Highly Active Photoreduction of Atmospheric-Concentration CO2 into CH3COOH over Palladium Particles on Nb2O5 Nanosheets.

The endeavor to drive CO2 photoreduction towards the synthesis of C2 products is largely thwarted by the colossal energy hurdle inherent in C-C coupling. Herein, we load active metal particles on metal oxide nanosheets to build the dual metal pair sites for steering C-C coupling to form C2 products. Taking Pd particles anchored on the Nb2O5 nanosheets as an example, the high-angle annular dark-field image and X-ray photoelectron spectroscopy demonstrate the presence of Pd-Nb metal pair sites on the Pd-Nb2O5 nanosheets. Density functional theory calculations reveal these sites exhibit a low reaction energy barrier of only 1.02 eV for C-C coupling, implying that the introduction of Pd particles effectively tailors the reaction step to form C2 products. Therefore, the Pd-Nb2O5 nanosheets achieve a CH3COOH evolution rate of 13.5 µmol g-1 h-1 in photoreduction of atmospheric-concentration CO2, outshining all other single photocatalysts reported to date under analogous conditions.

Recent progress in solar-driven CO2 reduction to multicarbon products.

Currently, most catalysts used for photoconverting carbon dioxide (CO2) typically produce C1 products. Achieving multicarbon (C2+) products, which are highly desirable due to their greater energy density and economic potential, still remains a significant challenge. This difficulty is primarily due to the kinetic hurdles associated with the C-C coupling step in the process. Given this, devising diverse strategies to accelerate C-C coupling for generating multicarbon products is requisite. Herein, we first give a classification of catalysts involved in the photoconversion of CO2 to C2+ fuels. We summarize metallic oxides, metallic sulfides, MXenes, and metal-organic frameworks as catalysts for CO2 photoreduction to C2+ products, attributing their efficacy to the inherent dual active sites facilitating C-C coupling. In addition, we survey covalent organic frameworks, carbon nitrides, metal phosphides, and graphene as cocatalysts for CO2 photoreduction to C2+ products, owing to the incorporated dual active sites that induce C-C coupling. In the end, we provide a brief conclusion and an outlook on designing new photocatalysts, understanding the catalytic mechanisms, and considering the practical application requirements for photoconverting CO2 into multicarbon products.

Regulated Photocatalytic CO2-to-CH3OH Pathway by Synergetic Dual Active Sites of Interlayer.

Herein, composites of nanosheets with van der Waals contacts are employed to disclose how the interlayer-microenvironment affects the product selectivity of carbon dioxide (CO2) photoreduction. The concept of composites of nanosheets with dual active sites is introduced to manipulate the bonding configuration and promote the thermodynamic formation of methanol (CH3OH). As a prototype, the CoNi2S4-In2O3 composites of nanosheets are prepared, in which high-resolution transmission electron microscopy imaging, X-ray photoelectron spectroscopy spectra, and zeta potential tests confirm the presence of van der Waals contacts rather than chemical bonding between the In2O3 nanosheets and the CoNi2S4 nanosheets within the composite. The fabricated CoNi2S4-In2O3 composites of nanosheets exhibit the detection of the key intermediate *CH3O during CO2 photoreduction through in situ Fourier transform infrared spectra, while the In2O3 nanosheets and CoNi2S4 nanosheets alone do not show this capability, further verified by the density functional theory calculations. Accordingly, the CoNi2S4-In2O3 composites of nanosheets show the ability to produce CH3OH, whereas the CoNi2S4 and In2O3 nanosheets solely generate carbon monoxide products from CO2 photoreduction.

Balancing efficacy and computational burden: weighted mean, multiple imputation, and inverse probability weighting methods for item non-response in reliable scales.

IMPORTANCE: Scales often arise from multi-item questionnaires, yet commonly face item non-response. Traditional solutions use weighted mean (WMean) from available responses, but potentially overlook missing data intricacies. Advanced methods like multiple imputation (MI) address broader missing data, but demand increased computational resources. Researchers frequently use survey data in the All of Us Research Program (All of Us), and it is imperative to determine if the increased computational burden of employing MI to handle non-response is justifiable. OBJECTIVES: Using the 5-item Physical Activity Neighborhood Environment Scale (PANES) in All of Us, this study assessed the tradeoff between efficacy and computational demands of WMean, MI, and inverse probability weighting (IPW) when dealing with item non-response. MATERIALS AND METHODS: Synthetic missingness, allowing 1 or more item non-response, was introduced into PANES across 3 missing mechanisms and various missing percentages (10%-50%). Each scenario compared WMean of complete questions, MI, and IPW on bias, variability, coverage probability, and computation time. RESULTS: All methods showed minimal biases (all <5.5%) for good internal consistency, with WMean suffered most with poor consistency. IPW showed considerable variability with increasing missing percentage. MI required significantly more computational resources, taking >8000 and >100 times longer than WMean and IPW in full data analysis, respectively. DISCUSSION AND CONCLUSION: The marginal performance advantages of MI for item non-response in highly reliable scales do not warrant its escalated cloud computational burden in All of Us, particularly when coupled with computationally demanding post-imputation analyses. Researchers using survey scales with low missingness could utilize WMean to reduce computing burden.

A polarized multicomponent foundation upholds ciliary central microtubules.

Cilia's back-and-forth beat pattern requires a central pair (CP) of microtubules. However, the mechanism by which the CP is upheld above the transition zone (TZ) remains unclear. Here, we showed that a rod-like substructure marked by Cep131 and ciliary Centrin serves as a polarized CP-supporting foundation. This CP-foundation (CPF) was assembled independently of the CP during ciliogenesis in mouse ependymal cells. It protruded from the distal end of the basal body out of the TZ to enwrap the proximal end of the CP. Through proximity labeling, we identified 26 potential CPF components, among which Ccdc148 specifically localized at the proximal region of Centrin-decorated CPF and was complementary to the Cep131-enriched distal region. Cep131 deficiency abolished the CPF, resulting in CP penetration into the TZ. Consequently, cilia became prone to ultrastructural abnormality and paralysis, and Cep131-deficient mice were susceptible to late-onset hydrocephalus. In addition to Centrin, phylogenetic analysis also indicated conservations of Ccdc131 and Ccdc148 from protists to mammals, suggesting that the CPF is an evolutionarily conserved multicomponent CP-supporting platform in cilia.

Design Photocatalysts to Boost Carrier Dynamics in Plastics Photoconversion into Fuels.

Solar-driven plastics conversion into valuable fuels has attracted broad attention in recent years, which has enormous potential for plastics recycling in the future. However, it usually encounters low conversion efficiency, where one of the reasons is attributed to the poor carrier dynamics in the photocatalytic process. In this Perspective, we critically review the developed strategies, involving defect engineering, doping engineering, heterojunction engineering, and composite construction, for boosted carrier separation efficiency. In addition, we provide an outlook for more potential strategies to engineer catalysts for promoted carrier dynamics. Finally, we also propose prospects for the future research direction of plastics photoconversion into fuels.

Germline loss in C. elegans enhances longevity by disrupting adhesion between niche and stem cells.

Ageing and fertility are intertwined. Germline loss extends the lifespan in various organisms, termed gonadal longevity. However, the original longevity signal from the somatic gonad remains poorly understood. Here, we focused on the interaction between germline stem cells (GSCs) and their niche, the distal tip cells (DTCs), to explore the barely known longevity signal from the somatic gonad in C. elegans. We found that removing germline disrupts the cell adhesions between GSC and DTC, causing a significant transcriptomic change in DTC through hmp-2/ß-catenin and two GATA transcription factors, elt-3 and pqm-1 in this niche cell. Inhibiting elt-3 and pqm-1 in DTC suppresses gonadal longevity. Moreover, we further identified the TGF-ß ligand, tig-2, as the cytokine from DTC upon the loss of germline, which evokes the downstream gonadal longevity signalling throughout the body. Our findings thus reveal the source of the longevity signalling in response to germline removal, highlighting the stem cell niche as a critical signalling hub in ageing.

Optimizing word embeddings for small dataset: a case study on patient portal messages from breast cancer patients.

Patient portal messages often relate to specific clinical phenomena (e.g., patients undergoing treatment for breast cancer) and, as a result, have received increasing attention in biomedical research. These messages require natural language processing and, while word embedding models, such as word2vec, have the potential to extract meaningful signals from text, they are not readily applicable to patient portal messages. This is because embedding models typically require millions of training samples to sufficiently represent semantics, while the volume of patient portal messages associated with a particular clinical phenomenon is often relatively small. We introduce a novel adaptation of the word2vec model, PK-word2vec (where PK stands for prior knowledge), for small-scale messages. PK-word2vec incorporates the most similar terms for medical words (including problems, treatments, and tests) and non-medical words from two pre-trained embedding models as prior knowledge to improve the training process. We applied PK-word2vec in a case study of patient portal messages in the Vanderbilt University Medical Center electric health record system sent by patients diagnosed with breast cancer from December 2004 to November 2017. We evaluated the model through a set of 1000 tasks, each of which compared the relevance of a given word to a group of the five most similar words generated by PK-word2vec and a group of the five most similar words generated by the standard word2vec model. We recruited 200 Amazon Mechanical Turk (AMT) workers and 7 medical students to perform the tasks. The dataset was composed of 1389 patient records and included 137,554 messages with 10,683 unique words. Prior knowledge was available for 7981 non-medical and 1116 medical words. In over 90% of the tasks, both reviewers indicated PK-word2vec generated more similar words than standard word2vec (p = 0.01).The difference in the evaluation by AMT workers versus medical students was negligible for all comparisons of tasks' choices between the two groups of reviewers ( p = 0.774 under a paired t-test). PK-word2vec can effectively learn word representations from a small message corpus, marking a significant advancement in processing patient portal messages.

Progress and Perspective for "Green" Strategies of Catalytic Plastics Conversion into Fuels by Regulating Half-Reactions.

Nowadays, plastic waste threatens public health and the natural ecosystems of our lives. It is highly beneficial to recycle plastic waste in order to maximize the reuse of its contained carbon sources for the development of other valuable products. Unfortunately, traditional techniques usually require significant energy consumption and result in the generation of hazardous waste. Herein, the up-to-date developments on the "green" strategies under mild conditions including electrocatalysis, photocatalysis, and photoelectrocatalysis of plastic wastes are presented. During the oxidation of plastics in these "green" strategies, corresponding reduction reactions usually exist, which affect the property of catalytic plastics conversion. Particularly, we mainly focus on how to design the corresponding half reactions, such as the water reduction, carbon dioxide reduction, and nitrate reduction. Finally, we provide forward-looking insight into the enhancement of these "green" strategies, the extension of more half reactions into other organic catalysis, a comprehensive exploration of the underlying mechanisms through in situ studies and theoretical analysis and the problems for practical applications that needs to be solved.

Confined CO in a sandwich structure promotes C-C coupling in electrocatalytic CO2 reduction.

Microenvironment regulation near the catalyst surface plays a critical role in heterogeneous electrocatalytic reactions. The local concentration of reactants and intermediates significantly affects the reaction kinetics and product selectivity. Herein, we propose an innovative strategy of utilizing the spatial confinement effect in a sandwich-structured C/Cu/C assembly to regulate kinetic mass transport during the electrocatalytic CO2 reduction reaction. The sandwich C/Cu/C assembly catalyst was successfully prepared using a simple bidirectional freezing and freeze-drying method. The sandwich structure changes the free diffusion pathway of the CO intermediate within the sandwich interlayer and helps confine CO with locally increased CO concentration near the catalyst surface, which in turn promotes C-C coupling and thus improves the reaction activity and doubles the C2 product selectivity compared to its disordered mixture counterpart. This kinetics regulation in the sandwich structure may provide a new insight into the catalyst design and inspire the understanding of the structure-performance relationship in electrocatalysis.

Interfacial-Assembly-Induced In Situ Transformation from Aligned 1D Nanowires to Quasi-2D Nanofilms.

As the dimensionality of materials generally affects their characteristics, thin films composed of low-dimensional nanomaterials, such as nanowires (NWs) or nanoplates, are of great importance in modern engineering. Among various bottom-up film fabrication strategies, interfacial assembly of nanoscale building blocks holds great promise in constructing large-scale aligned thin films, leading to emergent or enhanced collective properties compared to individual building blocks. As for 1D nanostructures, the interfacial self-assembly causes the morphology orientation, effectively achieving anisotropic electrical, thermal, and optical conduction. However, issues such as defects between each nanoscale building block, crystal orientation, and homogeneity constrain the application of ordered films. The precise control of transdimensional synthesis and the formation mechanism from 1D to 2D are rarely reported. To meet this gap, we introduce an interfacial-assembly-induced interfacial synthesis strategy and successfully synthesize quasi-2D nanofilms via the oriented attachment of 1D NWs on the liquid interface. Theoretical sampling and simulation show that NWs on the liquid interface maintain their lowest interaction energy for the ordered crystal plane (110) orientation and then rearrange and attach to the quasi-2D nanofilm. This quasi-2D nanofilm shows enhanced electric conductivity and unique optical properties compared with its corresponding 1D geometry materials. Uncovering these growth pathways of the 1D-to-2D transition provides opportunities for future material design and synthesis at the interface.

Perceptions and Utilization of Online Peer Support Among Informal Dementia Caregivers: Survey Study.

Background: Informal dementia caregivers are those who care for a person living with dementia and do not receive payment (eg, family members, friends, or other unpaid caregivers). These informal caregivers are subject to substantial mental, physical, and financial burdens. Online communities enable these caregivers to exchange caregiving strategies and communicate experiences with other caregivers whom they generally do not know in real life. Research has demonstrated the benefits of peer support in online communities, but this research is limited, focusing merely on caregivers who are already online community users. Objective: We aimed to investigate the perceptions and utilization of online peer support through a survey. Methods: Following the Andersen and Newman Framework of Health Services Utilization and using REDCap (Research Electronic Data Capture), we designed and administered a survey to investigate the perceptions and utilization of online peer support among informal dementia caregivers. Specifically, we collected types of information that influence whether an informal dementia caregiver accesses online peer support: predisposing factors, which refer to the sociocultural characteristics of caregivers, relationships between caregivers and people living with dementia, and belief in the value of online peer support; enabling factors, which refer to the logistic aspects of accessing online peer support (eg, eHealth literacy and access to high-speed internet); and need factors, which are the most immediate causes of seeking online peer support. We also collected data on caregivers' experiences with accessing online communities. We distributed the survey link on November 14, 2022, within two online locations: the Alzheimer's Association website (as an advertisement) and ALZConnected (an online community organized by the Alzheimer's Association). We collected all responses on February 23, 2023, and conducted a regression analysis to identifyn factors that were associated with accessing online peer support. Results: We collected responses from 172 dementia caregivers. Of these participants, 140 (81.4%) completed the entire survey. These caregivers were aged 19 to 87 (mean 54, SD 13.5) years, and a majority were female (123/140, 87.9%) and White (126/140, 90%). Our findings show that the behavior of accessing any online community was significantly associated with participants' belief in the value of online peer support (P=.006). Moreover, of the 40 non-online community caregivers, 33 (83%) had a belief score above 24-the score that was assigned when a neutral option was selected for each belief question. The most common reasons for not accessing any online community were having no time to do so (14/140, 10%) and having insufficient online information-searching skills (9/140, 6.4%). Conclusions: Our findings suggest that online peer support is valuable, but practical strategies are needed to assist informal dementia caregivers who have limited time or online information-searching skills.

Extracting Systemic Anticancer Therapy and Response Information From Clinical Notes Following the RECIST Definition.

PURPOSE: The RECIST guidelines provide a standardized approach for evaluating the response of cancer to treatment, allowing for consistent comparison of treatment efficacy across different therapies and patients. However, collecting such information from electronic health records manually can be extremely labor-intensive and time-consuming because of the complexity and volume of clinical notes. The aim of this study is to apply natural language processing (NLP) techniques to automate this process, minimizing manual data collection efforts, and improving the consistency and reliability of the results. METHODS: We proposed a complex, hybrid NLP system that automates the process of extracting, linking, and summarizing anticancer therapy and associated RECIST-like responses from narrative clinical text. The system consists of multiple machine learning-/deep learning-based and rule-based modules for diverse NLP tasks such as named entity recognition, assertion classification, relation extraction, and text normalization, to address different challenges associated with anticancer therapy and response information extraction. We then evaluated the system performances on two independent test sets from different institutions to demonstrate its effectiveness and generalizability. RESULTS: The system used domain-specific language models, BioBERT and BioClinicalBERT, for high-performance therapy mentions identification and RECIST responses extraction and categorization. The best-performing model achieved a 0.66 score in linking therapy and RECIST response mentions, with end-to-end performance peaking at 0.74 after relation normalization, indicating substantial efficacy with room for improvement. CONCLUSION: We developed, implemented, and tested an information extraction system from clinical notes for cancer treatment and efficacy assessment information. We expect this system will support future cancer research, particularly oncologic studies that focus on efficiently assessing the effectiveness and reliability of cancer therapeutics.

Optimizing Word Embeddings for Patient Portal Message Datasets with a Small Number of Samples.

Background: Patient portal messages often relate to specific clinical phenomena (e.g., patients undergoing treatment for breast cancer) and, as a result, have received increasing attention in biomedical research. These messages require natural language processing and, while word embedding models, such as word2vec, have the potential to extract meaningful signals from text, they are not readily applicable to patient portal messages. This is because embedding models typically require millions of training samples to sufficiently represent semantics, while the volume of patient portal messages associated with a particular clinical phenomenon is often relatively small. Objective: We introduce a novel adaptation of the word2vec model, PK-word2vec, for small-scale messages. Methods: PK-word2vec incorporates the most similar terms for medical words (including problems, treatments, and tests) and non-medical words from two pre-trained embedding models as prior knowledge to improve the training process. We applied PK-word2vec on patient portal messages in the Vanderbilt University Medical Center electric health record system sent by patients diagnosed with breast cancer from December 2004 to November 2017. We evaluated the model through a set of 1000 tasks, each of which compared the relevance of a given word to a group of the five most similar words generated by PK-word2vec and a group of the five most similar words generated by the standard word2vec model. We recruited 200 Amazon Mechanical Turk (AMT) workers and 7 medical students to perform the tasks. Results: The dataset was composed of 1,389 patient records and included 137,554 messages with 10,683 unique words. Prior knowledge was available for 7,981 non-medical and 1,116 medical words. In over 90% of the tasks, both reviewers indicated PK-word2vec generated more similar words than standard word2vec (p=0.01).The difference in the evaluation by AMT workers versus medical students was negligible for all comparisons of tasks' choices between the two groups of reviewers (p = 0.774 under a paired t-test). Conclusions: PK-word2vec can effectively learn word representations from a small message corpus, marking a significant advancement in processing patient portal messages.

All of Us participant perspectives on the return of value in research.

PURPOSE: To understand participant preferences for receiving specific types of research information, whether information preferences vary across sociodemographic groups, and the types of health providers participants could access to understand returned information. METHODS: All of Us Research Program participants completed a value of returning research information survey. Stratified sampling was implemented to enhance participant diversity and avoid noncoverage. We used weighted multivariable logistic regression to evaluate associations between the most valuable information types, access to providers, and sociodemographic variables. RESULTS: Participants (N = 20,405) were diverse in their race/ethnicity (eg, 52% were White, 18% were Hispanic/Latino or Spanish, 3% were Asian, and 20% were Black or African American). Most participants (78.6%) valued information about their risk of serious genetic diseases with available treatment. Primary care physicians, specialists, and genetic counselors were the top providers that participants could access for help understanding returned information. Information preferences and provider access varied across sociodemographic groups. For example, as income levels increased, the odds of placing value on genetic results indicating risk of serious disease with available treatment increased when compared with the lowest income levels (P value < .001). CONCLUSION: Although genetic information was most valuable to participants, preferences about specific information types varied across sociodemographic groups.

Identifying erroneous height and weight values from adult electronic health records in the All of Us research program.

INTRODUCTION: Electronic Health Records (EHR) are a useful data source for research, but their usability is hindered by measurement errors. This study investigated an automatic error detection algorithm for adult height and weight measurements in EHR for the All of Us Research Program (All of Us). METHODS: We developed reference charts for adult heights and weights that were stratified on participant sex. Our analysis included 4,076,534 height and 5,207,328 wt measurements from âˆ¼ 150,000 participants. Errors were identified using modified standard deviation scores, differences from their expected values, and significant changes between consecutive measurements. We evaluated our method with chart-reviewed heights (8,092) and weights (9,039) from 250 randomly selected participants and compared it with the current cleaning algorithm in All of Us. RESULTS: The proposed algorithm classified 1.4 % of height and 1.5 % of weight errors in the full cohort. Sensitivity was 90.4 % (95 % CI: 79.0-96.8 %) for heights and 65.9 % (95 % CI: 56.9-74.1 %) for weights. Precision was 73.4 % (95 % CI: 60.9-83.7 %) for heights and 62.9 (95 % CI: 54.0-71.1 %) for weights. In comparison, the current cleaning algorithm has inferior performance in sensitivity (55.8 %) and precision (16.5 %) for height errors while having higher precision (94.0 %) and lower sensitivity (61.9 %) for weight errors. DISCUSSION: Our proposed algorithm outperformed in detecting height errors compared to weights. It can serve as a valuable addition to the current All of Us cleaning algorithm for identifying erroneous height values.

Nitrogen Doping-Roused Synergistic Active Sites in Perovskite Enabling Highly Selective CO2 Photoreduction into CH4.

The intricate protonation process in carbon dioxide reduction usually makes the product unpredictable. Thus, it is significant to control the reactive intermediates to manipulate the reaction steps. Here, we propose that the synergistic La-Ti active sites in the N-La2Ti2O7 nanosheets enable the highly selective carbon dioxide photoreduction into methane. In the photoreduction of CO2 over N-La2Ti2O7 nanosheets, in situ Fourier transform infrared spectra are utilized to monitor the *CH3O intermediate, pivotal for methane production, whereas such monitoring is not conducted for La2Ti2O7 nanosheets. Also, theoretical calculations testify to the increased charge densities on the Ti and La atoms and the regulated formation energy barrier of *CO and *CH3O intermediates by the constructed synergistic active sites. Accordingly, the methane formation rate of 7.97 µL h-1 exhibited by the N-La2Ti2O7 nanosheets, along with an electron selectivity of 96.6%, exceeds that of most previously reported catalysts under similar conditions.

Randomized Controlled Comparative Effectiveness Trial of Risk Model-Guided Clinical Decision Support for Suicide Screening.

Suicide prevention requires risk identification, appropriate intervention, and follow-up. Traditional risk identification relies on patient self-reporting, support network reporting, or face-to-face screening with validated instruments or history and physical exam. In the last decade, statistical risk models have been studied and more recently deployed to augment clinical judgment. Models have generally been found to be low precision or problematic at scale due to low incidence. Few have been tested in clinical practice, and none have been tested in clinical trials to our knowledge. Methods: We report the results of a pragmatic randomized controlled trial (RCT) in three outpatient adult Neurology clinic settings. This two-arm trial compared the effectiveness of Interruptive and Non-Interruptive Clinical Decision Support (CDS) to prompt further screening of suicidal ideation for those predicted to be high risk using a real-time, validated statistical risk model of suicide attempt risk, with the decision to screen as the primary end point. Secondary outcomes included rates of suicidal ideation and attempts in both arms. Manual chart review of every trial encounter was used to determine if suicide risk assessment was subsequently documented. Results: From August 16, 2022, through February 16, 2023, our study randomized 596 patient encounters across 561 patients for providers to receive either Interruptive or Non-Interruptive CDS in a 1:1 ratio. Adjusting for provider cluster effects, Interruptive CDS led to significantly higher numbers of decisions to screen (42%=121/289 encounters) compared to Non-Interruptive CDS (4%=12/307) (odds ratio=17.7, p-value <0.001). Secondarily, no documented episodes of suicidal ideation or attempts occurred in either arm. While the proportion of documented assessments among those noting the decision to screen was higher for providers in the Non-Interruptive arm (92%=11/12) than in the Interruptive arm (52%=63/121), the interruptive CDS was associated with more frequent documentation of suicide risk assessment (63/289 encounters compared to 11/307, p-value<0.001). Conclusions: In this pragmatic RCT of real-time predictive CDS to guide suicide risk assessment, Interruptive CDS led to higher numbers of decisions to screen and documented suicide risk assessments. Well-powered large-scale trials randomizing this type of CDS compared to standard of care are indicated to measure effectiveness in reducing suicidal self-harm. ClinicalTrials.gov Identifier: NCT05312437.

Measuring social determinants of health in the All of Us Research Program.

To accelerate medical breakthroughs, the All of Us Research Program aims to collect data from over one million participants. This report outlines processes used to construct the All of Us Social Determinants of Health (SDOH) survey and presents the psychometric characteristics of SDOH survey measures in All of Us. A consensus process was used to select SDOH measures, prioritizing concepts validated in diverse populations and other national cohort surveys. Survey item non-response was calculated, and Cronbach's alpha was used to analyze psychometric properties of scales. Multivariable logistic regression models were used to examine associations between demographic categories and item non-response. Twenty-nine percent (N = 117,783) of eligible All of Us participants submitted SDOH survey data for these analyses. Most scales had less than 5% incalculable scores due to item non-response. Patterns of item non-response were seen by racial identity, educational attainment, income level, survey language, and age. Internal consistency reliability was greater than 0.80 for almost all scales and most demographic groups. The SDOH survey demonstrated good to excellent reliability across several measures and within multiple populations underrepresented in biomedical research. Bias due to survey non-response and item non-response will be monitored and addressed as the survey is fielded more completely.

Stress-induced ordering evolution of 1D segmented heteronanostructures and their chemical post-transformations.

Investigations of one-dimensional segmented heteronanostructures (1D-SHs) have recently attracted much attention due to their potentials for applications resulting from their structure and synergistic effects between compositions and interfaces. Unfortunately, developing a simple, versatile and controlled synthetic method to fabricate 1D-SHs is still a challenge. Here we demonstrate a stress-induced axial ordering mechanism to describe the synthesis of 1D-SHs by a general under-stoichiometric reaction strategy. Using the continuum phase-field simulations, we elaborate a three-stage evolution process of the regular segment alternations. This strategy, accompanied by easy chemical post-transformations, enables to synthesize 25 1D-SHs, including 17 nanowire-nanowire and 8 nanowire-nanotube nanostructures with 13 elements (Ag, Te, Cu, Pt, Pb, Cd, Sb, Se, Bi, Rh, Ir, Ru, Zn) involved. This ordering evolution-driven synthesis will help to investigate the ordering reconstruction and potential applications of 1D-SHs.