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[Display omitted] Exploring the transition-metal-based bifunctional electrocatalysts with high performance for efficient water-splitting and urea electrolysis is significant but challenging. This work presents the in situ preparation of holey NiFe-LDH nanosheets on Ni foam (H-NiFe-LDH/NF) via a one-step hydrothermal method in the presence of PEO-PPO-PEO as the soft template. The holey NiFe-LDH nanosheets provide a high electrochemical surface area, more edge catalytic sites, and abundant oxygen vacancies. Consequently, H-NiFe-LDH/NF exhibits excellent catalytic activity to oxygen evolution, urea oxidation, and hydrogen evolution reactions (OER, UOR, and HER) with good stability in alkaline electrolytes. This electrode requires an overpotential of 261 mV for the OER, a potential of 1.480 V for the UOR to achieve a current density of 100 mA cm−2 in alkaline solutions. By employing the self-supported electrode as both the anode and cathode, this electrolysis cell (H-NiFe-LDH/NF||H-NiFe-LDH/NF) gains current densities of 10 and 100 mA cm−2 at low cell voltages of 1.575 and 1.933 V in the 1.0 M KOH solution. After adding 0.33 M urea, the voltages to deliver 10 and 100 mA cm−2 respectively decrease to 1.418 and 1.691 V. The H-NiFe-LDH/NF electrode also shows excellent stability for water-splitting and urea electrolysis. This work not only contributes to developing a low-cost, high-efficiency, bifunctional electrocatalyst but also provides a practically feasible approach for urea-rich wastewater electrolysis. [ABSTRACT FROM AUTHOR]

S-NiMoO 4 @NiFe-LDH was prepared by growing NiFe-LDH layer on sulfur-modified NiMoO 4 nanorods supported by Ni foam and shows high OER, HER, and water-splitting activity in alkaline both simulated seawater and natural seawater electrolytes. [Display omitted] • S-NiMoO 4 @NiFe-LDH is synthesized by electrodepositing a NiFe-LDH layer on S-modified NiMoO 4 nanorods. • The NiFe-LDH layer contains amorphous and crystalline species and NiMoO 4 is just modified rather than transformed. • S-NiMoO 4 @NiFe-LDH shows outstanding OER and HER activities in alkaline simulated seawater and natural seawater. • The S-NiMoO 4 @NiFe-LDH electrolyzer exhibits better overall seawater-splitting performance than the IrO 2 ||Pt/C cell. Developing high-efficiency and earth-abundant electrocatalysts for electrochemical seawater-splitting is of great significance but remains a grand challenge due to the presence of high-concentration chloride. This work presents the synthesis of a three-dimensional core–shell nanostructure with an amorphous and crystalline NiFe-layered double hydroxide (NiFe-LDH) layer on sulfur-modified nickel molybdate nanorods supported by porous Ni foam (S-NiMoO 4 @NiFe-LDH/NF) through hydrothermal and electrodeposition. Benefiting from high intrinsic activity, plentiful active sites, and accelerated electron transfer, S-NiMoO 4 @NiFe-LDH/NF displays an outstanding bifunctional catalytic activity toward oxygen evolution reaction (OER) and hydrogen evolution reaction (HER) in both simulated alkaline seawater and natural seawater electrolytes. To reach a current density of 100 mA cm−2, this catalyst only requires overpotentials of 273 and 315 mV for OER and 170 and 220 mV for HER in 1 M KOH + 0.5 M NaCl freshwater and 1 M KOH + seawater electrolytes, respectively. Using S-NiMoO 4 @NiFe-LDH as both anode and cathode, the electrolyzer shows superb overall seawater-splitting activity, and respectively needs low voltages of 1.68 and 1.73 V to achieve a current density of 100 mA cm−2 in simulated alkaline seawater and alkaline natural seawater electrolytes with good Cl− resistance and satisfactory durability. The electrolyzer outperforms the benchmark IrO 2 ||Pt/C pair and many other reported bifunctional catalysts and exhibits great potential for realistic seawater electrolysis. [ABSTRACT FROM AUTHOR]

[Display omitted] Linking data across studies offers an opportunity to enrich data sets and provide a stronger basis for data-driven models for biomedical discovery and/or prognostication. Several techniques to link records have been proposed, and some have been implemented across data repositories holding molecular and clinical data. Not all these techniques guarantee appropriate privacy protection; there are trade-offs between (a) simple strategies that can be associated with data that will be linked and shared with any party and (b) more complex strategies that preserve the privacy of individuals across parties. We propose an intermediary, practical strategy to support linkage in studies that share de-identified data with Data Coordinating Centers. This technology can be extended to link data across multiple data hubs to support privacy preserving record linkage, considering data coordination centers and their awardees, which can be extended to a hierarchy of entities (e.g., awardees, data coordination centers, data hubs, etc.) b. [ABSTRACT FROM AUTHOR]

Big data and (deep) machine learning have been ambitious tools in digital medicine, but these tools focus mainly on association. Intervention in medicine is about the causal effects. The average treatment effect has long been studied as a measure of causal effect, assuming that all populations have the same effect size. However, no "one-size-fits-all" treatment seems to work in some complex diseases. Treatment effects may vary by patient. Estimating heterogeneous treatment effects (HTE) may have a high impact on developing personalized treatment. Lots of advanced machine learning models for estimating HTE have emerged in recent years, but there has been limited translational research into the real-world healthcare domain. To fill the gap, we reviewed and compared eleven recent HTE estimation methodologies, including meta-learner, representation learning models, and tree-based models. We performed a comprehensive benchmark experiment based on nationwide healthcare claim data with application to Alzheimer's disease drug repurposing. We provided some challenges and opportunities in HTE estimation analysis in the healthcare domain to close the gap between innovative HTE models and deployment to real-world healthcare problems. [ABSTRACT FROM AUTHOR]

Herein, an electroluminescence (ECL) biosensor was constructed by combining click chemistry with activators regenerated by electron transfer-atom transfer radical polymerization (ARGET-ATRP) to sensitively assay tobacco mosaic virus (TMV) RNA for the first time. First, hairpin DNA (hDNA) was self-assembled on the gold electrode surface through Au–S bonding. The hDNA hybridized with the tDNA to form tRNA/hDNA hybrids in the presence of TMV RNA (tRNA), so that the azide group labelled at the end of the hDNA was kept away from the electrode surface. Subsequently, the initiator for the ARGET-ATRP reaction was modified on the electrode surface by chemical bonds via click chemistry. Then, N-acryloxysuccinimide (NAS)-labelled polymer chains were successfully formed on the electrode surface by ARGET-ATRP. Under the optimized conditions, a good linear relationship existed with the ECL signal and the logarithm of tRNA concentration in the range of 0.1 pM–10 nM, and the limit of detection was 2.61 fM. In addition, this strategy can identify mismatched bases and performs well in recovery assays in real samples. For its high sensitivity, selectivity, simplicity and economy, the ECL biosensor shows great potential for practical applications. An electroluminescence (ECL) biosensor for the highly sensitive detection of TMV RNA was constructed based on click chemistry and ARGET ATRP. [Display omitted] • An RNA ECL biosensor was constructed by click chemistry and ARGET ATRP. • ARGET ATRP as signal amplification strategy guarantees the sensitivity of the sensor. • Immobilization of the initiator by click chemistry improves the reproducibility. • The method provides a new solution for the detection of nucleic acid molecules. [ABSTRACT FROM AUTHOR]

In this paper, we developed a feasible and efficient deep-learning-based framework to combine the United States (US) natality data for the last five decades, with changing variables and factors, into a consistent database. We constructed a graph based on the property and elements of databases, including variables, and conducted a graph convolutional network (GCN) to learn the embeddings of variables on the constructed graph, where the learned embeddings implied the similarity of variables. Specifically, we devised a loss function with a slack margin and a banlist mechanism (for a random walk) to learn the desired structure (two nodes sharing more information were more similar to each other.), and developed an active learning mechanism to conduct the harmonization. Toward a total of 9,321 variables from 49 databases (i.e., 783 stemmed variables, from 1970 to 2018), we applied our model iteratively together with human reviews for four rounds, then obtained 323 hyperchains of variables. During the harmonization, the first round of our model achieved recall and precision of 87.56%, 57.70%, respectively. Our harmonized graph neural network (HGNN) method provides a feasible and efficient way to connect relevant databases at a meta-level. Adapting to the database's property and characteristics, HGNN can learn patterns globally, which is powerful to discover the similarity between variables among databases. Our proposed method provides an effective way to reduce the manual effort in database harmonization and integration of fragmented data into useful databases for future research. [ABSTRACT FROM AUTHOR]

Complicated multifactorial diseases deteriorate from one disease to other diseases. For example, existing studies consider Alzheimer's disease (AD) a comorbidity of epilepsy, but also recognize epilepsy to occur more frequently in patients with AD than those without. It is important to understand the progress of disease that deteriorates to severe diseases. To this end, we develop a transitional phenotyping method based on both longitudinal and cross-sectional relationships between diseases and/or medications. For a cross-sectional approach, we utilized a skip-gram model to represent co-occurred disease or medication. For a longitudinal approach, we represented each patient as a transition probability between medical events and used supervised tensor factorization to decompose into groups of medical events that develop together. Then we harmonized both information to derive high-risk transitional patterns. We applied our method to disease progress from epilepsy to AD. An epilepsy-AD cohort of 600,000 patients were extracted from Cerner Health Facts data. Our experimental results suggested a causal relationship between epilepsy and later onset of AD, and also identified five epilepsy subgroups with distinct phenotypic patterns leading to AD. While such findings are preliminary, the proposed method combining representation learning with tensor factorization seems to be an effective approach for risk factor analysis. [ABSTRACT FROM AUTHOR]

• The first study that applies tPBM to (complex) sentence processing. • tPBM enhances sentence processing performances in both Mandarin L1 & L2 speakers. • A causal role of LIFG for sentence processing through active tPBM. • Opening up the promising application prospect for tPBM on sentence processing. This study investigated the causal enhancing effect of transcranial photobiomodulation (tPBM) over the left inferior frontal gyrus (LIFG) on syntactically complex Mandarin Chinese first language (L1) and second language (L2) sentence processing performances. Two (L1 and L2) groups of participants (thirty per group) were recruited to receive the double-blind, sham-controlled tPBM intervention via LIFG, followed by the sentence processing, the verbal working memory (WM), and the visual WM tasks. Results revealed a consistent pattern for both groups: (a) tPBM enhanced sentence processing performance but not verbal WM for linear processing of unstructured sequences and visual WM performances; (b) Participants with lower sentence processing performances under sham tPBM benefited more from active tPBM. Taken together, the current study substantiated that tPBM enhanced L1 and L2 sentence processing, and would serve as a promising and cost-effective noninvasive brain stimulation (NIBS) tool for future applications on upregulating the human language faculty. [ABSTRACT FROM AUTHOR]

Thanks to the advancements in bioinformatics, drugs, and other interventions that modulate microbes to treat diseases have been emerging continuously. In recent years, an increasing number of databases related to traditional Chinese medicine (TCM) or gut microbes have been established. However, a database combining the two has not yet been developed. To accelerate TCM research and address the traditional medicine and micro ecological system connection between short board, we have developed the most comprehensive micro-ecological database of TCM. This initiative includes the standardization of the following advantages: (1) A repeatable process achieved through the standardization of a retrieval strategy to identify literature. This involved identifying 419 experiment articles from PubMed and six authoritative databases; (2) High-quality data integration achieved through double-entry extraction of literature, mitigating uncertainties associated with natural language extraction; (3) Implementation of a similar strategy aiding in the prediction of mechanisms of action. Leveraging drug similarity, target entity similarity, and known drug-target entity association, our platform enables the prediction of the effects of a new herb or acupoint formulas using the existing data. In total, MicrobeTCM includes 171 diseases, 725 microbes, 1468 herb-formulas, 1032 herbs, 15780 chemical compositions, 35 acupoint-formulas, and 77 acupoints. For further exploration, please visit https://www.microbetcm.com. [Display omitted] [ABSTRACT FROM AUTHOR]

Vascular peroxidase 1 (VPO1) has been proved to be associated with vascular endothelial cell apoptosis by producing reactive oxygen species. However, the contribution of VPO1 to the development of vascular remodeling (VR) remains to be fully characterized. We explored the role of VPO1 in VR in spontaneously hypertensive rats (SHRs) and the underlying mechanism of losartan in inhibiting VR. Compared to Wistar-Kyoto (WKY) rats, the SHR showed remodeling of their vascular walls. The level of VPO1 and the hydrogen peroxide (H 2 O 2 ) concentration were increased in the SHRs. However, the SHRs pretreated with losartan showed significant inhibition of blood pressure and VR and decreased levels of VPO1 and H 2 O 2 compared to the non-treated SHRs. Angiotensin II significantly increased the expressions of MMP-2, MMP-9 and the concentrations of H 2 O 2 and hypochlorous acid (HOCl) in vascular smooth muscle cells (VSMCs). However, only the H 2 O 2 level increased in VSMCs when transfected with VPO1 shRNA. These results support a critical but previously unrecognized role of VPO1 in VR and suggest that therapies to reduce VPO1 may be novel approaches for VR. [ABSTRACT FROM AUTHOR]