Abstract: The present disclosure provides a Cas12i protein (e.g., non-naturally occurring, engineered) comprising an amino acid sequence having at least about 80% (e.g., at least about any of 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, 99.5%, or 100%) identity to the amino acid sequence as set forth in any one of SEQ ID NOs: 1-10 (preferably, SEQ ID NOs: 1-3 and 6, and more preferably, SEQ ID NO: 1).

Abstract: The present disclosure provides a Cas12i protein (e.g., non-naturally occurring, engineered) comprising an amino acid sequence having at least about 80% (e.g., at least about any of 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, 99.5%, or 100%) identity to the amino acid sequence as set forth in any one of SEQ ID NOs: 1-10 (preferably, SEQ ID NOs: 1-3 and 6, and more preferably, SEQ ID NO: 1).

Abstract: Techniques for a service provider network to allow users to quickly and easily establish a testing environment to test various virtual machine (VM) instance types for hosting their workloads. Rather than identifying and recommending optimized VM instance types for hosting workloads of users, the techniques allow for users to initially test the VM instance types and determine how well their workloads perform on the VM instance types. Users can quickly and easily (e.g., “one-click” input) request that a testing environment be established. The optimization service can then test one or more recommended VM instance types for the users' workloads in the testing environment. The optimization service can monitor the performance of the VM instance types while they host the “test workloads,” and provide the users with performance metrics to help them decide if they would like to migrate their workloads to the recommended VM instance types.

Abstract: Methods, systems, and computer-readable media for automatically detecting root causes of anomalies occurring in information technology (IT) systems are disclosed. In some embodiments, data of a service graph depicting dependencies between nodes or services of the IT infrastructure is traversed to determine propagation patterns of anomaly symptoms/alarms through the IT infrastructure. Also, a causal inference model is used to determine probabilities that an observed propagation pattern corresponds to a stored propagation pattern, wherein a close correspondence indicates that the current anomaly is likely caused by a similar root cause as a past anomaly that caused the stored propagation pattern.