3D Printable Polymeric Composite Material with Enhanced Conductivity Achieved by Affinity Matching.

Polymer-based conductive composites are lightweight, low-cost, and easily processable materials with important applications in various fields. However, achieving highly conductive and 3D printable polymer-based conductive composites remains challenging. In this study, we successfully developed a highly conductive composite suitable for direct ink writing 3D printing using unsaturated polyester resin as the polymer matrix and graphene nanosheets as conductive fillers and rheological modifiers. Due to the well-matched affinity between graphene nanosheets and unsaturated polyester, the graphene nanosheets aggregate within the unsaturated polyester, forming a 3D conductive network. Moreover, the shearing force during direct ink writing 3D printing induces the orientation of the 2D graphene nanosheets, significantly enhancing their conductivity along the printing direction. At room temperature, the unsaturated polyester resin/graphene composite shows a high conductivity of 69.9 S m-1 while maintaining excellent 3D printability. Structures printed using this material exhibit improved heat dissipation and electromagnetic shielding performance. The reported unsaturated polyester resin/graphene nanosheet composites demonstrate outstanding electrical and heat conductivity and excellent processability, making them promising candidates for applications in electromagnetic shielding, printed electronics, and other fields.

Deletion of ASPP1 in myofibroblasts alleviates myocardial fibrosis by reducing p53 degradation.

In the healing process of myocardial infarction, cardiac fibroblasts are activated to produce collagen, leading to adverse remodeling and heart failure. Our previous study showed that ASPP1 promotes cardiomyocyte apoptosis by enhancing the nuclear trafficking of p53. We thus explored the influence of ASPP1 on myocardial fibrosis and the underlying mechanisms. Here, we observed that ASPP1 was increased after 4 weeks of MI. Both global and myofibroblast knockout of ASPP1 in mice mitigated cardiac dysfunction and fibrosis after MI. Strikingly, ASPP1 produced the opposite influence on p53 level and cell fate in cardiac fibroblasts and cardiomyocytes. Knockdown of ASPP1 increased p53 levels and inhibited the activity of cardiac fibroblasts. ASPP1 accumulated in the cytoplasm of fibroblasts while the level of p53 was reduced following TGF-ß1 stimulation; however, inhibition of ASPP1 increased the p53 level and promoted p53 nuclear translocation. Mechanistically, ASPP1 is directly bound to deubiquitinase OTUB1, thereby promoting the ubiquitination and degradation of p53, attenuating myofibroblast activity and cardiac fibrosis, and improving heart function after MI.

Hypothesis tests in ordinal predictive models with optimal accuracy.

In real-world applications involving multi-class ordinal discrimination, a common approach is to aggregate multiple predictive variables into a linear combination, aiming to develop a classifier with high prediction accuracy. Assessment of such multi-class classifiers often utilizes the hypervolume under ROC manifolds (HUM). When dealing with a substantial pool of potential predictors and achieving optimal HUM, it becomes imperative to conduct appropriate statistical inference. However, prevalent methodologies in existing literature are computationally expensive. We propose to use the jackknife empirical likelihood method to address this issue. The Wilks' theorem under moderate conditions is established and the power analysis under the Pitman alternative is provided. We also introduce a novel network-based rapid computation algorithm specifically designed for computing a general multi-sample $U$-statistic in our test procedure. To compare our approach against existing approaches, we conduct extensive simulations. Results demonstrate the superior performance of our method in terms of test size, power, and implementation time. Furthermore, we apply our method to analyze a real medical dataset and obtain some new findings.

Silanol-Stabilized Atomically Dispersed Ptδ+-Ox-Sn Active Sites in Protozeolite for Propane Dehydrogenation.

Crystalline zeolites have been proven to be excellent supports for confining subnanometric metal catalysts to boost the propane dehydrogenation (PDH) reaction. However, the introduced metallic species may suffer from severe sintering and limited stability during the catalytic process, especially when utilizing an industrial impregnation method for metal incorporation. In this study, we developed a new type of support based on amorphous protozeolite (PZ), taking advantage of its adjustable silanol chemistry and zeolitic microporous characteristic for stabilizing atomically dispersed PtSn catalyst via a simple, cost-effective coimpregnation process. The combination of X-ray absorption spectroscopy, X-ray photoelectron spectroscopy, in situ diffuse reflectance infrared Fourier transform spectroscopy under CO atmosphere, and density functional theory calculations confirmed the formation of highly dispersed active Ptδ+-Ox-Sn species in PtSn/PZ. The PtSn/PZ catalyst exhibited a high propane conversion of 45.4% and a high propylene selectivity of 99% (WHSV= 3.6 h-1, 550 °C), with a high apparent rate coefficient of 565 molC3H6·gPt-1·h-1·bar-1 at a high WHSV of 108 h-1, presenting a top-level performance among the state-of-the-art Pt-based catalysts prepared by in situ synthesis and impregnation methods. The silanol density determined the chemical state of PtSn species, showing a change from atomically dispersed Ptδ+-Ox-Sn sites to PtSn alloy with decreasing silanol density of supports. This work provides a general strategy using silanol-rich amorphous protozeolite as support for stabilizing various metal catalysts by the simple impregnation method and also offers an effective way for fine tailoring the chemical state of metallic species via a silanol-engineered approach.

Eco-friendly versatile shielding revolution: Tannin tailored bamboo waste composite with wave-absorbing, flame retardancy, and antibacterial abilities.

The swift evolution of fifth-generation technology has intensified the need for lightweight, high-efficiency, and low-reflection multifunctional electromagnetic interference shielding materials, crucial in combating escalating electromagnetic pollution in complex application environments. To tackle these challenges, an innovative solution has emerged: a biocomposite crafted from discarded bamboo materials. This innovation incorporates a meticulously engineered functional coating composed of tannic acid, boric acid, and polyvinyl alcohol. Additionally, the integration of highly conductive Ti3C2Tx (MXene) nanosheets onto the surface of bamboo powders enhances the EMI shielding efficiency of composites, achieving an impressive ∼40.9 dB. Meanwhile, significant improvements in mechanical reinforcement have been achieved, along with increases in the relative values of key performance indicators: tensile strength (89.8 %), tensile modulus (79.6 %), flexural strength (51.6 %), flexural modulus (35.1 %), and impact strength (45.4 %). Furthermore, the introduction of functional components grants the composite exceptional flame retardancy and antibacterial properties against both Gram-negative and Gram-positive bacteria. Beyond these strides, the utilization of bamboo waste as a composite pioneer a paradigm shift in waste utilization, converting refuse into invaluable resources.

Full electrical manipulation of perpendicular exchange bias in ultrathin antiferromagnetic film with epitaxial strain.

Achieving effective manipulation of perpendicular exchange bias effect remains an intricate endeavor, yet it stands a significance for the evolution of ultra-high capacity and energy-efficient magnetic memory and logic devices. A persistent impediment to its practical applications is the reliance on external magnetic fields during the current-induced switching of exchange bias in perpendicularly magnetized structures. This study elucidates the achievement of a full electrical manipulation of the perpendicular exchange bias in the multilayers with an ultrathin antiferromagnetic layer. Owing to the anisotropic epitaxial strain in the 2-nm-thick IrMn3 layer, the considerable exchange bias effect is clearly achieved at room temperature. Concomitantly, a specific global uncompensated magnetization manifests in the IrMn3 layer, facilitating the switching of the irreversible portion of the uncompensated magnetization. Consequently, the perpendicular exchange bias can be manipulated by only applying pulsed current, notably independent of the presence of any external magnetic fields.

High internal phase emulsions gel ink for direct-ink-writing 3D printing of liquid metal.

3D printing of liquid metal remains a big challenge due to its low viscosity and large surface tension. In this study, we use Carbopol hydrogel and liquid gallium-indium alloy to prepare a liquid metal high internal phase emulsion gel ink, which can be used for direct-ink-writing 3D printing. The high volume fraction (up to 82.5%) of the liquid metal dispersed phase gives the ink excellent elastic properties, while the Carbopol hydrogel, as the continuous phase, provides lubrication for the liquid metal droplets, ensuring smooth flow of the ink during shear extrusion. These enable high-resolution and shape-stable 3D printing of three-dimensional structures. Moreover, the liquid metal droplets exhibit an electrocapillary phenomenon in the Carbopol hydrogel, which allows for demulsification by an electric field and enables electrical connectivity between droplets. We have also achieved the printing of ink on flexible, non-planar structures, and demonstrated the potential for alternating printing with various materials.

Renewable risk assessment of heterogeneous streaming time-to-event cohorts.

The analysis of streaming time-to-event cohorts has garnered significant research attention. Most existing methods require observed cohorts from a study sequence to be independent and identically sampled from a common model. This assumption may be easily violated in practice. Our methodology operates within the framework of online data updating, where risk estimates for each cohort of interest are continuously refreshed using the latest observations and historical summary statistics. At each streaming stage, we introduce parameters to quantify the potential discrepancy between batch-specific effects from adjacent cohorts. We then employ penalized estimation techniques to identify nonzero discrepancy parameters, allowing us to adaptively adjust risk estimates based on current data and historical trends. We illustrate our proposed method through extensive empirical simulations and a lung cancer data analysis.

Efficient purging of deleterious mutations contributes to the survival of a rare conifer.

Cupressaceae is a conifer family rich in plants of horticultural importance, including Cupressus, Chamaecyparis, Juniperus, and Thuja, yet genomic surveys are lacking for this family. Cupressus gigantea, one of the many rare conifers that are threatened by climate change and anthropogenic habitat fragmentation, plays an ever-increasing role in ecotourism in Tibet. To infer how past climate change has shaped the population evolution of this species, we generated a de novo chromosome-scale genome (10.92 Gb) and compared the species' population history and genetic load with that of a widespread close relative, C. duclouxiana. Our demographic analyses, based on 83 re-sequenced individuals from multiple populations of the two species, revealed a sharp decline of population sizes during the first part of the Quaternary. However, populations of C. duclouxiana then started to recover, while C. gigantea populations continued to decrease until recently. The total genomic diversity of C. gigantea is smaller than that of C. duclouxiana, but contrary to expectations, C. gigantea has fewer highly and mildly deleterious mutations than C. duclouxiana, and simulations and statistical tests support purifying selection during prolonged inbreeding as the explanation. Our results highlight the evolutionary consequences of decreased population size on the genetic burden of a long-lived endangered conifer with large genome size and suggest that genetic purging deserves more attention in conservation management.

Transition probabilities of diabetic retinopathy and death in an Asian population with diabetes.

PURPOSE: To evaluate the dynamic transitions in diabetic retinopathy (DR) severity over time and associated risk factors in an Asian population with diabetes. DESIGN: Longitudinal cohort study METHODS: We analyzed data from 9481 adults in the Singapore Integrated Diabetic Retinopathy Screening Program (2010-2015) with linkage to death registry. A multistate Markov model adjusted for age, sex, systolic blood pressure (SBP), diabetes duration, HbA1c, and body mass index (BMI) was applied to estimate annual transition probabilities between four DR states (no, mild, moderate, and severe/proliferative) and death, and the mean sojourn time in each state. RESULTS: The median assessment interval was 12 months, with most patients having 3 assessments. Annual probabilities for DR progression (no-to-mild, mild-to-moderate and moderate-to-severe/proliferative) were 6.1 %, 7.0 % and 19.3 %, respectively; and for regression (mild-to-no, moderate-to-mild and severe-to-moderate) were 55.4 %, 17.3 % and 4.4 %, respectively. Annual mortality rates from each DR state were 1.2 %, 2.0 %, 18.7 %, and 30.0 %. The sojourn time in each state were 8.2, 0.8, 0.8 and 2.2 years. Higher HbA1c and SBP levels were associated with progression of no-mild and mild-moderate DR, and diabetes duration with no-to-mild and moderate-to-severe/proliferative DR. Lower HbA1c levels were associated with regression from mild-to-no and moderate-to-mild, and higher BMI with mild-to-no DR. CONCLUSIONS: Our results suggest a prolonged duration (∼8 years) in developing mild DR, with faster transitions (within a year) from mild or moderate states. Moderate/above DR greatly increases the probability of progression and death as compared to mild DR/below. HbA1c was associated with both progression as well as regression.

Across two phylogeographic breaks: Quaternary evolutionary history of a mountain aspen (Populus rotundifolia) in the Hengduan Mountains.

Biogeographical barriers to gene flow are central to plant phylogeography. In East Asia, plant distribution is greatly influenced by two phylogeographic breaks, the Mekong-Salween Divide and Tanaka-Kaiyong Line, however, few studies have investigated how these barriers affect the genetic diversity of species that are distributed across both. Here we used 14 microsatellite loci and four chloroplast DNA fragments to examine genetic diversity and distribution patterns of 49 populations of Populus rotundifolia, a species that spans both the Mekong-Salween Divide and the Tanaka-Kaiyong Line in southwestern China. Demographic and migration hypotheses were tested using coalescent-based approaches. Limited historical gene flow was observed between the western and eastern groups of P. rotundifolia, but substantial flow occurred across both the Mekong-Salween Divide and Tanaka-Kaiyong Line, manifesting in clear admixture and high genetic diversity in the central group. Wind-borne pollen and seeds may have facilitated the dispersal of P. rotundifolia following prevalent northwest winds in the spring. We also found that the Hengduan Mountains, where multiple genetic barriers were detected, acted on the whole as a barrier between the western and eastern groups of P. rotundifolia. Ecological niche modeling suggested that P. rotundifolia has undergone range expansion since the last glacial maximum, and demographic reconstruction indicated an earlier population expansion around 600 Ka. The phylogeographic pattern of P. rotundifolia reflects the interplay of biological traits, wind patterns, barriers, niche differentiation, and Quaternary climate history. This study emphasizes the need for multiple lines of evidence in understanding the Quaternary evolution of plants in topographically complex areas.

CureAuxSP: An R package for estimating mixture cure models with auxiliary survival probabilities.

BACKGROUND AND OBJECTIVE: There is a rising interest in exploiting aggregate information from external medical studies to enhance the statistical analysis of a modestly sized internal dataset. Currently available software packages for analyzing survival data with a cure fraction ignore the potentially available auxiliary information. This paper aims at filling this gap by developing a new R package CureAuxSP that can include subgroup survival probabilities extracted outside into an interested internal survival dataset. METHODS: The newly developed R package CureAuxSP provides an efficient approach for information synthesis under the mixture cure models, including Cox proportional hazards mixture cure model and the accelerated failure time mixture cure model as special cases. It focuses on synthesizing subgroup survival probabilities at multiple time points and the underlying method development lies in the control variate technique. Evaluation of homogeneity assumption based on a test statistic can be automatically carried out by our package and if heterogeneity does exist, the original outputs can be further refined adaptively. RESULTS: The R package CureAuxSP provides a main function SMC.AxuSP() that helps us adaptively incorporate external subgroup survival probabilities into the analysis of an internal survival data. We also provide another function Print.SMC.AuxSP() for printing the results with a better presentation. Detailed usages are described, and implementations are illustrated with numerical examples, including a simulated dataset with a well-designed data generating process and a real breast cancer dataset. Substantial efficiency gain can be observed by our results. CONCLUSIONS: Our R package CureAuxSP can make the wide applications of utilizing auxiliary information possible. It is anticipated that the performance of mixture cure models can be improved for the survival data with a cure fraction, especially for those with small sample sizes.

Spexin Diminishes Atrial Fibrillation Vulnerability by Acting on Galanin Receptor 2.

BACKGROUND: G protein-coupled receptors play a critical role in atrial fibrillation (AF). Spexin is a novel ligand of galanin receptors (GALRs). In this study, we investigated the regulation of spexin and GALRs on AF and the underlying mechanisms. METHODS: Global spexin knockout (SPX-KO) and cardiomyocyte-specific GALRs knockout (GALR-cKO) mice underwent burst pacing electrical stimulation. Optical mapping was used to determine atrial conduction velocity and action potential duration. Atrial myocyte action potential duration and inward rectifying K+ current (IK1) were recorded using whole-cell patch clamps. Isolated cardiomyocytes were stained with Fluo-3/AM dye, and intracellular Ca2+ handling was examined by CCD camera. A mouse model of AF was established by Ang-II (angiotensin II) infusion. RESULTS: Spexin plasma levels in patients with AF were lower than those in subjects without AF, and knockout of spexin increased AF susceptibility in mice. In the atrium of SPX-KO mice, potassium inwardly rectifying channel subfamily J member 2 (KCNJ2) and sarcolipin (SLN) were upregulated; meanwhile, IK1 current was increased and Ca2+ handling was impaired in isolated atrial myocytes of SPX-KO mice. GALR2-cKO mice, but not GALR1-cKO and GALR3-cKO mice, had a higher incidence of AF, which was associated with higher IK1 current and intracellular Ca2+ overload. The phosphorylation level of CREB (cyclic AMP responsive element binding protein 1) was upregulated in atrial tissues of SPX-KO and GALR2-cKO mice. Chromatin immunoprecipitation confirmed the recruitment of p-CREB to the proximal promoter regions of KCNJ2 and SLN. Finally, spexin treatment suppressed CREB signaling, decreased IK1 current and decreased intracellular Ca2+ overload, which thus reduced the inducibility of AF in Ang-II-infused mice. CONCLUSIONS: Spexin reduces atrial fibrillation susceptibility by inhibiting CREB phosphorylation and thus downregulating KCNJ2 and SLN transcription by GALR2 receptor. The spexin/GALR2/CREB signaling pathway represents a novel therapeutic avenue in the development of agents against atrial fibrillation.

Effect of Mo and Cr on the Microstructure and Properties of Low-Alloy Wear-Resistant Steels.

Low-alloy wear-resistant steel often requires the addition of trace alloy elements to enhance its performance while also considering the cost-effectiveness of production. In order to comparatively analyze the strengthening mechanisms of Mo and Cr elements and further explore economically feasible production processes, we designed two types of low-alloy wear-resistant steels, based on C-Mn series wear-resistant steels, with individually added Mo and Cr elements, comparing and investigating the roles of the alloying elements Mo and Cr in low-alloy wear-resistant steels. Utilizing JMatPro software to calculate Continuous Cooling Transformation (CCT) curves, conducting thermal simulation quenching experiments using a Gleeble-3800 thermal simulator, and employing equipment such as a metallographic microscope, transmission electron microscope, and tensile testing machine, this study comparatively investigated the influence of Mo and Cr on the microstructural transformation and mechanical properties of low-alloy wear-resistant steels under different cooling rates. The results indicate that the addition of the Mo element in low-alloy wear-resistant steel can effectively suppress the transformation of ferrite and pearlite, reduce the martensitic transformation temperature, and lower the critical cooling rate for complete martensitic transformation, thereby promoting martensitic transformation. Adding Cr elements can reduce the austenite transformation zone, decrease the rate of austenite formation, and promote the occurrence of low-temperature phase transformation. Additionally, Mo has a better effect on improving the toughness of low-temperature impact, and Cr has a more significant improvement in strength and hardness. The critical cooling rates of C-Mn-Mo steel and C-Mn-Cr steel for complete martensitic transition are 13 °C/s and 24 °C/s, respectively. With the increase in the cooling rate, the martensitic tissues of the two experimental steels gradually refined, and the characteristics of the slats gradually appeared. In comparison, the C-Mn-Mo steel displays a higher dislocation density, accompanied by dislocation entanglement phenomena, and contains a small amount of residual austenite, while granular ε-carbides are clearly precipitated in the C-Mn-Cr steel. The C-Mn-Mo steel achieves its best performance at a cooling rate of 25 °C/s, whereas the C-Mn-Cr steel only needs to increase the cooling rate to 35 °C/s to attain a similar comprehensive performance to the C-Mn-Mo steel.

Upcycling of polyethylene to gasoline through a self-supplied hydrogen strategy in a layered self-pillared zeolite.

Conversion of plastic wastes to valuable carbon resources without using noble metal catalysts or external hydrogen remains a challenging task. Here we report a layered self-pillared zeolite that enables the conversion of polyethylene to gasoline with a remarkable selectivity of 99% and yields of >80% in 4 h at 240 °C. The liquid product is primarily composed of branched alkanes (selectivity of 72%), affording a high research octane number of 88.0 that is comparable to commercial gasoline (86.6). In situ inelastic neutron scattering, small-angle neutron scattering, solid-state nuclear magnetic resonance, X-ray absorption spectroscopy and isotope-labelling experiments reveal that the activation of polyethylene is promoted by the open framework tri-coordinated Al sites of the zeolite, followed by ß-scission and isomerization on Brönsted acids sites, accompanied by hydride transfer over open framework tri-coordinated Al sites through a self-supplied hydrogen pathway to yield selectivity to branched alkanes. This study shows the potential of layered zeolite materials in enabling the upcycling of plastic wastes.

The coupling coordination characteristics of China's health production efficiency and new urbanization and its influencing factors.

Urbanization leads to dramatic changes in habitat quality, which significantly affects population health. Research on the coupling coordination relationship between new urbanization and health production efficiency is conducive to improving residents' well-being and urban sustainable development. In this article, we adopted the super-efficient SBM model and entropy value method separately to evaluate the spatiotemporal variation characteristics of health production efficiency and new urbanization in China. Then, we used the coupling coordination degree model to investigate the interactive coercing relationship between new urbanization and health production efficiency. Finally, the panel Tobit model is used to analyze the factors influencing the coupled coordination of the two systems. The results showed that the new urbanization levels of 31 provinces in China have all steadily increased from 2003 to 2018. Health production efficiency exhibited a fluctuating but increasing trend, and its regional differences are gradually narrowing. Health production efficiency and new urbanization have developed in a more coordinated direction, with a spatial pattern of "high in the southeast and low in the northwest." Meanwhile, the relative development characteristics between the two systems have constantly changed, from the new urbanization lagged type to the two systems synchronized type and the health production efficiency lagged type. Population density, economic development level, government financial investment, and government health investment positively impact the coupling coordination degree of the two systems. In comparison, individual health investment harms the harmonization of the two systems.

Quantum random number generation based on phase reconstruction.

Quantum random number generator (QRNG) utilizes the intrinsic randomness of quantum systems to generate completely unpredictable and genuine random numbers, finding wide applications across many fields. QRNGs relying on the phase noise of a laser have attracted considerable attention due to their straightforward system architecture and high random number generation rates. However, traditional phase noise QRNGs suffer from a 50% loss of quantum entropy during the randomness extraction process. In this paper, we propose a phase-reconstruction quantum random number generation scheme, in which the phase noise of a laser is reconstructed by simultaneously measuring the orthogonal quadratures of the light field using balanced detectors. This enables direct discretization of uniform phase noise, and the min-entropy can achieve a value of 1. Furthermore, our approach exhibits inherent robustness against the classical phase fluctuations of the unbalanced interferometer, eliminating the need for active compensation. Finally, we conducted experimental validation using commercial optical hybrid and balanced detectors, achieving a random number generation rate of 1.96 Gbps at a sampling rate of 200 MSa/s.

Development and External Validation of Machine Learning Models for Diabetic Microvascular Complications: Cross-Sectional Study With Metabolites.

BACKGROUND: Diabetic kidney disease (DKD) and diabetic retinopathy (DR) are major diabetic microvascular complications, contributing significantly to morbidity, disability, and mortality worldwide. The kidney and the eye, having similar microvascular structures and physiological and pathogenic features, may experience similar metabolic changes in diabetes. OBJECTIVE: This study aimed to use machine learning (ML) methods integrated with metabolic data to identify biomarkers associated with DKD and DR in a multiethnic Asian population with diabetes, as well as to improve the performance of DKD and DR detection models beyond traditional risk factors. METHODS: We used ML algorithms (logistic regression [LR] with Least Absolute Shrinkage and Selection Operator and gradient-boosting decision tree) to analyze 2772 adults with diabetes from the Singapore Epidemiology of Eye Diseases study, a population-based cross-sectional study conducted in Singapore (2004-2011). From 220 circulating metabolites and 19 risk factors, we selected the most important variables associated with DKD (defined as an estimated glomerular filtration rate <60 mL/min/1.73 m2) and DR (defined as an Early Treatment Diabetic Retinopathy Study severity level ≥20). DKD and DR detection models were developed based on the variable selection results and externally validated on a sample of 5843 participants with diabetes from the UK biobank (2007-2010). Machine-learned model performance (area under the receiver operating characteristic curve [AUC] with 95% CI, sensitivity, and specificity) was compared to that of traditional LR adjusted for age, sex, diabetes duration, hemoglobin A1c, systolic blood pressure, and BMI. RESULTS: Singapore Epidemiology of Eye Diseases participants had a median age of 61.7 (IQR 53.5-69.4) years, with 49.1% (1361/2772) being women, 20.2% (555/2753) having DKD, and 25.4% (685/2693) having DR. UK biobank participants had a median age of 61.0 (IQR 55.0-65.0) years, with 35.8% (2090/5843) being women, 6.7% (374/5570) having DKD, and 6.1% (355/5843) having DR. The ML algorithms identified diabetes duration, insulin usage, age, and tyrosine as the most important factors of both DKD and DR. DKD was additionally associated with cardiovascular disease history, antihypertensive medication use, and 3 metabolites (lactate, citrate, and cholesterol esters to total lipids ratio in intermediate-density lipoprotein), while DR was additionally associated with hemoglobin A1c, blood glucose, pulse pressure, and alanine. Machine-learned models for DKD and DR detection outperformed traditional LR models in both internal (AUC 0.838 vs 0.743 for DKD and 0.790 vs 0.764 for DR) and external validation (AUC 0.791 vs 0.691 for DKD and 0.778 vs 0.760 for DR). CONCLUSIONS: This study highlighted diabetes duration, insulin usage, age, and circulating tyrosine as important factors in detecting DKD and DR. The integration of ML with biomedical big data enables biomarker discovery and improves disease detection beyond traditional risk factors.

Creation of Room-Temperature Sub-100 nm Antiferromagnetic Skyrmions in an Antiferromagnet IrMn through Interfacial Exchange Coupling.

Antiferromagnetic (AFM) skyrmions are magnetic vortices composed of antiparallell-aligned neighboring spins. In stark contrast to conventional skyrmions based on ferromagnetic order, AFM skyrmions have vanished stray fields, higher response frequencies, and rectified translational motion driven by an external force. Therefore, AFM skyrmions promise highly efficient spintronics devices with high bit mobility and density. Nevertheless, the experimental realization of intrinsic AFM skyrmions remains elusive. Here, we show that AFM skyrmions can be nucleated via interfacial exchange coupling at the surface of a room-temperature AFM material, IrMn, exploiting the particular response from uncompensated moments to the thermal annealing and imprinting effects. Further systematic magnetic characterizations validate the existence of such an AFM order at the IrMn/CoFeB interfaces. Such AFM skyrmions have a typical size of 100 nm, which presents pronounced robustness against field and temperature. Our work opens new pathways for magnetic topological devices based on AFM skyrmions.

Conservation genomic investigation of an endangered conifer, Thuja sutchuenensis, reveals low genetic diversity but also low genetic load.

Endangered species generally have small populations with low genetic diversity and a high genetic load. Thuja sutchuenensis is an endangered conifer endemic to southwestern China. It was once considered extinct in the wild, but in 1999 was rediscovered. However, little is known about its genetic load. We collected 67 individuals from five wild, isolated T. sutchuenensis populations, and used 636,151 SNPs to analyze the level of genetic diversity and genetic load in T. sutchuenensis to delineate the conservation units of T. sutchuenensis, based on whole transcriptome sequencing data, as well as target capture sequencing data. We found that populations of T. sutchuenensis could be divided into three groups. These groups had low levels genetic diversity and were moderately genetically differentiated. Our findings also indicate that T. sutchuenensis suffered two severe bottlenecks around the Last Glaciation Period and Last Glacial Maximum. Among Thuja species, T. sutchuenensis presented the lowest genetic load and hence might have purged deleterious mutations efficiently through purifying selection. However, distribution of fitness effects analysis indicated a high extinction risk for T. sutchuenensis. Multiple lines of evidence identified three management units for T. sutchuenensis. Although T. sutchuenensis possesses a low genetic load, low genetic diversity, suboptimal fitness, and anthropogenic pressures all present an extinction risk for this rare conifer. This might also hold true for many endangered plant species in the mountains all over the world.