Unveiling the Genetic Mechanism of Meat Color in Pigs through GWAS, Multi-Tissue, and Single-Cell Transcriptome Signatures Exploration.

Meat color traits directly influence consumer acceptability and purchasing decisions. Nevertheless, there is a paucity of comprehensive investigation into the genetic mechanisms underlying meat color traits in pigs. Utilizing genome-wide association studies (GWAS) on five meat color traits and the detection of selection signatures in pig breeds exhibiting distinct meat color characteristics, we identified a promising candidate SNP, 6_69103754, exhibiting varying allele frequencies among pigs with different meat color characteristics. This SNP has the potential to affect the redness and chroma index values of pork. Moreover, transcriptome-wide association studies (TWAS) analysis revealed the expression of candidate genes associated with meat color traits in specific tissues. Notably, the largest number of candidate genes were observed from transcripts derived from adipose, liver, lung, spleen tissues, and macrophage cell type, indicating their crucial role in meat color development. Several shared genes associated with redness, yellowness, and chroma indices traits were identified, including RINL in adipose tissue, ENSSSCG00000034844 and ITIH1 in liver tissue, TPX2 and MFAP2 in lung tissue, and ZBTB17, FAM131C, KIFC3, NTPCR, and ENGSSSCG00000045605 in spleen tissue. Furthermore, single-cell enrichment analysis revealed a significant association between the immune system and meat color. This finding underscores the significance of the immune system associated with meat color. Overall, our study provides a comprehensive analysis of the genetic mechanisms underlying meat color traits, offering valuable insights for future breeding efforts aimed at improving meat quality.

Superexchange-stabilized long-distance Cu sites in rock-salt-ordered double perovskite oxides for CO2 electromethanation.

Cu-oxide-based catalysts are promising for CO2 electroreduction (CO2RR) to CH4, but suffer from inevitable reduction (to metallic Cu) and uncontrollable structural collapse. Here we report Cu-based rock-salt-ordered double perovskite oxides with superexchange-stabilized long-distance Cu sites for efficient and stable CO2-to-CH4 conversion. For the proof-of-concept catalyst of Sr2CuWO6, its corner-linked CuO6 and WO6 octahedral motifs alternate in all three crystallographic dimensions, creating sufficiently long Cu-Cu distances (at least 5.4 Å) and introducing marked superexchange interaction mainly manifested by O-anion-mediated electron transfer (from Cu to W sites). In CO2RR, the Sr2CuWO6 exhibits significant improvements (up to 14.1 folds) in activity and selectivity for CH4, together with well boosted stability, relative to a physical-mixture counterpart of CuO/WO3. Moreover, the Sr2CuWO6 is the most effective Cu-based-perovskite catalyst for CO2 methanation, achieving a remarkable selectivity of 73.1% at 400 mA cm-2 for CH4. Our experiments and theoretical calculations highlight the long Cu-Cu distances promoting *CO hydrogenation and the superexchange interaction stabilizing Cu sites as responsible for the superb performance.

Extraordinary Thermal Stability and Sinter Resistance of Sub-2 nm Platinum Nanoparticles Anchored to a Carbon Support by Selenium.

Nanoparticle sintering has long been a major challenge in developing catalytic systems for use at elevated temperatures. Here we report an in situ electron microscopy study of the extraordinary sinter resistance of a catalytic system comprised of sub-2 nm Pt nanoparticles on a Se-decorated carbon support. When heated to 700 °C, the average size of the Pt nanoparticles only increased from 1.6 to 2.2 nm, while the crystal structure, together with the {111} and {100} facets, of the Pt nanoparticles was well retained. Our electron microscopy analyses suggested that the superior resistance against sintering originated from the Pt-Se interaction. Confirmed by energy-dispersive X-ray elemental mapping and electron energy loss spectra, the Se atoms surrounding the Pt nanoparticles could survive the heating. This work not only offers an understanding of the physics behind the thermal behavior of this catalytic material but also sheds light on the future development of sinter-resistant catalytic systems.

Other's shoes also fit well: AI technologies contribute to China's blue skies as well as carbon reduction.

Artificial intelligence (AI) technology represents a disruptive innovation that has garnered significant interest among researchers for its potential applications in ecological and environmental management. While many studies have investigated the impact of AI on carbon emissions, relatively few have delved into its relationship with air pollution. This study sets out to explore the causal mechanisms and constraints linking AI technologies and air pollution, using provincial panel data collected from 2007 to 2020 in China. Furthermore, this study examines the distinct pathways through which AI technology can ameliorate air pollution and reduce carbon emissions. The findings reveal the following key insights: (1) AI technologies have the capacity to significantly reduce air pollution, particularly in terms of PM2.5 and SO2 levels. (2) AI technologies contribute to enhanced air quality by facilitating adjustments in energy structures, improving energy efficiency, and strengthening digital infrastructure. Nonetheless, it is important to note that adjusting the energy structure remains the most practical approach for reducing carbon emissions. (3) The efficacy of AI in controlling air pollution is influenced by geographical location, economic development level, level of information technology development, resource dependence, and public attention. In conclusion, this study proposes novel policy recommendations to offer fresh perspectives to countries interested in leveraging AI for the advancement of ecological and environmental governance.

Predictive factors for persistent postoperative hydrocephalus in children undergoing surgical resection of periventricular tumors.

Objective: The aim of this study is to identify the factors predicting persistent hydrocephalus after periventricular tumor resection in children and assess the need and efficacy of perioperative cerebrospinal fluid (CSF) intervention. Methods: We performed a retrospective analysis of pediatric patients who underwent resection surgery of a periventricular tumor between March 2012 and July 2021 at the Department of Neurosurgery in Zhujiang Hospital of South Medical University. Demographic, radiographic, perioperative, and dispositional data were analyzed using univariate and multivariate models. Results: A total of 117 patients were enrolled in our study. Incidence of postoperative persistent hydrocephalus varied with tumor pathology (p = 0.041), tumor location (p = 0.046), surgical approach (p = 0.013), extension of resection (p = 0.043), tumor volume (p = 0.041), preoperative Evan's index (p = 0.002), and preoperative CSF diversion (p = 0.024). On logistic regression, posterior median approach (OR = 5.315), partial resection (OR = 20.984), volume > 90cm3 (OR = 5.768), and no preoperative CSF diversion (OR = 3.661) were independent predictors of postoperative persistent hydrocephalus. Preoperative Evan's index is significantly correlated with tumor volume (p = 0.019). Meanwhile, the need for preoperative CSF drainage in patients in this cohort was significantly correlated with tumor location (p = 0.019). Conclusion: Tumor pathology, location, surgical approach, the extension of resection, tumor volume, preoperative Evan's index, and preoperative CSF diversion were considered to be predictive factors for postoperative persistent hydrocephalus. Notably, posterior median approach, partial resection, and tumor volume > 90cm3, without preoperative CSF diversion, were identified as independent risk factors for persistent postoperative hydrocephalus. Preoperative identification of children at risk of developing persistent postoperative hydrocephalus would avoid delays in planning the cerebrospinal fluid diversion. Active and effective preoperative hydrocephalus intervention in children with periventricular tumors is beneficial to reduce the incidence of persistent hydrocephalus and ventriculoperitoneal shunt surgery after resection.

The Role of SOCS3 in Regulating Meat Quality in Jinhua Pigs.

Meat quality is an important economic trait that influences the development of the pig industry. Skeletal muscle development and glycolytic potential (GP) are two crucial aspects that significantly impact meat quality. It has been reported that abnormal skeletal muscle development and high glycogen content results in low meat quality. However, the genetic mechanisms underlying these factors are still unclear. Compared with intensive pig breeds, Chinese indigenous pig breeds, such as the Jinhua pig, express superior meat quality characteristics. The differences in the meat quality traits between Jinhua and intensive pig breeds make them suitable for uncovering the genetic mechanisms that regulate meat quality traits. In this study, the Jinhua pig breed and five intensive pig breeds, including Duroc, Landrace, Yorkshire, Berkshire, and Pietrain pig breeds, were selected as experimental materials. First, the FST and XP-EHH methods were used to screen the selective signatures on the genome in the Jinhua population. Then, combined with RNA-Seq data, the study further confirmed that SOCS3 could be a key candidate gene that influences meat quality by mediating myoblast proliferation and glycometabolism because of the down-regulated expression of SOCS3 in Jinhua pigs compared with Landrace pigs. Finally, through SOCS3 knockout (KO) and overexpression (OE) experiments in mouse C2C12 cells, the results showed that SOCS3 regulated the cell proliferation of myoblasts. Moreover, SOCS3 is involved in regulating glucose uptake by the IRS1/PI3K/AKT signaling pathway. Overall, these findings provide a basis for the genetic improvement of meat quality traits in the pig industry.

Integrated microbiome-metabolome-genome axis data of Laiwu and Lulai pigs.

Excessive fat deposition can trigger metabolic diseases, and it is crucial to identify factors that can break the link between fat deposition and metabolic diseases. Healthy obese Laiwu pigs (LW) are high in fat content but resistant to metabolic diseases. In this study, we compared the fecal microbiome, fecal and blood metabolome, and genome of LW and Lulai pigs (LU) to identify factors that can block the link between fat deposition and metabolic diseases. Our results show significant differences in Spirochetes and Treponema, which are involved in carbohydrate metabolism, between LW and LU. The fecal and blood metabolome composition was similar, and some anti-metabolic disease components of blood metabolites were different between the two breeds of pigs. The predicted differential RNA is mainly enriched in lipid metabolism and glucose metabolism, which is consistent with the functions of differential microbiota and metabolites. The down-regulated gene RGP1 is strongly negatively correlated with Treponema. Our omics data would provide valuable resources for further scientific research on healthy obesity in both human and porcine.

Integration of Selection Signatures and Protein Interactions Reveals NR6A1, PAPPA2, and PIK3C2B as the Promising Candidate Genes Underlying the Characteristics of Licha Black Pig.

Licha black (LI) pig has the specific characteristics of larger body length and appropriate fat deposition among Chinese indigenous pigs. Body length is one of the external traits that affect production performance, and fat deposition influences meat quality. However, the genetic characteristics of LI pigs have not yet been systematically uncovered. Here, the genomic information from 891 individuals of LI pigs, commercial pigs, and other Chinese indigenous pigs was used to analyze the breed characteristics of the LI pig with runs of homozygosity, haplotype, and FST selection signatures. The results showed the growth traits-related genes (i.e., NR6A1 and PAPPA2) and the fatness traits-related gene (i.e., PIK3C2B) were the promising candidate genes that closely related to the characteristics of LI pigs. In addition, the protein-protein interaction network revealed the potential interactions between the promising candidate genes and the FASN gene. The RNA expression data from FarmGTEx indicated that the RNA expression levels of NR6A1, PAPPA2, PIK3C2B, and FASN were highly correlated in the ileum. This study provides valuable molecular insights into the mechanisms that affect pig body length and fat deposition, which can be used in the further breeding process to improve meat quality and commercial profitability.

A web tool for the global identification of pig breeds.

BACKGROUND: Natural and artificial selection for more than 9000 years have led to a variety of domestic pig breeds. Accurate identification of pig breeds is important for breed conservation, sustainable breeding, pork traceability, and local resource registration. RESULTS: We evaluated the performance of four selectors and six classifiers for breed identification using a wide range of pig breeds (N = 91). The internal cross-validation and external independent testing showed that partial least squares regression (PLSR) was the most effective selector and partial least squares-discriminant analysis (PLS-DA) was the most powerful classifier for breed identification among many breeds. Five-fold cross-validation indicated that using PLSR as the selector and PLS-DA as the classifier to discriminate 91 pig breeds yielded 98.4% accuracy with only 3K single nucleotide polymorphisms (SNPs). We also constructed a reference dataset with 124 pig breeds and used it to develop the web tool iDIGs ( http://alphaindex.zju.edu.cn/iDIGs_en/ ) as a comprehensive application for global pig breed identification. iDIGs allows users to (1) identify pig breeds without a reference population and (2) design small panels to discriminate several specific pig breeds. CONCLUSIONS: In this study, we proved that breed identification among a wide range of pig breeds is feasible and we developed a web tool for such pig breed identification.

Meta-analysis of genome-wide association studies uncovers shared candidate genes across breeds for pig fatness trait.

BACKGROUND: Average backfat thickness (BFT) is a critical complex trait in pig and an important indicator for fat deposition and lean rate. Usually, genome-wide association study (GWAS) was used to discover quantitative trait loci (QTLs) of BFT in a single population. However, the power of GWAS is limited by sample size in a single population. Alternatively, meta-analysis of GWAS (metaGWAS) is an attractive method to increase the statistical power by integrating data from multiple breeds and populations. The aim of this study is to identify shared genetic characterization of BFT across breeds in pigs via metaGWAS.  RESULTS: In this study, we performed metaGWAS on BFT using 15,353 pigs (5,143 Duroc, 7,275 Yorkshire, and 2,935 Landrace) from 19 populations. We detected 40 genome-wide significant SNPs (Bonferroni corrected P < 0.05) and defined five breed-shared QTLs in across-breed metaGWAS. Markers within the five QTL regions explained 7 ~ 9% additive genetic variance and showed strong heritability enrichment. Furthermore, by integrating information from multiple bioinformatics databases, we annotated 46 candidate genes located in the five QTLs. Among them, three important (MC4R, PPARD, and SLC27A1) and seven suggestive candidate genes (PHLPP1, NUDT3, ILRUN, RELCH, KCNQ5, ITPR3, and U3) were identified. CONCLUSION: QTLs and candidate genes underlying BFT across breeds were identified via metaGWAS from multiple populations. Our findings contribute to the understanding of the genetic architecture of BFT and the regulating mechanism underlying fat deposition in pigs.

Event-triggered H∞ consensus for uncertain nonlinear systems using integral sliding mode based adaptive dynamic programming.

This paper studies a robust optimal consensus problem for uncertain nonlinear multi-agent systems, where the uncertainties include both input and external disturbances. Adaptive distributed observer, integral sliding mode control and H∞ adaptive dynamic programming are integrated to obtain a sub-optimal control protocol for each follower. Firstly, an adaptive distributed observer is designed for state estimation of the leader, which serves as the reference of the ADP algorithm. Then, an H∞ ADP algorithm is presented to make each follower track the reference in real-time. An integral sliding manifold-based discontinuous control is designed to eliminate the matched uncertainty, and continuous control is obtained by solving the Hamilton-Jacobi-Isaac equation under the H∞ tracking framework. Two event-triggered rules are developed to relieve the communication pressure. For simplicity, a critic-only structure is used to numerically implement the proposed algorithm, and a concurrent learning technique is employed to update weights of neural networks. All signals in the closed-loop system are proven to be uniformly ultimately bounded. Finally, a simulation is conducted to demonstrate demonstrates the effectiveness of the method.

Runs of Homozygosity Revealed Reproductive Traits of Hu Sheep.

Hu sheep, a famous breed in the Taihu Basin, has the advantages of non-seasonal estrus, multiple fetuses, coarse feeding tolerance, and suitability for house feeding. Runs of homozygosity (ROHs) were found to be an effective tool to detect the animal population structure and economic traits. The detection of ROHs is beneficial for reducing the incidence of inbreeding as well as identifying harmful variants in the genome. However, there is a lack of systemic reports on ruminants in previous studies of ROHs. Here, we sequenced 108 Hu sheep, detected ROHs in Hu sheep to calculate their inbreeding coefficient, and selected genes of Hu sheep breeds within the ROH islands which are relevant to agricultural economic characteristics. Then, we compared the characteristics of the occurrences of SNPs between Hu sheep and other sheep breeds, and also investigated the distribution of the frequencies of SNPs within specific gene regions of Hu sheep breeds to select their breed-specific genes. Furthermore, we performed a comparative genome and transcriptome analysis in human and sheep breeds to identify important reproduction-related genes. In this way, we found some significant SNPs, and mapped these with a set of interesting candidate genes which are related to the productive value of livestock (FGF9, BMPR1B, EFNB3, MICU2, GFRA3), healthy characteristics (LGSN, EPHA5, ALOX15B), and breed specificity (FGF9, SAP18, MICU2). These results in our study describe various production traits of Hu sheep from a genetic perspective, and provide insights into the genetic management and complementary understanding of Hu sheep.

Perovskite-Socketed Sub-3 nm Copper for Enhanced CO2 Electroreduction to C2.

In situ socketing metal nanoparticles onto perovskite oxides has shown great potential in heterogeneous catalysis, but its employment in boosting ambient CO2 electroreduction (CER) is unexplored. Here, a CER catalyst of perovskite-socketed sub-3 nm Cu equipped with strong metal-support interactions (SMSIs) is constructed to promote efficient and stable CO2 -to-C2+ conversion. For such a catalyst, plentiful sub-3 nm ellipsoid Cu particles are homogeneously and epitaxially anchored on the perovskite backbones, with concomitant creation of significant SMSIs. These SMSIs are able to not only modulate electronic structure of active Cu and facilitate adsorption/activation of key intermediates, but also to strengthen perovskite-Cu adhesion and intensify resistance to structural degradation. Beneficial from these advantageous merits, when evaluated in CER, it performs comparably to or better than most reported Cu-based heteronanostructures. Relative to a physical-mixture counterpart, it features marked improvements (up to 6.2 folds) in activity and selectivity for C2+ , together with greatly boosted stability (>80 h). This work gives a new avenue to rationally design more advanced Cu-based heteronanostructures for CER.

Insights into the architecture of human-induced polygenic selection in Duroc pigs.

BACKGROUND: As one of the most utilized commercial composite boar lines, Duroc pigs have been introduced to China and undergone strongly human-induced selection over the past decades. However, the efficiencies and limitations of previous breeding of Chinese Duroc pigs are largely understudied. The objective of this study was to uncover directional polygenic selection in the Duroc pig genome, and investigate points overlooked in the past breeding process. RESULTS: Here, we utilized the Generation Proxy Selection Mapping (GPSM) on a dataset of 1067 Duroc pigs with 8,766,074 imputed SNPs. GPSM detected a total of 5649 putative SNPs actively under selection in the Chinese Duroc pig population, and the potential functions of the selection regions were mainly related to production, meat and carcass traits. Meanwhile, we observed that the allele frequency of variants related to teat number (NT) relevant traits was also changed, which might be influenced by genes that had pleiotropic effects. First, we identified the direction of selection on NT traits by [Formula: see text], and further pinpointed large-effect genomic regions associated with NT relevant traits by selection signature and GWAS. Combining results of NT relevant traits-specific selection signatures and GWAS, we found three common genome regions, which were overlapped with QTLs related to production, meat and carcass traits besides "teat number" QTLs. This implied that there were some pleiotropic variants underlying NT and economic traits. We further found that rs346331089 has pleiotropic effects on NT and economic traits, e.g., litter size at weaning (LSW), litter weight at weaning (LWW), days to 100 kg (D100), backfat thickness at 100 kg (B100), and loin muscle area at 100 kg (L100) traits. CONCLUSIONS: The selected loci that we identified across methods displayed the past breeding process of Chinese Duroc pigs, and our findings could be used to inform future breeding decision.

Heterozygosity and homozygosity regions affect reproductive success and the loss of reproduction: A case study with litter traits in pigs.

Runs of heterozygosity (ROHet) and homozygosity (ROH) harbor useful information related to traits of interest. There is a lack of investigating the effect of ROHet and ROH on reproductive success and the loss of reproduction in mammals. Here, we detected and characterized the ROHet and ROH patterns in the genomes of Chinese indigenous pigs (i.e., Jinhua, Chun'an, Longyou Black, and Shengxian Spotted pigs), revealing the similar genetic characteristics of indigenous pigs. Later, we highlighted the underlying litter traits-related ROHet and ROH using association analysis with linear model in these four indigenous pig breeds. To pinpoint the promising candidate genes associated with litter traits, we further in-depth explore the selection patterns of other five pig breeds (i.e., Erhualian, Meishan, Minzhu, Rongchang, and Diqing pigs) with different levels of reproduction performance at the underlying litter traits-related ROHet and ROH using F ST and genetic diversity ratio. Then, we identified a set of known and novel candidate genes associated with reproductive performance in pigs. For the novel candidate genes (i.e., CCDC91, SASH1, SAMD5, MACF1, MFSD2A, EPC2, and MBD5), we obtained public available datasets and performed multi-omics analyses integrating transcriptome-wide association studies and comparative single-cell RNA-seq analyses to uncover the roles of them in mammalian reproductive performance. The genes have not been widely reported to be fertility-related genes and can be complementally considered as prior biological information to modify genomic selections models that benefits pig genetic improvement of litter traits. Besides, our findings provide new insights into the function of ROHet and ROH in mammals.

Defect Engineering of Ceria Nanocrystals for Enhanced Catalysis via a High-Entropy Oxide Strategy.

Introducing transition-metal components to ceria (CeO2) is important to tailor the surface redox properties for a broad scope of applications. The emergence of high-entropy oxides (HEOs) has brought transformative opportunities for oxygen defect engineering in ceria yet has been hindered by the difficulty in controllably introducing transition metals to the bulk lattice of ceria. Here, we report the fabrication of ceria-based nanocrystals with surface-confined atomic HEO layers for enhanced catalysis. The increased covalency of the transition-metal-oxygen bonds at the HEO-CeO2 interface promotes the formation of surface oxygen vacancies, enabling efficient oxygen activation and replenishment for enhanced CO oxidation capabilities. Understanding the structural heterogeneity involving bulk and surface oxygen defects in nanostructured HEOs provides useful insights into rational design of atomically precise metal oxides, whose increased compositional and structural complexities give rise to expanded functionalities.

PHARP: a pig haplotype reference panel for genotype imputation.

Pigs not only function as a major meat source worldwide but also are commonly used as an animal model for studying human complex traits. A large haplotype reference panel has been used to facilitate efficient phasing and imputation of relatively sparse genome-wide microarray chips and low-coverage sequencing data. Using the imputed genotypes in the downstream analysis, such as GWASs, TWASs, eQTL mapping and genomic prediction (GS), is beneficial for obtaining novel findings. However, currently, there is still a lack of publicly available and high-quality pig reference panels with large sample sizes and high diversity, which greatly limits the application of genotype imputation in pigs. In response, we built the pig Haplotype Reference Panel (PHARP) database. PHARP provides a reference panel of 2012 pig haplotypes at 34 million SNPs constructed using whole-genome sequence data from more than 49 studies of 71 pig breeds. It also provides Web-based analytical tools that allow researchers to carry out phasing and imputation consistently and efficiently. PHARP is freely accessible at http://alphaindex.zju.edu.cn/PHARP/index.php . We demonstrate its applicability for pig commercial 50 K SNP arrays, by accurately imputing 2.6 billion genotypes at a concordance rate value of 0.971 in 81 Large White pigs (~ 17 × sequencing coverage). We also applied our reference panel to impute the low-density SNP chip into the high-density data for three GWASs and found novel significantly associated SNPs that might be casual variants.

Genetic diversity and selection signatures of four indigenous pig breeds from eastern China.

Chinese indigenous pig breeds have been undergoing selection for thousands of years, and have become invaluable genetic sources over the world. To investigate the population structure and genetic diversity of Jinhua (JH), Longyou Black (LYW), Shengxian Spotted (SXH), and Lanxi Spotted (LXH) breeds, a total of 200 pigs belonging to 10 diverse population were genotyped using SNP chips. The results showed that LYW pigs exhibited higher level of heterozygosity than the other indigenous pigs. In addition, gene introgression from intensively reared commercial pig breeds to LYW pigs was detected. Moreover, selection signature analysis revealed the possibility of differences between Chinese indigenous and intensively reared commercial pig breeds were mainly present for meat and carcass traits. Furthermore, we found that ANXA13, DISP1, and SRSF6 were the nearest genes located around the common selection signatures detected between each indigenous pig breed and Chinese wild boars. Our findings provide new insights into the selection signatures of Chinese indigenous pigs, and may contribute to future pig breeding.

Mn2+-activated dual-wavelength emitting materials toward wearable optical fibre temperature sensor.

Photothermal sensing is crucial for the creation of smart wearable devices. However, the discovery of luminescent materials with suitable dual-wavelength emissions is a great challenge for the construction of stable wearable optical fibre temperature sensors. Benefiting from the Mn2+-Mn2+ superexchange interactions, a dual-wavelength (530/650 nm)-emitting material Li2ZnSiO4:Mn2+ is presented via simple increasing the Mn2+ concentration, wherein the two emission bands have different temperature-dependent emission behaviours, but exhibit quite similar excitation spectra. Density functional theory calculations, coupled with extended X-ray absorption fine structure and electron-diffraction analyses reveal the origins of the two emission bands in this material. A wearable optical temperature sensor is fabricated by incorporating Li2ZnSiO4:Mn2+ in stretchable elastomer-based optical fibres, which can provide thermal-sensitive emissions at dual- wavelengths for stable ratiometric temperature sensing with good precision and repeatability. More importantly, a wearable mask integrated with this stretchable fibre sensor is demonstrated for the detection of physiological thermal changes, showing great potential for use as a wearable health monitor. This study also provides a framework for creating transition-metal-activated luminescence materials.