MIBiG 4.0: advancing biosynthetic gene cluster curation through global collaboration.

Specialized or secondary metabolites are small molecules of biological origin, often showing potent biological activities with applications in agriculture, engineering and medicine. Usually, the biosynthesis of these natural products is governed by sets of co-regulated and physically clustered genes known as biosynthetic gene clusters (BGCs). To share information about BGCs in a standardized and machine-readable way, the Minimum Information about a Biosynthetic Gene cluster (MIBiG) data standard and repository was initiated in 2015.

Stimulant use disorder and the likelihood of stroke: Analysis of a national database in the United States.

Background: Stimulant use has been associated with an increased risk of stroke, though data on clinical characteristics and exact risk are limited. This retrospective case-control study examines stroke risk in individuals with stimulant use disorder using data from a national U.S.

In-vivo non-contact multispectral oral disease image dataset with segmentation.

In imaging spectroscopy, gathering oral tissue spectral data from resected samples may not accurately represent tissue signatures due to time-dependent changes, blood loss, protein degeneration, and preservation chemicals. In-vivo spectral imaging is employed to address these limitations, but it poses challenges like device dimensions, tissue accessibility, and motion artifacts, impacting data quality and reliability. Our study publishes a dataset of spectral images focusing on oral diseases, addressing these challenges.

Redesigning payments for ecosystem services to increase cost-effectiveness.

Payments for Ecosystem Services (PES) are a widely used approach to incentivize conservation efforts such as avoided deforestation. Although PES effectiveness has received significant scholarly attention, how PES design modifications can improve program outcomes is less explored. We present findings from a randomized trial in Mexico that tested whether a PES contract that requires enrollees to enroll all of their forest is more effective than the traditional PES contract that allows them to choose which forest parcels to enroll.

Optimal directed acyclic graph federated learning model for energy-efficient IoT communication networks.

Federated learning (FL) stimulates distributed on-device computation systems to process an optimum technique efficiency by communicating local process upgrades and global method distribution from aggregation averaging procedure. On-device FL is a standard application in wireless environments, with several mobile devices participating as nodes in the FL network. Managing extensive multi-dimensional process upgrades and resource-constrained computations in large-scale heterogeneous IoT cellular networks can be challenging.