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Summary: Background: Neuroendocrine neoplasia (NEN) incidence is rising internationally. We aimed to evaluate the epidemiology of NEN in England and examine changes in survival over time. Methods: A retrospective, population-based study using nationally representative data between 1995 and 2018 from the National Cancer Registry and Analysis Service (NCRAS) in England was conducted on 63,949 tumours. Age-standardized incidence was calculated using Office for National Statistics (ONS) data. Overall survival (OS) was calculated using the Kaplan-Meier estimator. Multivariable analysis was performed using an accelerated failure time model. Findings: Of 63,949 cases, 50.5% (32,309) were female. Age-adjusted incidence increased 3.7-fold between 1995 and 2018 from 2.35 to 8.61 per 100,000. In 2018, highest incidence occurred in lung (1.47 per 100,000), small intestine (1.46 per 100,000), pancreas (1.00 per 100,000) and appendix (0.95 per 100,000). In multivariable analysis, age, sex, morphology, stage, site and deprivation were independent predictors of survival (p

Fracture toughness, rather than strength, is often the limiting factor of structural materials. Developing new base materials with improved fracture toughness often takes more than a decade. Alternatively, topological design has recently been expanded by additive manufacturing. In the present study, architected planes of internal porosity mimicking a weak interface were found capable of arresting and deflecting a propagating crack, delaying fracture. This concept was demonstrated experimentally in solid beams of polymeric 3D printed material, and in gyroid metamaterials constructed from either a brittle polymer or stainless steel. Improvements in fracture toughness ranged from 22% to 300% depending on the material. Especially with topological optimization, toughening via designed porosity provides an avenue for cost-effective and simple toughening across a range of materials.

Metamaterials, otherwise known as architected or programmable materials, enable designers to tailor mesoscale topology and shape to achieve unique material properties that are not present in nature. Additionally, with the recent proliferation of additive manufacturing tools across industrial sectors, the ability to readily fabricate geometrically complex metamaterials is now possible. However, in many high-performance applications involving complex multi-physics interactions, design of novel lattice metamaterials is still difficult. Design is primarily guided by human intuition or gradient optimization for simple problems. In this work, we show how machine learning guides discovery of new unit cells that are Pareto optimal for multiple competing objectives; specifically, maximizing elastic stiffness during static loading and minimizing wave speed through the metamaterial during an impact event. Additionally, we show that our artificial intelligence approach works with relatively few (3500) simulation calls.

While lattice metamaterials can achieve exceptional energy absorption by tailoring periodically distributed heterogeneous unit cells, relatively little focus has been placed on engineering heterogeneity above the unit-cell level. In this work, the energy-absorption performance of lattice metamaterials with a heterogeneous spatial layout of different unit cell architectures was studied. Such multi-morphology lattices can harness the distinct mechanical properties of different unit cells while being composed out of a single base material. A rational design approach was developed to explore the design space of these lattices, inspiring a non-intuitive design which was evaluated alongside designs based on mixture rules. Fabrication was carried out using two different base materials: 316L stainless steel and Vero White photopolymer. Results show that multi-morphology lattices can be used to achieve higher specific energy absorption than homogeneous lattice metamaterials. Additionally, it is shown that a rational design approach can inspire multi-morphology lattices which exceed rule-of-mixtures expectations.

Summary: Eclosion hormone (EH) was originally identified as a brain-derived hormone capable of inducing the behavioral sequences required for molting across insect species. However, its role in this process (called ecdysis) has since been confounded by discrepancies in the effects of genetic and cellular manipulations of EH function in Drosophila. Although knock-out of the Eh gene results in severe ecdysis-associated deficits accompanied by nearly complete larval lethality, ablation of the only neurons known to express EH (i.e. Vm neurons) is only partially lethal and surviving adults emerge, albeit abnormally. Using new tools for sensitively detecting Eh gene expression, we show that EH is more widely expressed than previously thought, both within the nervous system and in somatic tissues, including trachea. Ablating all Eh-expressing cells has effects that closely match those of Eh gene knock-out; developmentally suppressing them severely disrupts eclosion. Our results thus clarify and extend the scope of EH action.

Summary: The neuromodulator dopamine (DA) plays a key role in motor control, motivated behaviors, and higher-order cognitive processes. Dissecting how these DA neural networks tune the activity of local neural circuits to regulate behavior requires tools for manipulating small groups of DA neurons. To address this need, we assembled a genetic toolkit that allows for an exquisite level of control over the DA neural network in Drosophila. To further refine targeting of specific DA neurons, we also created reagents that allow for the conversion of any existing GAL4 line into Split GAL4 or GAL80 lines. We demonstrated how this toolkit can be used with recently developed computational methods to rapidly generate additional reagents for manipulating small subsets or individual DA neurons. Finally, we used the toolkit to reveal a dynamic interaction between a small subset of DA neurons and rearing conditions in a social space behavioral assay. : The rapid analysis of how dopaminergic circuits regulate behavior is limited by the genetic tools available to target and manipulate small numbers of these neurons. Xie et al. present genetic tools in Drosophila that allow rational targeting of sparse dopaminergic neuronal subsets and selective knockdown of dopamine signaling. Keywords: dopamine, genetics, behavior, neural circuits, neuromodulation, Drosophila

Genetically encoded effectors are important tools for probing cellular function in living animals, but improved methods for directing their expression to specific cell types are required. Here, we introduce a simple, versatile method for achieving cell-type-specific expression of transgenes that leverages the untapped potential of “coding introns” (i.e., introns between coding exons). Our method couples the expression of a transgene to that of a native gene expressed in the cells of interest using intronically inserted “plug-and-play” cassettes (called “Trojan exons”) that carry a splice acceptor site followed by the coding sequences of T2A peptide and an effector transgene. We demonstrate the efficacy of this approach in Drosophila using lines containing suitable MiMIC (Minos-mediated integration cassette) transposons and a palette of Trojan exons capable of expressing a range of commonly used transcription factors. We also introduce an exchangeable, MiMIC-like Trojan exon construct that can be targeted to coding introns using the Crispr/Cas system.