Accelerated Discovery of 3D Printing Materials Using Data-Driven Multi-Objective Optimization

Additive manufacturing has become one of the forefront technologies in fabrication, enabling new products impossible to manufacture before. Although many materials exist for additive manufacturing, they typically suffer from performance trade-offs preventing them from replacing traditional manufacturing techniques. Current materials are designed with inefficient human-driven intuition-based methods, leaving them short of optimal solutions. We propose a machine learning approach to accelerate the discovery of additive manufacturing materials with optimal trade-offs in mechanical performance. A multi-objective optimization algorithm automatically guides the experimental design by proposing how to mix primary formulations to create better-performing materials. The algorithm is coupled with a semi-autonomous fabrication platform to significantly reduce the number of performed experiments and overall time to solution. Without any prior knowledge of the primary formulations, the proposed methodology autonomously uncovers twelve optimal composite formulations and enlarges the discovered performance space 288 times after only 30 experimental iterations. This methodology could easily be generalized to other material formulation problems and enable completely automated discovery of a wide variety of material designs

A genome-wide association study of the longitudinal course of executive functions

Executive functions are metacognitive capabilities that control and coordinate mental processes. In the transdiagnostic PsyCourse Study, comprising patients of the affective-to-psychotic spectrum and controls, we investigated the genetic basis of the time course of two core executive subfunctions: set-shifting (Trail Making Test, part B (TMT-B)) and updating (Verbal Digit Span backwards) in 1338 genotyped individuals. Time course was assessed with four measurement points, each 6 months apart. Compared to the initial assessment, executive performance improved across diagnostic groups. We performed a genome-wide association study to identify single nucleotide polymorphisms (SNPs) associated with performance change over time by testing for SNP-by-time interactions using linear mixed models. We identified nine genome-wide significant SNPs for TMT-B in strong linkage disequilibrium with each other on chromosome 5. These were associated with decreased performance on the continuous TMT-B score across time. Variant rs150547358 had the lowest P value = 7.2 × 10(−10) with effect estimate beta = 1.16 (95% c.i.: 1.11, 1.22). Implementing data of the FOR2107 consortium (1795 individuals), we replicated these findings for the SNP rs150547358 (P value = 0.015), analyzing the difference of the two available measurement points two years apart. In the replication study, rs150547358 exhibited a similar effect estimate beta = 0.85 (95% c.i.: 0.74, 0.97). Our study demonstrates that longitudinally measured phenotypes have the potential to unmask novel associations, adding time as a dimension to the effects of genomics

Adaptation and Convergent Evolution within the Jamesonia-Eriosorus Complex in High-Elevation Biodiverse Andean Hotspots

The recent uplift of the tropical Andes (since the late Pliocene or early Pleistocene) provided extensive ecological opportunity for evolutionary radiations. We test for phylogenetic and morphological evidence of adaptive radiation and convergent evolution to novel habitats (exposed, high-altitude páramo habitats) in the Andean fern genera Jamesonia and Eriosorus. We construct time-calibrated phylogenies for the Jamesonia-Eriosorus clade. We then use recent phylogenetic comparative methods to test for evolutionary transitions among habitats, associations between habitat and leaf morphology, and ecologically driven variation in the rate of morphological evolution. Páramo species (Jamesonia) display morphological adaptations consistent with convergent evolution in response to the demands of a highly exposed environment but these adaptations are associated with microhabitat use rather than the páramo per se. Species that are associated with exposed microhabitats (including Jamesonia and Eriorsorus) are characterized by many but short pinnae per frond whereas species occupying sheltered microhabitats (primarily Eriosorus) have few but long pinnae per frond. Pinnae length declines more rapidly with altitude in sheltered species. Rates of speciation are significantly higher among páramo than non-páramo lineages supporting the hypothesis of adaptation and divergence in the unique Páramo biodiversity hotspot

p Multi-year cloud and precipitation statistics observed with remote sensors at the high-altitude Environmental Research Station Schneefernerhaus in the German Alps

Clouds and precipitation over mountainous terrain are a challenge for models and observations alike. In this study, we exploit a unique, nearly one decade long dataset of collocated microwave radiometer, radar, ceilometer, and auxiliary observations collected at the Environmental Research Station Schneeferner-haus (UFS). Located at 2650 m a.s.l. just 300 m below the summit of Zugspitze, Germany's highest mountain, this dataset allows a combined view on water vapor, clouds, and precipitation. Annual and diurnal cycles of water vapor, cloud liquid water, cloud ice, rainfall, and snowfall rate are investigated. Strong diurnal cycles during summer in several observables indicate a strong coupling with the surface and convective transport of air from the surrounding valleys to the level of UFS resulting in maximum amounts in integrated water vapor (IWV), cloud liquid water path (LWP) and rain during the afternoon. In contrast, no diurnal cycle is found during winter, which points to the predominance of advection of cloud systems associated with large scale dynamics during winter. Daily precipitation estimates for snowfall and rainfall derived from a verti-cally pointing, low-cost micro rain radar (MRR) are found to be in good agreement with manual observations from the German Weather Service at the summit. Exploiting the synergy of MRR and microwave radiometer measurements revealed that almost 90 % of the snow clouds contained signif cant amounts of super-cooled LWP but only a weak correlation between snowfall rate and LWP is found. The still growing data set at this very particular location, also in combination with further observations, such as trace gases and aerosols, has a unique potential for many applications, e.g. to investigate cloud processes, evaluate high resolution models, and to validate satellite products

Accelerated discovery of 3D printing materials using data-driven multiobjective optimization

Machine learning can aid the discovery of useful 3D printing material formulations.</jats:p

Multi-endpoint toxicological assessment of polystyrene nano- and microparticles in different biological models in vitro

Nanoplastics (NP) and microplastics (MP) accumulate in our environment as a consequence of the massive consumption of plastics. Huge knowledge-gaps exist regarding uptake and fate of plastic particles in micro- and nano-dimensions in humans as well as on their impact on human health. This study investigated the transport and effects of 50 nm and 0.5 μm COOH-modified polystyrene (PS) particles, as representatives for NP and MP, in different biological models in vitro. Acute toxicity and potential translocation of the particles were studied at the human intestinal and placental barrier using advanced in vitro co-culture models. Furthermore, embryotoxicity and genotoxicity were investigated as highly sensitive endpoints. Polystyrene was not acutely toxic in both sizes (nano- and microparticles). No transport across the intestinal and placental barrier but a cellular uptake and intracellular accumulation of PS nano- and microparticles were determined. The particles were identified as weak embryotoxic and non-genotoxic. In contrast to single-organ studies, this multi-endpoint study is providing a data-set with the exact same type of particles to compare organ-specific outcomes. Our study clearly shows the need to investigate other types of plastics as well as towards long-term or chronic effects of plastic particles in different biological models in vitro