Exploring the Effect of Pd on the Oxygen Reduction Performance of Pt by In Situ Raman Spectroscopy.

Directly monitoring the oxygen reduction reaction (ORR) process in situ is very important to deeply understand the reaction mechanism and is a critical guideline for the design of high-efficiency catalysts, but there is still lack of definite in situ evidence to clarify the effect between adsorbed intermediates and the strain/electronic effect for enhanced ORR performance. Herein, in situ surface-enhanced Raman spectroscopy (SERS) was employed to detect the intermediates during the ORR process on the Au@Pd@Pt core/shell heterogeneous nanoparticles (NPs). Direct spectroscopic evidence of the *OOH intermediate was obtained, and an obvious red shift of the *OOH frequency was identified with the controllable shell thickness of Pd. Detailed experimental characterizations and density functional theory (DFT) calculations demonstrated that such improved ORR activity after inducing Pd into Au@Pt NPs can be attributed to the optimized adsorbate-substrate interaction due to the strain and electronic effect, leading to a higher Pt-O binding energy and a lower O-O binding energy, which was conducive to O-O dissociation and promoted the subsequent reaction. Notably, this work illustrates a relationship between the performance and strain/electronic effect via the intermediate detected by SERS and paves the way for the construction of ORR electrocatalysts with high performance.

Unmasking the Critical Role of the Ordering Degree of Bimetallic Nanocatalysts on Oxygen Reduction Reaction by In Situ Raman Spectroscopy.

Precise control and accurate understanding of the ordering degree of bimetallic nanocatalysts (BNs) are challenging yet crucial to acquire advanced materials for the oxygen reduction reaction (ORR). AuCu BNs with various ordering degrees were synthesized to evaluate the influence of ordering degree on the ORR at a molecular level using in situ Raman spectroscopy. The activity of AuCu BNs was improved by over 2 times after a disorder-to-order transition, making the performance of highly ordered AuCu BNs exceed that of benchmark Pt/C. Direct Raman spectroscopic evidence of key intermediate (*OH) demonstrates that the active site is the combination site of Au and Cu. Moreover, two distinct *OH species are observed on the ordered and disordered structure, and the ordered site is more beneficial for ORR due to its lower affinity to *OH. This work deepens the understanding on the important role of ordering degree on BNs and enables the design of improved catalysts.

In Situ Raman Probing of Hot-Electron Transfer at Gold-Graphene Interfaces with Atomic Layer Accuracy.

Plasmonic metals under photoexcitation can generate energetic hot electrons to directly induce chemical reactions. However, the capability and fundamental insights of the transportation of these hot electrons at plasmonic metal-2D material interfaces remain unclear. Herein, hot-electron transfer at Au-graphene interfaces has been in situ studied using surface-enhanced Raman spectroscopy (SERS) with atomic layer accuracy. Combining in situ SERS studies with density functional theory calculations, it is proved that hot electrons can be injected from plasmonic Au nanoparticles to graphene and directly penetrate graphene to trigger photocatalytic reactions. With increasing graphene layers, the transportation of hot electrons decays rapidly and would be completely blocked after five layers of graphene. Moreover, the transfer of hot electrons can be modulated by applying an external electric field, and the hot-electron transfer efficiency under electrochemical conditions is improved by over three times in the presence of a monolayer of graphene.

In Situ Raman Observation of Oxygen Activation and Reaction at Platinum-Ceria Interfaces during CO Oxidation.

Understanding the fundamental insights of oxygen activation and reaction at metal-oxide interfaces is of significant importance yet remains a major challenge due to the difficulty in in situ characterization of active oxygen species. Herein, the activation and reaction of molecular oxygen during CO oxidation at platinum-ceria interfaces has been in situ explored using surface-enhanced Raman spectroscopy (SERS) via a borrowing strategy, and different active oxygen species and their evolution during CO oxidation at platinum-ceria interfaces have been directly observed. In situ Raman spectroscopic evidence with isotopic exchange experiments demonstrate that oxygen is efficiently dissociated to chemisorbed O on Pt and lattice Ce-O species simultaneously at interfacial Ce3+ defect sites under CO oxidation, leading to a much higher activity at platinum-ceria interfaces compared to that at Pt alone. Further in situ time-resolved SERS studies and density functional theory simulations reveal a more efficient molecular pathway through the reaction between adsorbed CO and chemisorbed Pt-O species transferred from the interfaces. This work deepens the fundamental understandings on oxygen activation and CO oxidation at metal-oxide interfaces and offers a sensitive technique for the in situ characterization of oxygen species under working conditions.

Estimating the Size of a COVID-19 Epidemic from Surveillance Systems.

Public health policy makers in countries with Coronavirus Disease 2019 (COVID-19) outbreaks face the decision of when to switch from measures that seek to contain and eliminate the outbreak to those designed to mitigate its effects. Estimates of epidemic size are complicated by surveillance systems that cannot capture all cases, and by the need for timely estimates as the epidemic is ongoing. This article provides a Bayesian methodology to estimate outbreak size from one or more surveillance systems such as virologic testing of pneumonia cases or samples from a network of general practitioners.

Optimal Design of Population-Level Financial Incentives of Influenza Vaccination for the Elderly.

OBJECTIVES: To identify how monetary incentives affect influenza vaccination uptake rate using a randomized control experiment and to subsequently design an optimal incentive program in Singapore, a high-income country with a market-based healthcare system. METHODS: 4000 people aged ≥65 were randomly assigned to 4 treatment groups (1000 each) and were offered a monetary incentive (in shopping vouchers) if they chose to participate. The baseline group was invited to complete a questionnaire with incentives of 10 Singapore dollars (SGD; where 1 SGD ≈ 0.73 USD), whereas the other three groups were invited to complete the questionnaire and be vaccinated against influenza at their own cost of around 32 SGD, in return for incentives of 10, 20, or 30 SGD. RESULTS: Increasing the total incentive for vaccination and reporting from 10 to 20 SGD increased participation in vaccination from 4.5% to 7.5% (P < .001). Increasing the total incentive from 20 to 30 SGD increased the participation rate to 9.2%, but this was not statistically significantly different from a 20-SGD incentive. The group of nonworking elderly were more sensitive to changes in incentives than those who worked. In addition to working status, the effects of increasing incentives on influenza vaccination rates differed by ethnicity, socio-economic status, household size, and a measure of social resilience. There were no significant differential effects by age group, gender, or education, however. The cost of the program per completed vaccination under a 20-SGD incentive is 36.80 SGD, which was the lowest among the three intervention arms. For a hypothetical population-level financial incentive program to promote influenza vaccination among the elderly, accounting for transmission dynamics, an incentive between 10 and 20 SGD minimizes the cost per completed vaccination from both governmental and health system perspectives. CONCLUSIONS: Appropriate monetary incentives can boost influenza vaccination rates. Increasing monetary incentives for vaccination from 10 to 20 SGD can improve the influenza vaccination uptake rate, but further increasing the monetary incentive to 30 SGD results in no additional gains. A partial incentive may therefore be considered to improve vaccination coverage in this high-risk group.

Subgroup identification via homogeneity pursuit for dense longitudinal/spatial data.

In the clinical trial community, it is usually not easy to find a treatment that benefits all patients since the reaction to treatment may differ substantially across different patient subgroups. The heterogeneity of treatment effect plays an essential role in personalized medicine. To facilitate the development of tailored therapies and improve the treatment efficacy, it is important to identify subgroups that exhibit different treatment effects. We consider a very general framework for subgroup identification via the homogeneity pursuit methods usually employed in econometric time series analysis. The change point detection algorithm in our procedure is most suitable for analyzing dense longitudinal or spatial data which are quite common for biomedical studies these days. We demonstrate that our proposed method is fast and accurate through extensive numerical studies. In particular, our method is illustrated by analyzing a diffusion tensor imaging data set.

Cost-Effectiveness Analysis for Influenza Vaccination Coverage and Timing in Tropical and Subtropical Climate Settings: A Modeling Study.

BACKGROUND: The lack of seasonality in influenza epidemics in the tropics makes the application of well-established temperate zone national vaccination plans challenging. OBJECTIVES: We developed an individual-based simulation model to study optimal vaccination scheduling and assess cost-effectiveness of these vaccination schedules in scenarios of no influenza seasonality and the seasonality regimes of Singapore, Taipei, and Tokyo. METHODS: The simulation models heterogeneities in human contact networks, levels of protective antibodies following infection, the effectiveness of the influenza vaccine, and seasonality. Using a no intervention baseline, we consider 3 alternative vaccination strategies: (1) annual vaccination for a percentage of the elderly, (2) biannual vaccination for a percentage of the elderly, and (3) annual vaccination for all elderly and a fraction of the remaining population. We considered 5 vaccination uptake rates for each strategy and modeled the estimated costs, quality-adjusted life years, and incremental cost-effectiveness ratios (ICERs), indicating the cost-effectiveness of each scenario. RESULTS: In Singapore, annual vaccination for a proportion of elderly is largely cost-effective. However, with fixed uptake rates, partial biannual vaccination for the elderly yields a higher ICER than partial annual vaccination for the elderly, resulting in a cost-ineffective ICER. The most optimal strategy is the total vaccination of all the elderly and a proportion of individuals from other age groups, which results in a cost-saving ICER. This finding is consistent across different seasonality regimes. CONCLUSIONS: Tropical countries like Singapore can have comparably cost-effective vaccination strategies as found in countries with winter epidemics. The vaccination of all the elderly and a proportion of other age groups is the most cost-effective strategy, supporting the need for an extensive national influenza vaccination program.

Sparse boosting for high-dimensional survival data with varying coefficients.

Motivated by high-throughput profiling studies in biomedical research, variable selection methods have been a focus for biostatisticians. In this paper, we consider semiparametric varying-coefficient accelerated failure time models for right censored survival data with high-dimensional covariates. Instead of adopting the traditional regularization approaches, we offer a novel sparse boosting (SparseL2 Boosting) algorithm to conduct model-based prediction and variable selection. One main advantage of this new method is that we do not need to perform the time-consuming selection of tuning parameters. Extensive simulations are conducted to examine the performance of our sparse boosting feature selection techniques. We further illustrate our methods using a lung cancer data analysis.

Luteolin Inhibits Tumorigenesis and Induces Apoptosis of Non-Small Cell Lung Cancer Cells via Regulation of MicroRNA-34a-5p.

Luteolin (LTL) exerts remarkable tumor suppressive activity on various types of cancers, including non-small cell lung cancer (NSCLC). However, it is not completely understood whether the mechanism of its action against NSCLC is related to microRNAs (miRNAs). In the present study, we investigated the anti-tumor effects of LTL on NSCLC in vitro and in vivo. The results revealed that LTL could inhibit cell proliferation and induce apoptosis in both A549 and H460 cells. In a H460 xenograft tumor model of nude mice, LTL significantly suppressed tumor growth, inhibited cell proliferation, and induced apoptosis. miRNA microarray and quantitative PCR (qPCR) analysis indicated that miR-34a-5p was dramatically upregulated upon LTL treatment in tumor tissues. Furthermore, MDM4 was proved to be a direct target of miR-34a-5p by luciferase reporter gene assay. LTL treatment was associated with increased p53 and p21 protein expressions and decreased MDM4 protein expression in both NSCLC cells and tumor tissues. When miR-34a-5p was inhibited in vitro, the protein expressions of Bcl-2 and MDM4 were recovered, while that of p53, p21, and Bax were attenuated. Moreover, caspase-3 and caspase-9 activation induced by LHL treatment in vitro were also suppressed by miR-34a-5p inhibition. Overall, LTL could inhibit tumorigenesis and induce apoptosis of NSCLC cells by upregulation of miR-34a-5p via targeting MDM4. These findings provide novel insight into the molecular functions of LTL that suggest its potential as a therapeutic agent for human NSCLC.

Assessment of the coupling coordination relationship between the green financial system and the sustainable development system across China.

Green finance (GF) is recognized as a key driver of sustainable development. While existing studies have extensively discussed the relationship between GF and the Sustainable Development Goals (SDGs), few have explored the coupling coordination relationship between GF and SDGs. In this paper, we use data from thirty Chinese provinces (municipalities and autonomous regions) from 2008-2021 to examine the degree of coupling coordination development (CCD) between GF and the SDGs systems using the CCD model. We find that most SDGs and their sub-goals exhibit a significant upward trend, except for SDG8, 14-16. GF presents a fluctuating upward trend, with a significant decline in 2010 and 2019. The CCDs between GF and SDGs and their sub-goals generally show an M-shaped upward trend in most regions, with most of them experiencing a synchronous decline in 2011-2012 and 2019. In the analysis of regional heterogeneity, the eastern region performs better in SDG8-9, the central region performs better in SDG3, 14-15, while the western region performs better in SDG7. This paper provides empirical evidence for a further in-depth understanding of the relationship between GF and SDGs, which can contribute to advancing GF development and the SDG process.

Direct S-H Evidence Revealing the Photo-electrocatalytic Hydrogen Evolution Reaction Mechanism on CdS Using Surface-Enhanced Raman Spectroscopy.

Photoelectrocatalytic water splitting using metal sulfides is a promising method for green hydrogen production. However, in situ probing of the hydrogen evolution reaction (HER) on sulfides with excellent performance remains a challenge. Here, we construct Au@CdS core-shell nanoparticles to study the HER on CdS, a typical HER catalyst, by surface-enhanced Raman spectroscopy (SERS) using a "borrowing" strategy. We directly capture the spectroscopic evidence of S-H intermediate under HER condition, further verified by isotopic experiments. Moreover, the population of S-H intermediates is improved by injecting charge carriers through light illumination and the S-H bond is weakened by introducing Pt to form a Au@Pt@CdS structure to change the interfacial electronic structure, both of them resulting in significant HER performance improvement. These findings can deepen the understanding of the HER mechanism and offer strategies for designing of cost-effective HER catalyst with high performance.

Direct Capturing and Regulating Key Intermediates for High-Efficiency Oxygen Evolution Reactions.

Developing efficient oxygen evolution reaction (OER) electrocatalysts can greatly advance the commercialization of proton exchange membrane (PEM) water electrolysis. However, the unclear and disputed reaction mechanism and structure-activity relationship of OER pose significant obstacles. Herein, the active site and intermediate for OER on AuIr nanoalloys are simultaneously identified and correlated with the activity, through the integration of in situ shell-isolated nanoparticle-enhanced Raman spectroscopy and X-ray absorption spectroscopy. The AuIr nanoalloys display excellent OER performance with an overpotential of only 246 mV to achieve 10 mA cm-2 and long-term stability under strong acidic conditions. Direct spectroscopic evidence demonstrates that * OO adsorbed on IrOx sites is the key intermediate for OER, and it is generated through the O-O coupling of adsorbed oxygen species directly from water, providing clear support for the adsorbate evolution mechanism. Moreover, the Raman information of the * OO intermediate can serve as a universal "in situ descriptor" that can be obtained both experimentally and theoretically to accelerate the catalyst design. It unveils that weakening the interactions of * OO on the catalysts and facilitating its desorption would boost the OER performance. This work deepens the mechanistic understandings on OER and provides insightful guidance for the design of more efficient OER catalysts.

In situ Raman spectroscopy reveals the structure evolution and lattice oxygen reaction pathway induced by the crystalline-amorphous heterojunction for water oxidation.

One of the most successful approaches for balancing the high stability and activity of water oxidation in alkaline solutions is to use amorphous and crystalline heterostructures. However, due to the lack of direct evidence at the molecular level, the nano/micro processes of amorphous and crystalline heterostructure electrocatalysts, including self-reconstruction and reaction pathways, remain unknown. Herein, the Leidenfrost effect assisted electrospray approach combined with phase separation was used for the first time to create amorphous NiO x /crystalline α-Fe2O3 (a-NiO x /α-Fe2O3) nanowire arrays. The results of in situ Raman spectroscopy demonstrate that with the increase of the potential at the a-NiO x /α-Fe2O3 interface, a significant accumulation of OH can be observed. Combining with XAS spectra and DFT calculations, we believe that more OH adsorption on the Ni centers can facilitate Ni2+ deprotonation to achieve the high-valence oxidation of Ni4+ according to HSAB theory (Fe3+ serves as a strong Lewis acid). This result promotes the electrocatalysts to follow the lattice oxygen activation mechanism. This work, for the first time, offers direct spectroscopic evidence for deepening the fundamental understanding of the Lewis acid effect of Fe3+, and reveals the synergistic effect on water oxidation via the unique amorphous and crystalline heterostructures.

Ultrafast and field-based detection of methamphetamine in hair with Au nanocake-enhanced Raman spectroscopy.

The disaster and devastation from abuse of Methamphetamine (MAMP) have a serious impact on people's mental and physical health. Developing a rapid and accurate method to screen drug suspects and thus control MAMP abuse is essential to social security. Hair analysis for MAMP detection is considered to be one of the most potential methods for monitoring drug abuse due to its convenient sample collection, easy for storage and long traceability period. However, the current accurate detection of MAMP in hair primarily utilizes hyphenated mass spectrometry (MS) techniques, but it is not suitable for field-based detection due to the bulky instrument. Hence, developing alternative portable detection techniques for rapid on-site detection of MAMP in hair is an urgent problem to be solved. Here, the high-performance Au nanocakes (Au NCs) were constructed as surface-enhanced Raman spectroscopy (SERS) substrates to detect MAMP in hair, realizing 5 min ultrafast and ultrasensitive detection utilizing a portable Raman spectrometer. Experiments and finite-difference time-domain (FDTD) simulations show that Au NCs have stronger enhancement than Au nanospheres (Au NPs), and 0.5 ppb (3.35 × 10-9 M) MAMP standard is stably detected by Au NCs as an enhanced substrate. A strategy of liquid-liquid microextraction was exploited to eliminate the interference of complex matrices in hair. This method exhibited excellent reproducibility and temporal stability across different drug addicts (relative standard deviation was 5.14% within 160 s). Our approach shows great promise in public safety, providing a rapid and accurate method to detect in hair by SERS.

Neuroprotective assessment of prolonged local hypothermia post contusive spinal cord injury in rodent model.

BACKGROUND CONTEXT: Although general hypothermia is recognized as a clinically applicable neuroprotective intervention, acute moderate local hypothermia post contusive spinal cord injury (SCI) is being considered a more effective approach. Previously, we have investigated the feasibility and safety of inducing prolonged local hypothermia in the central nervous system of a rodent model. PURPOSE: Here, we aimed to verify the efficacy and neuroprotective effects of 5 and 8 hours of local moderate hypothermia (30±0.5°C) induced 2 hours after moderate thoracic contusive SCI in rats. STUDY DESIGN: Rats were induced with moderate SCI (12.5 mm) at its T8 section. Local hypothermia (30±0.5°C) was induced 2 hours after injury induction with an M-shaped copper tube with flow of cold water (12°C), from the T6 to the T10 region. Experiment groups were divided into 5-hour and 8-hour hypothermia treatment groups, respectively, whereas the normothermia control group underwent no hypothermia treatment. METHODS: The neuroprotective effects were assessed through objective weekly somatosensory evoked potential (SSEP) and motor behavior (basso, beattie and bresnahan Basso, Beattie and Bresnahan (BBB) scoring) monitoring. Histology on spinal cord was performed until at the end of day 56. All authors declared no conflict of interest. This work was supported by the Singapore Institute for Neurotechnology Seed Fund (R-175-000-121-733), National University of Singapore, Ministry of Education, Tier 1 (R-172-000-414-112.). RESULTS: Our results show significant SSEP amplitudes recovery in local hypothermia groups starting from day 14 post-injury onward for the 8-hour treatment group, which persisted up to days 28 and 42, whereas the 5-hour group showed significant improvement only at day 42. The functional improvement plateaued after day 42 as compared with control group of SCI with normothermia. This was supported by both 5-hour and 8-hour improvement in locomotion as measured by BBB scores. Local hypothermia also observed insignificant changes in its SSEP latency, as compared with the control. In addition, 5- and 8-hour hypothermia rats' spinal cord showed higher percentage of parenchyma preservation. CONCLUSIONS: Early local moderate hypothermia can be induced for extended periods of time post SCI in the rodent model. Such intervention improves functional electrophysiological outcome and motor behavior recovery for a long time, lasting until 8 weeks.

Improvement Screening for Ultra-High Dimensional Data with Censored Survival Outcomes and Varying Coefficients.

Motivated by risk prediction studies with ultra-high dimensional bio markers, we propose a novel improvement screening methodology. Accurate risk prediction can be quite useful for patient treatment selection, prevention strategy or disease management in evidence-based medicine. The question of how to choose new markers in addition to the conventional ones is especially important. In the past decade, a number of new measures for quantifying the added value from the new markers were proposed, among which the integrated discrimination improvement (IDI) and net reclassification improvement (NRI) stand out. Meanwhile, C-statistics are routinely used to quantify the capacity of the estimated risk score in discriminating among subjects with different event times. In this paper, we will examine these improvement statistics as well as the norm-based approach for evaluating the incremental values of new markers and compare these four measures by analyzing ultra-high dimensional censored survival data. In particular, we consider Cox proportional hazards models with varying coefficients. All measures perform very well in simulations and we illustrate our methods in an application to a lung cancer study.