Biology subjects

Neethirajan, S.

Publications and source records attributed to Neethirajan, S..

At least 19 recordsLinked to original sources

bioRxiv2026

Protocol-Guided Cross-Domain Transfer Learning for Bovine Facial Pain Recognition under Weak Dairy-Farm Labels

Livestock welfare models are developed under controlled experimental conditions but deployed across farms, breeds, management systems and label regimes, where reliability remains uncertain. We introduce the Protocol-Driven Transfer Evaluation (PDTE) framework, which treats the adaptation protocol, comprising label mapping, objective design, domain alignment, model selection, calibration and threshold policy, as the experimental variable and evaluates transfer through animal-level external validation with uncertainty quantification. We apply PDTE to a bovine welfare task involving transfer of a facial pain representation from postoperative beef cattle to dairy cows under shifts in breed, sex, production system, clinical etiology, recording environment and label fidelity. Using an author-collected Canadian Holstein and Jersey dataset with an independent eight-cow test cohort, direct source-domain transfer was weak, with sequence AUC 0.418 and cow-level AUC 0.400. PDTE identified two failure modes under weak supervision: threshold collapse, in which adaptation converges to a single prediction class, and calibration-induced collapse, in which score ranking is preserved while decision behavior deteriorates. Across protocols, objective design dominated performance. Class-balanced focal adaptation achieved stable operating behavior (sequence AUC 0.611; cow-level AUC 0.667), while a target-only model attained comparable performance without source initialization (sequence AUC 0.596; paired p = 0.984), indicating that protocol design and operating-point choices contributed more than pretraining under weak-label conditions. Animal-level uncertainty remained substantial, with a bootstrap 95% confidence interval of 0.20 to 1.00, exceeding the transfer effect. These findings show that transferability limits cannot be inferred from source-domain performance alone and require protocol-controlled, uncertainty-aware evaluation in livestock AI.

animal behavior and cognition↗

bioRxiv2026

Beyond Accuracy: Reliability-Aware Cross-Farm Evaluation of Dairy Cow Vocalization Models

Automated analysis of dairy cow vocalizations has largely relied on supervised classifiers evaluated within a single farm, a setting that inflates apparent performance and gives no measure of how far predictions can be trusted. We address this with a three-layer framework that separates acoustic structure discovery, proxy-state inference, and reliability assessment, evaluated on 569 annotated clips from three commercial dairy farms. A frozen self-supervised speech encoder, latent-space segmentation, and stability-guided clustering convert continuous recordings into discrete acoustic units without behavioral labels. Proxy-state signal is then tested under audio-only, audio-plus-context, and leave-one-farm-out (LOFO) protocols designed to separate transferable acoustic structure from farm-specific shortcuts. The results suggest that cross-farm generalizability differs substantially across biologically distinct vocalization categories. Non-vocal physiological sounds transfer across farms (LOFO macro-F1 = 0.763) and calibrate well (expected calibration error reduced from 0.087 to 0.023), whereas resource-related calls collapse to a majority-class baseline (macro-F1 = 0.500) and distress-related calls degrade under farm holdout. Selective prediction improves the retained-set score of the multiclass functional proxy (0.407 to 0.430), and an end-to-end convolutional baseline matches or exceeds the framework on raw accuracy for the easier targets yet yields a roughly two- to six-fold larger calibration error and offers no abstention. Random cross-validation consistently overstates cross-farm utility. These findings show that acoustic models for livestock monitoring require reliability-aware evaluation rather than flat classification.

animal behavior and cognition↗

bioRxiv2025

Video-Based Cattle Behavior Detection for Digital Twin Development in Precision Dairy Systems

Digital twins in dairy systems require reliable behavioral inputs. We develop a video-based framework that detects and tracks individual cows and classifies seven behaviors under commercial barn conditions. From 4,964 annotated clips, expanded to 9,600 through targeted augmentation, we couple YOLOv11 detection with ByteTrack for identity persistence and evaluate SlowFast versus TimeSformer for behavior recognition. TimeSformer achieved 85.0% overall accuracy (macro-F1 0.84) and real-time throughput of 22.6 fps on RTX A100 hardware. Attention visualizations concentrated on anatomically relevant regions (head/muzzle for feeding and drinking; torso/limbs for postures), supporting biological interpretability. Structured outputs (cow ID, start-end times, durations, confidence) enable downstream use in nutritional modeling and 3D digital-twin visualization. The pipeline delivers continuous, per-animal activity streams suitable for individualized nutrition, predictive health, and automated management, providing a practical behavioral layer for scalable dairy digital twins.

animal behavior and cognition↗

bioRxiv2025

Decoding Bovine Communication with AI and Multimodal Systems ~ Advancing Sustainable Livestock Management and Precision Agriculture

Achieving sustainability in livestock farming requires advanced, non-invasive monitoring systems that enhance both productivity and animal welfare. Traditional methods for assessing dairy cow ingestive behavior, such as manual observation and sensor-based tracking, are often limited in scalability and accuracy. This study advances precision livestock farming by integrating multimodal artificial intelligence (AI) to decode bovine vocalizations in real time. Our approach leverages acoustic recordings, video analysis, and biometric sensor data to create a comprehensive system capable of detecting subtle patterns in feeding behavior and physiological well-being. By employing Generative AI and Large Language Models, our framework not only classifies ingestive behaviors but also interprets vocal signals linked to stress, health, and environmental conditions. The extracted features are transformed into spectrograms and fused with biometric indicators, enabling early detection of anomalies. This information is delivered through an intuitive dashboard, empowering farmers with real-time insights to optimize feeding strategies, reduce resource wastage, and mitigate welfare concerns. Unlike conventional deep learning approaches, which struggle with environmental variability, our system adapts dynamically across diverse farm settings, ensuring robustness and generalizability. This work directly contributes to global sustainability goals by improving resource efficiency, enhancing dairy herd management, and reducing the environmental footprint of livestock production. By integrating cutting-edge AI with practical farm applications, we pave the way for a more intelligent, responsive, and ethical approach to animal agriculture--where technology serves as a bridge between scientific advancements and on-farm decision-making.

animal behavior and cognition↗

bioRxiv2024

Decoding Poultry Vocalizations - Natural Language Processing and Transformer Models for Semantic and Emotional Analysis

Deciphering the acoustic "language" of chickens opens new frontiers in animal welfare and ecological informatics, illuminating how subtle vocal signals encode health status, emotional states, and interactions within ecosystems. By uncovering the semantics of these vocalizations, we gain a powerful tool for interpreting their functional vocabulary--how each call serves a purpose within the social and environmental context. Here, we leverage state-of-the-art Natural Language Processing (NLP) and transformer-based models to translate bioacoustic data into meaningful insights. Our approach integrates Wave2Vec 2.0 for raw audio feature extraction with a fine-tuned Bidirectional Encoder Representations from Transformers (BERT) model, pretrained on a broad corpus of animal sounds and adapted to poultry-specific tasks. This novel pipeline decodes poultry vocalizations into interpretable categories--such as distress calls, feeding signals, and mating vocalizations--while revealing subtle emotional nuances often overlooked by traditional spectrogram-based analyses. Achieving 92% accuracy in classifying key vocalization types, our methodology demonstrates the feasibility of real-time, automated monitoring of flock health and stress levels. By continuously tracking this functional vocabulary, farmers can respond proactively to environmental or behavioral changes, enhancing poultry welfare, reducing stress-induced productivity losses, and promoting more sustainable farm management. Beyond its direct agricultural applications, this work enriches our understanding of computational ecology. Gaining access to the semantic foundation of animal calls provides a window into the ecological networks of which poultry are a part, potentially serving as indicators of biodiversity, environmental stressors, and species interactions. In bridging animal behavior, machine learning, and ecosystem analysis, our framework lays a foundation for integrative studies that harness acoustic data to inform ecological decision-making and develop more resilient, ethically aligned agricultural systems.

animal behavior and cognition↗

bioRxiv2024

Dairy DigiD ~ A Deep Learning-Based, Non-Invasive Biometric Identification System for Dairy Cattle Using Detectron2

Precision livestock farming demands accurate and reliable individual animal identification for optimizing health monitoring, resource allocation, and overall herd productivity. Conventional identification methods--such as ear tagging and wearable sensors--are often invasive, potentially causing animal discomfort and affecting natural behaviors. To address these challenges, we introduce Dairy DigiD, a non-invasive biometric classification system for dairy cattle using facial image analysis. Leveraging deep learning and the Detectron2 framework, Dairy DigiD classifies cattle into four categories--young (<2 years), mature milking, pregnant, and old--using over 2,500 high-resolution facial images. A DenseNet121 model achieved a classification accuracy of 97%, showcasing its strong discriminative capability. Further, Detectron2 attained an average recall of 87% in category identification, an average precision of 96% in cattle detection, and an overall detection accuracy of 93%, demonstrating its robustness and adaptability to varied environmental conditions. Unlike traditional approaches that rely on fixed facial landmarks and can be sensitive to environmental variability, Dairy DigiDs flexible deep learning architecture enables stable and scalable facial feature extraction. This non-invasive method minimizes animal stress, enhances data integrity, and facilitates improved herd management and welfare monitoring. In comparison to existing convolutional neural network (CNN)-based strategies, Dairy DigiD exhibits superior adaptability to real-world farm conditions, including variable lighting and complex poses. This work highlights the potential of integrating advanced computer vision and deep learning methodologies into precision dairy farming, offering an ethically aligned, efficient, and scalable approach to modern livestock management.

animal behavior and cognition↗

bioRxiv2024

Development of a Cloud-Based IoT System for Livestock Health Monitoring Using AWS and Python

The agriculture industry is currently facing significant challenges in effectively monitoring the health of livestock. Traditional methods of health monitoring are often labor-intensive, inefficient, and insufficiently responsive to the needs of modern farming. As the number of IoT devices in agriculture proliferates, issues of scalability and computational load have become prominent, necessitating efficient and scalable solutions. This research introduces a cloud-based architecture aimed at enhancing livestock health monitoring. This system is designed to track critical health indicators such as movement patterns, body temperature, and heart rate, utilizing AWS for robust data handling and Python for data processing and real-time analytics. The proposed system incorporates Narrow Band IoT (Nb IoT) technology, which is optimized for low-bandwidth, long-range communication, making it suitable for rural and remote farming locations. The architectures scalability allows for the effective management of varying numbers of IoT devices, which is essential for adapting to changing herd sizes and farm scales. Preliminary experiments conducted to assess the systems performance have demonstrated its durability and effectiveness, indicating a successful integration of AWS IoT Cloud services with the deployed IoT devices. Furthermore, the study explores the implementation of predictive analytics to facilitate proactive health management in livestock. By predicting potential health issues before they become apparent, the system can offer significant improvements in animal welfare and farm efficiency. The integration of cloud computing and IoT not only meets the growing technological needs of modern agriculture but also sets a new benchmark in the development of sustainable farming practices. The findings from this research could have broad implications for the future of livestock management, potentially leading to widespread adoption of technology-driven health monitoring systems in agriculture. This would help in optimizing the health management of livestock globally, thereby enhancing productivity and sustainability in the agricultural sector.

animal behavior and cognition↗

bioRxiv2024

Artificial Intelligence driven Benchmarking Tool for Emission Reduction in Canadian Dairy Farms

This study develops an Artificial Intelligence-driven benchmarking tool to reduce methane emissions in Canadian dairy farms, responding to the urgent need to mitigate environmental impacts from agriculture. Utilizing a comprehensive dataset from over 1000 dairy farms and processors across Canada, combined with satellite-driven methane emission data, we apply advanced machine learning technologies and data analytics, including geospatial analysis and time series forecasting. This approach identifies critical emission hotspots and temporal trends. We tested several predictive models--ARIMA, LSTM, GBR, and PROPHET--with the LSTM model showing the greatest accuracy in forecasting emissions, demonstrated by the lowest Root Mean Squared Error (RMSE) of 15.40. Our results highlight the transformative potential of AI tools in agricultural environmental management by providing dairy farmers and policymakers with precise, real-time emission insights. This facilitates informed decision-making and the implementation of effective emission reduction strategies. This study not only advances understanding of emission dynamics in dairy farming but also underscores the role of technology in sustainable agricultural practices and achieving environmental targets consistent with global agreements.

bioengineering↗

bioRxiv2024

Decoding the Language of Chickens - An Innovative NLP Approach to Enhance Poultry Welfare

This research investigates the utilization of the Natural Language Processing-based WHISPER model for decoding chicken vocalizations, with the goal of comprehending the semantics and emotions embedded in their vocal communications. By leveraging advanced acoustic analysis techniques, the study focuses on interpreting the syntax and temporal patterns inherent in the vocalizations to discern the underlying affective states of chickens. This approach facilitates a non-invasive method of monitoring poultry welfare, enhancing traditional animal welfare assessments which often rely on direct human observation and can induce stress in the animals. The principal results from the application of the WHISPER model demonstrate its efficacy in classifying various chicken vocalizations into distinct categories that reflect specific emotional states such as distress, contentment, and fear. This classification is achieved by analyzing the frequency, duration, and intensity of vocalizations, thus providing a detailed insight into the emotional well-being of the animals. Our findings indicate that real-time monitoring of chicken vocalizations using NLP techniques can significantly improve the responsiveness and precision of welfare interventions. This method reduces the need for human interaction, minimizes stress for the animals, and allows for the continuous assessment of their well-being in a farming environment. Furthermore, the research highlights the potential of NLP tools in recognizing and interpreting complex animal vocalizations, which could lead to advancements in automated animal welfare monitoring systems. This study underscores the transformative potential of integrating sophisticated computational models like the WHISPER NLP model into animal welfare practices. By providing a more humane and efficient approach to monitoring animal welfare, this research contributes to the broader field of precision livestock farming, suggesting a shift towards more scientifically informed and welfare-centric farming practices. The application of such technologies not only aids in the immediate improvement of animal welfare but also supports sustainable farming operations by promoting the health and productivity of poultry through enhanced welfare standards.

animal behavior and cognition↗

bioRxiv2023

Vocalization Patterns in Laying Hens - An Analysis of Stress-Induced Audio Responses

This study leverages Convolutional Neural Networks (CNN) and Mel Frequency Cepstral Coefficients (MFCC) to analyze the vocalization patterns of laying hens, focusing on their responses to both visual (umbrella opening) and auditory (dog barking) stressors at different ages. The aim is to understand how these diverse stressors, along with the hens age and the timing of stress application, affect their vocal behavior. Utilizing a comprehensive dataset of chicken vocal recordings, both from stress-exposed and control groups, the research enables a detailed comparative analysis of vocal responses to varied environmental stimuli. A significant outcome of this study is the distinct vocal patterns exhibited by younger chickens compared to older ones, suggesting developmental variations in stress response. This finding contributes to a deeper understanding of poultry welfare, demon-strating the potential of non-invasive vocalization analysis for early stress detection and aligning with ethical live-stock management practices. The CNN models ability to distinguish between pre- and post-stress vocalizations highlights the substantial impact of stressor application on chicken vocal behavior. This study not only sheds light on the nuanced interactions between stress stimuli and animal behavior but also marks a significant advancement in smart farming. It paves the way for real-time welfare assessments and more informed decision-making in poultry management. Looking forward, the study suggests avenues for longitudinal research on chronic stress and the application of these methodologies across different species and farming contexts. Ultimately, this research represents a pivotal step in integrating technology with animal welfare, offering a promising approach to transforming welfare assessments in animal husbandry.

animal behavior and cognition↗

bioRxiv2022

Quantifying the Effect of an Acute Stressor in Laying Hens using Thermographic Imaging and Vocalizations

The laying hen sector has multiple issues concerning the animals welfare. One crucial factor negatively impacts chicken welfare is stress. The conventional way of measuring and assessing chicken stress is time-consuming and subjective to the assessor. On the other hand, sensing and sensor technologies can be used to obtain objective, continuous and non-invasive/contactless measures of animal behavioural and physiological welfare indicators. The present study aims to investigate the use of thermographic imaging and microphones (sound) in obtaining objective indicators for acute stress in laying hens. During this study, 40 laying hens were stressed by opening an umbrella as a stressor starting from one day age until nine-weeks old. The birds were stressed every other day. Another 12 birds were housed in another cage. These birds were not stressed and served as a control group. The surface temperatures of the birds comb and beak decreased (1{degrees}C and 2.5{degrees}C respectively) in response to the applied stressor. This effect was only seen in the treatment group and not in the control birds. The number of vocalisations the birds produced significantly decreased shortly after stress. The number of calls in the stressed group decreased from 39.5 to 12.1 calls/minute, where the control group decreased from 27.8 to 22.5 calls/minute. It was hypothesized that the number of vocalisations would increase after stress. This difference could be due to the daily behavioural rhythm performed by the birds. The birds might naturally produce more calls at certain hours of the day because of certain behaviours they perform. Three different neural network algorithms were employed to differentiate between the vocalisation of stressed and the control group. This was done by converting the audio files to images and feeding them to the pretrained convolutional neural networks (CNN). The Resnet CNN had the highest categorising accuracy with an overall accuracy of 86 percent. The changes in surface temperature of the beak, comb, eye, and head, as well as the results from the audio analysis could serve as potential indicator for acute stress in laying hens. Future research is warranted to validate the methodologies and findings under different environmental conditions and stressors.

bioengineering↗

bioRxiv2022

Understanding Chicks' Emotions: Are Eye Blinks & Facial Temperatures Reliable Indicators?

In commercial farming systems, chicks are reared without a mother. This absence of maternal influence can cause welfare problems when the chicks become older. Chicks imprint on their mothers they are young, and this mediates their stress and fear response. It is important to recognise problems early in the development of chicks to avoid welfare issues when they are older. One way to assess welfare is by measuring affective states. Research has shown chickens can display empathy, both towards their offspring and towards conspecifics. Measures of negative and positive affective states, either behavioural or physiological, could be good welfare indicators. This study employed non-invasive methods to measure affective states in laying hen chicks. Using video and thermal imaging, it analysed temperature changes in the peripheral areas and head region as well as changes in blinking behaviour before and after exposure to a stressor. The prediction was that the temperature would decrease in the eye and peripheral regions in response to a stressor and that the blinking rate would decrease. These changes would be indicative of a negative affective state. The results showed that the eye temperature as well as the blinking rate both decreased, whereas the temperature in the head region and the beak area increased. These results could be indicative of a negative affective state.

animal behavior and cognition↗

bioRxiv2021

Happy Cow or Thinking Pig? WUR Wolf Facial Coding Platform for Measuring Emotions in Farm Animals

Emotions play an indicative and informative role in the investigation of farm animal behaviors. Systems that respond and can measure emotions provide a natural user interface in enabling the digitalization of animal welfare platforms. The faces of farm animals can be one of the richest channels for expressing emotions. We present WUR Wolf (Wageningen University & Research: Wolf Mascot)--a real-time facial expression recognition platform that can automatically code the emotions of farm animals. Using Python-based algorithms, we detect and track the facial features of cows and pigs, analyze the appearance, ear postures, and eye white regions, and correlate with the mental/emotional states of the farm animals. The system is trained on dataset of facial features of images of the farm animals collected in over 6 farms and has been optimized to operate with an average accuracy of 85%. From these, we infer the emotional states of animals in real time. The software detects 13 facial actions and 9 emotional states, including whether the animal is aggressive, calm, or neutral. A real-time emotion recognition system based on YoloV3, and Faster YoloV4-based facial detection platform and an ensemble Convolutional Neural Networks (RCNN) is presented. Detecting expressions of farm animals simultaneously in real time makes many new interfaces for automated decision-making tools possible for livestock farmers. Emotions sensing offers a vast amount of potential for improving animal welfare and animal-human interactions.

bioengineering↗

bioRxiv2018

Aptamer-based biosensor for food allergen determination using graphene oxide/gold nanocomposite on a paper-assisted analytical device

The detection of allergens in food are currently conducted by techniques that are time-consuming and complicated which can deter consistent sampling for allergens, which could potentially cause an anaphylactic shock in the consumer by cross-contamination. The need for a technique that is rapid, on-site, cost-effective, disposable, highly sensitive and accurate to identify these molecules urges the development of a point-of-care device. The aim of this work is to develop a microfluidic paper-assisted analytical device (PAD) using hydrophobic channels, set by a wax printer on filter paper, and functionalized gold nanoparticles (AuNP) to help identify the allergens arachin (Ara h 1) for peanuts, {beta}-lactoglobulin ({beta} LG) for milk, and tropomyosin (Pen a 1) for shrimp and other shellfish presence by a colorimetric test. Synthesized AuNP were conjugated with biotinylated aptamers, using the biotin-streptavidin interaction, to make the specific detection of target allergens. Functionalized AuNP are incubated with the sample and are absorbed by graphene oxide (GO), creating GO-AuNP complexes, if the aptamers have not become structured due to conjugation with allergenic proteins. The PAD device is used to filter the resultant mixture which provides superior sensitivity to detect the allergens present down to the nanogram range (allergens were measured from 25 nM - 1000 nM with a LOD of 7.8 nM, 12.4 nM and 6.2 nM for peanut, milk and shrimp allergens respectively), in contrast to the microgram range of commonly used enzymatic immunoassays. The simple color indicator, varying from clear to pink in the presences of allergens allows the readout to be utilized without the need for highly specific equipment or training. Alternatively, the results can be quantified by taking a picture and measuring the color. This presented PAD can provide results in real time and has the potential to become a rapid, low-cost, and accurate portable point-of-care device to avoid cross-reactivity of food-borne allergens.

bioengineering↗

bioRxiv2018

Direct immunosensing of avian influenza A virus in whole blood using hybrid nanocomposites

A sandwich-based electrochemical immunosensor was designed for detection of avian influenza virus (AIV) strains H5N1 and H4N6. This sensor was developed using gold-graphene nanocomposites, immobilized viral antibodies, and CdTe quantum dot electrochemical tagging. The nanocomposites were formed by the simultaneous reduction of a gold salt and graphene using hydroquinone as the reducing agent, thus producing non-spherical gold nanoparticles on graphene sheets. Viral antibodies were immobilized on nanocomposites and CdTe quantum dots through N-(3-dimethylaminopropyl)-N'-ethylcarbodiimide and N-hydroxysuccinimide chemistry. Cyclic voltammetry studies were used to validate the detection of H5N1 surface protein and H4N6 inactivated virus. The immunosensor detected H5 protein in phosphate buffer solution (pH 7.4) with a limit of detection (LOD) of 10 fg/mL and a linear detection range was established for 10 ng/mL to 10 pg/mL. The biosensor detected H4N6 in three parts diluted whole chicken blood with a LOD of 1.28x10-7 hemagglutinating units (HAU). Commercial ELISA testing for H5N1 and H4N6 showed limits of detection of 10 ng/mL and 0.128 HAU, respectively. The sensor showed 106-fold increased detection of H4N6 virus in blood in comparison to its commercial ELISA kit counterpart. The developed immunosensor effectively change the way avian influenza is detected, monitored, and controlled; transforming time-consuming reactive methods, into rapid predictive technology.

bioengineering↗

bioRxiv2017

Rapid Detection of Norovirus Using Paper-based Microfluidic Device

Noroviruses (NoV) are the leading cause of outbreak of acute gastroenteritis worldwide. A substantial effort has been made in the development of analytical devices for rapid and sensitive food safety monitoring via the detection of foodborne bacteria, viruses and parasites. Conventional analytical approaches for noroviruses suffer from some critical weaknesses: labor-intensive, time-consuming, and relatively low sensitivity. In this study, we developed a rapid and highly sensitive biosensor towards point-of-care device for noroviruses based on 6-carboxyfluorescein (6-FAM) labeled aptamer and nanomaterials, multi-walled carbon nanotubes (MWCNTs) and graphene oxide (GO). In an assay, the fluorescence of 6-FAM labeled aptamer was quenched by MWCNTs or GO via fluorescence resonance energy transfer (FRET). In the presence of norovirus, the fluorescence would be recovered due to the release of the 6-FAM labeled aptamer from MWCNTs or GO. An easy-to-make paper-based microfluidic platform made by nitrocellulose membrane was used to conduct the assay. The quantitative detection of norovirus virus-like particles (NoV VLPs) was successfully performed. A linear range of 0-12.9 g/mL with a detection limit of 40 pM and 30 pM was achieved for the MWCNTs and GO based paper sensors, respectively. The results suggested the developed paper-based microfluidic device is simple, cost-effective and holds the potential of rapid in situ visual determination for noroviruses with remarkable sensitivity and specificity, which provides a new way for early identification of NoV and thereby an early intervention for preventing the spread of an outbreak.

bioengineering↗

bioRxiv2017

Self-assembled Star-shaped Chiroplasmonic Gold Nanoparticles for Ultrasensitive Chiro-immunosensor of Viruses

Near field optics and optical tunneling light-matter interaction in the superstructure of chiral nanostructures and semiconductor quantum dots exhibit strong optical rotation activity that may open a new window for chiral-based bioanalytes detection. Herein we report an ultrasensitive, chiro-immunosensor using superstructure of chiral gold nanohybrids (CAu NPs) and quantum dots (QDs). Self-assembling techniques were employed to create asymmetric plasmonic chiral nanostructures for extending the spectral range of circular dichroism (CD) response for obtaining superior plasmonic resonant coupling with the QDs excitonic state; this may help to achieve lower the limit of detection (LOD) values. As a result, the designed probe exhibited avian influenza A (H5N1) viral concentration at picomolar level, a significant improvement in sensitivity in comparison to a non-assembled CAu NPs based chiroassay. The practicability of the proposed sensing system was successfully demonstrated on several virus cultures including, avian influenza A (H4N6) virus, fowl adenovirus and coronavirus in blood samples. The results of our study highlights that exciton-plasmon interaction changes chirality and the self-assembled nanostructures are an efficient strategy for enhancing the sensitivity of plasmonic nanosensors.

bioengineering↗

bioRxiv2017

Paper based microfluidic aptasensor for food safety

Food analysis is requiring rapid, accurate, sensitive and cost-effective methods to monitor and guarantee the safety and quality to fulfill the strict food legislation and consumer demands. In our study, a nano-materials enhanced multipurpose paper based microfluidic aptasensor was demonstrated as a sensing tool for accurate detection of food allergens and food toxins. Graphene oxide (GO) and specific aptamer-functionalized quantum dots (QDs) were employed as probes, the fluorescence quenching and recovering of the QDs caused by the interaction among GO, aptamer-functionalized QDs and the target protein were investigated to quantitatively analyze the target concentration. The homogenous assay was performed on the paper based microfluidic chip, which significantly decreased the sample and reagent consumptions and reduced the assay time. Egg white lysozyme, {beta}-conglutin lupine and food toxins, okadaic acid and brevetoxin standard solutions and spiked food samples were successfully assayed by the presented aptasensor. Dual-target assay was completed within 5 min, and superior sensitivities were achieved when testing the samples with commercial ELISA kits side by side.

bioengineering↗