Passenger Mutations in More Than 2,500 Cancer Genomes: Overall Molecular Functional Impact and Consequences.

The dichotomous model of "drivers" and "passengers" in cancer posits that only a few mutations in a tumor strongly affect its progression, with the remaining ones being inconsequential. Here, we leveraged the comprehensive variant dataset from the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) project to demonstrate that-in addition to the dichotomy of high- and low-impact variants-there is a third group of medium-impact putative passengers. Moreover, we also found that molecular impact correlates with subclonal architecture (i.e., early versus late mutations), and different signatures encode for mutations with divergent impact. Furthermore, we adapted an additive-effects model from complex-trait studies to show that the aggregated effect of putative passengers, including undetected weak drivers, provides significant additional power (∼12% additive variance) for predicting cancerous phenotypes, beyond PCAWG-identified driver mutations. Finally, this framework allowed us to estimate the frequency of potential weak-driver mutations in PCAWG samples lacking any well-characterized driver alterations.

Growing three-dimensional objects with light.

Vat photopolymerization (VP) additive manufacturing enables fabrication of complex 3D objects by using light to selectively cure a liquid resin. Developed in the 1980s, this technique initially had few practical applications due to limitations in print speed and final part material properties. In the four decades since the inception of VP, the field has matured substantially due to simultaneous advances in light delivery, interface design, and materials chemistry. Today, VP materials are used in a variety of practical applications and are produced at industrial scale. In this perspective, we trace the developments that enabled this printing revolution by focusing on the enabling themes of light, interfaces, and materials. We focus on these fundamentals as they relate to continuous liquid interface production (CLIP), but provide context for the broader VP field. We identify the fundamental physics of the printing process and the key breakthroughs that have enabled faster and higher-resolution printing, as well as production of better materials. We show examples of how in situ print process monitoring methods such as optical coherence tomography can drastically improve our understanding of the print process. Finally, we highlight areas of recent development such as multimaterial printing and inorganic material printing that represent the next frontiers in VP methods.

Multivariate genetic architecture reveals testosterone-driven sexual antagonism in contemporary humans.

Sex difference (SD) is ubiquitous in humans despite shared genetic architecture (SGA) between the sexes. A univariate approach, i.e., studying SD in single traits by estimating genetic correlation, does not provide a complete biological overview, because traits are not independent and are genetically correlated. The multivariate genetic architecture between the sexes can be summarized by estimating the additive genetic (co)variance across shared traits, which, apart from the cross-trait and cross-sex covariances, also includes the cross-sex-cross-trait covariances, e.g., between height in males and weight in females. Using such a multivariate approach, we investigated SD in the genetic architecture of 12 anthropometric, fat depositional, and sex-hormonal phenotypes. We uncovered sexual antagonism (SA) in the cross-sex-cross-trait covariances in humans, most prominently between testosterone and the anthropometric traits - a trend similar to phenotypic correlations. 27% of such cross-sex-cross-trait covariances were of opposite sign, contributing to asymmetry in the SGA. Intriguingly, using multivariate evolutionary simulations, we observed that the SGA acts as a genetic constraint to the evolution of SD in humans only when selection is sexually antagonistic and not concordant. Remarkably, we found that the lifetime reproductive success in both the sexes shows a positive genetic correlation with anthropometric traits, but not with testosterone. Moreover, we demonstrated that genetic variance is depleted along multivariate trait combinations in both the sexes but in different directions, suggesting absolute genetic constraint to evolution. Our results indicate that testosterone drives SA in contemporary humans and emphasize the necessity and significance of using a multivariate framework in studying SD.

Bio-catalyzed oxidation self-charging zinc-polymer batteries.

Oxidation self-charging batteries have emerged with the demand for powering electronic devices around the clock. The low efficiency of self-charging has been the key challenge at present. Here, a more efficient autoxidation self-charging mechanism is realized by introducing hemoglobin (Hb) as a positive electrode additive in the polyaniline (PANI)-zinc battery system. The heme acts as a catalyst that reduces the energy barrier of the autoxidation reaction by regulating the charge and spin state of O2. To realize self-charging, the adsorbed O2 molecules capture electrons of the reduced (discharged state) PANI, leading to the desorption of zinc ions and the oxidation of PANI to complete self-charging. The battery can discharge for 12 min (0.5 C) after 50 self-charging/discharge cycles, while there is nearly no discharge capacity in the absence of Hb. This biology-inspired electronic regulation strategy may inspire new ideas to boost the performance of self-charging batteries.

Physics of microscale freeform 3D printing of ice.

Ice is emerging as a promising sacrificial material in the rapidly expanding area of advanced manufacturing for creating precise 3D internal geometries. Freeform 3D printing of ice (3D-ICE) can produce microscale ice structures with smooth walls, hierarchical transitions, and curved and overhang features. However, controlling 3D-ICE is challenging due to an incomplete understanding of its complex physics involving heat transfer, fluid dynamics, and phase changes. This work aims to advance our understanding of 3D-ICE physics by combining numerical modeling and experimentation. We developed a 2D thermo-fluidic model to analyze the transition from layered to continuous printing and a 3D thermo-fluidic model for the oblique deposition, which enables curved and overhang geometries. Experiments are conducted and compared with model simulations. We found that high droplet deposition rates enable the continuous deposition mode with a sustained liquid cap on top of the ice, facilitating smooth geometries. The diameter of ice structures is controlled by the droplet deposition frequency. Oblique deposition causes unidirectional spillover of the liquid cap and asymmetric heat transfer at the freeze front, rotating the freeze front. These results provide valuable insights for reproducible 3D-ICE printing that could be applied across various fields, including tissue engineering, microfluidics, and soft robotics.

Direct-ink-write cross-linkable bottlebrush block copolymers for on-the-fly control of structural color.

Additive manufacturing capable of controlling and dynamically modulating structures down to the nanoscopic scale remains challenging. By marrying additive manufacturing with self-assembly, we develop a UV (ultra-violet)-assisted direct ink write approach for on-the-fly modulation of structural color by programming the assembly kinetics through photo-cross-linking. We design a photo-cross-linkable bottlebrush block copolymer solution as a printing ink that exhibits vibrant structural color (i.e., photonic properties) due to the nanoscopic lamellar structures formed post extrusion. By dynamically modulating UV-light irradiance during printing, we can program the color of the printed material to access a broad spectrum of visible light with a single ink while also creating color gradients not previously possible. We unveil the mechanism of this approach using a combination of coarse-grained simulations, rheological measurements, and structural characterizations. Central to the assembly mechanism is the matching of the cross-linking timescale with the assembly timescale, which leads to kinetic trapping of the assembly process that evolves structural color from blue to red driven by solvent evaporation. This strategy of integrating cross-linking chemistry and out-of-equilibrium processing opens an avenue for spatiotemporal control of self-assembled nanostructures during additive manufacturing.

Programmable heterogeneous lamellar lattice architecture for dual mechanical protection.

Shear bands frequently appear in lattice architectures subjected to compression, leading to an unstable stress-strain curve and global deformation. This deformation mechanism reduces their energy absorption and loading-bearing capacity and causes the architectures to prioritize mechanical protection of external components at the expense of the entire structure. Here, we leverage the design freedom offered by additive manufacturing and the geometrical relation of dual-phase nanolamellar crystals to fabricate heterogeneous lamellar lattice architectures consisting of body-centered cubic (BCC) and face-centered cubic (FCC) unit cells in alternating lamella. The lamellar lattice demonstrates more than 10 and 9 times higher specific energy absorption and energy absorption efficiency, respectively, compared to the BCC lattice. The drastic improvement arises as the nucleation of shear bands is inhibited by the discrete energy threshold for plastic buckling of adjacent heterogeneous lattice lamella during loading. Despite its lower density than the FCC lattice, the lamellar lattice exhibits significant enhancement in plateau stress and crushing force efficiency, attributed to the strengthening effect induced by simultaneous deformation of unit cells in the BCC lattice lamella and the resulting cushion shielding effect. The design improves the global mechanical properties, making lamellar lattices compare favorably against numerous materials proposed for mechanical protection. Additionally, it provides opportunities to program the local mechanical response, achieving programmable internal protection alongside overall external protection. This work provides a different route to design lattice architecture by combining internal and external dual mechanical protection, enabling a generation of multiple mechanical protectors in aerospace, automotive, and transportation fields.

Role of genetic architecture in phenotypic plasticity.

Phenotypic plasticity, the ability of an organism to display different phenotypes across environments, is widespread in nature. Plasticity aids survival in novel environments. Herein, we review studies from yeast that allow us to start uncovering the genetic architecture of phenotypic plasticity. Genetic variants and their interactions impact the phenotype in different environments, and distinct environments modulate the impact of genetic variants and their interactions on the phenotype. Because of this, certain hidden genetic variation is expressed in specific genetic and environmental backgrounds. A better understanding of the genetic mechanisms of phenotypic plasticity will help to determine short- and long-term responses to selection and how wide variation in disease manifestation occurs in human populations.

Development and validation of an explainable machine learning model for predicting multidimensional frailty in hospitalized patients with cirrhosis.

We sought to develop and validate a machine learning (ML) model for predicting multidimensional frailty based on clinical and laboratory data. Moreover, an explainable ML model utilizing SHapley Additive exPlanations (SHAP) was constructed. This study enrolled 622 patients hospitalized due to decompensating episodes at a tertiary hospital. The cohort data were randomly divided into training and test sets. External validation was carried out using 131 patients from other tertiary hospitals. The frail phenotype was defined according to a self-reported questionnaire (Frailty Index). The area under the receiver operating characteristics curve was adopted to compare the performance of five ML models. The importance of the features and interpretation of the ML models were determined using the SHAP method. The proportions of cirrhotic patients with nonfrail and frail phenotypes in combined training and test sets were 87.8% and 12.2%, respectively, while they were 88.5% and 11.5% in the external validation dataset. Five ML algorithms were used, and the random forest (RF) model exhibited substantially predictive performance. Regarding the external validation, the RF algorithm outperformed other ML models. Moreover, the SHAP method demonstrated that neutrophil-to-lymphocyte ratio, age, lymphocyte-to-monocyte ratio, ascites, and albumin served as the most important predictors for frailty. At the patient level, the SHAP force plot and decision plot exhibited a clinically meaningful explanation of the RF algorithm. We constructed an ML model (RF) providing accurate prediction of frail phenotype in decompensated cirrhosis. The explainability and generalizability may foster clinicians to understand contributors to this physiologically vulnerable situation and tailor interventions.

The ecology of human-caused mortality for a protected large carnivore.

Mitigating human-caused mortality for large carnivores is a pressing global challenge for wildlife conservation. However, mortality is almost exclusively studied at local (within-population) scales creating a mismatch between our understanding of risk and the spatial extent most relevant to conservation and management of wide-ranging species. Here, we quantified mortality for 590 radio-collared mountain lions statewide across their distribution in California to identify drivers of human-caused mortality and investigate whether human-caused mortality is additive or compensatory. Human-caused mortality, primarily from conflict management and vehicles, exceeded natural mortality despite mountain lions being protected from hunting. Our data indicate that human-caused mortality is additive to natural mortality as population-level survival decreased as a function of increasing human-caused mortality and natural mortality did not decrease with increased human-caused mortality. Mortality risk increased for mountain lions closer to rural development and decreased in areas with higher proportions of citizens voting to support environmental initiatives. Thus, the presence of human infrastructure and variation in the mindset of humans sharing landscapes with mountain lions appear to be primary drivers of risk. We show that human-caused mortality can reduce population-level survival of large carnivores across large spatial scales, even when they are protected from hunting.

Generational differences in mental health trends in the twenty-first century.

Given the observed deterioration in mental health among Australians over the past decade, this study investigates to what extent this differs in people born in different decades-i.e., possible birth cohort differences in the mental health of Australians. Using 20 y of data from a large, nationally representative panel survey (N = 27,572), we find strong evidence that cohort effects are driving the increase in population-level mental ill-health. Deteriorating mental health is particularly pronounced among people born in the 1990s and seen to a lesser extent among the 1980s cohort. There is little evidence that mental health is worsening with age for people born prior to the 1980s. The findings from this study highlight that it is the poorer mental health of Millennials that is driving the apparent deterioration in population-level mental health. Understanding the context and changes in society that have differentially affected younger people may inform efforts to ameliorate this trend and prevent it continuing for emerging cohorts.

Integration of graph network with kernel SVM and logistic regression for identification of biomarkers in SCA12 and its diagnosis.

Spinocerebellar ataxia type 12 is a hereditary and neurodegenerative illness commonly found in India. However, there is no established noninvasive automatic diagnostic system for its diagnosis and identification of imaging biomarkers. This work proposes a novel four-phase machine learning-based diagnostic framework to find spinocerebellar ataxia type 12 disease-specific atrophic-brain regions and distinguish spinocerebellar ataxia type 12 from healthy using a real structural magnetic resonance imaging dataset. Firstly, each brain region is represented in terms of statistics of coefficients obtained using 3D-discrete wavelet transform. Secondly, a set of relevant regions are selected using a graph network-based method. Thirdly, a kernel support vector machine is used to capture nonlinear relationships among the voxels of a brain region. Finally, the linear relationship among the brain regions is captured to build a decision model to distinguish spinocerebellar ataxia type 12 from healthy by using the regularized logistic regression method. A classification accuracy of 95% and a harmonic mean of precision and recall, i.e. F1-score of 94.92%, is achieved. The proposed framework provides relevant regions responsible for the atrophy. The importance of each region is captured using Shapley Additive exPlanations values. We also performed a statistical analysis to find volumetric changes in spinocerebellar ataxia type 12 group compared to healthy. The promising result of the proposed framework shows that clinicians can use it for early and timely diagnosis of spinocerebellar ataxia type 12.

Identifying engaging bird species and traits with community science observations.

Identifying rates at which birders engage with different species can inform the impact and efficacy of conservation outreach and the scientific use of community-collected biodiversity data. Species that are thought to be "charismatic" are often prioritized in conservation, and previous researchers have used sociological experiments and digital records to estimate charisma indirectly. In this study, we take advantage of community science efforts as another record of human engagement with animals that can reveal observer biases directly, which are in part driven by observer preference. We apply a multistage analysis to ask whether opportunistic birders contributing to iNaturalist engage more with larger, more colorful, and rarer birds relative to a baseline approximated from eBird contributors. We find that body mass, color contrast, and range size all predict overrepresentation in the opportunistic dataset. We also find evidence that, across 472 modeled species, 52 species are significantly overreported and 158 are significantly underreported, indicating a wide variety of species-specific effects. Understanding which birds are highly engaging can aid conservationists in creating impactful outreach materials and engaging new naturalists. The quantified differences between two prominent community science efforts may also be of use for researchers leveraging the data from one or both of them to answer scientific questions of interest.

Net effect of environmental fluctuations in multiple global-change drivers across the tree of life.

Jensen's inequality predicts that the response of any given system to average constant conditions is different from its average response to varying ones. Environmental fluctuations in abiotic conditions are pervasive on Earth; yet until recently, most ecological research has addressed the effects of multiple environmental drivers by assuming constant conditions. One could thus expect to find significant deviations in the magnitude of their effects on ecosystems when environmental fluctuations are considered. Drawing on experimental studies published during the last 30 years reporting more than 950 response ratios (n = 5,700), we present a comprehensive analysis of the role that environmental fluctuations play across the tree of life. In contrast to the predominance of interactive effects of global-change drivers reported in the literature, our results show that their cumulative effects were additive (58%), synergistic (26%), and antagonistic (16%) when environmental fluctuations were present. However, the dominant type of interaction varied by trophic level (autotrophs: interactive; heterotrophs: additive) and phylogenetic group (additive in Animalia; additive and positive antagonism in Chromista; negative antagonism and synergism in Plantae). In addition, we identify the need to tackle how complex communities respond to fluctuating environments, widening the phylogenetic and biogeographic ranges considered, and to consider other drivers beyond warming and acidification as well as longer timescales. Environmental fluctuations must be taken into account in experimental and modeling studies as well as conservation plans to better predict the nature, magnitude, and direction of the impacts of global change on organisms and ecosystems.

Estimating the total treatment effect in randomized experiments with unknown network structure.

Randomized experiments are widely used to estimate the causal effects of a proposed treatment in many areas of science, from medicine and healthcare to the physical and biological sciences, from the social sciences to engineering, and from public policy to the technology industry. Here we consider situations where classical methods for estimating the total treatment effect on a target population are considerably biased due to confounding network effects, i.e., the fact that the treatment of an individual may impact its neighbors' outcomes, an issue referred to as network interference or as nonindividualized treatment response. A key challenge in these situations is that the network is often unknown and difficult or costly to measure. We assume a potential outcomes model with heterogeneous additive network effects, encompassing a broad class of network interference sources, including spillover, peer effects, and contagion. First, we characterize the limitations in estimating the total treatment effect without knowledge of the network that drives interference. By contrast, we subsequently develop a simple estimator and efficient randomized design that outputs an unbiased estimate with low variance in situations where one is given access to average historical baseline measurements prior to the experiment. Our solution does not require knowledge of the underlying network structure, and it comes with statistical guarantees for a broad class of models. Due to their ease of interpretation and implementation, and their theoretical guarantees, we believe our results will have significant impact on the design of randomized experiments.

A Statistical Testing Strategy Accounting for Random and Nonrandom (Skewed) X-Chromosome Inactivation Identifies Lung Cancer Susceptibility Loci among Smokers.

INTRODUCTION: Lung cancer is the most common cancer worldwide in mortality and the second in incidence. Epidemiological studies found a higher lung cancer risk for smoking women in comparison to men, but these sex differences, irrespective of smoking habits, remain controversial. One of the hypotheses concerns the genetic contribution of the sex chromosomes. However, while genome-wide association studies identified many lung cancer susceptibility loci, these analyses have excluded X-linked loci. METHODS: To account for nongenetic factors, we first presented an association test based on an additive-multiplicative hazard model accounting for random/nonrandom X-inactivation process. A simulation study was performed to investigate the properties of the proposed test as compared with the Wald test from a Cox model with random X-inactivation process and the partial likelihood ratio test proposed by Xu et al. accounting for nonrandom X-inactivation process. Then, we performed an X chromosome-wide association study on 9,261 individuals from the population-based cohort CARTaGENE to identify susceptibility loci for lung cancer among current and past smokers. We adjusted for the PLCOm2012 lung cancer risk score used in screening programs. RESULTS: Simulation results show the good behavior of the proposed test in terms of power and type I error probability as compared to the Xu et al. and the Wald test. Using the proposed test statistic and adjusting for the PLCOm2012 score, the X chromosome-wide statistical analysis identified two SNPs in low-linkage disequilibrium located in the IL1RAPL1 (IL-1 R accessory protein-like) gene: rs12558491 (p = 2.75×10-9) and rs12835699 (p = 1.26×10-6). For both SNPs, the minor allele was associated with lower lung cancer risk. Adjusting for multiple testing, no signal was detected using the Wald or the Xu et al. likelihood ratio tests. CONCLUSION: By taking into account smoking behavior and the X-inactivation process, the investigation of the X chromosome has shed a new light on the association between X-linked loci and lung cancer. We identified two loci associated with lung cancer located in the IL1RAPL1 gene. This finding would have been overlooked by examining only results from other test statistics.

Laser Metal Printing from Nanoparticles Prepared by a Gas Aggregation Source.

This study demonstrates the use of nanoparticles prepared by a gas aggregation source for fabricating structures by combining laser sintering and ablation. At first, the morphology and optical properties of prepared nanoparticle coatings were characterized. Then, the response of coatings to laser irradiation at different powers or exposure times was studied by in situ time-of-flight mass spectrometry, followed by scanning electron microscopy measurements of the resulting structures. By comparing the numbers of detected Ag ions, that were ablated and desorbed, with changes in morphology after irradiation, the optimum conditions for laser sintering and ablation of Ag nanoparticle coatings were found. As a proof of concept we fabricated micromirrors from sintered metal, microwires from both sintered metal and interconnected nanoparticles, and arbitrary metallic bulky or nanoparticle patterns. Vacuum compatibility and the possibility of fabrication of both metallic and nanoparticle structures in one step predetermines applications of developed method in electronics or sensing.

Trace Dual-Salt Electrolyte Additive Enabling a LiF-Rich Solid Electrolyte Interphase for High-Performance Lithium Metal Batteries.

The composition and physiochemical properties of the solid electrolyte interphase (SEI) significantly impact the electrochemical cyclability of the Li metal. Here, we introduce a trace dual-salt electrolyte additive (TDEA) that accelerates LiF production from FEC decomposition and improves the LiF distribution, resulting in earlier LiF precipitation and the formation of a LiF-rich SEI on the Li anode. TDEA at a millimolar-level concentration was found to alter the morphology of deposited Li, suppress Li dendrite formation, and increase the cycling time and operating current density for Li anodes. Li∥NCM811 full cells using TDEA-based electrolytes exhibited approximately two times longer lifespan than those without additives. Additionally, the TDEA-based electrolytes enabled a high energy density of 347 Wh kg-1 for 500-mAh pouch cells, maintaining stable cycling over 180 cycles under stringent conditions (N/P = 1.26 and E/C = 2.2 g A h-1). Our findings suggest that the proposed TDEA strategy offers a promising path to achieving high-performance Li metal batteries.

Laser-Induced Controllable Porosity in Additive Manufacturing Boosts Efficiency of Electrocatalytic Water Splitting.

In laser-based additive manufacturing (AM), porosity and unmelted metal powder are typically considered undesirable and harmful. Nevertheless in this work, precisely controlling laser parameters during printing can intentionally introduce controllable porosity, yielding a porous electrode with enhanced catalytic activity for the oxygen evolution reaction (OER). This study demonstrates that deliberate introduction of porosity, typically considered a defect, leads to improved gas molecule desorption, enhanced mass transfer, and increased catalytically active sites. The optimized P-93% electrode displays superior OER performance with an overpotential of 270 mV at 20 mA cm-2. Furthermore, it exhibits remarkable long-term stability, operating continuously for over 1000 h at 10 mA cm-2 and more than 500 h at 500 mA cm-2. This study not only provides a straightforward and mass-producible method for efficient, binder-free OER catalysts but also, if optimized, underscores the potential of laser-based AM driven defect engineering as a promising strategy for industrial water splitting.

Modulating Nucleation and Crystal Growth of Tin Perovskite Films for Efficient Solar Cells.

The performance of tin halide perovskite solar cells (PSCs) has been severely limited by the rapid crystallization of tin perovskites, which usually leads to an undesirable film quality. In this work, we tackle this issue by regulating the nucleation and crystal growth of tin perovskite films using a small Lewis base additive, urea. The urea-SnI2 interaction facilitates the formation of larger and more uniform clusters, thus accelerating the nucleation process. Additionally, the crystal growth process is extended, resulting in a high-quality tin perovskite film with compact morphology, increased crystallinity, and reduced defects. Consequently, the efficiency of tin PSCs is significantly increased from 10.42% to 14.22%. This work highlights the importance of manipulating the nucleation and crystal growth of tin perovskites to realize efficient tin PSCs.