Targeting Properties Research Articles

Pacifying problem places: How problem property interventions increase guardianship and reduce disorder and crime

AbstractCrime is highly concentrated at places that lack capable place managers (i.e., landlords and their delegates). In response, numerous cities have instituted problem property interventions that pressure landowners to better manage properties suffering from decay, nuisance, or crime. This approach is distinctive in that it both targets a place and incentivizes those legally responsible to improve its management, yet little is known about the efficacy of such interventions. We assess the short‐ and long‐term impacts of such interventions in Boston, Massachusetts, using matched difference‐in‐difference analyses. Problem property interventions reduced crime and disorder relative to comparable matched properties. They also led to property investment and landowner turnover, suggesting strengthened place management. In addition, drops in crime and disorder were observed at other properties on the same street, although not at other properties with the same owner throughout the city. This study, therefore, provides evidence that problem property interventions compel landowners to better manage the targeted property and that these effects have a diffusion of benefits on surrounding properties. The effect on place management, however, was limited to the target property and did not reliably generalize to the landowner's other holdings. This study reveals nuance in the ways that problem property interventions can benefit communities.

Mitochondria-Targeting and Oxygen Self-Supplying Eccentric Hollow Nanoplatform for Enhanced Breast Cancer Photodynamic Therapy

Photodynamic therapy (PDT) has received increasing attention for tumor therapy due to its minimal invasiveness and spatiotemporal selectivity. However, the poor targeting of photosensitizer and hypoxia of the tumor microenvironment limit the PDT efficacy. Herein, eccentric hollow mesoporous organic silica nanoparticles (EHMONs) are prepared by anisotropic encapsulation and hydrothermal etching for constructing PDT nanoplatforms with targeting and hypoxia-alleviating properties. The prepared EHMONs possess a unique eccentric hollow structure, a uniform size (300 nm), a large cavity, and ordered mesoporous channels (2.3 nm). The EHMONs are modified with the mitochondria-targeting molecule triphenylphosphine (CTPP) and photosensitizers chlorin e6 (Ce6). Oxygen-carrying compound perfluorocarbons (PFCs) are further loaded in the internal cavity of EHMONs. Hemolytic assays and in vitro toxicity experiments show that the EHMONs-Ce6-CTPP possesses very good biocompatibility and can target mitochondria of triple-negative breast cancer, thus increasing the accumulation of photosensitizers Ce6 at mitochondria after entering cancer cells. The EHMONs-Ce6-CTPP@PFCs with oxygen-carrying ability can alleviate hypoxia after entering in the cancer cell. Phantom and cellular experiments show that the EHMONs-Ce6-CTPP@PFCs produce more singlet oxygen reactive oxygen species (ROSs). Thus, in vitro and in vivo experiments demonstrated that the EHMONs-Ce6-CTPP@PFCs showed excellent treatment effects for triple-negative breast cancer. This research provides a new method for a targeting and oxygen-carrying nanoplatform for enhancing PDF effectiveness.

Use of Artificial Neural Network to Develop Surrogates for Hydrotreated Vegetable Oil with Experimental Validation in Ignition Quality Tester

<div>This article presents surrogate mixtures that simulate the physical and chemical properties in the auto-ignition of hydrotreated vegetable oil (HVO). Experimental investigation was conducted in the Ignition Quality Tester (IQT) to validate the auto-ignition properties with respect to those of the target fuel. The surrogate development approach is assisted by artificial neural network (ANN) embedded in MATLAB optimization function. Aspen HYSYS is used to calculate the key physical and chemical properties of hundreds of mixtures of representative components, mainly alkanes—the dominant components of HVO, to train the learning algorithm. Binary and ternary mixtures are developed and validated in the IQT. The target properties include the derived cetane number (DCN), density, viscosity, surface tension, molecular weight, and volatility represented by the distillation curve. The developed surrogates match the target fuel in terms of ignition delay and DCN within 6% error range. This investigation will be of value to developing high-fidelity models to investigate HVO combustion and spray behavior. This will be beneficial to researchers advancing the design and development of compression ignition engines to efficiently operate on renewable fuels such as HVO.</div>

Prediction of Lithium Oilfield Brines Based on Seismic Data: A Case Study from L Area, Northeastern Sichuan Basin, China

Lithium is an important mineral resource and a critical element in the production of lithium batteries, which are currently in high demand. Oilfield brine has significant value as a raw material for lithium extraction. However, it is often considered a byproduct of oil and gas production and is either abandoned or reinjected underground. Exploration and development of oilfield brines can enhance the economic benefits of oilfields and avoid wasting resources. Current methods for predicting brine distribution rely on geological genetic analysis, which results in low accuracy and reliability. To address this issue, we propose a workflow for lithium brine prediction that uses seismic and logging data. We introduced waveform clustering control and used the mapping relationship between seismic waveforms and well-logging curves to predict high-quality reservoirs based on the electrical and physical properties of lithium brine reservoirs. In this workflow, the seismic waveforms were first clustered using singular value decomposition. The sample sets of well-logging properties were established for the target location. The target properties were divided into high- and low-frequency components and predicted separately. The predicted results of the high-quality reservoirs in the study area were verified using elemental content test results to demonstrate the effectiveness of the method. Our study indicates that well-logging property prediction constrained by waveform clustering can predict lithium brines in a carbonate reservoir.

Fairness- and Uncertainty-Aware Data Generation for Data-Driven Design Based on Active Learning

Abstract The design dataset is the backbone of data-driven design. Ideally, the dataset should be fairly distributed in both shape and property spaces to efficiently explore the underlying relationship. However, the classical experimental design focuses on shape diversity and thus yields biased exploration in the property space. Recently developed methods either conduct subset selection from a large dataset or employ assumptions with severe limitations. In this paper, fairness- and uncertainty-aware data generation (FairGen) is proposed to actively detect and generate missing properties starting from a small dataset. At each iteration, its coverage module computes the data coverage to guide the selection of the target properties. The uncertainty module ensures that the generative model can make certain and thus accurate shape predictions. Integrating the two modules, Bayesian optimization determines the target properties, which are thereafter fed into the generative model to predict the associated shapes. The new designs, whose properties are analyzed by simulation, are added to the design dataset. This constructs an active learning mechanism that iteratively samples new data to improve data representativeness and machine learning model performance. An S-slot design dataset case study was implemented to demonstrate the efficiency of FairGen in auxetic structural design. Compared with grid and randomized sampling, FairGen increased the coverage score at twice the speed and significantly expanded the sampled region in the property space. As a result, the generative models trained with FairGen-generated datasets showed consistent and significant reductions in mean absolute errors.

Accelerating Elastic Property Prediction in Fe-C Alloys through Coupling of Molecular Dynamics and Machine Learning.

The scarcity of high-quality data presents a major challenge to the prediction of material properties using machine learning (ML) models. Obtaining material property data from experiments is economically cost-prohibitive, if not impossible. In this work, we address this challenge by generating an extensive material property dataset comprising thousands of data points pertaining to the elastic properties of Fe-C alloys. The data were generated using molecular dynamic (MD) calculations utilizing reference-free Modified embedded atom method (RF-MEAM) interatomic potential. This potential was developed by fitting atomic structure-dependent energies, forces, and stress tensors evaluated at ground state and finite temperatures using ab-initio. Various ML algorithms were subsequently trained and deployed to predict elastic properties. In addition to individual algorithms, super learner (SL), an ensemble ML technique, was incorporated to refine predictions further. The input parameters comprised the alloy's composition, crystal structure, interstitial sites, lattice parameters, and temperature. The target properties were the bulk modulus and shear modulus. Two distinct prediction approaches were undertaken: employing individual models for each property prediction and simultaneously predicting both properties using a single integrated model, enabling a comparative analysis. The efficiency of these models was assessed through rigorous evaluation using a range of accuracy metrics. This work showcases the synergistic power of MD simulations and ML techniques for accelerating the prediction of elastic properties in alloys.

Machine learning for rapid discovery of laminar flow channel wall modifications that enhance heat transfer

Numerical simulation of fluid flow plays an essential role in modeling many physical phenomena, which enables technological advancements, contributes to sustainable practices, and expands our understanding of various natural and engineered systems. The calculation of heat transfer in fluid flow in simple flat channels is a relatively easy task for various simulation methods. However, once the channel geometry becomes more complex, numerical simulations become a bottleneck in optimizing wall geometries. We present a combination of accurate numerical simulations of arbitrary, flat, and non-flat channels as well as machine learning models trained on simulated data to predict the drag coefficient and Stanton number. We show that convolutional neural networks (CNNs) can accurately predict target properties at a fraction of the computational cost of numerical simulations. We use CNN models in a virtual high-throughput screening approach to explore a large number of possible, randomly generated wall architectures. Data augmentation techniques are incorporated to enforce physical invariances toward shifting and flipping, contributing to precise prediction for fluid flow and heat transfer characteristics. Moreover, we approach the interpretation of the trained model to better understand relevant channel structures and their influence on heat transfer. The general approach is not only applicable to simple flow setups as presented here but can be extended to more complex tasks, such as multiphase or even reactive unit operations in chemical engineering.

Using slisemap to interpret physical data.

Manifold visualisation techniques are commonly used to visualise high-dimensional datasets in physical sciences. In this paper, we apply a recently introduced manifold visualisation method, slisemap, on datasets from physics and chemistry. slisemap combines manifold visualisation with explainable artificial intelligence. Explainable artificial intelligence investigates the decision processes of black box machine learning models and complex simulators. With slisemap, we find an embedding such that data items with similar local explanations are grouped together. Hence, slisemap gives us an overview of the different behaviours of a black box model, where the patterns in the embedding reflect a target property. In this paper, we show how slisemap can be used and evaluated on physical data and that it is helpful in finding meaningful information on classification and regression models trained on these datasets.

GT-00AxIL15, a Novel Tumor-Targeted IL-15-Based Immunocytokine for the Treatment of TA-MUC1-Positive Solid Tumors: Preclinical In Vitro and In Vivo Pharmacodynamics and Biodistribution Studies.

GT-00AxIL15 is a novel interleukin-15-based immunocytokine targeting a tumor-specific, glycosylated epitope of MUC1 (TA-MUC1). We characterized mode of action, pharmacokinetic (PK) and pharmacodynamic (PD) properties and investigated the relevance of TA-MUC1 binding for the concept of delivering IL-15 to solid tumors. In vitro pharmacology was analyzed in binding and cell-based assays. The in vivo PK profile and IL-15-mediated PD effects of GT-00AxIL15 were investigated in tumor-free mice. Tumor accumulation, immune infiltration and anti-tumor activity were assessed in TA-MUC1+ syngeneic and xenogeneic murine tumor models. GT-00AxIL15 was shown to specifically bind TA-MUC1 on tumor cells via its mAb moiety, to IL-15 receptors on immune cells via its IL-15 fusion modules and to FcγRs via its functional Fc-part. In vitro, NK, NKT and CD8+ T cells were activated and proliferated, leading to anti-tumor cytotoxicity and synergism with antibody-dependent cellular cytotoxicity (ADCC)-mediating mAbs. In vivo, GT-00AxIL15 exhibited favorable PK characteristics with a serum half-life of 13 days and specifically accumulated in TA-MUC1+ tumors. In the tumor microenvironment, GT-00AxIL15 induced robust immune activation and expansion and mediated anti-metastatic and anti-tumor effects in syngeneic and xenograft tumor models. These results support the rationale to improve PK and anti-tumor efficacy of IL-15 by increasing local concentrations at the tumor site via conjugation to a TA-MUC1 binding mAb. The tumor-selective expression pattern of TA-MUC1, powerful immune activation and anti-tumor cytotoxicity, long serum half-life and tumor targeting properties, render GT-00AxIL15 a promising candidate for treatment of solid tumors with high medical need, e.g., ovarian, lung and breast cancer.

Construction Strategy of Functionalized Liposomes and Multidimensional Application.

Liposomes are widely used in the biological field due to their good biocompatibility and surface modification properties. With the development of biochemistry and material science, many liposome structures and their surface functional components have been modified and optimized one by one, pushing the liposome platform from traditional to functionalized and intelligent, which will better satisfy and expand the needs of scientific research. However, a main limiting factor effecting the efficiency of liposomes is the complicated environmental conditions in the living body. Currently, in order to overcome the above problem, functionalized liposomes have become a very promising strategy. In this paper, binding strategies of liposomes with four main functional elements, namely nucleic acids, antibodies, peptides, and stimuli-responsive motif have been summarized for the first time. In addition, based on the construction characteristics of functionalized liposomes, such as drug-carrying, targeting, long-circulating, and stimulus-responsive properties, a comprehensive overview of their features and respective research progress are presented. Finally, the paper critically presents the limitations of these functionalized liposomes in the current applications and also prospectively suggests the future development directions, aiming to accelerate realization of their industrialization.

Prognostic and predictive value of super-enhancer-derived signatures for survival and lung metastasis in osteosarcoma

BackgroundRisk stratification and personalized care are crucial in managing osteosarcoma due to its complexity and heterogeneity. However, current prognostic prediction using clinical variables has limited accuracy. Thus, this study aimed to explore potential molecular biomarkers to improve prognostic assessment.MethodsHigh-throughput inhibitor screening of 150 compounds with broad targeting properties was performed and indicated a direction towards super-enhancers (SEs). Bulk RNA-seq, scRNA-seq, and immunohistochemistry (IHC) were used to investigate SE-associated gene expression profiles in osteosarcoma cells and patient tissue specimens. Data of 212 osteosarcoma patients who received standard treatment were collected and randomized into training and validation groups for retrospective analysis. Prognostic signatures and nomograms for overall survival (OS) and lung metastasis-free survival (LMFS) were developed using Cox regression analyses. The discriminatory power, calibration, and clinical value of nomograms were evaluated.ResultsHigh-throughput inhibitor screening showed that SEs significantly contribute to the oncogenic transcriptional output in osteosarcoma. Based on this finding, focus was given to 10 SE-associated genes with distinct characteristics and potential oncogenic function. With multi-omics approaches, the hyperexpression of these genes was observed in tumor cell subclusters of patient specimens, which were consistently correlated with poor outcomes and rapid metastasis, and the majority of these identified SE-associated genes were confirmed as independent risk factors for poor outcomes. Two molecular signatures were then developed to predict survival and occurrence of lung metastasis: the SE-derived OS-signature (comprising LACTB, CEP55, SRSF3, TCF7L2, and FOXP1) and the SE-derived LMFS-signature (comprising SRSF3, TCF7L2, FOXP1, and APOLD1). Both signatures significantly improved prognostic accuracy beyond conventional clinical factors.ConclusionsOncogenic transcription driven by SEs exhibit strong associations with osteosarcoma outcomes. The SE-derived signatures developed in this study hold promise as prognostic biomarkers for predicting OS and LMFS in patients undergoing standard treatments. Integrative prognostic models that combine conventional clinical factors with these SE-derived signatures demonstrate substantially improved accuracy, and have the potential to facilitate patient counseling and individualized management.

Cytotoxicity prediction of nano metal oxides on different lung cells via Nano-QSAR

In recent years, nanomaterials have found extensive applications across diverse domains owing to their distinctive physical and chemical characteristics. It is of great importance in theoretical and practical terms to carry out the relationship between structural characteristics of nanomaterials and different cytotoxicity and to achieve practical assessment and prediction of cytotoxicity. This study investigated the intrinsic quantitative constitutive relationships between the cytotoxicity of nano-metal oxides on human normal lung epithelial cells and human lung adenocarcinoma cells. We first employed quasi-SMILES-based nanostructural descriptors by selecting the five physicochemical properties that are most closely related to the cytotoxicity of nanometal oxides, then established SMILES-based descriptors that can effectively describe and characterize the molecular structure of nanometal oxides, and then built the corresponding Nano-Quantitative Structure-Activity Relationship (Nano-QSAR) prediction models, finally, combined with the theory of reactive oxygen species (ROS) biotoxicity, to reveal the mechanism of toxicity and differences between the two cell types. The established model can efficiently and accurately predict the properties of targets, reveal the corresponding toxicity mechanisms, and guide the safe design, synthesis, and application of nanometal oxides.

Half‐Full Filled Aerogels with a 348% Increment in Energy Absorption and a Retained High Electromagnetic Shielding Performance

AbstractAerogels, as kinetic energy absorbing materials, can find crucial applications for safeguarding in transportation, sports, buildings, construction, and aerospace. However, the highly porous structure makes it extremely fragile for endurance usage. In this study, a half‐full filled structure has been proposed, and the concept has been demonstrated based on shear thickening fluid (STF) and chemically vapor deposited carbon nanotube aerogels (CNTAs), in which the outer part of CNTA is filled with STF while the inner core keeping unfilled. Chemical vapor deposition significantly enhances the elasticity and electromagnetic shielding performance of the native CNTA. The half‐full filled aerogels (HFFA) show a 348% increase in energy absorption compared to the CNTA. At the same time, the density, electronic conductivity, and electromagnetic interference shielding effectiveness (EMI SE) of the HFFAs are 0.117 g cm−3, 1213 S m−1, and 69.52 dB (which is a neglectable reduction of 0.63% compared to native CNTA), respectively. The HFFA strategy provides an alternative route to fabricate robust aerogels with a remarkable increase of target properties while maintaining other properties, such as low density, high pore volume, and conductivity, with limited changes.

Design method of extractant for liquid–liquid extraction based on elements and chemical bonds

In the petrochemical industry process, the relative volatility between the components to be separated is close to 1 or the azeotrope that systems are difficult to separate. Liquid–liquid extraction is a common and effective separation method, and selecting an extraction agent is the key to extraction technology research. In this paper, a design method of extractants based on elements and chemical bonds was proposed. A knowledge-based molecular design method was adopted to pre-select elements and chemical bond groups. The molecules were automatically synthesized according to specific combination rules to avoid the problem of "combination explosion" of molecules. The target properties of the extractant were set, and the extractant meeting the requirements was selected by predicting the correlation physical properties of the generated molecules. Based on the separation performance of the extractant in liquid–liquid extraction and the relative importance of each index, the fuzzy comprehensive evaluation membership function was established, the analytic hierarchy process determined the weight ratio of each index, and the consistency test results were passed. The results of case study based on quantum chemical analysis demonstrated that effective determination of extractants for the analysis of benzene–cyclohexane systems. The results unanimously prove that the method has important theoretical significance and application value.

Detangling Solvatochromic Effects by the Effective Fragment Potential Method.

Understanding molecular interactions in complex systems opens avenues for the efficient design of new materials with target properties. Energy decomposition methods provide a means to obtain a detailed picture of intermolecular interactions. This work introduces a molecular modeling approach for decomposing the solvatochromic shifts of the electronic excited states into the contributions of the individual molecular fragments of the environment surrounding the chromophore. The developed approach is implemented for the QM/EFP (quantum mechanics/effective fragment potential) model that provides a rigorous first-principles-based description of the electronic states of the chromophores in complex polarizable environments. On the example of two model systems, water pentamer and hydrated uracil, we show how the decomposition of the solvatochromic shifts into the contributions of individual solvent water molecules provides a detailed picture of the intermolecular interactions in the ground and excited states of these systems. The analysis also demonstrates the nonadditivity of solute-solvent interactions and the significant contribution of solute polarization to the total values of solvatochromic shifts.

Physico- and phytochemical properties of Brassica juncea as affected by agroclimatic conditions

Physicochemical and phytochemical assessment of leaf mustard (Brassica juncea L.) grown in different agroclimatic conditions is essential to highlight their compositional variability and evaluate the most suitable bunch of agroclimatic and agronomic practices. B. juncea is one of the important leafy vegetables that serve as source of vitamin A and C and iron, and plenty of antioxidants. This in situ research was executed to assess the quality variability of B. juncea grown in different agroecosystems. Leaves’ samples of B. juncea were procured from 15 farmers’ fields exhibiting different agroclimatic conditions i.e., elevation, nutrient management, temperature, irrigation, and tillage practices. Leaves’ samples were subjected to physicochemical and phytochemical analysis, i.e., moisture, pH, TSS, ascorbic acid, carotenoids, phenolics, flavonoids, and antioxidant potential. In the leaves’ samples of B. juncea, the target properties were found to vary significantly (P ≤ 0.05) in different agroclimatic conditions. The moisture content, ascorbic acid, phenolic content, carotenoids, and antioxidants were found in the range of 62.7–79.3%, 74–91 mg/100 g, 49.2–49.2 mg GAE/100 g, 436.3–480 mg β carotene/100 g, 32.7–46.67%, respectively. This study elaborates the significant variation of physicochemical and phytochemical attributes of B. juncea due to the prevailing agroclimatic conditions. This necessitates the appropriate choice of B. juncea concerning its composition and ecological conditions of its cultivation in the prospective health benefits.

INTransformer: Data augmentation-based contrastive learning by injecting noise into transformer for molecular property prediction

Molecular property prediction plays an essential role in drug discovery for identifying the candidate molecules with target properties. Deep learning models usually require sufficient labeled data to train good prediction models. However, the size of labeled data is usually small for molecular property prediction, which brings great challenges to deep learning-based molecular property prediction methods. Furthermore, the global information of molecules is critical for predicting molecular properties. Therefore, we propose INTransformer for molecular property prediction, which is a data augmentation method via contrastive learning to alleviate the limitations of the labeled molecular data while enhancing the ability to capture global information. Specifically, INTransformer consists of two identical Transformer sub-encoders to extract the molecular representation from the original SMILES and noisy SMILES respectively, while achieving the goal of data augmentation. To reduce the influence of noise, we use contrastive learning to ensure the molecular encoding of noisy SMILES is consistent with that of the original input so that the molecular representation information can be better extracted by INTransformer. Experiments on various benchmark datasets show that INTransformer achieved competitive performance for molecular property prediction tasks compared with the baselines and state-of-the-art methods.

Inference of Robust Reachability Constraints

Characterization of bugs and attack vectors is in many practical scenarios as important as their finding. Recently, Girol et. al. have introduced the concept of robust reachability, which ensures a perfect reproducibility of the reported violations by distinguishing inputs that are under the control of the attacker (controlled inputs) from those that are not (uncontrolled inputs), and proposed first automated analysis for it. While it is a step toward distinguishing severe bugs from benign ones, it fails for example to describe violations that are mostly reproducible, i.e., when triggering conditions are likely to happen, meaning that they happen for all uncontrolled inputs but a few corner cases. To address this issue, we propose to leverage theory-agnostic abduction techniques to generate constraints on the uncontrolled program inputs that ensure that a target property is robustly satisfied. Our proposal comes with an extension of robust reachability that is generic on the type of trace property and on the technology used to verify the properties. We show that our approach is complete w.r.t its inference language, and we additionally discuss strategies for the efficient exploration of the inference space. We demonstrate the feasibility of the method and its practical ability to refine the notion of robust reachability with an implementation that uses robust reachability oracles to generate constraints on standard benchmarks from software verification and security analysis. We illustrate the use of our implementation to a vulnerability characterization problem in the context of fault injection attacks. Our method overcomes a major limitation of the initial proposal of robust reachability, without complicating its definition. From a practical view, this is a step toward new verification tools that are able to characterize program violations through high-level feedback.

Advancing tablet lubrication: A systematic comparison of feed frame lubrication and internal lubrication

For the continuous manufacturing of tablets, internal lubrication is often utilized. Here, lubricant is added to a pre-blend in an additional blending step. However, over-lubrication may occur, with a negative impact on the tablet quality. This study consequently focuses on the systematic comparison of internal lubrication to an alternative, named feed frame lubrication. This method represents a new lubrication type, where the lubricant is fed into the feed frame without prior blending.A design of experiments with shear number and lubricant content as factors was used. Target properties were tensile strength, disintegration time and contact angle. Based on the data, multiple linear regression models were created and optimized using backward stepwise regression.Feed frame lubrication exhibited similar or even improved tablet properties compared to internal lubrication. The tablets turned out to be harder with decreased disintegration times. Finally, feed frame lubrication reduces the need for an additional blending step.

The microvillar protocadherin CDHR5 associates with EBP50 to promote brush border assembly.

Transporting epithelial cells of the gut and kidney interact with their luminal environment through a densely packed collection of apical microvilli known as a brush border (BB). Proper brush border assembly depends on the intermicrovillar adhesion complex (IMAC), a protocadherin-based adhesion complex found at the distal tips of microvilli that mediates adhesion between neighboring protrusions to promote their organized packing. Loss of the IMAC adhesion molecule Cadherin-related family member 5 (CDHR5) results in significant brush border defects, though the functional properties of this protocadherin have not been thoroughly explored. Here, we show that the cytoplasmic tail of CDHR5 contributes to its correct apical targeting and functional properties in an isoform-specific manner. Library screening identified the Ezrin-associated scaffolds EBP50 and E3KARP as cytoplasmic binding partners for CDHR5. Consistent with this, loss of EBP50 disrupted proper brush border assembly with cells exhibiting markedly reduced apical IMAC levels. Together, our results shed light on the apical targeting determinants of CDHR5 and further define the interactome of the IMAC involved in brush border assembly.