Multiple Input Sensing and Signal Integration Using a Split Cas12a System.

The ability to integrate biological signals and execute a functional response when appropriate is critical for sophisticated cell engineering using synthetic biology. Although the CRISPR-Cas system has been harnessed for synthetic manipulation of the genome, it has not been fully utilized for complex environmental signal sensing, integration, and actuation. Here, we develop a split dCas12a platform and show that it allows for the construction of multi-input, multi-output logic circuits in mammalian cells. The system is highly programmable and can generate expandable AND gates with two, three, and four inputs. It can also incorporate NOT logic by using anti-CRISPR proteins as an OFF switch. By coupling the split dCas12a design to multiple tumor-relevant promoters, we provide a proof of concept that the system can implement logic gating to specifically detect breast cancer cells and execute therapeutic immunomodulatory responses.

Thermal AND Gate Using a Monolayer Graphene Nanoribbon.

The first ever implementation of a thermal AND gate, which performs logic calculations with phonons, is presented using two identical thermal diodes composed of asymmetric graphene nanoribbons (GNRs). Employing molecular dynamics simulations, the characteristics of this AND gate are investigated and compared with those for an electrical AND gate. The thermal gate mechanism originates through thermal rectification due to asymmetric phonon boundary scattering in the two diodes, which is only effective at the nanoscale and at the temperatures much below the room temperature. Due to the high phonon velocity in graphene, the gate has a fast switching time of ≈100 ps.

A revocable storage CP-ABE scheme with constant ciphertext length in cloud storage.

The ciphertext policy attribute-based encryption (CP-ABE) is widely used in cloud storage. It not only provides a secure data sharing scheme but also has the characteristics of fine-grained access control. However, most CP-ABE schemes have problems such as the ciphertext length increases with the complexity of the access policy, the encryption scheme is complex, the computational efficiency is low, and the fine-grained revocation cannot be performed. In view of the above problems, this pa-per proposes an efficient CP-ABE scheme with fine-grained revocable storage and constant ciphertext length. The scheme combines proxy re-encryption with CP-ABE technology, adopts the flexible access strategy AND-gates on multi-valued attributes with wildcards (AND∗m ), and realizes revocable storage and fixed-length ciphertext. At the same time, in order to reduce the amount of user decryption calcu-lation, the complex operation in the decryption process is outsourced to the third-party server and the decryption result is verified to ensure the correctness of the information. Finally, the security of the scheme is proved under the decisional bilinear Diffie-Hellman (DBDH) assumption. In addition, the performance analysis shows that the scheme is efficient and feasible in cloud storage.

An improved differential evolution algorithm for learning high-fidelity quantum controls.

Precisely and efficiently designing control pulses for the preparation of quantum states and quantum gates are the fundamental tasks for quantum computation. Gradient-based optimal control methods are the routine to design such pulses. However, the gradient information is often difficult to calculate or measure, especially when the system is not well calibrated or in the presence of various uncertainties. Gradient-free evolutionary algorithm is an alternative choice to accomplish this task but usually with low-efficiency. Here, we design an efficient mutation rule by using the information of the current and the former individuals together. This leads to our improved differential evolution algorithm, called daDE. To demonstrate its performance, we numerically benchmark the pulse optimization for quantum states and quantum gates preparations on small-scale NMR system. Further numerical comparisons with conventional differential evolution algorithms show that daDE has great advantages on the convergence speed and robustness to several uncertainties including pulse imperfections and measurement errors.