Association of nightmares with cardio-cerebrovascular disease, hypertension and hyperlipidemia in older adults: A population-based cross-sectional study.

OBJECTIVE: This cross-sectional study investigated the relationship of nightmares with cardio-cerebrovascular disease (CVD), hypertension and hyperlipidemia which are major preceding diseases of CVD in older adults. METHODS: Participants (n = 2824; mean age 63.6 ± 6.6 years, females 49.3%) completed the Disturbing Dream and Nightmare Severity Index (DDNSI), which was used to divide the sample into either the Nightmare or Non-Nightmare group (cut-off score ≥ 10). Demographic information, history of CVD (cerebrovascular disease, myocardial infarction, congestive heart failure, coronary artery disease, and arrhythmia), hypertension, hyperlipidemia, and self-report questionnaires about stress (Perceived Stress Scale), depression (Beck Depression Inventory), sleep quality (Pittsburgh Sleep Quality Index), and insomnia symptoms were also collected. RESULTS: Among the sample, 379 participants (13.4%) reported experiencing nightmares more than once a year, and 73 participants (2.6%) were classified as having nightmare disorder based on DDNSI scores (≥10). 11.3% of participants (n = 319) reported having more than one CVD. Approximately half of the participants reported a history of hypertension (52.1%, n = 1471) and hyperlipidemia (47.7%, n = 1346). Logistic regression analysis indicated the Nightmare group was 2.04 times at higher risk for hyperlipidemia (OR = 2.04, 95% CI 1.22-3.40, p = .006) after controlling for covariates compared to the Non-Nightmare group. Although non-significant, there was a trend toward a higher risk of hypertension in the Nightmare group (OR = 1.67, 95% CI 0.99-2.84, p = .056). CONCLUSIONS: Results of this study indicate frequent nightmares in older adults may be associated with hyperlipidemia, which are risk factors for CVD. Further studies are needed to explore nightmares' directionality and health consequences in an aging population.

Prognostic score-based methods for estimating center effects based on survival probability: Application to post-kidney transplant survival.

In evaluating the performance of different facilities or centers on survival outcomes, the standardized mortality ratio (SMR), which compares the observed to expected mortality has been widely used, particularly in the evaluation of kidney transplant centers. Despite its utility, the SMR may exaggerate center effects in settings where survival probability is relatively high. An example is one-year graft survival among U.S. kidney transplant recipients. We propose a novel approach to estimate center effects in terms of differences in survival probability (ie, each center versus a reference population). An essential component of the method is a prognostic score weighting technique, which permits accurately evaluating centers without necessarily specifying a correct survival model. Advantages of our approach over existing facility-profiling methods include a metric based on survival probability (greater clinical relevance than ratios of counts/rates); direct standardization (valid to compare between centers, unlike indirect standardization based methods, such as the SMR); and less reliance on correct model specification (since the assumed model is used to generate risk classes as opposed to fitted-value based 'expected' counts). We establish the asymptotic properties of the proposed weighted estimator and evaluate its finite-sample performance under a diverse set of simulation settings. The method is then applied to evaluate U.S. kidney transplant centers with respect to graft survival probability.

Imaging Thermally Fluctuating Néel Vectors in van der Waals Antiferromagnet NiPS3.

Studying antiferromagnetic domains is essential for fundamental physics and potential spintronics applications. Despite their importance, few systematic studies have been performed on antiferromagnet (AFM) domains with high spatial resolution in van der Waals (vdW) materials, and direct probing of the Néel vectors remains challenging. In this work, we found multidomain states in the vdW AFM NiPS3, a material extensively investigated for its unique magnetic exciton. We employed photoemission electron microscopy combined with the X-ray magnetic linear dichroism (XMLD-PEEM) to image the NiPS3's magnetic structure. The nanometer-spatial resolution of XMLD-PEEM allows us to determine local Néel vector orientations and discover thermally fluctuating Néel vectors that are independent of the crystal symmetry even at 65 K, well below the TN of 155 K. We demonstrate that an in-plane orbital moment of the Ni ion is responsible for the weak magnetocrystalline anisotropy. The observed thermal fluctuations of the antiferromagnetic domains may explain the broadening of magnetic exciton peaks at higher temperatures.

Snowflake relocated Cu2O electrocatalyst on an Ag backbone template for the production of liquid C2+ chemicals from CO2.

In this study, we performed the CO2 reduction reaction (CO2RR) using a structural composite catalyst of cuprous oxide (Cu2O) and silver (Ag) that was simultaneously electrodeposited. While the underneath Ag electrodeposits maintained their spiky backbone structures even after the CO2RR, the Cu2O deposits were reduced to Cu(111) and relocated on the backbone template. The structural changes in Cu2O to Cu increase the active area of the Cu-Ag interface, resulting in a remarkable production rate of 125.01 µmol h-1 of liquid C2+ chemicals via the stabilization of the C-C coupling of the key intermediate species of acetaldehyde. This study provides new insights into designing a bimetallic catalyst for producing sustainable C2+ products from CO2 without any selectivity towards the production of methane.

Conversion of Layered WS2 Crystals into Mixed-Domain Electrochemical Catalysts by Plasma-Assisted Surface Reconstruction.

Electrocatalytic water splitting is crucial to generate clean hydrogen fuel, but implementation at an industrial scale remains limited due to dependence on expensive platinum (Pt)-based electrocatalysts. Here, an all-dry process to transform electrochemically inert bulk WS2 into a multidomain electrochemical catalyst that enables scalable and cost-effective implementation of the hydrogen evolution reaction (HER) in water electrolysis is reported. Direct dry transfer of WS2 flakes to a gold thin film deposited on a silicon substrate provides a general platform to produce the working electrodes for HER with tunable charge transfer resistance. By treating the mechanically exfoliated WS2 with sequential Ar-O2 plasma, mixed domains of WS2, WO3, and tungsten oxysulfide form on the surfaces of the flakes, which gives rise to a superior HER with much greater long-term stability and steady-state activity compared to Pt. Using density functional theory, ultraefficient atomic sites formed on the constituent nanodomains are identified, and the quantification of atomic-scale reactivities and resulting HER activities fully support the experimental observations.

Quantitative Measurement of Tau Aggregation in Genetically Modified Rats with Neurodegeneration.

Animal models of neurodegenerative diseases have helped us to better understand the pathogenesis of neurodegenerative diseases. However, recent failure to translate pre-clinical model studies to the clinic urges us to develop more rigorous and faithful animal models in neurodegenerative diseases. As genetic manipulation of rats becomes much more accessible due to availability of CRISPR-Cas9 and other genomic editing toolboxes, rats have been emerging as a new model system for neurodegenerative diseases. Even though mouse models have been dominant over the last decades, rats may provide advantages over mice. Rats are more genetically and physiologically closer to humans than to mice. Also, certain rat models can represent deposition of tau, which is one of the key pathological features of Alzheimer's diseases and tauopathies. However, there is an unmet need for standardized, rigorous testing in rat models. We adopted two commonly used biochemical and immunofluorescence methods from mice and human postmortem brains to measure tau aggregation. Due to the intrinsic differences between mice and rats, e.g., size of rat brains, certain equipment is required for rat models to study tau pathologies. Along with specific tools, here we describe the detailed methods for rat models of neurodegenerative diseases.

Time-of-flight detection of terahertz phonon-polariton.

A polariton is a fundamental quasiparticle that arises from strong light-matter interaction and as such has attracted wide scientific and practical interest. When light is strongly coupled to the crystal lattice, it gives rise to phonon-polaritons (PPs), which have been proven useful in the dynamical manipulation of quantum materials and the advancement of terahertz technologies. Yet, current detection and characterization methods of polaritons are still limited. Traditional techniques such as Raman or transient grating either rely on fine-tuning of external parameters or complex phase extraction techniques. To overcome these inherent limitations, we propose and demonstrate a technique based on a time-of-flight measurement of PPs. We resonantly launch broadband PPs with intense terahertz fields and measure the time-of-flight of each spectral component with time-resolved second harmonic generation. The time-of-flight information, combined with the PP attenuation, enables us to resolve the real and imaginary parts of the PP dispersion relation. We demonstrate this technique in the van der Waals magnets NiI2 and MnPS3 and reveal a hidden magnon-phonon interaction. We believe that this approach will unlock new opportunities for studying polaritons across diverse material systems and enhance our understanding of strong light-matter interaction.

Amygdala electrical stimulation for operant conditioning in rat navigation.

There have been several attempts to navigate the locomotion of animals by neuromodulation. The most common method is animal training with electrical brain stimulation for directional cues and rewards; the basic principle is to activate dopamine-mediated neural reward pathways such as the medial forebrain bundle (MFB) when the animal correctly follows the external commands. In this study, the amygdala, which is the brain region responsible for fear modulation, was targeted for punishment training. The brain regions of MFB, amygdala, and barrel cortex were electrically stimulated for reward, punishment, and directional cues, respectively. Electrical stimulation was applied to the amygdala of rats when they failed to follow directional commands. First, two different amygdala regions, i.e., basolateral amygdala (BLA) and central amygdala (CeA), were stimulated and compared in terms of behavior responses, success and correction rates for training, and gene expression for learning and memory. Then, the training was performed in three groups: group R (MFB stimulation for reward), group P (BLA stimulation for punishment), and group RP (both MFB and BLA stimulation for reward and punishment). In group P, after the training, RNA sequencing was conducted to detect gene expression and demonstrate the effect of punishment learning. Group P showed higher success rates than group R, and group RP exhibited the most effective locomotion control among the three groups. Gene expression results imply that BLA stimulation can be more effective as a punishment in the learning process than CeA stimulation. We developed a new method to navigate rat locomotion behaviors by applying amygdala stimulation.

Egg microneedles for transdermal vaccination of inactivated influenza virus.

The use of dissolving microneedles (DMNs) is a drug delivery technique in which drug dissolution occurs once it is administered into the skin. The skin is a remarkable site for vaccination due to its significant immunologic properties. Compared to the traditional hypodermic intramuscular (IM) injection, vaccination via DMN does not require cold chains and allows for minimal invasive drug delivery. On account of the significance of skin vaccination, preceding studies have been conducted to elucidate the importance of the DMN technology in vaccination. Most of these studies focused on formulations that maintain the activity of the vaccine, so formulations designed to be specific to the mechanical properties of the microneedle could not be used together independently. In this study, we have developed influenza vaccine loaded egg microneedles (EMN) and characterized the specificity of layer-specific functions of EMN by distinguishing between formulations that can maintain the activity of the vaccine and have the mechanical strength. By the use of in vitro tests such as ELISA and SRID assays, we quantitively evaluated the antigen activity of the formulation candidates to be 87% and 91%, respectively. In vivo tests were also conducted as mouse groups were inoculated with the formulation constructed into egg microneedles (FLU-EMN) to determine the protective efficacy against infection. The results demonstrated that FLU-EMN with functionalized formulations successfully enabled protective immune response even with a fractional dose compared to IM injection.

Calculating risk and prevalence ratios and differences in R: developing intuition with a hands-on tutorial and code.

Epidemiologic research questions often focus on evaluating binary outcomes, yet curricula and scientific literature do not always provide clear guidance or examples on selecting and calculating an appropriate measure of association in these scenarios. Reporting inappropriate measures may lead to misleading statistical conclusions. We present a hands-on tutorial that includes annotated code written in an open-source statistical programming language (R) showing readers how to apply, compare, and understand four methods used to estimate a risk or prevalence ratio (or difference), rather than presenting an odds ratio. We will provide guidance on when to use each method, discussing the strengths and limitations of each approach, and compare the results obtained across them. Ultimately, we aim to help trainees, public health researchers, and interdisciplinary professionals develop an intuition for these methods and empower them to implement and interpret these methods in their own research.

Terahertz Spectroscopy and DFT Analysis of Phonon Dynamics of the Layered Van der Waals Semiconductor Nb3 X 8 (X = Cl, I).

We have conducted a terahertz spectroscopic study and a density functional theory analysis of the phonon dynamics of the layered van der Waals semiconductors Nb3Cl8 and Nb3I8. Several infrared-active phonon modes were observed in the terahertz region, and their frequencies were found to be in excellent agreement with our first-principles lattice dynamics calculations. For Nb3Cl8, the observed phonon spectra are consistent with a structural transition at 90 K from the high-temperature P3̅m1 phase to the low-temperature R3̅m phase. Also, our study confirmed that the structural and magnetic transitions were coupled in Nb3Cl8. For Nb3I8, which is nonmagnetic at and below room temperature, no significant temperature or magnetic field dependence was observed in the phonon spectra. Our study provides an intriguing connection between the structural properties and the paramagnetic-nonmagnetic transitions in Nb3Cl8 and Nb3I8.

Free-standing TiO2 nanograssy tubular hybrid membrane for polysulfide trapping in Li-S battery.

During the growth of anodic TiO2 nanotubes with a high layer thickness of greater than 20 µm, "nanograss" structures are typically formed on the outermost surface. This happens due to the fact that the engraving of the oxide tubes arises during prolonged exposure to an F- ion containing electrolyte. These TiO2 nanotubular layers have a high aspect ratio with astonishing bundles of nanograss structures on the tube top and especially a high surface area with anatase crystallites in the tubes. By two-step anodization in synergy with the hybridization of a rubber polymer binder, freestanding nanotubular layers consisting of nanograssy surfaces with nano-crystalline particles in the tubes were successfully obtained. Under the highly efficient polysulfide trapping and electrolyte perturbation, this nanotubular hybrid membrane could deliver an enriched performance with a capacity of 618 mA h g-1 after 100 cycles at 0.1C in Li-S batteries.

Methods for Assessing Spillover in Network-Based Studies of HIV/AIDS Prevention among People Who Use Drugs.

Human Immunodeficiency Virus (HIV) interventions among people who use drugs (PWUD) often have spillover, also known as interference or dissemination, which occurs when one participant's exposure affects another participant's outcome. PWUD are often members of networks defined by social, sexual, and drug-use partnerships and their receipt of interventions can affect other members in their network. For example, HIV interventions with possible spillover include educational training about HIV risk reduction, pre-exposure prophylaxis, or treatment as prevention. In turn, intervention effects frequently depend on the network structure, and intervention coverage levels and spillover can occur even if not measured in a study, possibly resulting in an underestimation of intervention effects. Recent methodological approaches were developed to assess spillover in the context of network-based studies. This tutorial provides an overview of different study designs for network-based studies and related methodological approaches for assessing spillover in each design. We also provide an overview of other important methodological issues in network studies, including causal influence in networks and missing data. Finally, we highlight applications of different designs and methods from studies of PWUD and conclude with an illustrative example from the Transmission Reduction Intervention Project (TRIP) in Athens, Greece.

Finding influential subjects in a network using a causal framework.

Researchers across a wide array of disciplines are interested in finding the most influential subjects in a network. In a network setting, intervention effects and health outcomes can spill over from one node to another through network ties, and influential subjects are expected to have a greater impact than others. For this reason, network research in public health has attempted to maximize health and behavioral changes by intervening on a subset of influential subjects. Although influence is often defined only implicitly in most of the literature, the operative notion of influence is inherently causal in many cases: influential subjects are those we should intervene on to achieve the greatest overall effect across the entire network. In this work, we define a causal notion of influence using potential outcomes. We review existing influence measures, such as node centrality, that largely rely on the particular features of the network structure and/or on certain diffusion models that predict the pattern of information or diseases spreads through network ties. We provide simulation studies to demonstrate when popular centrality measures can agree with our causal measure of influence. As an illustrative example, we apply several popular centrality measures to the HIV risk network in the Transmission Reduction Intervention Project and demonstrate the assumptions under which each centrality can represent the causal influence of each participant in the study.

Somatosensory ECoG-based brain-machine interface with electrical stimulation on medial forebrain bundle.

Brain-machine interface (BMI) provides an alternative route for controlling an external device with one's intention. For individuals with motor-related disability, the BMI technologies can be used to replace or restore motor functions. Therefore, BMIs for movement restoration generally decode the neural activity from the motor-related brain regions. In this study, however, we designed a BMI system that uses sensory-related neural signals for BMI combined with electrical stimulation for reward. Four-channel electrocorticographic (ECoG) signals were recorded from the whisker-related somatosensory cortex of rats and converted to extract the BMI signals to control the one-dimensional movement of a dot on the screen. At the same time, we used operant conditioning with electrical stimulation on medial forebrain bundle (MFB), which provides a virtual reward to motivate the rat to move the dot towards the desired center region. The BMI task training was performed for 7 days with ECoG recording and MFB stimulation. Animals successfully learned to move the dot location to the desired position using S1BF neural activity. This study successfully demonstrated that it is feasible to utilize the neural signals from the whisker somatosensory cortex for BMI system. In addition, the MFB electrical stimulation is effective for rats to learn the behavioral task for BMI.

Quality improvement of outpatient clinical chemistry tests through a novel middleware-laboratory information system solution.

OBJECTIVES: Rapid and accurate laboratory tests are essential to support clinical decision-making. Despite the various efforts to control quality in the laboratory, our outpatient chemistry turnaround time (TAT) has deteriorated since 2018. Moreover, these difficulties have accelerated further due to the COVID-19 pandemic. Therefore, we aimed to improve laboratory work efficiency by identifying and eliminating the causes of reduced laboratory work efficiency. DESIGN & METHODS: We surveyed to identify tasks that reduce work efficiency. Based on our survey, a new-concept of work assistance middleware linked to laboratory information system (LIS) was developed. The middleware supports test end-time prediction, automatic real-time TAT monitoring, and urgent test requests so that medical technologists can focus on their chemistry tests. The developed middleware was used for 6 months in laboratory and outpatient clinics, and its effectiveness was evaluated. RESULTS: The median TAT for outpatient chemistry tests was reduced by 6.6 min, from 72.4 min to 65.8 min. And not only did the maximum TAT for the sample decrease from 353 min to 214 min, but the proportion of samples exceeding the TAT target (120 min) also decreased by 77%; from 2.00% in 2010 (1,905 out of 94,989 samples) to 0.46% in 2021 (453 out of 98,117 samples). 2,199 samples were urgently requested through middleware, and they were processed about 15% faster than other samples, effectively performing urgent tests. The test end-time prediction showed an error of 8.6 min in the evaluation using the MAE (Mean Absolute Error) index. CONCLUSIONS: Through this study, the quality and efficiency of the laboratory were improved, and while reducing the workload of medical staff, it contributed to enhancing patient safety and satisfaction.

Distinguishing nontuberculous mycobacterial lung disease and Mycobacterium tuberculosis lung disease on X-ray images using deep transfer learning.

BACKGROUND: Nontuberculous mycobacterial lung disease (NTM-LD) and Mycobacterium tuberculosis lung disease (MTB-LD) have similar clinical characteristics. Therefore, NTM-LD is sometimes incorrectly diagnosed with MTB-LD and treated incorrectly. To solve these difficulties, we aimed to distinguish the two diseases in chest X-ray images using deep learning technology, which has been used in various fields recently. METHODS: We retrospectively collected chest X-ray images from 3314 patients infected with Mycobacterium tuberculosis (MTB) or nontuberculosis mycobacterium (NTM). After selecting the data according to the diagnostic criteria, various experiments were conducted to create the optimal deep learning model. A performance comparison was performed with the radiologist. Additionally, the model performance was verified using newly collected MTB-LD and NTM-LD patient data. RESULTS: Among the implemented deep learning models, the ensemble model combining EfficientNet B4 and ResNet 50 performed the best in the test data. Also, the ensemble model outperformed the radiologist on all evaluation metrics. In addition, the accuracy of the ensemble model was 0.85 for MTB-LD and 0.78 for NTM-LD on an additional validation dataset consisting of newly collected patients. CONCLUSIONS: In previous studies, it was known that it was difficult to distinguish between MTB-LD and NTM-LD in chest X-ray images, but we have successfully distinguished the two diseases using deep learning methods. This study has the potential to aid clinical decisions if the two diseases need to be differentiated.

Doubly robust nonparametric instrumental variable estimators for survival outcomes.

Instrumental variable (IV) methods allow us the opportunity to address unmeasured confounding in causal inference. However, most IV methods are only applicable to discrete or continuous outcomes with very few IV methods for censored survival outcomes. In this article, we propose nonparametric estimators for the local average treatment effect on survival probabilities under both covariate-dependent and outcome-dependent censoring. We provide an efficient influence function-based estimator and a simple estimation procedure when the IV is either binary or continuous. The proposed estimators possess double-robustness properties and can easily incorporate nonparametric estimation using machine learning tools. In simulation studies, we demonstrate the flexibility and double robustness of our proposed estimators under various plausible scenarios. We apply our method to the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial for estimating the causal effect of screening on survival probabilities and investigate the causal contrasts between the two interventions under different censoring assumptions.

OH scavenging pyridinic N for facile oxygen reduction reaction on free Pt surface.

Compared to hydrogen oxidation reaction, oxygen reduction reaction (ORR) is a sluggish reaction in the proton exchange membrane fuel cell. Many studies have focused on the development of complex synthesis methods for new catalysts. In this study, we introduce a simple catalyst layer preparation method using an additive based on physical mixing for facile ORR in acid media. N-doped carbon containing mainly pyridinic N was used as the additive in the Pt or PtCo catalyst layer. The adjacent pyridinic N near the Pt catalyst works as a Lewis base and removes hydroxyl ions from Pt, thus leading to the simultaneous suppression of Pt oxidation. Based on electrochemical and spectroscopic analyses, we found that pyridinic N reacts with hydroxyl ions and is oxidized. Consequently, Pt catalyst without Pt oxide species exhibited higher ORR activity than pristine Pt or PtCo catalyst.

Broadband high-performance terahertz polarizer based on a dense array of 5†nm gap slit antennas.

Critical factors for terahertz polarizers include broadband operation, high transmittance, and a good extinction ratio. In this paper, using a 5 nm-wide metallic slit array with a 200 nm periodicity as a wire grid polarizer, we achieved over 95% transmittance with an average extinction ratio of 40 dB, over the entire spectrum as defined by the terahertz time-domain spectroscopy (0.4 ∼ 2 THz). Theoretical calculations revealed that the slit array can show 100% transmission up to 5 THz, and wider bandwidths with a higher cutoff frequency can be achieved by reducing the slit periodicity. These results provide a novel approach for achieving a broadband THz polarizer and open a new path for seamless integration of the polarizers with nanophotonic applications.