Hybrid dual mode sensor for simultaneous detection of two serum metabolites

Metabolites are the ultimate readout of disease phenotype that plays a significant role in the study of human disease. Multiple metabolites sometimes serve as biomarkers for a single metabolic disease. Therefore, simultaneous detection and analysis of those metabolites facilitate early diagnostics of the disease. Conventional approaches to detect and quantify metabolites include mass spectrometry and nuclear magnetic resonance that require bulky and expensive equipment. Here, we present a disposable sensing platform that is based on complementary metal–oxide–semiconductor process. It contains two sensors: an ion sensitive field-effect transistor and photodiode that can work independently for detection of pH and color change produced during the metabolite-enzyme reaction. Serum glucose and cholesterol have been detected and quantified simultaneously with the new platform, which shows good sensitivity within the physiological range. Low cost and easy manipulation make our device a prime candidate for personal metabolome sensing diagnostics

Hybrid localized surface plasmon resonance and quartz crystal microbalance sensor for label free biosensing

We report on the design and fabrication of a hybrid sensor that integrates transmission-mode localized surface plasmonic resonance (LSPR) into a quartz crystal microbalance (QCM) for studying biochemical surface reactions. The coupling of LSPR nanostructures and a QCM allows optical spectra and QCM resonant frequency shifts to be recorded simultaneously and analyzed in real time for a given surface adsorption process. This integration simplifies the conventional combination of SPR and QCM and has the potential to be miniaturized for application in point-of-care (POC) diagnostics. The influence of antibody-antigen recognition effect on both the QCM and LSPR has been analyzed and discussed.`

Boosting Multi-modal Model Performance with Adaptive Gradient Modulation

While the field of multi-modal learning keeps growing fast, the deficiency of the standard joint training paradigm has become clear through recent studies. They attribute the sub-optimal performance of the jointly trained model to the modality competition phenomenon. Existing works attempt to improve the jointly trained model by modulating the training process. Despite their effectiveness, those methods can only apply to late fusion models. More importantly, the mechanism of the modality competition remains unexplored. In this paper, we first propose an adaptive gradient modulation method that can boost the performance of multi-modal models with various fusion strategies. Extensive experiments show that our method surpasses all existing modulation methods. Furthermore, to have a quantitative understanding of the modality competition and the mechanism behind the effectiveness of our modulation method, we introduce a novel metric to measure the competition strength. This metric is built on the mono-modal concept, a function that is designed to represent the competition-less state of a modality. Through systematic investigation, our results confirm the intuition that the modulation encourages the model to rely on the more informative modality. In addition, we find that the jointly trained model typically has a preferred modality on which the competition is weaker than other modalities. However, this preferred modality need not dominate others. Our code will be available at https://github.com/lihong2303/AGM_ICCV2023.Comment: Accepted by ICCV202

Reconstructing human activities via coupling mobile phone data with location-based social networks

In the era of big data, the ubiquity of location-aware portable devices provides an unprecedented opportunity to understand inhabitants' behavior and their interactions with the built environments. Among the widely used data resources, mobile phone data is the one passively collected and has the largest coverage in the population. However, mobile operators cannot pinpoint one user within meters, leading to the difficulties in activity inference. To that end, we propose a data analysis framework to identify user's activity via coupling the mobile phone data with location-based social networks (LBSN) data. The two datasets are integrated into a Bayesian inference module, considering people's circadian rhythms in both time and space. Specifically, the framework considers the pattern of arrival time to each type of facility and the spatial distribution of facilities. The former can be observed from the LBSN Data and the latter is provided by the points of interest (POIs) dataset. Taking Shanghai as an example, we reconstruct the activity chains of 1,000,000 active mobile phone users and analyze the temporal and spatial characteristics of each activity type. We assess the results with some official surveys and a real-world check-in dataset collected in Shanghai, indicating that the proposed method can capture and analyze human activities effectively. Next, we cluster users' inferred activity chains with a topic model to understand the behavior of different groups of users. This data analysis framework provides an example of reconstructing and understanding the activity of the population at an urban scale with big data fusion

A colorimetric CMOS-based platform for rapid total serum cholesterol quantification

Elevated cholesterol levels are associated with a greater risk of developing cardiovascular disease and other illnesses, making it a prime candidate for detection on a disposable biosensor for rapid point of care diagnostics. One of the methods to quantify cholesterol levels in human blood serum uses an optically mediated enzyme assay and a bench top spectrophotometer. The bulkiness and power hungry nature of the equipment limits its usage to laboratories. Here, we present a new disposable sensing platform that is based on a complementary metal oxide semiconductor process for total cholesterol quantification in pure blood serum. The platform that we implemented comprises readily mass-manufacturable components that exploit colorimetric changes of cholesterol oxidase and cholesterol esterase reactions. We have shown that our quantification results are comparable to that obtained by a bench top spectrophotometer. Using the implemented device, we have measured cholesterol concentration in human blood serum as low as 29 μM with a limit of detection at 13 μM, which is approximately 400 times lower than average physiological range, implying that our device also has the potential to be used for applications that require greater sensitivity

Disposable paper-on-CMOS platform for real-time simultaneous detection of metabolites

Objective: Early stage diagnosis of sepsis without overburdening health services is essential to improving patient outcomes. Methods: A fast and simple-to-use platform that combines an integrated circuit with paper microfluidics for simultaneous detection of multiple-metabolites appropriate for diagnostics was presented. Paper based sensors are a primary candidate for widespread deployment of diagnostic or test devices. However, the majority of devices today use a simple paper strip to detect a single marker using the reflectance of light. However, for many diseases such as sepsis, one biomarker is not sufficient to make a unique diagnosis. In this work multiple measurements are made on patterned paper simultaneously. Using laser ablation to fabricate microfluidic channels on paper provides a flexible and direct approach for mass manufacture of disposable paper strips. A reusable photodiode array on a complementary metal oxide semiconductor chip is used as the transducer. Results: The system measures changes in optical absorbance in the paper to achieve a cost-effective and easily implemented system that is capable of multiple simultaneous assays. Potential sepsis metabolite biomarkers glucose and lactate have been studied and quantified with the platform, achieving sensitivity within the physiological range in human serum. Conclusion: We have detailed a disposable paper-based CMOS photodiode sensor platform for real-time simultaneous detection of metabolites for diseases such as sepsis. Significance: A combination of a low-cost paper strip with microfluidic channels and a sensitive CMOS photodiode sensor array makes our platform a robust portable and inexpensive biosensing device for multiple diagnostic tests in many different applications

Pixel-Reasoning-Based Robotics Fine Grasping for Novel Objects with Deep EDINet Structure.

Robotics grasp detection has mostly used the extraction of candidate grasping rectangles; those discrete sampling methods are time-consuming and may ignore the potential best grasp synthesis. This paper proposes a new pixel-level grasping detection method on RGB-D images. Firstly, a fine grasping representation is introduced to generate the gripper configurations of parallel-jaw, which can effectively resolve the gripper approaching conflicts and improve the applicability to unknown objects in cluttered scenarios. Besides, the adaptive grasping width is used to adaptively represent the grasping attribute, which is fine for objects. Then, the encoder-decoder-inception convolution neural network (EDINet) is proposed to predict the fine grasping configuration. In our findings, EDINet uses encoder, decoder, and inception modules to improve the speed and robustness of pixel-level grasping detection. The proposed EDINet structure was evaluated on the Cornell and Jacquard dataset; our method achieves 98.9% and 96.1% test accuracy, respectively. Finally, we carried out the grasping experiment on the unknown objects, and the results show that the average success rate of our network model is 97.2% in a single object scene and 93.7% in a cluttered scene, which out-performs the state-of-the-art algorithms. In addition, EDINet completes a grasp detection pipeline within only 25 ms