An electrochemiluminescence resonance energy transfer biosensor for the detection of circulating tumor DNA from blood plasma.

A liquid biopsy is a noninvasive approach for detecting double-stranded circulating tumor DNA (ctDNA) of 90-320 nucleotides in blood plasma from patients with cancer. Most techniques employed for ctDNA detection are time consuming and require expensive DNA purification kits. Electrochemiluminescence resonance energy transfer (ECL-RET) biosensors exhibit high sensitivity, a wide response range, and are promising for straightforward sensing applications. Until now, ECL-RET biosensors have been designed for sensing short single-stranded oligonucleotides of less than 45 nucleotides. In this work, an ECL-RET biosensor comprising graphitic carbon nitride quantum dots was assessed for the amplification-free detection in the blood plasma of DNA molecules coding for the EGFR L858R mutation, which is associated with non-small-cell lung cancer. Following a low-cost pre-treatment, the highly specific ECL-RET biosensor quantified double-stranded EGFR L858R DNA of 159 nucleotides diluted into the blood within a linear range of 0.01 fM to 1 pM, demonstrating its potential for noninvasive biopsies.

Chinese Sign Language Recognition Based on an Optimized Tree-Structure Framework.

Chinese Sign Language (CSL) subword recognition based on surface electromyography (sEMG), accelerometer (ACC), and gyroscope (GYRO) sensors was explored in this paper. In order to fuse effectively the information of these three kinds of sensors, the classification abilities of sEMG, ACC, GYRO, and their combinations in three common sign components (one or two handed, hand orientation, and hand amplitude) were evaluated first and then an optimized tree-structure classification framework was proposed for CSL subword recognition. Eight subjects participated in this study and recognition experiments under different testing conditions were implemented on a target set consisting of 150 CSL subwords. The proposed optimized tree-structure classification framework based on sEMG, ACC, and GYRO obtained the best performance among seven different testing conditions with single sensor, paired-sensor fusion, and three-sensor fusion, and the overall recognition accuracies of 94.31% and 87.02% were obtained for 150 CSL subwords in a user-specific test and user-independent test, respectively. Our study could lay a basis for the implementation of large-vocabulary sign language recognition system based on sEMG, ACC, and GYRO sensors.

A Component-Based Vocabulary-Extensible Sign Language Gesture Recognition Framework.

Sign language recognition (SLR) can provide a helpful tool for the communication between the deaf and the external world. This paper proposed a component-based vocabulary extensible SLR framework using data from surface electromyographic (sEMG) sensors, accelerometers (ACC), and gyroscopes (GYRO). In this framework, a sign word was considered to be a combination of five common sign components, including hand shape, axis, orientation, rotation, and trajectory, and sign classification was implemented based on the recognition of five components. Especially, the proposed SLR framework consisted of two major parts. The first part was to obtain the component-based form of sign gestures and establish the code table of target sign gesture set using data from a reference subject. In the second part, which was designed for new users, component classifiers were trained using a training set suggested by the reference subject and the classification of unknown gestures was performed with a code matching method. Five subjects participated in this study and recognition experiments under different size of training sets were implemented on a target gesture set consisting of 110 frequently-used Chinese Sign Language (CSL) sign words. The experimental results demonstrated that the proposed framework can realize large-scale gesture set recognition with a small-scale training set. With the smallest training sets (containing about one-third gestures of the target gesture set) suggested by two reference subjects, (82.6 ± 13.2)% and (79.7 ± 13.4)% average recognition accuracy were obtained for 110 words respectively, and the average recognition accuracy climbed up to (88 ± 13.7)% and (86.3 ± 13.7)% when the training set included 50~60 gestures (about half of the target gesture set). The proposed framework can significantly reduce the user's training burden in large-scale gesture recognition, which will facilitate the implementation of a practical SLR system.