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Breaking barrier of binding buffer in colorimetric aptasensing of tetracycline in food fish using peroxidase mimic gold NanoZyme

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Methods 2025年 240卷 145-153页

作者： Sarkar, Dhruba Jyoti Raja, Ramij Santhana Kumar, V. Bhattacharyya, Soumyadeb Pal, Souvik Mukherjee, Subhankar Das, Basanta Kumar Aquatic Environmental Biotechnology Division ICAR-Central Inland Fisheries Research Institute Barrackpore West Bengal Kolkata 700120 India Centre for Development of Advanced Computing-Kolkata West Bengal Kolkata 700091 India

Tetracycline is extensively used in aquaculture as a therapeutic agent that needs to be monitored due to food safety concerns. Aptasensing has been revealed as a suitable diagnostic platform for tetracycline sensing in food matrix due to its quick, low cost and robust nature. But, the colorimetric aptasensing of tetracycline employing the peroxidase activity of gold nanoparticles (AuNPs) to 3,3,5,5-tetramethylbenzidine (TMB) was unsuitable until now owing to the aptamer-specific alkaline binding buffer. The present study developed a method with an optimized reaction protocol diminishing the inhibitory effect of binding buffer on the sensor probe (AuNPs-aptamer + TMB + H₂O₂). The overall peroxidase activity of the sensor probe was only inhibited by tetracycline through selective adsorption on the AuNPs-aptamer complex. The peroxidase inhibition percentage in the test range of 0.01 to 0.5 mg L^-1 tetracycline gave a logarithmic response (R², 0.99) with a detection limit of 0.017 mg L^-1 which is less than the prescribed limit (0.1 mg L^-1) set by EU and FSSAI. The developed sensing system in fish muscle showed high recovery (111–115 %) with great potential for rapid detection of tetracycline in fish muscle. © 2025 Elsevier Inc.

关键词： Aptamer Fish Gold nanoparticle NanoZyme Tetracycline

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Peroxidase mimic feroxyhyte (FeOOH) nanoparticles enabled highly specific colorimetric detection of arsenate in water and fish

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Spectrochimica Acta - Part A: Molecular and Biomolecular Spectroscopy 2025年 339卷 126299页

作者： Sarkar, Dhruba Jyoti Raja, Ramij Das, Basanta Kumar Bhattacharyya, Soumyadeb Pal, Souvik Mukherjee, Subhankar Aquatic Environmental Biotechnology Division ICAR-Central Inland Fisheries Research Institute Barrackpore West Bengal Kolkata700120 India Centre for Development of Advanced Computing-Kolkata West Bengal Kolkata700091 India

Arsenic poses a serious health risk to humans. Hence, a simple and robust analytical approach for monitoring arsenic levels in water and food matrixes is required. We present a simple and rapid approach for quantifying arsenate in water and fish using feroxyhyte (FeOOH) nanoparticles as a sensor probe. The FeOOH nanoparticles showed peroxidase mimetic activity oxidizing 3,5,3′5′-tetramethylbenzidine (TMB) to a blue product (oxTMB, λmax 650 nm) in the presence of H2O2. However, arsenate's presence inhibits the peroxidase activity of FeOOH nanoparticles through binding on the catalytic active sites. Based on this principle, the presently developed method obtained a good linear response (R2, 0.99) over the range of 0.005 to 5.000 mg L−1 arsenate with 0.006 mg L−1 as the detection limit which is less than the prescribed limit (0.010 mg L−1) by WHO for drinking water. The average recoveries at different fortification levels ranged from 89.51 to 115.61 % in water and 101.11 to 106.99 % in fish muscle. The present analytical technique showed good selectivity due to pronounced peroxidase inhibition alibility (60.53–103.78 %) by arsenate than other non-target ions like PO43−, NO3−, Cr2O72−, etc. The FeOOH nanoparticles showed a promising application prospect for colorimetric detection of arsenate with a wide detection range in water and fish samples. © 2025 Elsevier B.V.

关键词： Nanoparticles

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Image analysis and fine-tuned ResNet50 model for effective malware detection

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Procedia Computer Science 2025年 260卷 1098-1106页

作者： Sanjeev Kumar Yudhishthira Sapru Jitendra Kumar Sugandha Sapru Cyber Security Technology Division Centre for Development of Advanced Computing Mohali India Indian Computer Emergency Response Team CERT-In Delhi India Department of Mathematics Bioinformatics and Computer Applications MANIT Bhopal India Department of Mathematics PGGC Panjab University Chandigarh India

With the rapid growth of connected assets to the Internet, malware attacks have increased exponentially, often causing substantial damage to these connected assets. Early detection of evolving malware attacks can avoid such damage to the Internet ecosystem. However, it requires an efficient and robust approach with little reliance on existing signature-based approaches, as these are no longer effective in detecting obfuscated and complex malware attacks. This study presents a new malware detection and classification approach using image analysis and a fine-tuned ResNet50 convolution neural network (CNN) model through transfer learning. The raw binary executable programs are transformed into grayscale images and resized to 224 × 224 × 1 image size to be fed to the deep CNN model for feature extraction. The pre-trained ResNet50 model is customized to extract the textural features by retaining the learned weight and bias from the source ImageNet data and making the last fully connected layer non-trainable. After that, a fully connected dense layer is added for dimension reduction of the feature map into N × 128, with N as the number of malware files. Two public datasets are used in experiments - MalImg and Microsoft BIG. This study uses comprehensive performance metrics to evaluate the model and obtained an accuracy of 98.50% for the MalImg dataset and 94.85% for the Microsoft BIG data set. The experiment results show that the proposed methodology can handle malware obfuscated by standard polymorphic techniques and is better than similar methods in the literature.

关键词： Cybersecurity Convolution neural network ResNet Malware classification Transfer learning

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Intelligent Malware Detection Using Sentence Transformer and Hybrid Deep Learning Architecture

Intelligent Malware Detection Using Sentence Transformer and...

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International Conference on Signal Processing and Communication (ICSC)

作者： Sanjeev Kumar Yudhishthira Sapru Sugandha Sapru B. Janet Cyber Security Technology Division Centre for Development of Advanced Computing Mohali India Indian Computer Emergency Response Team CERT-In Delhi India Department of Mathematics PGGC Panjab University Chandigarh India Department of Computer Application National Institute of Technology Tiruchirappalli India

ISBN: (数字)9798331511890

ISBN: (纸本)9798331511906

The complex evasion techniques adopted by the malware creator make signature-based malware detection approaches unable to detect unknown malware attacks. This study proposes a new malware detection and classification approach with significantly less dependence on manual feature engineering. The bidirectional encoder representations from transformers (BERT) are used to model the opcode sequences of the malware file as the semantic behavior of malware. After that, the vectorized feature vector is fed to the hybrid one-dimensional convolution neural network (1D-CNN) and the gated recurrent unit (GRU) model for feature reduction and normalization before training a Softmax classifier. A hybrid learning architecture is designed with a custom 1D-CNN and a GRU model to take the strengths of both types of networks. The proposed scheme is evaluated using the Microsoft BIG dataset, consisting of 1625 assembly files belonging to 9 distinct malware families. The proposed method produced 95.77% accuracy even with the smaller dataset, which we believe will boost performance by adding a more diverse dataset. It uses the next-generation BERT language model, which gives a promising future area for malware defenders. This research presents malware detection without much reliance on manual feature engineering or signature-based detection, making it suitable for detecting unknown malware attacks.

关键词： Training Semantics Manuals Bidirectional control Transformers Feature extraction Malware Encoding Vectors Next generation networking

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Repository of arbitration cores (ROAC) for scheduling in various multi-processor SOCs

Repository of arbitration cores (ROAC) for scheduling in var...

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2016 International Conference on Control Instrumentation Communication and Computational Technologies, ICCICCT 2016

作者： Sharma, Kirtika Singh, Anurag Singh, Balwinder ACS Division Centre for Development of Advanced Computing Mohali India

ISBN: (纸本)9781509052400

Increasing demand of multiple functions in a single device or with smaller area leads to more market for devices with System on Chip (SOC). An SOC have multiple Intellectual Properties (IPs) such as Processors, memory, peripherals etcetera on a single chip. These IPs need to have an efficient bus architecture for the communication purpose which further decides the device performance. The most used bus architectures like AMBA AHB, AMBA ASB, AMBA AXI, PCI etcetera uses arbitration algorithms to deal with the data traffic issues between these IP cores on a single silicon chip. Therefore a repository of various resizable and reusable arbitration methods has been presented in this paper for scheduling purposes in various multiprocessor system on chips (SOCs). The library consists of five arbitration methods such as Dynamic first in first out (DFIFO), Circular queue, Priority, Dynamic last in first out (DLIFO), and multilevel queue, which are compatible with various Hardware Design and Verification Languages (HDVL). The repository proposed is independent of platform, target, vendor and tool chain. It is generic and helps in fast implementation of IP cores. Every arbitration method in this library can be molded according to the specification needed or required for the SOC. The results are comparatively observed on the basis of area, power and delay. As the results shows that the proposed Circular Queue Scheduling technique is more proficient arbitration method as it take very less time delay and power. It proves to be a starvation free method. The area utilized by Circular queue Scheduler on Spartan 6 XC6SLX45 FPGA is also minimum amongst the other arbitration techniques presented in this repository. © 2016 IEEE.

关键词： System-on-chip

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On board EMG signal compression for portable devices-a study

On board EMG signal compression for portable devices-a study

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2013 International Conference on Machine Intelligence Research and Advancement, ICMIRA 2013

作者： Sharma, Sushant Singh, Mandeep ACSD Centre for Development of Advanced Computing Mohali India Engineer Digital Electronics and Communication Division Centre for Development of Advanced Computing Mohali India

ISBN: (纸本)9780769550138

Electromyography signals are used for biomedical applications and clinical diagnosis. This paper presents a system to acquire high quality EMG (electromyography) signal and compress it for high data transfer rate. For acquiring EMG signals we are using off-the-shelf integrated circuit AFE which generates 24 bit data stream output. After acquiring the signal from front end amplifier, the signal is processed (filtration, compression). This is achieved by making use of MSP430 MCU (micro controller). We are using AFE and MSP430 because it is less power consuming, reliable, and both are compatible with each other. The detail of the hardware and software is explained in the further section of the paper. This paper also describes the compression algorithm. Compression is done so that whenever the data is send wirelessly using Bluetooth, Zigbee etc, it requires less bandwidth. © 2013 IEEE.

关键词： Data transfer

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Federated Machine Learning Architecture for Heterogeneous Quantum Devices 1

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3rd International Conference on "Emerging Trends and Technologies on Intelligent Systems" (ETTIS-2023)

作者： Singh, Nagendra Tiwari, Abhishek Khaneja, Vivek Embedded Systems Division Centre for Development of Advanced Computing Noida India

ISBN: (数字)9789819939633

ISBN: (纸本)9789819939626

Quantum computing shows immense potential for solving traditional, near to impossible, complex problems. It also shows how the performance and speed can be improved for the solution of existing classical problems by using quantum mechanics. However, the computation power of current computers, servers, and supercomputers is well enough to speed up the existing problem solutions. But as data generated from various resources increases rapidly, in current scenarios more than 2.5 quintillion bytes of data is generated daily and is expected to be 200+ zettabytes on the cloud by 2025. Handling and storing such a massive amount of data is a significant issue for current computing and storage devices. Quantum computing has the capability to resolve this issue with quantum rules. In this paper, the task of pattern finding and recognizing in data (i.e., machine learning) is explored with the help of quantum computing and introducing a collaborative learning architecture. It enables collaborative computation from heterogeneous quantum computing devices for machine learning tasks. Also, it can be adapted to any quantum machine learning approach and speed up the learning phase with better data privacy. The proposed scheme is implemented with the MNIST dataset to compare with current existing research work. Results show that the proposed architecture matches existing research work’s performance with the additional features of privacy and heterogeneity of quantum devices. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Machine learning

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Comparative Analysis of Different Operational Transconductance Amplifiers 5

Comparative Analysis of Different Operational Transconductan...

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5th IEEE India Council International Subsections Conference, INDISCON 2024

作者： Yadav, Kareena Srivastava, Pawan Singh, Balwinder Kumar, Parveen Centre for Development of Advanced Computing Academic and Consultancy Division Mohali India

ISBN: (纸本)9798350376753

An operational-transconductance amplifier (OTA) is a type of amplifier that has the capability to convert an input voltage signal into an output current signal. Traditional Op-amp amplifies voltage signals directly while the OTA amplifies the current making it suitable for applications such as filters, voltage-controlled oscillators, and active filters. Similar to operational amplifiers, OTAs rely on both the supply voltage and gain. To maximize the advantage, the analog circuitry depends on various parameters such as bandwidth, common mode rejection ratio, gain, noise, and power consumption. High gain and less power consumption are preferred to fulfill the desired requirements. It is observed that the circuit which is based on the current mode is preferable as compared to the voltage mode. This paper analyzes and compares various operational-transconductance amplifiers while utilizing basic current mirrors, cascode, and cascade current mirrors. The OTA having a current mirror load and current sink shows better performance as it gives high gain and consumes less power. All three circuits are simulated in 90nm CMOS technology in Cadence Virtuoso. © 2024 IEEE.

关键词： Operational amplifiers

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DICOM search in medical image archive solution e-Sushrut Chhavi

DICOM search in medical image archive solution e-Sushrut Chh...

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International Conference on Electronic Computer Technology

作者： Sahu, B.K. Verma, Raghvendra Health Informatics Division Centre for Development of Advanced Computing Noida India

ISBN: (纸本)9781424486779

Digital Imaging and Communication in Medicine (DICOM) standard promotes the communication of digital image in the medical imaging environment. A picture archiving and communication system(PACS) is an electronic and ideally film less information system for acquiring, transporting, storing and electronically displaying medical images. It plays a key role in provision of clinical information in the form of images and reports to informed decision making and provision of care. e-Sushrut Chhavi is such an enterprise PACS Solution developed by CDAC, Noida that goes beyond the Radiology department which caters to the need of any department of the hospital that deals with medical images, DICOM or Non-DICOM. DICOM search is the frequently used feature available in e-Sushrut Chhavi and the DICOM Standard for providing Query/Retrieve facility in the image archiving solutions. In e-Sushrut Chhavi this search mechanism is provided for DICOM images and also for converted DICOM images. By using this search facility any user can quickly access Patient images through a multilevel query User Interface. To achieve a better DICOM search in the archive storage the archive server extracts Patient, Study, Series and image level information from the received image and puts in the archive database. So any search related with user given values with respect to search keywords webPACS system only searches the archive database and generate the results depending on the available image information at that time. So the addition any Patient's images in the archive storage can be checked at any point of time. And also any addition of a Series of images or Study of a patient in the archive storage will reflect in the search result. The method of extracting information from the DICOM image is to look for the desired tag and its value. In e-Sushrut Chhavi all the images converted to DICOM have similar Study Instance UID, So when user want to list images generated by e-Chhavi modality or application it a

关键词： Picture archiving and communication systems

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Towards Scalability and Performance: Framework for Heterogeneous Cluster Integration in Deep Learning Accelerators 4

Towards Scalability and Performance: Framework for Heterogen...

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4th IEEE International Conference on VLSI Systems, Architecture, Technology and Applications, VLSI SATA 2024

作者： Bhagirath, K. Pant, Dheeraj Tiwari, Abhishek Khaneja, Vivek Centre For Development Of Advanced Computing Embedded Systems Division Noida India

ISBN: (纸本)9798350362268

This paper presents a streamlined approach to address the challenges of integrating heterogeneous clusters in deep learning(DL) accelerators. As the demand for scalable and high-performance computing in DL continues to grow, the proposed framework offers an easy implementation solution, ensuring efficiency without compromise. The method details core components and design principles, guiding the inclusion of heterogeneous clusters step by step. Extensive experiments show the framework's adaptability, handling diverse workloads and cluster architectures. Our exploration integrates various dataflows, dynamically selected by NN during runtime for optimal performance. Results indicate improved scalability and robustness compared to existing methods. This research provides a valuable resource for practitioners seeking efficient and accessible solutions for harnessing the power of heterogeneous clusters in DL acceleration, with implications for advancements in AI and computational efficiency. © 2024 IEEE.

关键词： Computational efficiency

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