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Results in Chemistry 2025年 16卷

作者： Walid Abdelfattah Munthar Kadhim Abosaoda Hardik Doshi H.S. Shreenidhi Manoranjan Parhi Devendra Singh Prabhjot Singh Bilakshan Purohit Kamal Kant Joshi Ahmad Abumalek Department of Mathematics College of Science Northern Border University Arar Saudi Arabia College of pharmacy the Islamic University Najaf Iraq College of pharmacy The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Marwadi Universitly Research Center Department of Computer Engineering Faculty of Engineering & Technology Marwadi University Rajkot Gujarat India Assistant Professor Department of Computer Science and Engineering School of Engineering and Technology JAIN (Deemed to be University) Bangalore Karnataka India Department of Computer Science and Engineering Siksha 'O' Anusandhan (Deemed to be University) Bhubaneswar Odisha 751030 India Department of Computer science & Engineering Uttaranchal Institute of Technology Uttaranchal University Dehradun 248007 Uttarakhand India Department of Computer Application Chandigarh Engineering College Chandigarh Group of Colleges-Jhanjeri Mohali 140307 Punjab India Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura 140401 Punjab India Department of Allied Science Graphic Era Hill University Dehradun India Graphic Era Deemed to be University Dehradun Uttarakhand India Faculty of Engineering Balkh University Balkh Afghanistan

This study develops advanced machine learning algorithms to accurately predict the refractive index of polyethylene glycol (PEG) polymers using temperature and molecular weight as key input variables. A databank comprising 99 experimental measurements were utilized to train and assess multiple algorithms, including Extra Trees, Random Forests, Decision Trees, Ensemble Learning, AdaBoost, K-Nearest Neighbors, Multilayer Perceptron Artificial Neural Networks (MLP-ANN), Linear Regression, and Elastic Net. The dataset demonstrated a strong correlation between molecular weight and refractive index ( r = 0.64), and an inverse correlation with temperature ( r = −0.72). Model performance was assessed using R 2 , MSE, and AARE%, revealing that MLP-ANN, Linear Regression, and Elastic Net models yielded the highest accuracy on the testing dataset. Furthermore, SHAP analysis identified molecular weight as the dominant factor influencing refractive index predictions. The proposed approach significantly reduces the need for experimental measurements and offers a reliable, time-efficient solution for estimating the optical behavior of PEG polymers under varying conditions.

关键词： Machine learning Artificial intelligence Polyethylene glycol Refractive index Outlier detection SHAP analysis

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FlowDesign: Improved design of antibody CDRs through flow matching and better prior distributions

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Cell systems 2025年第6期16卷 101270页

作者： Jun Wu Xiangzhe Kong Ningguan Sun Jing Wei Sisi Shan Fuli Feng Feng Wu Jian Peng Linqi Zhang Yang Liu Jianzhu Ma University of Science and Technology of China Hefei China. Department of Computer Science and Technology Tsinghua University Beijing China Institute for AI Industry Research Tsinghua University Beijing China. Helixon US Inc. Champaign Illinois USA. Comprehensive AIDS Research Center Center for Infection Biology Pandemic Research Alliance School of Basic Medical Sciences Tsinghua Medicine Tsinghua University Beijing China. Institute for AI Industry Research Tsinghua University Beijing China. Electronic address: liuyang2011@***. Institute for AI Industry Research Tsinghua University Beijing China Department of Electronic Engineering Tsinghua University Beijing China. Electronic address: majianzhu@***.

Designing antibodies with desired binding specificity and affinity is essential for pharmaceutical research. While diffusion-based models have advanced the co-design of the complementarity-determining region (CDR) sequences and structures, challenges remain, including non-informative priors, incompatibility with discrete amino acid types, and impractical computational costs in large-scale sampling. To address these, we propose FlowDesign, a sequence-structure co-design approach via flow matching, offering (1) flexible prior selection, (2) direct matching of discrete distributions, and (3) enhanced efficiency for large-scale sampling. By leveraging various priors, data-driven structural models proved the most informative. FlowDesign outperformed baselines in amino acid recovery (AAR), root-mean-square deviation (RMSD), and Rosetta energy. We also applied FlowDesign to design antibodies targeting the HIV-1 receptor CD4. FlowDesign yielded antibodies with improved binding affinity and neutralizing potency compared with the antibody ibalizumab across multiple HIV mutants, validated by biolayer interferometry (BLI) and pseudovirus neutralization. This highlights FlowDesign's potential in antibody and protein design. A record of this paper's transparent peer review process is included in the supplemental information.

关键词： HIV antibody antibody design flow matching structure-based drug design

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Multidisciplinary Consensus Prostate Contours on Magnetic Resonance Imaging: Educational Atlas and Reference Standard for Artificial Intelligence Benchmarking

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International Journal of Radiation Oncology Biology Physics 2025年

作者： Song, Yuze Dornisch, Anna M. Dess, Robert T. Margolis, Daniel J.A. Weinberg, Eric P. Barrett, Tristan Cornell, Mariel Fan, Richard E. Harisinghani, Mukesh Kamran, Sophia C. Lee, Jeong Hoon Li, Cynthia Xinran Liss, Michael A. Rusu, Mirabela Santos, Jason Sonn, Geoffrey A. Vidic, Igor Woolen, Sean A. Dale, Anders M. Seibert, Tyler M. Department of Radiation Medicine and Applied Sciences University of California San Diego La JollaCalifornia United States Department of Electrical and Computer Engineering University of California San Diego La JollaCalifornia United States Department of Radiation Oncology University of Michigan Ann ArborMI United States Department of Radiology Cornell University IthacaNew York United States Department of Clinical Imaging Sciences University of Rochester Medical Center RochesterNew York United States Department of Radiology University of Cambridge United Kingdom Cambridge United Kingdom Radformation New YorkNew York United States Department of Urology Stanford School of Medicine Palo AltoCalifornia United States Department of Radiology Massachusetts General Hospital BostonMassachusetts United States Department of Radiation Oncology Massachusetts General Hospital BostonMassachusetts United States Department of Radiology Stanford School of Medicine Palo AltoCalifornia United States Institute for Computational and Mathematical Engineering Stanford University Palo AltoCalifornia United States Department of Urology University of Texas Health Sciences Center San Antonio San AntonioTexas United States Department of Biomedical Data Science Stanford University Palo AltoCalifornia United States Quibim New YorkNew York United States Cortechs.ai San DiegoCalifornia United States Department of Radiology and Biomedical Imaging University of California San Francisco San FranciscoCalifornia United States Department of Radiology University of California San Diego La JollaCalifornia United States Department of Neurosciences University of California San Diego La JollaCalifornia United States Halıcıoğlu Data Science Institute University of California San Diego La JollaCalifornia United States Department of Bioengineering University of California San Diego La JollaCalifornia United States Department of Urology University of California San Diego La Jolla

Purpose: Evaluation of artificial intelligence (ai) algorithms for prostate segmentation is challenging because ground truth is lacking. We aimed to: (1) create a reference standard data set with precise prostate contours by expert consensus, and (2) evaluate various ai tools against this standard. Methods and Materials: We obtained prostate magnetic resonance imaging cases from six institutions from the Qualitative Prostate Imaging Consortium. A panel of 4 experts (2 genitourinary radiologists and 2 prostate radiation oncologists) meticulously developed consensus prostate segmentations on axial T2-weighted series. We evaluated the performance of 6 ai tools (3 commercially available and 3 academic) using Dice scores, distance from reference contour, and volume error. Results: The panel achieved consensus prostate segmentation on each slice of all 68 patient cases included in the reference data set. We present 2 patient examples to serve as contouring guides. Depending on the ai tool, median Dice scores (across patients) ranged from 0.80 to 0.94 for whole prostate segmentation. For a typical (median) patient, ai tools had a mean error over the prostate surface ranging from 1.3 to 2.4 mm. They maximally deviated 3.0 to 9.4 mm outside the prostate and 3.0 to 8.5 mm inside the prostate for a typical patient. Error in prostate volume measurement for a typical patient ranged from 4.3% to 31.4%. Conclusions: We established an expert consensus benchmark for prostate segmentation. The best-performing ai tools have typical accuracy greater than that reported for radiation oncologists using computed tomography scans (the most common clinical approach for radiation therapy planning). Physician review remains essential to detect occasional major errors. © 2025 The Authors

关键词： Volume measurement

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A Reliable Method to Estimate the Bispectral Index Value Using a Single Frontal EEG Channel for Intra and Inter Subject Variability

A Reliable Method to Estimate the Bispectral Index Value Usi...

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IEEE International Workshop on Medical Measurement and applications (MEMEA)

作者： Mohammad Shahbakhti Matin Beiramvand Roza Krycinska Erfan Nasiri Wei Chen Jordi Solé-Casals Michal Wierzchon Anna Broniec-Wójcik Piotr Augustyniak Vaidotas Marozas Biomedical Engineering Institute Kaunas University of Technology Kaunas Lithuania Faculty of Information Technology and Communication Tampere University Tempere Finland Doctoral School in Medical and Health Sciences Jagiellonian University Medical College Krakow Poland Department of Interdisciplinary Intensive Care Jagiellonian University Collegium Medicum Krakow Poland Faculty of Statistics Mathematics and Computer Allameh Tabataba’i University Tehran Iran Center for Intelligent Medical Electronics School of Information Science and Technology Fudan University Shanghai China Data and Signal Processing Research Group University of Vic-Central University of Catalonia Vic Spain Institute of Psychology Jagiellonian University Krakow Poland Department of Biocybernetics and Biomedical Engineering AGH University of Science and Technology Krakow Poland

Objective: Monitoring the depth of anesthesia (DoA) plays an important role for administering the drug injection during a surgery, i.e., preventing undesired awareness and inordinate anesthetic depth. Although the bispectral index (BIS) monitor is the golden standard system for the DoA monitoring, it is still not affordable for the developing countries. Alternatively, a low-cost electroencephalogram (EEG) headband can be used. The objective of this paper is to present a new algorithm for estimating the BIS values using a single frontal EEG channel. Method: In the first step, the EEG signal is filtered for the elimination of artifacts and is split into its sub-bands. In the second step, several linear and nonlinear features are extracted from each sub-band and fed to a random forest regression model in order to estimate the BIS. The performance of the proposed algorithm is assessed using EEG data recorded from twenty-four subjects during the general anesthesia and is validated in terms of correlation coefficient (CC) and absolute error (AE) between the reference and estimated BIS values. Results: The proposed algorithm achieved the mean CC of 0.83 and AE of 6.5 for intra subject variability and mean CC of 0.87 and AE of 5.5 for inter subject variability. Significance: Given the similar results for both intra and inter subject variability, the proposed algorithm has the potential to be used in the real-world scenario.

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Multi-phase liver tumor segmentation with spatial aggregation and uncertain region inpainting

arXiv

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arXiv 2021年

作者： Zhang, Yue Peng, Chengtao Peng, Liying Huang, Huimin Tong, Ruofeng Lin, Lanfen Li, Jingsong Chen, Yen-Wei Chen, Qingqing Hu, Hongjie Peng, Zhiyi College of Computer Science and Technology Zhejiang University Hangzhou China Department of Electronic Engineering and Information Science University of Science and Technology of China Hefei China Research Center for Healthcare Data Science Zhejiang Lab Hangzhou China College of Information Science and Engineering Ritsumeikan University Kusatsu Japan Department of Radiology Sir Run Run Shaw Hospital Hangzhou China Department of Radiology The First Affiliated Hospital College of Medicine Zhejiang University Hangzhou China

Multi-phase computed tomography (CT) images provide crucial complementary information for accurate liver tumor segmentation (LiTS). State-of-the-art multi-phase LiTS methods usually fused cross-phase features through phase-weighted summation or channel-attention based concatenation. However, these methods ignored the spatial (pixel-wise) relationships between different phases, hence leading to insufficient feature integration. In addition, the performance of existing methods remains subject to the uncertainty in segmentation, which is particularly acute in tumor boundary regions. In this work, we propose a novel LiTS method to adequately aggregate multi-phase information and refine uncertain region segmentation. To this end, we introduce a spatial aggregation module (SAM), which encourages per-pixel interactions between different phases, to make full use of cross-phase information. Moreover, we devise an uncertain region inpainting module (URIM) to refine uncertain pixels using neighboring discriminative features. Experiments on an in-house multi-phase CT dataset of focal liver lesions (MPCT-FLLs) demonstrate that our method achieves promising liver tumor segmentation and outperforms state-of-the-arts. Copyright © 2021, The Authors. All rights reserved.

关键词： Pixels

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A normalized cross-correlation convolutional neural network (CNN-NCC) for exemplar-based object detection

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aiP Conference Proceedings 2023年第1期2734卷

作者： Yaya Wihardi Winda M. Kristy Erlangga Arjon Turnip Intan N. Yulita Endroyono Computer Science Education Department Faculty of Mathematic and Natural Science Education Universitas Pendidikan Indonesia Bandung Indonesia Artificial Intelligence and Big Data Research Center Padjadjaran University Sumedang Indonesia Department of Electrical Engineering Sepuluh Nopember Institute of Technology Surabaya Indonesia

Object detection in dynamic environments, such as CCTV surveillance systems and robotics, requires a special approach. New objects that have not been previously trained to the model are often captured by sensors, and it is very important to recognize them in the future. One approach that can be used to solve this problem is an exemplar-based approach. On this study, we try to build an exemplar-based object detector based on Convolutional Neural Network with Normalized Cross Correlation matching. Our proposed method did not need to re-train the model to identify unknown objects that had not been acknowledged by the model during the training stage. The results show that the model achieves 0.449 mAP on the COCO dataset.

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A hybridized parallel bats algorithm for combinatorial problem of traveling salesman

A hybridized parallel bats algorithm for combinatorial probl...

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作者： Nguyen, Trong-The Qiao, Yu Pan, Jeng-Shyang Chu, Shu-Chuan Chang, Kuo-Chi Xue, Xingsi Dao, Thi-Kien Intelligent Information Processing Research Center Fujian University of Technology Fujian Fuzhou China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fujian China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China Department of Information Technology University of Manage and Technology Haiphong Viet Nam

Metaheuristic algorithms have been applied widely for real-world problems in many fields, e.g., engineering, financial, healthcare. Bats algorithm (BA) is a recent metaheuristic algorithm with considering as a robust optimization method that can outperform existing algorithms. However, when dealing with complicated combinatorial problems such as traveling salesman problems (TSP), the BA can be fallen in a local optimum. This paper proposes a new hybridizing Parallel BA (HPBA) with a mutation in local-search to escape such its drawback scenario for TSP. A graph theory mutation method is used to embed for hybridizing BA with exploiting similarities among individuals. The proposed method is extensively evaluated in TSP with series instances of the benchmark from TSPLIB to test its performance. The compared experimental result with the previous method and the best-known solutions (B.K.S) in the literature shows that the proposed approach offers competitive results. © 2020 - IOS Press and the authors. All rights reserved.

关键词： Traveling salesman problem

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A Robust Ensemble Algorithm for Ischemic Stroke Lesion Segmentation: Generalizability and Clinical Utility Beyond the ISLES Challenge

arXiv

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arXiv 2024年

作者： de la Rosa, Ezequiel Reyes, Mauricio Liew, Sook-Lei Hutton, Alexandre Wiest, Roland Kaesmacher, Johannes Hanning, Uta Hakim, Arsany Zubal, Richard Valenzuela, Waldo Robben, David Sima, Diana M. Anania, Vincenzo Brys, Arne Meakin, James A. Mickan, Anne Broocks, Gabriel Heitkamp, Christian Gao, Shengbo Liang, Kongming Zhang, Ziji Siddiquee, Md Mahfuzur Rahman Myronenko, Andriy Ashtari, Pooya Van Huffel, Sabine Jeong, Hyun-Su Yoon, Chi-Ho Kim, Chulhong Huo, Jiayu Ourselin, Sebastien Sparks, Rachel Clèrigues, Albert Oliver, Arnau Lladó, Xavier Chalcroft, Liam Pappas, Ioannis Bertels, Jeroen Heylen, Ewout Moreau, Juliette Hatami, Nima Frindel, Carole Qayyum, Abdul Mazher, Moona Puig, Domenec Lin, Shao-Chieh Juan, Chun-Jung Hu, Tianxi Boone, Lyndon Goubran, Maged Liu, Yi-Jui Wegener, Susanne Kofler, Florian Ezhov, Ivan Shit, Suprosanna Hernandez Petzsche, Moritz R. Menze, Bjoern Kirschke, Jan S. Wiestler, Benedikt Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Department of Informatics Technical University Munich Germany Icometrix Leuven Belgium ARTORG Center for Biomedical Research University of Bern Bern Switzerland Department of Radiation Oncology University Hospital Bern University of Bern Switzerland University of Bern Bern Switzerland Chan Division of Occupational Science and Occupational Therapy University of Southern California Los AngelesCA United States Stevens Neuroimaging and Informatics Institute Department of Neurology Keck School of Medicine University of Southern California United States University Institute of Diagnostic and Interventional Neuroradiology Inselspital Bern Switzerland University Institute of Diagnostic and Interventional Neuroradiology University Hospital Bern Inselspital University of Bern Bern Switzerland Department of Diagnostic and Interventional neuroradiology University Medical Center Hamburg-Eppendorf Hamburg Germany Department of Medical Imaging Radboud University Medical Center Institute for Health Sciences Nijmegen Netherlands Deepwise AI Lab Beijing China Beijing University of Posts and Telecommunications Bejing China School of Computing and Augmented Intelligence Arizona State University TempeAZ United States NVIDIA Santa ClaraCA United States STADIUS Center for Dynamical Systems Signal Processing and Data Analytics KU Leuven Leuven Belgium Pohang Korea Republic of School of Biomedical Engineering & Imaging Sciences King’s College London United Kingdom Institute of Computer Vision and Robotics University of Girona Spain Wellcome Centre for Human Neuroimaging University College London London United Kingdom Laboratory of Neuro Imaging Stevens Institute for Neuroimaging and Informatics Keck School of Medicine University of Southern California Los Angeles United States KU Leuven Leuven Belgium CREATIS Université Lyon1 CNRS UMR5220 INSERM U1206 INSA-Lyon Villeurbanne696

Diffusion-weighted MRI (DWI) is essential for stroke diagnosis, treatment decisions, and prognosis. However, image and disease variability hinder the development of generalizable ai algorithms with clinical value. We address this gap by presenting a novel ensemble algorithm derived from the 2022 Ischemic Stroke Lesion Segmentation (ISLES) challenge. ISLES’22 provided 400 patient scans with ischemic stroke from various medical centers, facilitating the development of a wide range of cutting-edge segmentation algorithms by the research community. By assessing them against a hidden test set, we identified strengths, weaknesses, and potential biases. Through collaboration with leading teams, we combined top-performing algorithms into an ensemble model that overcomes the limitations of individual solutions. Our ensemble model combines the individual algorithms’ strengths and achieved superior ischemic lesion detection and segmentation accuracy (median Dice score: 0.82, median lesion-wise F1 score: 0.86) on our internal test set compared to individual algorithms. This accuracy generalized well across diverse image and disease variables. Furthermore, the model excelled in extracting clinical biomarkers like lesion types and affected vascular territories. Notably, in a Turing-like test, neuroradiologists consistently preferred the algorithm’s segmentations over manual expert efforts, highlighting increased comprehensiveness and precision. Validation using a real-world external dataset (N=1686) confirmed the model’s generalizability (median Dice score: 0.82, median lesion-wise F1 score: 0.86). The algorithm’s outputs also demonstrated strong correlations with clinical scores (admission NIHSS and 90-day mRS) on par with or exceeding expert-derived results, underlining its clinical relevance. This study offers two key findings. First, we present an ensemble algorithm that detects and segments ischemic stroke lesions on DWI across diverse scenarios on par with expert (neuro)rad

关键词： Diagnosis

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The Minimum Information about CLinical Artificial Intelligence Checklist for Generative Modeling research (MI-CLaiM-GEN)

arXiv

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arXiv 2024年

作者： Miao, Brenda Y. Chen, Irene Y. Williams, Christopher Y.K. Davidson, Jaysón Garcia-Agundez, Augusto Sun, Shenghuan Zack, Travis Saria, Suchi Arnaout, Rima Quer, Giorgio Sadaei, Hossein J. Torkamani, Ali Beaulieu-Jones, Brett Yu, Bin Gianfrancesco, Milena Butte, Atul J. Norgeot, Beau Sushil, Madhumita Bakar Computational Health Sciences Institute University of California San Francisco San FranciscoCA United States UCSF-UC Berkeley Joint Program in Computational Precision Health University of California Berkeley University of California San Francisco BerkeleyCA United States Department of Electrical Engineering and Computer Sciences University of California Berkeley BerkeleyCA United States Berkeley AI Research University of California Berkeley BerkeleyCA United States Department of Medicine Division of Rheumatology University of California San Francisco San FranciscoCA United States Helen Diller Family Comprehensive Cancer Center University of California San Francisco San FranciscoCA United States Center for Data-driven Insights and Innovation University of California Office of the President OaklandCA United States Bayesian Health New YorkNY10282 United States Department of Computer Science Johns Hopkins University Whiting School of Engineering BaltimoreMD United States Department of Health Policy & Management Johns Hopkins University Bloomberg School of Public Health BaltimoreMD United States Department of Medicine Johns Hopkins Medicine BaltimoreMD21205 United States Departments of Medicine Radiology and Pediatrics University of California San Francisco San FranciscoCA United States Scripps Research Translational Institute La Jolla CA United States Department of Integrative Structural and Computational Biology Scripps Research La Jolla CA92037 United States Department of Medicine University of Chicago ChicagoIL United States Department of Statistics University of California Berkeley BerkeleyCA United States Center for Computational Biology University of California Berkeley BerkeleyCA United States Qualified Health PBC Palo AltoCA United States

Recent advances in generative models, including large language models (LLMs), vision language models (VLMs), and diffusion models, have accelerated the field of natural language and image processing in medicine and marked a significant paradigm shift in how biomedical models can be developed and deployed. While these models are highly adaptable to new tasks, scaling and evaluating their usage presents new challenges not addressed in previous frameworks. In particular, the ability of these models to produce useful outputs with little to no specialized training data ("zero-" or "few-shot" approaches), as well as the open-ended nature of their outputs, necessitate the development of new guidelines for robust reporting of clinical generative model research. In response to gaps in standards and best practices for the development of clinical ai tools identified by US Executive Order 141103 and several emerging national networks for clinical ai evaluation, we begin to formalize some of these guidelines by building on the original MI-CLaiM checklist. The new checklist, MI-CLaiM-GEN (Table 1), aims to address differences in training, evaluation, interpretability, and reproducibility of new generative models compared to non-generative ("predictive") ai models. This MI-CLaiM-GEN checklist also seeks to clarify cohort selection reporting with unstructured clinical data and adds additional items on alignment with ethical standards for clinical ai research. © 2024, CC BY.

关键词： Generative adversarial networks

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Generative Adversarial Networks for Image Super-Resolution: A Survey

arXiv

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arXiv 2022年

作者： Tian, Chunwei Zhang, Xuanyu Zhu, Qi Zhang, Bob Lin, Jerry Chun-Wei School of Software Northwestern Polytechnical University the National Engineering Laboratory for Integrated Aero-space-Ground-Ocean Big Data Application Technology 1 Dongxiang Rd Shaanxi Xi’an710129 China School of Software Northwestern Polytechnical University 1 Dongxiang Rd Shaanxi Xi’an710129 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics 101 Section 2 Kuang-Fu Road Jiangsu Nanjing China PAMI Research Group University of Macau 101 Section 2 Kuang-Fu Road Macao999078 China Department of Computer Science Western Norway University of Applied Sciences Bergen Norway

Single image super-resolution (SISR) has played an important role in the field of image processing. Recent generative adversarial networks (GANs) can achieve excellent results on low-resolution images with small samples. However, there are little literatures summarizing different GANs in SISR. In this paper, we conduct a comparative study of GANs from different perspectives. We first take a look at developments of GANs. Second, we present popular architectures for GANs in big and small samples for image applications. Then, we analyze motivations, implementations and differences of GANs based optimization methods and discriminative learning for image super-resolution in terms of supervised, semi-supervised and unsupervised manners, where these GANs are analyzed via integrating different network architectures, prior knowledge, loss functions and multiple tasks. Next, we compare performance of these popular GANs on public datasets via quantitative and qualitative analysis in SISR. Finally, we highlight challenges of GANs and potential research points for SISR. © 2022, CC BY-NC-ND.

关键词： Generative adversarial networks

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