs) are capable of undergoing liquid-liquid phase separation to form mesoscale condensates that function as biochemical niches with the ability to concentrate or sequester macromolecules and regulate cellular activity. Engineered disordered proteins have been used to generate programmable synthetic membraneless organelles in cells. Phase separation is governed by the strength of interactions among polypeptides with multivalency enhancing phase separation at lower concentrations. Previously, we and others demonstrated enzymatic control of IDR valency from multivalent precursors to dissolve condensed phases. Here, we develop noncovalent strategies to multimerize an individual IDR, the RGG domain of LAF-1, using protein interaction domains to regulate condensate formation in vitro and in living cells. First, we characterize modular dimerization of RGG domains at either terminus using cognate high-affinity coiled-coil pairs to form stable condensates in vitro. Second, we demonstrate temporal control over phase separation of RGG domains fused to FRB and FKBP in the presence of dimerizer. Further, using a photocaged dimerizer, we achieve optically induced condensation both in cell-sized emulsions and within live cells. Collectively, these modular tools allow multiple strategies to promote phase separation of a common core IDR for tunable control of condensate assembly.

Lipase-Mediated Mechanoenzymatic Synthesis of Sugar Esters in Dissolved Unconventional and Neat Reaction Systems.

Mechanochemical and biocatalytic approaches in modern research are two major assets to develop greener processes. In the present study, these modular tools of sustainability are pointed toward the production of versatile and daily employed compounds such as surfactants. Toward this aim, glycolipids, a class of nonionic surfactants composed of ubiquitous and primary metabolites such as sugar and fatty acid moieties, represent a promising alternative to petroleum-derived surface-active agents. Therefore, the combination of biocatalysis with mechanochemistry aiming at glycolipid synthesis seemed a logical step that was taken in this study for the first time. The monoacylated model compound glucose-6-O-decanoate was synthesized with the help of a bead mill apparatus using two different unconventional dissolved reaction systems, namely, menthol-based hydrophobic deep eutectic solvents and 2-methyl-2-butanol, thus reaching up to 12% yield in the latter based on the conversion of vinyl decanoate, after only 90 min of reaction. In addition, a neat reaction system using an excess of vinylated fatty ester as an adjuvant allowed a 27 mM/h space-time yield. The overall significant increase in productivities, up to 6 times, compared to standard heating and shaking methods, shows the tremendous potential of mechanoenzymatic synthesis.

Downscaling Satellite Soil Moisture Using a Modular Spatial Inference Framework

Soil moisture is an important parameter that regulates multiple ecosystem processes and provides important information for environmental management and policy decision-making. Spaceborne sensors provide soil moisture information over large areas, but information is commonly available at coarse resolution with spatial and temporal gaps. Here, we present a modular spatial inference framework to downscale satellite-derived soil moisture using terrain parameters and test the performance of two modeling methods (Kernel-Weighted K-Nearest Neighbor &lt;KKNN&gt; and Random Forest &lt;RF&gt;). We generate monthly and weekly gap-free spatial predictions on soil moisture at 1 km using data from the European Space Agency Climate Change Initiative (ESA-CCI; version 6.1) over two regions in the conterminous United States. RF was the method that performed better in cross-validation when comparing with the reference ESA-CCI data, but KKNN showed a slightly higher agreement with ground-truth information as part of independent validation. We postulate that more heterogeneous landscapes (i.e., high topographic variation) may be more challenging for downscaling and predicting soil moisture; therefore, moisture networks should increase monitoring efforts across these complex landscapes. Future opportunities for development of modular cyberinfrastructure tools for downscaling satellite-derived soil moisture are discussed.

A General Strategy for the Design and Evaluation of Heterobifunctional Tools: Applications to Protein Localization and Phase Separation.

To mimic the levels of spatiotemporal control that exist in nature, tools for chemically induced dimerization (CID) are employed to manipulate protein-protein interactions. Although linker composition is known to influence speed and efficiency of heterobifunctional compounds, modeling or in vitro experiments are often insufficient to predict optimal linker structure. This can be attributed to the complexity of ternary complex formation and the overlapping factors that impact the effective concentration of probe within the cell, such as efflux and passive permeability. Herein, we synthesize a library of modular chemical tools with varying linker structures and perform quantitative microscopy in live cells to visualize dimerization in real-time. We use our optimized probe to demonstrate our ability to recruit a protein of interest (POI) to the mitochondria, cell membrane, and nucleus. Finally, we induce and monitor local and global phase separation. We highlight the importance of quantitative approaches to linker optimization for dynamic systems and introduce new, synthetically accessible tools for the rapid control of protein localization.