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.

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.

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.

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.

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.

Integrated evaluation of biomechanical and biological properties of the biomimetic structural bone scaffold: Biomechanics, simulation analysis, and osteogenesis.

A porous structure is essential for bone implants because it increases the bone ingrowth space and improves mechanical and biological properties. The biomimetically designed porous Voronoi scaffold can reconstruct the structure and function of cancellous bone; however, its comprehensive properties need to be investigated further. In this study, algorithms based on scaling factors were used to design the Voronoi scaffolds. Classic approaches, such as computer-aided design and the implicit surface method, have been used to design Diamond, Gyroid, and I-WP scaffolds as controls. All scaffolds were prepared by selective laser melting of titanium alloys and three-dimensional printing. Mechanical tests, finite element analysis, and in vitro and in vivo experiments were performed to investigate the biomechanical, cytologic, and osteogenic performance of the scaffolds, while computational fluid dynamics simulations were used to explore the underlying mechanisms. Diamond scaffolds have a better loading capacity, and the mechanical behaviors and fluid flow of Voronoi scaffolds are similar to those of the human trabecular bone. Cells showed more proliferation and distribution on the Diamond and Voronoi scaffolds and exhibited evident differentiation on Gyroid and Voronoi scaffolds. Bone formation was apparent on the inner part of the Gyroid, the outer part of the I-WP, and the entire Diamond and Voronoi scaffolds. The hydrodynamic properties and stimulus response of cells influenced by the porous structure account for the varied biological performance of the scaffolds. The Voronoi scaffolds with bionic mechanical behavior and an appropriate hydrodynamic response exhibit evident cell growth and osteogenesis, making them preferable for porous structural bone implants.

A meta-analysis of the efficacy and safety of the traditional Chinese medicine formula Kaixinsan decoction for depression.

BACKGROUND: Kaixinsan (KXS) decoction is a traditional Chinese herbal formulation commonly used to treat depression. This study aimed to evaluate the efficacy and safety of KXS, which is widely used, alone and in combination with other therapies, for the treatment of depression. The main objective was to assess the efficacy and safety of KXS in the treatment of depression as a single agent or in combination with other methods. METHODS: Randomized controlled trials of KXS in the treatment of depression were systematically searched from several Chinese and English databases with no language restriction. Patients in these studies met the relevant diagnostic criteria for depression. Data on HAMD, SDS, practical situations, and occurrence of side effects in the studies were extracted. Finally, the methodological quality and risk of bias of the included studies were assessed using the Jadad scale and Cochrane bias evaluation tool. RESULTS: Twelve studies with 1034 patients were included after screening. The Jadad scale and Cochrane bias evaluation tool indicated that the quality of the studies ranged from fair to good, with 41.7% categorized as good and 58.3% as poor. Egger test and funnel plots showed that the publication bias remain low. CONCLUSION: The results showed that the frequency of side effects in the control group was higher than that in the treatment group, and there was a statistically significant difference. KXS was comparable or superior to antidepressants in treating depression and has fewer side effects. The data analysis showed that effectiveness and other indicators differed significantly by geographic area and dosage form, which has implications for future clinical work.

Tough and Self-Healing Waterborne Polyurethane Elastomers via Dynamic Hydrogen Bonds Design for Flexible Conductive Substrate Applications.

Balancing the mechanical strength and self-healing performance of polyurethane (PU) remains a significant challenge in achieving excellent self-repairing PU materials. In this study, a self-healing waterborne PU elastomer was designed from a bionic concept by incorporating 2'-deoxythymidine (2'-dT) and isophorone diamine (IPDA) into the polymer chain. The loose stacking of IPDA's irregular cycloaliphatic structure resulted in the irregular arrangement of urethane bonds in the hard domain. The formation of sextuple hydrogen bonds between 2'-dT and urethane bonds, as well as quadruple hydrogen bonds between urethane bonds themselves, enhanced the mechanical properties of the material. The multiple hydrogen bonds can dissociate, recombine, and dissipate energy, thereby improving the material's repair capability. The hierarchical self-assembly of hydrogen bonds enabled the PU to achieve a tensile strength of 15.3 MPa and toughness of 100.75 MJ/m3. The prepared PU film is highly transparent and has a transmittance of more than 90%. Additionally, it can undergo rapid repair under high temperatures or under trace solvent conditions. When used as a flexible conductive substrate, it quickly restored the conductivity and enhanced the material's lifespan after surface damage. This environmentally friendly and self-healing waterborne PU elastomer will hold broad application prospects in the field of flexible electronic devices.

Outcomes of covered vs bare metal stents for the treatment of aortoiliac occlusive disease.

OBJECTIVE: We retrospectively compared the clinical outcomes of self-expanding covered stents (CSs) and bare metal stents (BMSs) in the treatment of aortoiliac occlusive disease (AIOD) at a single center between 2016 and 2022. METHODS: All patients with AIOD receiving endovascular therapy at a single center from January 2016 to October 2022 were continuously analyzed, including patients with lesions of all classes according to the Trans-Atlantic Inter-Society Consensus II (TASC-II). Relevant clinical and baseline data were collected, and propensity score matching was performed to compare CSs and BMSs in terms of baseline characteristics, surgical factors, 30-day outcomes, 5-year primary patency, and limb salvage. The follow-up results were analyzed by Kaplan-Meier curves. Cox proportional hazard models were used to identify predictors of primary patency. RESULTS: A total of 209 patients with AIOD were enrolled in the study, including 135 patients (64.6%) in the CS group and 74 patients (35.4%) in the BMS group. Surgical success rates (100% vs 100%; P = 1.00), early (<30-day) mortality rates (0% vs 0%; P = 1.00), cumulative surgical complication rate (12.0% vs 8.0%; P = .891), 5-year primary patency rate (83.4% vs 86.9%; P = .330), secondary patency rate (96% vs 100%; P = .570), and limb salvage rate (100% vs 100%; P = 1.00) did not exhibit significant differences between the two groups. Patients in the CS group had a lower preoperative ankle-brachial index (0.48 ± 0.26 vs 0.52 ± 0.19; P = .032), more cases of complex AIOD (especially TASC D) (47.4% vs 9.5%; P < .001), more chronic total occlusive lesions (77.0% vs 31.1%; P < .001), and more severe calcification (20.7% vs 14.9%; P < .036). After propensity score matching, 50 patients (25 with CS and 25 with BMS) were selected. The results showed that only severe calcification (32.0% vs 8.0%; P = .034) and ankle-brachial index increase (0.45 ± 0.15 vs 0.41 ± 0.22; P = .038) were significantly different between the groups. In terms of surgical factors, patients in the CS group had more use of bilateral femoral or combined brachial artery percutaneous access (60.0% vs 12.0%; P < .001), more number of stents used (2.3 ± 1.2 vs 1.3 ± 0.7; P < .001), longer mean stent length (9.3 ± 3.3 vs 5.8 ± 2.6 cm; P < .001), and more catheter-directed thrombolysis treatment (32.0% vs 4.0%; P = .009). Multivariate Cox survival analysis showed that severe calcification (hazard ratio, 1.32; 95% confidence interval, 1.04-1.85; P = .048) was the only independent predictor of the primary patency rate. CONCLUSIONS: All patients with AIOD who underwent endovascular therapy were included and achieved good outcomes with both CSs and BMSs. The influence of confounding factors in the two groups was minimized by propensity score matching, and the 5-year patency rates were generally similar in the unmatched and matched cohorts. Postoperative hemodynamic improvement was more obvious in patients in the CS group. For more complex lesions, CS is recommended to be preferred. Especially for severe calcification lesions, which is the only independent predictor of primary patency, CS showed obvious advantages. Further studies with more samples are needed to investigate the role of stent types in AIOD treatment.

Alterations in brain network functional connectivity and topological properties in DRE patients.

Objective: The study aimed to find the difference in functional network topology on interictal electroencephalographic (EEG) between patients with drug-resistant epilepsy (DRE) and healthy people. Methods: We retrospectively analyzed the medical records as well as EEG data of ten patients with DRE and recruited five sex-age-matched healthy controls (HC group). Each participant remained awake while undergoing video-electroencephalography (vEEG) monitoring. After excluding data that contained abnormal discharges, we screened EEG segments that were free of artifacts and put them together into 20-min segments. The screened data was bandpass filtered to different frequency bands (delta, theta, alpha, beta, and gamma). The weighted phase lag index (wPLI) and the network properties were calculated to evaluate changes in the topology of the functional network. Finally, the results were statistically analyzed, and the false discovery rate (FDR) was used to correct for differences after multiple comparisons. Results: In the full frequency band (0.5-45 Hz), the functional connectivity in the DRE group during the interictal period was significantly lower than that in the HC group (p < 0.05). Compared to the HC group, in the full frequency band, the DRE group exhibited significantly decreased clustering coefficient (CC), node degree (D), and global efficiency (GE), while the characteristic path length (CPL) significantly increased (p < 0.05). In the sub-frequency bands, the functional connectivity of the DRE group was significantly lower than that of the HC group in the delta band but higher in the alpha, beta, and gamma bands (p < 0.05). The statistical results of network properties revealed that in the delta band, the DRE group had significantly decreased values for D, CC, and GE, but in the alpha, beta, and gamma bands, these values were significantly increased (p < 0.05). Additionally, the CPL of the DRE group significantly increased in the delta and theta bands but significantly decreased in the alpha, beta, and gamma bands (p < 0.05). Conclusion: The topology structure of the functional network in DRE patients was significantly changed compared with healthy people, which was reflected in different frequency bands. It provided a theoretical basis for understanding the pathological network alterations of DRE.

Tongue texture may contribute to the assessment of malignant risk of thyroid nodules.

In the present study, it was aimed to evaluate whether there is an objective tongue image indicator that could be used to evaluate malignant risk of thyroid nodules through a cross sectional study. From December 2018 to December 2020, the TFDA-1 digital tongue-face diagnostic instrument was used to collect the tongue images. TDAS 2.0 software was used for tongue image analysis. A standardized database was constructed by combining patient physical examination results and tongue image analysis results. The relationship between tongue image index and TI-RADS classification of thyroid nodules was tested. A total of 5,900 cases were collected and 4,615 cases were included in the present study after excluding 154 cases due to incomplete information, 1,221 cases with thyroid nodules were separated into 417 cases TI-RADS 2 group, 693 cases in TI-RADS 3 group and 111 cases in TI-RADS 4 group. Without considering confounding factors, tongue image indexes zhiCon, zhiASM, zhiENT, zhiMEAN, zhiClrB, zhiClrR, zhiClrG, zhiClrI, zhiClrL and zhiClrY were significantly different among the three groups (P<0.05). Excluding the influence of age, sex, body mass index, smoking and drinking, the results of one-way variance linear trend analysis showed that the values of zhiCon, zhiENT and zhiMEAN increased with the increasing TI-RADS category, while the values of zhiASM decreased with the increase of TI-RADS category. Tongue texture index may be helpful for differentiating the benign and malignant of thyroid nodules.

Prediction of diabetic kidney disease risk using machine learning models: A population-based cohort study of Asian adults.

Background: Machine learning (ML) techniques improve disease prediction by identifying the most relevant features in multidimensional data. We compared the accuracy of ML algorithms for predicting incident diabetic kidney disease (DKD). Methods: We utilized longitudinal data from 1365 Chinese, Malay, and Indian participants aged 40-80 y with diabetes but free of DKD who participated in the baseline and 6-year follow-up visit of the Singapore Epidemiology of Eye Diseases Study (2004-2017). Incident DKD (11.9%) was defined as an estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m2 with at least 25% decrease in eGFR at follow-up from baseline. A total of 339 features, including participant characteristics, retinal imaging, and genetic and blood metabolites, were used as predictors. Performances of several ML models were compared to each other and to logistic regression (LR) model based on established features of DKD (age, sex, ethnicity, duration of diabetes, systolic blood pressure, HbA1c, and body mass index) using area under the receiver operating characteristic curve (AUC). Results: ML model Elastic Net (EN) had the best AUC (95% CI) of 0.851 (0.847-0.856), which was 7.0% relatively higher than by LR 0.795 (0.790-0.801). Sensitivity and specificity of EN were 88.2 and 65.9% vs. 73.0 and 72.8% by LR. The top 15 predictors included age, ethnicity, antidiabetic medication, hypertension, diabetic retinopathy, systolic blood pressure, HbA1c, eGFR, and metabolites related to lipids, lipoproteins, fatty acids, and ketone bodies. Conclusions: Our results showed that ML, together with feature selection, improves prediction accuracy of DKD risk in an asymptomatic stable population and identifies novel risk factors, including metabolites. Funding: This study was supported by the National Medical Research Council, NMRC/OFLCG/001/2017 and NMRC/HCSAINV/MOH-001019-00. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Examining Impacts of Healthy Families America on Infant Health Care.

OBJECTIVE: Healthy Families America (HFA) is an evidence-based home visiting program that provides parenting education with the overall goal of preventing child maltreatment and optimizing child development. This study compares the health care utilization and vaccination of infants enrolled in HFA with similar infants not in the program. METHODS: From January 2014 to December 2020, 604 children served statewide by HFA in Arkansas were identified in vital statistics and administrative medical claims records. Using propensity score matching, infants in HFA were matched with nonprogram control infants based on family demographics and birth characteristics. Double propensity-score adjustment method and generalized linear models were used to estimate program effects. RESULTS: In the first year after birth hospital discharge and compared to a propensity score matched cohort of infants not enrolled, those enrolled in HFA had significantly greater use of the emergency department overall (incidence rate ratios (IRR) = 1.24 [95% confidence interval (CI) = 1.07-1.43], P = .004) and for less complex conditions (IRR = 1.22 [95% CI = 1.04-1.44], P = .01), more outpatient medical appointments for child wellness (IRR = 1.09 [95% CI = 1.03-1.15], P = .003), illness (IRR = 1.17 [95% CI = 1.07-1.29], P = .001), and speech and language therapies (IRR = 2.86 [95% CI = 1.18-6.93], P = .02), and more medical visits during which vaccinations were provided (IRR = 1.09 [95% CI = 1.04-1.15], P = .002). CONCLUSIONS: Findings from the current study suggest that HFA supports increased health care utilization and visits in which vaccinations occurred during the first year of life.

A Bayesian network meta-analysis for acute thrombosis after lower extremity artery endovascular treatment.

BACKGROUND: Various endovascular treatment devices have been widely used in the lower extremity arterial disease (LEAD). Their patency efficiency for target lesions has been well studied and reported. Comparison of the risk of acute thrombosis events between the different endovascular treatment devices is unclear. AIMS: To rank the risk of acute thrombosis events when bare metal stents (BMSs), covered stents (CSs), drug-eluting stents (DESs), drug-coated balloons (DCBs), and conventional percutaneous transluminal balloon angioplasty (PTA) are used to treat LEAD through Bayesian network meta-analysis. METHODS: We performed a network meta-analysis of randomized controlled trials comparing the risk of 1-year postoperative acute thrombosis between BMSs, CSs, DESs, DCBs, and PTA for treating LEAD. Bayesian random models were used for pooled endovascular treatment modality comparisons. We ranked these treatment modalities via the Bayesian method according to their surface under the cumulative ranking curve (SUCRA) and estimated probabilities. RESULTS: Nineteen studies (38 study arms; 2758 patients) were included. The Bayesian network ranking of treatments indicated that DCB had the lowest risk of acute thrombosis, PTA had the second-lowest risk of thrombosis, and CS, BMS, and DES had the highest risk of thrombosis. Regarding the treatment efficacy, the OR values of the loss of primary patency were significantly lower for DCB (OR = 0.44, 95% CI: 0.30-0.62), DES (OR = 0.36, 95% CI: 0.14-0.94), and CS (OR = 0.31, 95% CI: 0.18,0.56) than for PTA. When BMS was used as a reference, only the OR for CS was significantly lower (OR = 0.41, 95% CI = 0.21-0.82). Correspondingly, the Bayesian ranking of treatments from better to worse target lesion primary patency was CS, DES, DCB, BMS, and PTA. CONCLUSION: With the available research evidence and according to the network analysis ranking, DES appears to have the highest risk of acute thrombosis and DCB appears to have the lowest risk.

Adequacy of Nutrient Intake and Malnutrition Risk in Older Adults: Findings from the Diet and Healthy Aging Cohort Study.

There is a lack of data on the adequacy of nutrient intake and prevalence of malnutrition risk in Asian populations. The aim was to report on the nutrient intake and prevalence of malnutrition risk in a community sample of older adults in Singapore. Analysis was performed on 738 (n = 206 male, n = 532 male, aged 67.6 ± 6.0 years) adults 60 years and above. Intakes of macro- and micronutrients were evaluated against the Recommended Dietary Allowances (RDAs). Malnutrition risk was assessed using the Nutrition Screening Initiative Determine Your Nutritional Health checklist. It was found that 90.5% older adults exceeded the sugar intake, 68.5% males and 57.1% females exceeded the intake limit for saturated fat, and 33% males had inadequate dietary fiber intake when compared to the RDAs. Inadequate dietary calcium intake was found in 49.5% males and 55.3% females. There were 22.3% of older adults at moderate to high malnutrition risk. Singaporean older adults need to reduce their dietary intakes of sugar and saturated fat and increase their intakes in dietary fiber and calcium. Current findings provide public health awareness on the importance of healthy eating and will facilitate decision making by health promotors to deliver targeted nutrition care programs.

Instrumental variable model average with applications in Mendelian randomization.

Mendelian randomization is a technique used to examine the causal effect of a modifiable exposure on a trait using an observational study by utilizing genetic variants. The use of many instruments can help to improve the estimation precision but may suffer bias when the instruments are weakly associated with the exposure. To overcome the difficulty of high-dimensionality, we propose a model average estimator which involves using different subsets of instruments (single nucleotide polymorphisms, SNPs) to predict the exposure in the first stage, followed by weighting the submodels' predictions using penalization by common penalty functions such as least absolute shrinkage and selection operator (LASSO), smoothly clipped absolute deviation (SCAD) and minimax concave penalty (MCP). The model averaged predictions are then used as a genetically predicted exposure to obtain the estimation of the causal effect on the response in the second stage. The novelty of our model average estimator also lies in that it allows the number of submodels and the submodels' sizes to grow with the sample size. The practical performance of the estimator is examined in a series of numerical studies. We apply the proposed method on a real genetic dataset investigating the relationship between stature and blood pressure.

Deterministic Magnetization Switching via Tunable Noncollinear Spin Configurations in Canted Magnets.

Spin obit torque (SOT) driven magnetization switching has been used widely for encoding consumption-efficient memory and logic. However, symmetry breaking under a magnetic field is required to realize the deterministic switching in synthetic antiferromagnets with perpendicular magnetic anisotropy (PMA), which limits their potential applications. Herein, we report all electric-controlled magnetization switching in the antiferromagnetic Co/Ir/Co trilayers with vertical magnetic imbalance. Besides, the switching polarity could be reversed by optimizing the Ir thickness. By using the polarized neutron reflection (PNR) measurements, the canted noncollinear spin configuration was observed in Co/Ir/Co trilayers, which results from the competition of magnetic inhomogeneity. In addition, the asymmetric domain walls demonstrated by micromagnetic simulations result from introducing imbalance magnetism, leading to the deterministic magnetization switching in Co/Ir/Co trilayers. Our findings highlight a promising route to electric-controlled magnetism via tunable spin configuration, improve our understanding of physical mechanisms, and significantly promote industrial applications in spintronic devices.

Field-free spin-orbit torque switching via out-of-plane spin-polarization induced by an antiferromagnetic insulator/heavy metal interface.

Manipulating spin polarization orientation is challenging but crucial for field-free spintronic devices. Although such manipulation has been demonstrated in a limited number of antiferromagnetic metal-based systems, the inevitable shunting effects from the metallic layer can reduce the overall device efficiency. In this study, we propose an antiferromagnetic insulator-based heterostructure NiO/Ta/Pt/Co/Pt for such spin polarization control without any shunting effect in the antiferromagnetic layer. We show that zero-field magnetization switching can be realized and is related to the out-of-plane component of spin polarization modulated by the NiO/Pt interface. The zero-field magnetization switching ratio can be effectively tuned by the substrates, in which the easy axis of NiO can be manipulated by the tensile or compressive strain from the substrates. Our work demonstrates that the insulating antiferromagnet based heterostructure is a promising platform to enhance the spin-orbital torque efficiency and achieve field-free magnetization switching, thus opening an avenue towards energy-efficient spintronic devices.