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CRISPR-Cas13 systems have recently been employed for targeted RNA degradation in various organisms. However, collateral degradation of bystander RNAs has imposed a major barrier for their in vivo applications. We designed a dual-fluorescent reporter system for detecting collateral effects and screening Cas13 variants in mammalian cells. Among over 200 engineered variants, several Cas13 variants (including Cas13d and Cas13X) exhibit efficient on-target activity but markedly reduced collateral activity. Furthermore, transcriptome-wide off-targets and cell growth arrest induced by Cas13 are absent for these variants. Importantly, high-fidelity Cas13 variants show comparable RNA knockdown activity with wild-type Cas13 but no detectable collateral damage in transgenic mice and adeno-associated virus-mediated somatic cell targeting. Thus, high-fidelity Cas13 variants with minimal collateral effect are now available for targeted degradation of RNAs in basic research and therapeutic applications.

This paper presents a summary of the Masked Face Recognition Competitions (MFR) held within the 2021 International Joint Conference on Biometrics (IJCB 2021). The competition attracted a total of 10 participating teams with valid submissions. The affiliations of these teams are diverse and associated with academia and industry in nine different countries. These teams successfully submitted 18 valid solutions. The competition is designed to motivate solutions aiming at enhancing the face recognition accuracy of masked faces. Moreover, the competition considered the deployability of the proposed solutions by taking the compactness of the face recognition models into account. A private dataset representing a collaborative, multisession, real masked, capture scenario is used to evaluate the submitted solutions. In comparison to one of the topperforming academic face recognition solutions, 10 out of the 18 submitted solutions did score higher masked face verification accuracy.

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CRISPR-Cas13 systems have recently been employed for targeted RNA degradation in various organisms. However, collateral degradation of bystander RNAs has imposed a major barrier for their in vivo applications. We designed a dual-fluorescent reporter system for detecting collateral effects and screening Cas13 variants in mammalian cells. Among over 200 engineered variants, several Cas13 variants (including Cas13d and Cas13X) exhibit efficient on-target activity but markedly reduced collateral activity. Furthermore, transcriptome-wide off-targets and cell growth arrest induced by Cas13 are absent for these variants. Importantly, high-fidelity Cas13 variants show comparable RNA knockdown activity with wild-type Cas13 but no detectable collateral damage in transgenic mice and adeno-associated virus-mediated somatic cell targeting. Thus, high-fidelity Cas13 variants with minimal collateral effect are now available for targeted degradation of RNAs in basic research and therapeutic applications.

We explored the main factors affecting the activity of compounds by different statistical and computational methods.

-Numerous approaches to sensing limb position for controlling neural prostheses have been proposed, evaluated and even incorporated into commercial products. In general, these technologies have focused on the goals of accuracy, convenience and cost. Here we propose an approach to sensing upper limb posture for a stroke rehabilitation system that does not require any devices attached to the subject. This is achieved through the use of a machine vision approach, which involves focusing a digital video camera on the subject and processing the video stream using a specialized algorithm running on a PC. This algorithm will produce a trigger signal whenever the arm posture conforms to a predefined profile. While the approach itself can be applied to a variety of sensing and control applications, we have demonstrated it by developing and characterizing an algorithm that can accurately sense elbow flexion and extension. The machine vision algorithm performs 3-D recovery of the arm position and calculates the elbow angle accordingly, which we have compared to a commercially available goniometer. It also involves a model based prediction and correction technique that improves the accuracy where the model is trained at the outset of a sensing session. The system uses a commercial off-the-shelf webcam, which is widely available and cost effective. The experiments were done in vivo, and the results have shown that the accuracy of the system is about 90% accurate on average compared to our benchmarking device, and that it has strong potential to facilitate control of neural prostheses.