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Hybrid Nanocomposites of PCL- PLA with Graphene and Cellulose Nanocrystal to Prepare Foams for Bone Regeneration

POLYMERS FOR ADVANCED TECHNOLOGIES 2025年第3期36卷

作者： kalantari, masoumeh Makaremy, Mohammad Amin Bazrpash, Sina Hosseini, Saadi Nourany, Mohammad Amirkabir Univ Technol Polymer Engn & Color Technol Tehran Iran Pasteur Inst Iran Lab Regenerat Med Tehran Iran

Three-dimensional (3D) and open-porous scaffolds with the capability to induce osteogenesis were of interest to researchers in the field of biomedical engineering. Poly (epsilon-caprolactone) was proven to be highly biocompatible, with the capability to induce osteogenesis of human mesenchymal stem cells (hMSCs). However, its high crystallinity and fast crystallization limit its capability to produce a stable porous structure due to pore shrinkage. Its low mechanical strength is another issue, as higher stiffness is a requirement for directing the fate of hMSCs to bone formation. In this regard, PCL was blended with polylactic acid (PLA) with a weight ratio of 60:40 using 1,4-Dioxane. Salt leaching and freeze-thaw methods were used to create porous structures. Certain nanoparticles were also found to be osteoinductive, such as graphene and cellulose nanocrystal (CNC). The nanoparticles, with the kinetical and thermodynamical tendency toward PCL, were used to suppress the coalescence of the PLA droplets to form a uniform blend. The rheological and crystallization behavior, as well as the morphology of the blend in both states (film and porous structure) were evaluated. The selected foams were further used as scaffolds for bone regeneration. The scaffolds were highly biocompatible, with the capability to induce osteogenesis.

关键词： 3D scaffolds graphene- cellulose nanocrystal osteogenesis PCL- PLA

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To be or not to be: navigating the influence of MicroRNAs on cervical cancer cell death

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CANCER CELL INTERNATIONAL 2025年第1期25卷 1-23页

作者： Taghizadieh, Mohammad kalantari, masoumeh Bakhshali, Roksana Kobravi, Sepehr Khalilollah, Shayan Baghi, Hossein Bannazadeh Bayat, Mobina Nahand, Javid Sadri Akhavan-Sigari, Reza Tabriz Univ Med Sci Fac Med Dept Pathol Tabriz Iran Tehran Univ Hlth Sci Dept Biol Tehran Iran Omid Canc Ctr Ahvaz Iran Islamic Azad Univ Fac Dent Dept Oral & Maxillofacial Surg Tehran Med Sci Branch Tehran Iran Islamic Azad Univ Fac Med Dept Neurosurg Tehran Med Sci Tehran Iran Tabriz Univ Med Sci Infect & Trop Dis Res Ctr Tabriz Iran Univ Med Ctr Tuebingen Dept Neurosurg Tubingen Germany Coll Humanum Warsaw Management Univ Dept Hlth Care Management & Clin Res Warsaw Poland

With all diagnostic and therapeutic advances, such as surgery, radiation- and chemo-therapy, cervical cancer (CC) is still ranked fourth among the most frequent cancers in women globally. New biomarkers and therapeutic targets are warranted to be discovered for the early detection, treatment, and prognosis of CC. As component of the non-coding RNA's family, microRNAs (miRNAs) participate in several cellular functions such as cell proliferation, gene expression, many signaling cascades, apoptosis, angiogenesis, etc. MiRNAs can suppress or induce programmed cell death (PCD) pathways by altering their regulatory genes. Besides, abnormal expression of miRNAs weakens or promotes various signaling pathways associated with PCD, resulting in the development of human diseases such as CC. For that reason, understanding the effects that miRNAs exert on the various modes of tumor PCD, and evaluating the potential of miRNAs to serve as targets for induction of cell death and reappearance of chemotherapy. The current study aims to define the effect that miRNAs exert on cell apoptosis, autophagy, pyroptosis, ferroptosis, and anoikis in cervical cancer to investigate possible targets for cervical cancer therapy. Manipulating the PCD pathways by miRNAs could be considered a primary therapeutic strategy for cervical cancer.

关键词： Cervical cancer MicroRNAs Apoptosis Ferroptosis Pyroptosis Autophagy

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Effect of Sparse Representation of Time Series Data on Learning Rate of Time-Delay Neural Networks

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CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2021年第6期40卷 3007-3032页

作者： kalantari Khandani, masoumeh Mikhael, Wasfy B. Univ Cent Florida Dept Elect & Comp Engn Orlando FL 32816 USA

In this paper, we examine how sparsifying input to a time-delay neural network (TDNN) can significantly improve the learning time and accuracy of the TDNN for time series data. The sparsifying of input is done through a sparse transform input layer. Many applications that involve prediction or forecasting of the state of a dynamic system can be formulated as a time series forecasting problem. Here, the task is to forecast some state variable, which is represented as a time series in applications such as weather forecasting, energy consumption prediction or predicting future state of a moving object. While there are many tools for time-delay forecasting, TDNNs have recently received more attention. We show that through applying a sparsifying input transform layer to the TDNN, we can considerably improve the learning time and accuracy. Through analyzing the learning process, we demonstrate the mathematical reasons for this improvement. Experiments with several datasets are used to show the improvement and the reason behind it. We use data from national weather forecast datasets, vehicle speed time series and synthetic data. Several different sparse representations are evaluated including principal component analysis (PCA), discrete cosine transform (DCT) and a mixture of DCT and Haar transforms. It is observed that the higher sparsity leads to better performance. The relative simplicity of TDNNs, compared with deep networks, and the use of sparse transforms for quicker learning open up possibilities for online learning in small embedded devices that do not have powerful computing capabilities.

关键词： Time series forecasting Time-delay neural networks Sparse representation PCA DCT Haar transforms

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Enhancing Convolutional Neural Network Performance Using Domain Transforms in Constrained Networks

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CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2022年第9期41卷 5160-5182页

作者： Khandani, masoumeh kalantari Mikhael, Wasfy B. Univ Cent Florida Dept Elect & Comp Engn Orlando FL 32816 USA

Convolutional neural network (CNN) has emerged as one of the primary tools for image classification. In particular, deep CNNs are now considered the main tool for this purpose. However, these networks are often large and require computing and storage power that may not be available in very small sensor devices such as IoT (Internet of Things) devices;their training is also time and computing power consuming. As a result, in some applications, reducing the size of input data (images) and the processing network becomes necessary. Such reduction usually comes at the cost of reduced classification performance. In this paper, we consider networks with under 200k learnable parameters, as opposed to millions in deeper networks. We examine how domain transforms can be used for efficient size reduction and improvement of classification accuracy for small networks. We emphasize that finding optimal hyperparameters or network configurations is not our objective in this paper. It is shown that by using transforms such as discrete wavelet transforms (DWT) or discrete cosine transform (DCT), it is possible to efficiently improve the performance of size-reduced networks and inputs. We demonstrate that in most cases, the improvement can be traced to higher entropy of resized input using transforms. While transforms such as DCT allow variable input and network sizes to be utilized, DWT proves to be very effective when significant size reduction is needed (improving the result by up to 5%). It is also shown that input size reduction of up to 75% is possible, without loss of classification accuracy in some cases. We use two datasets of small images, including Fashion MNIST and CIFAR-10, to evaluate the performance of size reduction methods.

关键词： Image classification Convolutional neural networks Sparse representation DWT DCT Domain transforms

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Investigating the viscoelastic and hysteresis behavior of nano silica/carbon black hybrid reinforced styrene-butadiene rubber vulcanizates via surface silanization

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POLYMER ENGINEERING AND SCIENCE 2023年第7期63卷 2309-2320页

作者： kalantari, masoumeh Abdi, Roxana Katbab, Ali Asghar Amirkabir Univ Technol Dept Polymer Engn & Color Technol Tehran Iran Azad Univ Sci & Res Branch Tehran Iran

With the thought of green tires and the need of replacing carbon reinforcing fillers with noncarbon silica filler, the effectiveness of silane surface modification on dispersion state, dynamic viscoelastic, hysteresis, and abrasion resistance of SBR vulcanizates filled with a hybrid of carbon black (CB)/nano SiO2 is investigated. In situ surface modification of nano SiO2 is carried out using bis-(?-triethoxysilylpropyl)-tetrasulfide (TESPT), and various compounds with different ratios of CB/SiO2 are prepared via a melt mixing process in an internal bath mixer. Scanning electron microscopy (SEM) and FT-IR spectroscopy revealed improved dispersion of silica and CB clusters with enhanced molecular interactions using in situ surface modification with 5 phr of TESPT is performed. Evidence for enhanced molecular interactions included lower dynamic loss (tand) and less hysteresis measured via dynamic mechanical thermal analysis (DMTA). Our results indicate that appropriate modification of silica allows partial replacement of carbon black with silica for production of eco-friendly rubber products.

关键词： hysteresis silica/carbon black hybrid styrene-butadiene rubber (SBR) viscoelastic

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A Review of Enabling Technologies for Magnetic Particle Imaging 65

A Review of Enabling Technologies for Magnetic Particle Imag...

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IEEE 65th International Midwest Symposium on Circuits and Systems (MWSCAS)

作者： Khandani, masoumeh kalantari Vosoughi, Azadeh Elect & Comp Engn Gainesville FL 32603 USA

ISBN: (数字)9781665402798

ISBN: (纸本)9781665402798

This paper offers a brief survey of Magnetic Particle Imaging (MPI) and its enabling technologies. MPI is an emerging molecular imaging modality that provides a superior alternative to other medical imaging methods such as magnetic resonance imaging (MRI) and X-Ray in some applications. MPI utilizes imaging concepts and hardware that are like MRI;however, its physics is different from that of MRI. MPI relies on measuring magnetic fields created by super-paramagnetic nanoparticles as tracers, offering a much higher contrast and sensitivity. Different from MRI, MPI can only measure and see the magnetized particles and not the tissue. MPI relieves the need for x-rays or harmful agents or positron emission tomography (PET) in many applications because it does not need ionizing radiation. The biocompatible iron oxide nanoparticles are used as tracers for in-vivo MPI measurements. This makes it possible to generate real-time in-vivo results from dynamic organs (e.g., a beating heart, blood flow in veins) allowing for producing 4D medical images (3D videos) of the dynamics of the internal organs, which will be indispensable in treatment planning and diagnosis of many serious diseases such as cancer, stroke, or cardiovascular ailments. Enabling MPI requires functionalities and technologies that are different from existing medical imaging and image processing techniques. We review some of the recent works on these technologies and point to the currently missing functionalities. For example, the conventional medical image analysis techniques are incapable of fully utilizing the hidden information in 4D data which could be produced by MPI. (1)

关键词： MPI Image Processing Magnetic Particle Imaging Survey superparamagnetic iron oxide nanoparticles (SPIONS) tracers

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A Study on Network Size Reduction Using Sparse Input Representation in Time Delay Neural Networks 63

A Study on Network Size Reduction Using Sparse Input Represe...

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63rd IEEE International Midwest Symposium on Circuits and Systems (MWSCAS)

作者： Khandani, masoumeh kalantari Mikhael, Wasfy B. Univ Cent Florida Dept Elect & Comp Engn Orlando FL 32816 USA

ISBN: (数字)9781728180588

ISBN: (纸本)9781728180588

Neural networks are being increasingly used in many applications. While large deep neural networks are quickly advancing, for computing devices that lack powerful processors, it is desirable to use smaller neural networks. In this paper, we focus on smaller neural networks than common deep networks and examine how their size can be made even smaller without sacrificing the performance. We show that for some data types it is possible to make the networks smaller using sparse representation of the input. We have studied time delay neural networks (TDNN) for time series prediction using three different datasets. It is found that sparsifying input to the TDNN, using Discrete Cosine Transform (DCT), or Principal Component Analysis (PCA), can result in performance improvement. The improved performance can be traded off for network size reduction. Therefore, we can make the network smaller and maintain the same performance. It is found that for data that has more randomness and sudden changes in value (higher frequencies present), sparse representation methods using discrete cosine transform or through principal component analysis allow reducing network size by up to 40%.

关键词： time series forecasting time delay neural networks DCT PCA transforms

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Efficient Time Series Forecasting Using Time Delay Neural Networks with Domain Pre-Transforms 62

Efficient Time Series Forecasting Using Time Delay Neural Ne...

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62nd IEEE International Midwest Symposium on Circuits and Systems (MWSCAS)

作者： Khandani, masoumeh kalantari Mikhael, Wasfy B. Univ Cent Florida Dept Elect & Comp Engn Orlando FL 32816 USA

ISBN: (纸本)9781728127880

Many applications that involve prediction or forecasting of the state of a dynamic system can be formulated as a time series forecasting problem. In these cases, the state of the system is represented as a time series. Among countless examples are weather forecasting, predicting energy consumption of a system, predicting future position of a vehicle, or prediction of economic metrics. Time Delay Neural Networks (TDNNs) are one of the main tools for this purpose that have recently received attention. In this paper, we examine how a transform layer applied to the beginning layer of the TDNN (called pre-transform layer) can significantly improve learning time, while also moderately improving accuracy. We take the example of vehicle speed time series and show that TDNNs can significantly improve the prediction accuracy;applying a Discrete Cosine Transform (DCT) or a mix of DCT and Haar transforms improves the accuracy even more, while significantly reducing the training time. We also observe that these shallow networks (e.g., 3 layers) are able to learn the inverse transform automatically. This means that the network output does not have to go through an inverse transform. The results are verified using two different data sets.

关键词： time series forecasting time delay neural networks DCT Haar transforms mixed transforms

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Training Strategies for Convolutional Neural Networks with Transformed Input

Training Strategies for Convolutional Neural Networks with T...

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IEEE International Midwest Symposium on Circuits and Systems (MWSCAS)

作者： Khandani, masoumeh kalantari Mikhael, Wasfy B. Univ Cent Florida Dept Elect & Comp Engn Orlando FL 32816 USA

ISBN: (纸本)9781665424615

Convolutional Neural Network (CNN) are now considered the main tool for image classification. However, most networks studied for classification are large and have extensive computing and storage requirements. Their training time is also usually very long. Such costly computational and storage requirements cannot be met in many applications with simple devices such as small processors or Internet of things (IoTs) devices. Therefore, reducing the size of networks and input sizes become necessary. However, such reductions are not easy and may reduce the classification performance. We examine how domain transforms under different training strategies can be used for efficient size reduction and improvement of classification accuracy. In this paper, we consider networks with under 220K learnable parameters, as opposed to millions in deeper networks. We show that by representing the input to a CNN using appropriately selected domain transforms, such as discrete wavelet transforms (DWT) or discrete cosine transform (DCT), it is possible to efficiently improve the performance of size-reduced networks. For example, DWT proves to be very effective when significant size reduction is needed (improving the result by up to 9%). It is also shown that by tuning training strategies such as the number of epochs and mini batch size, the performance can be further improved by up to 4% under fixed training time.

关键词： image classification Convolutional neural networks DCT DWT domain transforms

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Using Mixed DCT and Haar Transforms for Efficient Compression of Car Trajectory Data 61

Using Mixed DCT and Haar Transforms for Efficient Compressio...

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61st IEEE International Midwest Symposium on Circuits and Systems (MWSCAS)

作者： Khandani, masoumeh kalantari Mikhael, Wasfy B. Univ Cent Florida Dept Elect & Comp Engn Orlando FL 32816 USA

ISBN: (纸本)9781538673928

Data compression is an important component of communication and storage technologies, directly contributing to the efficiency of these systems. Lossy data or signal compression, while not applicable to all applications, usually allows for much higher efficiency. In this paper we propose a method for adopting the concept of mixed- transforms and evaluate it for vehicle trajectory data. In mixed- transform methods, compression residuals from the first representation of a signal are passed to a second transformation stage where further compression is applied in that domain. The final residual which represents error resulting from the compression and decompression process, is then used as a cost to be minimized by adjusting the configuration of each transformation stage. An adaptive algorithm from earlier works is adopted to iteratively adjust the configuration of each compression stage (selection of coefficients from each transform) to minimize the error. A hierarchical realization of the iterative adaptive algorithm is presented that uses preset compression ratios and optimizes the coefficient selection accordingly. We show that using DCT followed by Haar in the proposed method, it is possible to efficiently compress vehicle trajectory data. The results show that a higher quality for reconstructed data can be achieved using this method, compared to using a single transform compression. For trajectories with sudden movements, the method has a more notable improvement.

关键词： signal compression DCT Haar transforms mixed transforms nonorthogonal basis

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