Exploring the rules of chimeric antigen receptor phenotypic output using combinatorial signaling motif libraries and machine learning

Chimeric antigen receptor (CAR) costimulatory domains steer the phenotypic output of therapeutic T cells. In most cases these domains are derived from native immune receptors, composed of signaling motif combinations selected by evolution. To explore if non-natural combinations of signaling motifs could drive novel cell fates of interest, we constructed a library of CARs containing ~2,300 synthetic costimulatory domains, built from combinations of 13 peptide signaling motifs. The library produced CARs driving diverse fate outputs, which were sensitive motif combinations and configurations. Neural networks trained to decode the combinatorial grammar of CAR signaling motifs allowed extraction of key design rules. For example, the non-native combination of TRAF- and PLCg1-binding motifs was found to simultaneously enhance cytotoxicity and stemness, a clinically desirable phenotype associated with effective and durable tumor killing. The neural network accurately predicts that addition of PLCg1-binding motifs improves this phenotype when combined with TRAF-binding motifs, but not when combined with other immune signaling motifs (e.g. PI3K- or Grb2- binding motifs). This work shows how libraries built from the minimal building blocks of signaling, combined with machine learning, can efficiently guide engineering of receptors with desired phenotypes.

Seismic Design of Bolted Connections in Steel Structures—A Critical Assessment of Practice and Research

The importance of connections in steel structures is paramount, not only because it greatly influences the cost of construction and provides room for innovations, but also due to the connections’ impact on global structural behaviour. Therefore, research into innovative connections for seismic applications and related design criteria has significantly grown in recent years. However, it has been pursued mostly on local—connection or frame—levels, leaving the system analysis and code compliance levels with a meagre investigation. Moreover, less than 1% of published papers concerning steel connections and earthquake engineering are review articles. To overcome this gap, this systematic review of more than 240 references, including scientific contributions and design codes in the field aimed to cover both recent research and current shortcomings in practice and regulations. It has been found that European design rules updated to a fully performance-based design philosophy is imminent and is deemed to bring pre-qualified joints and increased complexity. Design rules have been systematized, and current hindrances have been highlighted. A deeper look into research needs and trends showed that investigations in connections for concentrically X braced frames are still a necessity, while developments in self-centring and replaceable connections as well as in simple solutions for increasing damping are expected to modify how joints are designed, as soon as semi-rigid and partial strength connections are more easily allowed by design codes.

Bim Machine Learning and Design Rules to Improve the Assembly Time in Steel Construction Projects

Integrating the knowledge and experience of fabrication during the design phase can help reduce the cost and duration of steel construction projects. Building Information Modeling (BIM) are technologies and processes that reduce the cost and duration of construction projects by integrating parametric digital models as support of information. These models can contain information about the performance of previous projects and allow a classification by linear regression of design criteria with a high impact on the duration of the fabrication. This paper proposes a quantitative approach that applies linear regressions on previous projects’ BIM models to identify some design rules and production improvement points. A case study applied on 55,444 BIM models of steel joists validates this approach. This case study shows that the camber, the weight of the structure, and its reinforced elements greatly influence the fabrication time of the joists. The approach developed in this article is a practical case where machine learning and BIM models are used rather than interviews with professionals to identify knowledge related to a given steel structure fabrication system.