Biological and Bioinspired Thermal Energy Regulation and Utilization.

The regulation and utilization of thermal energy is increasingly important in modern society due to the growing demand for heating and cooling in applications ranging from buildings, to cooling high power electronics, and from personal thermal management to the pursuit of renewable thermal energy technologies. Over billions of years of natural selection, biological organisms have evolved unique mechanisms and delicate structures for efficient and intelligent regulation and utilization of thermal energy. These structures also provide inspiration for developing advanced thermal engineering materials and systems with extraordinary performance. In this review, we summarize research progress in biological and bioinspired thermal energy materials and technologies, including thermal regulation through insulation, radiative cooling, evaporative cooling and camouflage, and conversion and utilization of thermal energy from solar thermal radiation and biological bodies for vapor/electricity generation, temperature/infrared sensing, and communication. Emphasis is placed on introducing bioinspired principles, identifying key bioinspired structures, revealing structure-property-function relationships, and discussing promising and implementable bioinspired strategies. We also present perspectives on current challenges and outlook for future research directions. We anticipate that this review will stimulate further in-depth research in biological and bioinspired thermal energy materials and technologies, and help accelerate the growth of this emerging field.

Multiply robust estimation of natural indirect effects with multiple ordered mediators.

Multiple mediation analysis is a powerful methodology to assess causal effects in the presence of multiple mediators. Several methodologies, such as G-computation and inverse-probability-weighting, have been widely used to draw inferences about natural indirect effects (NIEs). However, a limitation of these methods is their potential for model misspecification. Although powerful semiparametric methods with high robustness and consistency have been developed for inferring average causal effects and for analyzing the effects of a single mediator, a comparably robust method for multiple mediation analysis is still lacking. Therefore, this theoretical study proposes a method of using multiply robust estimators of NIEs in the presence of multiple ordered mediators. We show that the proposed estimators not only enjoy the multiply robustness to model misspecification, they are also consistent and asymptotically normal under regular conditions. We also performed simulations for empirical comparisons of the finite-sample properties between our multiply robust estimators and existing methods. In an illustrative example, a dataset for liver disease patients in Taiwan is used to examine the mediating roles of liver damage and liver cancer in the pathway from hepatitis B/C virus infection to mortality. The model is implemented in the open-source R package "MedMR."

Robust inference for causal mediation analysis of recurrent event data.

Recurrent events, including cardiovascular events, are commonly observed in biomedical studies. Understanding the effects of various treatments on recurrent events and investigating the underlying mediation mechanisms by which treatments may reduce the frequency of recurrent events are crucial tasks for researchers. Although causal inference methods for recurrent event data have been proposed, they cannot be used to assess mediation. This study proposed a novel methodology of causal mediation analysis that accommodates recurrent outcomes of interest in a given individual. A formal definition of causal estimands (direct and indirect effects) within a counterfactual framework is given, and empirical expressions for these effects are identified. To estimate these effects, a semiparametric estimator with triple robustness against model misspecification was developed. The proposed methodology was demonstrated in a real-world application. The method was applied to measure the effects of two diabetes drugs on the recurrence of cardiovascular disease and to examine the mediating role of kidney function in this process.

Sodium-Glucose Cotransporter 2 Inhibitors Reduce the Risk of Hospitalization for Heart Failure and Amputation Rate Compared With Incretin-Based Therapy in Patients With Diabetic Foot Disease: A Nationwide Population-Based Study.

OBJECTIVE: Major adverse cardiovascular event (MACE) outcomes associated with sodium-glucose cotransporter 2 inhibitor (SGLT2i) and glucagon-like peptide-1 receptor agonist (GLP-1 RA) therapies remain unclear in patients with type 2 diabetes and newly diagnosed diabetic foot complications (DFCs). This study examined the impact of SGLT2i and GLP-1 RA use on the rates of MACEs and amputations in patients with type 2 diabetes and without cardiovascular disease. METHODS: Data from the Taiwan National Health Insurance Research Database (2004-2017) were analyzed, focusing on patients with type 2 diabetes without previous MACE and newly diagnosed DFCs. The primary outcome was the first MACE occurrence, and the secondary outcomes included MACE components, all-cause mortality, and lower extremity amputation (LEA) rates. RESULTS: SGLT2i users showed a significant decrease in the MACE (hazard ratio [HR], 0.64; 95% confidence interval [CI], 0.46-0.88) and hospitalization for heart failure (HR, 0.54; 95% CI, 0.35-0.83) rates compared with dipeptidyl peptidase-4 inhibitor users. The amputation rates were also lower in SGLT2i users without LEA at the first DFC diagnosis (HR, 0.28; 95% CI, 0.10-0.75) and did not increase in those with a history of peripheral artery disease or LEA. No significant differences were observed between dipeptidyl peptidase-4 inhibitor and GLP-1 RA users in terms of the primary or secondary outcomes. CONCLUSION: In patients with type 2 diabetes initially diagnosed with DFC, SGLT2i are effective in significantly reducing the hospitalization for heart failure and MACE rates. SGLT2i lower the amputation rates, especially in patients who have not previously had a LEA, than the dipeptidyl peptidase-4 inhibitor therapy.

Human hand as a powerless and multiplexed infrared light source for information decryption and complex signal generation.

With the increasing pursuit of intelligent systems, the integration of human components into functional systems provides a promising route to the ultimate human-compatible intelligent systems. In this work, we explored the integration of the human hand as the powerless and multiplexed infrared (IR) light source in different functional systems. With the spontaneous IR radiation, the human hand provides a different option as an IR light source. Compared to engineered IR light sources, the human hand brings sustainability with no need of external power and also additional level of controllability to the functional systems. Besides the whole hand, each finger of the hand can also independently provide IR radiation, and the IR radiation from each finger can be selectively diffracted by specific gratings, which helps the hand serve as a multiplexed IR light source. Considering these advantages, we show that the human hand can be integrated into various engineered functional systems. The integration of hand in an encryption/decryption system enables both unclonable and multilevel information encryption/decryption. We also demonstrate the use of the hand in complex signal generation systems and its potential application in sign language recognition, which shows a simplified recognition process with a high level of accuracy and robustness. The use of the human hand as the IR light source provides an alternative sustainable solution that will not only reduce the power used but also help move forward the effort in the integration of human components into functional systems to increase the level of intelligence and achieve ultimate control of these systems.

Causal Mediation Analysis with Multiple Time-varying Mediators.

In longitudinal studies with time-varying exposures and mediators, the mediational g-formula is an important method for the assessment of direct and indirect effects. However, current methodologies based on the mediational g-formula can deal with only one mediator. This limitation makes these methodologies inapplicable to many scenarios. Hence, we develop a novel methodology by extending the mediational g-formula to cover cases with multiple time-varying mediators. We formulate two variants of our approach that are each suited to a distinct set of assumptions and effect definitions and present nonparametric identification results of each variant. We further show how complex causal mechanisms (whose complexity derives from the presence of multiple time-varying mediators) can be untangled. We implemented a parametric method, along with a user-friendly algorithm, in R software. We illustrate our method by investigating the complex causal mechanism underlying the progression of chronic obstructive pulmonary disease. We found that the effects of lung function impairment mediated by dyspnea symptoms accounted for 14.6% of the total effect and that mediated by physical activity accounted for 11.9%. Our analyses thus illustrate the power of this approach, providing evidence for the mediating role of dyspnea and physical activity on the causal pathway from lung function impairment to health status. See video abstract at, http://links.lww.com/EDE/B988 .

On the Conventional Definition of Path-specific Effects: Fully Mediated Interaction With Multiple Ordered Mediators.

Path-specific effects are a critical measure for assessing mediation in the presence of multiple mediators. However, the conventional definition of path-specific effects has generated controversy because it often causes misinterpretation of the results of multiple mediator analysis. For in-depth analysis of this issue, we propose the concept of decomposing fully mediated interaction from the average causal effect. We show that misclassification of fully mediated interaction is the main cause of misinterpretation of path-specific effects. We propose two strategies for specifying fully mediated interaction: isolating and reclassifying fully mediated interaction. The choice of strategy depends on the objective. Isolating fully mediated interaction is the superior strategy when the main objective is elucidating the mediation mechanism, whereas reclassifying it is superior when the main objective is precisely interpreting the mediation analysis results. To compare performance, this study used the two proposed strategies and the conventional decomposition strategy to analyze the mediating roles of dyspnea and anxiety in the effect of impaired lung function on poor health status in a population of patients with chronic obstructive pulmonary disease. The estimation result showed that the conventional decomposition strategy underestimates the importance of dyspnea as a mechanism of this disease. Specifically, the strategy of reclassifying fully mediated interaction revealed that 50% of the average causal effect is attributable to mediating effects, particularly the mediating effect of dyspnea.

Genome-wide causal mediation analysis identifies genetic loci associated with uterine fibroids mediated by age at menarche.

STUDY QUESTION: Could the direct contribution of genetic variants to the pathophysiology of uterine fibroids and the contribution mediated by age at menarche be different? SUMMARY ANSWER: Age at menarche plays a mediation role in the genetic influence on uterine fibroids, and four causal genetic mechanisms underlying the age at menarche-mediated effects of common genetic loci on uterine fibroid development were identified. WHAT IS KNOWN ALREADY: Uterine fibroids are common benign tumors developing from uterine smooth muscle. Genome-wide association studies (GWASs) have identified over 30 genetic loci associated with uterine fibroids in different ethnic populations. Several genetic variations in or nearby these identified loci were also associated with early age at menarche, one of the major risk factors of uterine fibroids. Although the results of GWASs reveal how genetic variations affect uterine fibroids, the genetic mechanism of uterine fibroids mediated by age at menarche remains elusive. STUDY DESIGN, SIZE, DURATION: In this study, we conducted a genome-wide causal mediation analysis in two cohorts covering a total of 69 552 females of Han Chinese descent from the Taiwan Biobank (TWB). TWB is an ongoing community- and hospital-based cohort aiming to enroll 200 000 individuals from the general Taiwanese population between 30 and 70 years old. It has been enrolling Taiwanese study participants since 2012 and has extensive phenotypic data collected from 148 291 individuals as of May 2021. PARTICIPANTS/MATERIALS, SETTING, METHODS: We recruited individuals in two cohorts, with 13 899 females in TWB1 and 55 653 females in TWB2. The two sets of individuals are almost distinct, with only 730 individuals enrolled in both cohorts. Over 99% of the participants are Han Chinese. Approximately 21% of participants developed uterine fibroids. DNA samples from both cohorts were genotyped using two different customized chips (TWB1 and TWB2 arrays). After quality control and genotype imputation, 646 973 TWB1 single-nucleotide polymorphisms (SNPs) and 686 439 TWB2 SNPs were assessed in our analysis. There were 99 939 SNPs which overlapped between the TWB1 and TWB2 arrays, 547 034 TWB1 array-specific SNPs and 586 500 TWB2 array-specific SNPs. We performed GWASs for screening potential risk SNPs for age at menarche and for uterine fibroids. We subsequently identified causal mediation effects of risk SNPs on uterine fibroids mediated by age at menarche. MAIN RESULTS AND THE ROLE OF CHANCE: In addition to known loci at LIN28B associated with age at menarche and loci at WNT4 associated with uterine fibroids, we identified 162 SNPs in 77 transcripts that were associated with menarche-mediated causal effects on uterine fibroids via four different causal genetic mechanisms: a both-harmful group with 52 SNPs, a both-protective group with 34 SNPs, a mediator-harmful group with 22 SNPs and a mediator-protective group with 54 SNPs. Among these SNPs, rs809302 in SLK significantly increased the risk of developing uterine fibroids by 3.92% through a mechanism other than age at menarche (P < 10-10), and rs371721345 in HLA-DOB was associated with a 2.70% decreased risk (P < 10-10) in the occurrence of uterine fibroids, mediated by age at menarche. These findings provide insights into the mechanism underlying the effect of genetic loci on uterine fibroids mediated by age at menarche. LIMITATIONS, REASONS FOR CAUTION: A potential issue is that the present study relied upon self-reported age at menarche and uterine fibroid information. Due to the experimental design, the consistency between self-reports and medical records for uterine fibroids in Taiwan cannot be checked. Fortunately, the literature support that self-reporting even years later remains a practical means for collecting data on menarche and uterine fibroids. We found that the impact of under-reporting of uterine fibroids is less in our study. In addition, the rate of reporting a diagnosis of uterine fibroids was within the rates of medical diagnosis based on national health insurance data. Future work investigating the consistency between self-reports and medical records in Taiwan can remedy this issue. WIDER IMPLICATIONS OF THE FINDINGS: This study is the first to investigate whether and to what extent age at menarche mediates the causal effects of genetic variants on uterine fibroids by using genome-wide causal mediation analysis. By treating age at menarche as a mediator, this report provides an insight into the genetic risk factors for developing uterine fibroids. Thus, this article represents a step forward in deciphering the role of intermediated risk factors in the genetic mechanism of disease. STUDY FUNDING/COMPETING INTEREST(S): This work was supported by the China Medical University, Taiwan (CMU110-ASIA-13 and CMU107-Z-04), the Ministry of Science and Technology, Taiwan (MOST 110-2314-B-039-058) and the International Joint Usage/Research Center, the Institute of Medical Science, the University of Tokyo, Japan (K2104). The authors have no competing interests. TRIAL REGISTRATION NUMBER: N/A.

Identification and robust estimation of swapped direct and indirect effects: Mediation analysis with unmeasured mediator-outcome confounding and intermediate confounding.

Counterfactual-model-based mediation analysis can yield substantial insight into the causal mechanism through the assessment of natural direct effects (NDEs) and natural indirect effects (NIEs). However, the assumptions regarding unmeasured mediator-outcome confounding and intermediate mediator-outcome confounding that are required for the determination of NDEs and NIEs present practical challenges. To address this problem, we introduce an instrumental blocker, a novel quasi-instrumental variable, to relax both of these assumptions, and we define a swapped direct effect (SDE) and a swapped indirect effect (SIE) to assess the mediation. We show that the SDE and SIE are identical to the NDE and NIE, respectively, based on a causal interpretation. Moreover, the empirical expressions of the SDE and SIE are derived with and without an intermediate mediator-outcome confounder. Then, a multiply robust estimation method is derived to mitigate the model misspecification problem. We prove that the proposed estimator is consistent, asymptotically normal, and achieves the semiparametric efficiency bound. As an illustration, we apply the proposed method to genomic datasets of lung cancer to investigate the potential role of the epidermal growth factor receptor in the treatment of lung cancer.

Robust inference on effects attributable to mediators: A controlled-direct-effect-based approach for causal effect decomposition with multiple mediators.

Effect decomposition is a critical technique for mechanism investigation in settings with multiple causally ordered mediators. Causal mediation analysis is a standard method for effect decomposition, but the assumptions required for the identification process are extremely strong. Moreover, mediation analysis focuses on addressing mediating mechanisms rather than interacting mechanisms. Mediation and interaction for mediators both contribute to the occurrence of disease, and therefore unifying mediation and interaction in effect decomposition is important to causal mechanism investigation. By extending the framework of controlled direct effects, this study proposes the effect attributable to mediators (EAM) as a novel measure for effect decomposition. For policymaking, EAM represents how much an effect can be eliminated by setting mediators to certain values. From the perspective of mechanism investigation, EAM contains information about how much a particular mediator or set of mediators is involved in the causal mechanism through mediation, interaction, or both. EAM is more appropriate than the conventional path-specific effect for application in clinical or medical studies. The assumptions of EAM for identification are considerably weaker than those of causal mediation analysis. We develop a semiparametric estimator of EAM with robustness to model misspecification. The asymptotic property is fully realized. We applied EAM to assess the magnitude of the effect of hepatitis C virus infection on mortality, which was eliminated by controlling alanine aminotransferase and treating hepatocellular carcinoma.

Integrated multiple mediation analysis: A robustness-specificity trade-off in causal structure.

Recent methodological developments in causal mediation analysis have addressed several issues regarding multiple mediators. However, these developed methods differ in their definitions of causal parameters, assumptions for identification, and interpretations of causal effects, making it unclear which method ought to be selected when investigating a given causal effect. Thus, in this study, we construct an integrated framework, which unifies all existing methodologies, as a standard for mediation analysis with multiple mediators. To clarify the relationship between existing methods, we propose four strategies for effect decomposition: two-way, partially forward, partially backward, and complete decompositions. This study reveals how the direct and indirect effects of each strategy are explicitly and correctly interpreted as path-specific effects under different causal mediation structures. In the integrated framework, we further verify the utility of the interventional analogues of direct and indirect effects, especially when natural direct and indirect effects cannot be identified or when crossworld exchangeability is invalid. Consequently, this study yields a robustness-specificity trade-off in the choice of strategies. Inverse probability weighting is considered for estimation. The four strategies are further applied to a simulation study for performance evaluation and for analyzing the Risk Evaluation of Viral Load Elevation and Associated Liver Disease/Cancer dataset from Taiwan to investigate the causal effect of hepatitis C virus infection on mortality.

Survival mediation analysis with the death-truncated mediator: The completeness of the survival mediation parameter.

In medical research, the development of mediation analysis with a survival outcome has facilitated investigation into causal mechanisms. However, studies have not discussed the death-truncation problem for mediators, the problem being that conventional mediation parameters cannot be well defined in the presence of a truncated mediator. In the present study, we systematically defined the completeness of causal effects to uncover the gap, in conventional causal definitions, between the survival and nonsurvival settings. We propose a novel approach to redefining natural direct and indirect effects, which are generalized forms of conventional causal effects for survival outcomes. Furthermore, we developed three statistical methods for the binary outcome of survival status and formulated a Cox model for survival time. We performed simulations to demonstrate that the proposed methods are unbiased and robust. We also applied the proposed method to explore the effect of hepatitis C virus infection on mortality, as mediated through hepatitis B viral load.

Light-Driven Nanodroplet Generation Using Porous Membranes.

A simple, fast, and contactless alternative for the generation of nanodroplets in solution is to apply light to stimulate their formation at a surface. In this work, a light-driven mechanism for the generation of nanodroplets is demonstrated by using a porous membrane. The membrane is placed at the interface between oil and water during the nanodroplet generation process. As light illuminates the membrane a photothermal conversion process induces the growth and release of water vapor bubbles into the aqueous phase. This release leads to the fluctuation of local pressure around the pores and enables the generation of oil nanodroplets. A computational simulation of the fluid dynamics provides insight into the underlying mechanism and the extent to which it is possible to increase nanodroplet concentrations. The ability to form nanodroplets in solutions without the need for mechanical moving parts is significant for the diverse biomedical and chemical applications of these materials.

Six-way decomposition of causal effects: Unifying mediation and mechanistic interaction.

The sufficient component cause (SCC) model and counterfactual model are two common methods for causal inference, each with their own advantages: the SCC model allows the mechanistic interaction to be detailed, whereas the counterfactual model features a systemic framework for quantifying causal effects. Hence, integrating the SCC and counterfactual models may facilitate the conceptualization of causation. Based on the marginal SCC (mSCC) model, we propose a novel counterfactual mSCC framework that includes the steps of definition, identification, and estimation. We further propose a six-way effect decomposition for assessing mediation and the mechanistic interaction. The results demonstrate that when all variables are binary, the six-way decomposition is an extension of four-way decomposition and that without agonism, the six-way decomposition is reduced to four-way decomposition. To illustrate the utility of the proposed decomposition, we apply it to a Taiwanese cohort to examine the mechanism of hepatitis C virus (HCV)-induced hepatocellular carcinoma (HCC) with liver inflammation measured by alanine aminotransferase (ALT) as a mediator. Among the HCV-induced HCC cases, 62.27% are not explained by either mediation or interaction in relation to ALT; 9.32% are purely mediated by ALT; 16.53% are caused by the synergistic effect of HCV and ALT; and 9.31% are due to the mediated synergistic effect of HCV and ALT. In summary, we introduce an SCC model framework based on counterfactual theory and detail the required identification assumptions and estimation procedures; we also propose a six-way effect decomposition to unify mediation and mechanistic interaction analyses.

Regioselective C-C cross-coupling of 1,2,4-thiadiazoles with maleimides through iridium-catalyzed C-H activation.

A regioselective C-C cross-coupling of 1,2,4-thiadiazoles with maleimides through iridium catalysis was developed. This transformation tactically linked the 1,2,4-thiadiazoles and succinimides together, and the novel molecules formed may have potential biological activity.

Nonlinear optical and multi-photon absorption properties in graphene-ZnO nanocomposites.

Graphene-ZnO (GZO) nanocomposites were synthesized by a modified solvothermal method, and characterized by transmission electron microscopy, x-ray diffraction, Raman spectra, and UV-vis absorption spectra. The controllable nonlinear optical (NLO) properties of as-prepared GZO nanocomposites were tested by an open-aperture Z-scan method with 1030 nm fs laser pulses; the tested results showed that there were five-photon absorption (5PA) at 46.8 GW cm-2, 3PA at 28.1 GW cm-2, 2PA at 18.7 GW cm-2, and a vital change from saturable absorption (SA) to reverse SA (RSA) with the increase of incident intensity. This was the first time that 5PA was found in GZO nanocomposites at such a low intensity, 46.8 GW cm-2. The tunable NLO property from SA to RSA and controllable multi-photon absorption provided a facile approach for their applications in optical, optoelectronic devices, and information storage.

Causal inference of gene regulation with subnetwork assembly from genetical genomics data.

Deciphering the causal networks of gene interactions is critical for identifying disease pathways and disease-causing genes. We introduce a method to reconstruct causal networks based on exploring phenotype-specific modules in the human interactome and including the expression quantitative trait loci (eQTLs) that underlie the joint expression variation of each module. Closely associated eQTLs help anchor the orientation of the network. To overcome the inherent computational complexity of causal network reconstruction, we first deduce the local causality of individual subnetworks using the selected eQTLs and module transcripts. These subnetworks are then integrated to infer a global causal network using a random-field ranking method, which was motivated by animal sociology. We demonstrate how effectively the inferred causality restores the regulatory structure of the networks that mediate lymph node metastasis in oral cancer. Network rewiring clearly characterizes the dynamic regulatory systems of distinct disease states. This study is the first to associate an RXRB-causal network with increased risks of nodal metastasis, tumor relapse, distant metastases and poor survival for oral cancer. Thus, identifying crucial upstream drivers of a signal cascade can facilitate the discovery of potential biomarkers and effective therapeutic targets.

Chameleon-inspired tunable multi-layered infrared-modulating system via stretchable liquid metal microdroplets in elastomer film.

This report presents liquid metal-based infrared-modulating materials and systems with multiple modes to regulate the infrared reflection. Inspired by the brightness adjustment in chameleon skin, shape-morphing liquid metal droplets in silicone elastomer (Ecoflex) matrix are used to resemble the dispersed "melanophores". In the system, Ecoflex acts as hormone to drive the deformation of liquid metal droplets. Both total and specular reflectance-based infrared camouflage are achieved. Typically, the total and specular reflectances show change of ~44.8% and 61.2%, respectively, which are among the highest values reported for infrared camouflage. Programmable infrared encoding/decoding is explored by adjusting the concentration of liquid metal and applying areal strains. By introducing alloys with different melting points, temperature-dependent infrared painting/writing can be achieved. Furthermore, the multi-layered structure of infrared-modulating system is designed, where the liquid metal-based infrared modulating materials are integrated with an evaporated metallic film for enhanced performance of such system.

Manipulation of Convection Using Infrared Light Emitted from Human Hands.

Control of convection plays an important role in heat transfer regulation, bio/chemical sensing, phase separation, etc. Current convection controlling systems generally depend on engineered energy sources to drive and manipulate the convection, which brings additional energy consumption into the system. Here the use of human hand as a natural and sustainable infrared (IR) radiation source for the manipulation of liquid convection is demonstrated. The fluid can sense the change of the relative position or the shape of the hand with the formation of different convection patterns. Besides the generation of static complex patterns, dynamic manipulation of convections can also be realized via moving of hand or finger. The use of such sustainable convections to control the movement of a floating "boat" is further achieved. The use of human hands as the natural energy sources provides a promising approach for the manipulation of liquid convection without the need of extra external energy, which may be further utilized for low-cost and intelligent bio/chemical sensing and separation.