Temperature-Immune, Wide-Range Flexible Robust Pressure Sensors for Harsh Environments.

Flexible pressure sensors possess vast potential for various applications such as new energy batteries, aerospace engines, and rescue robots owing to their exceptional flexibility and adaptability. However, the existing sensors face significant challenges in maintaining long-term reliability and environmental resilience when operating in harsh environments with variable temperatures and high pressures (∼MPa), mainly due to possible mechanical mismatch and structural instability. Here, we propose a composite scheme for a flexible piezoresistive pressure sensor to improve its robustness by utilizing material design of near-zero temperature coefficient of resistance (TCR), radial gradient pressure-dividing microstructure, and flexible interface bonding process. The sensing layer comprising multiwalled carbon nanotubes (MWCNTs), graphite (GP), and thermoplastic polyurethane (TPU) was optimized to achieve a near-zero temperature coefficient of resistance over a temperature range of 25-70 °C, while the radial gradient microstructure layout based on pressure division increases the range of pressure up to 2 MPa. Furthermore, a flexible interface bonding process introduces a self-soluble transition layer by direct-writing TPU bonding solution at the bonding interface, which enables the sensor to achieve signal fluctuations as low as 0.6% and a high interface strength of up to 1200 kPa. Moreover, it has been further validated for its capability of monitoring the physiological signals of athletes as well as the long-term reliable environmental resilience of the expansion pressure of the power cell. This work demonstrates that the proposed scheme sheds new light on the design of robust pressure sensors for harsh environments.

Successes and Barriers of Health Information Exchange Participation Across Hospitals in South Carolina From 2014 to 2020: Longitudinal Observational Study.

BACKGROUND: The 2009 Health Information Technology for Economic and Clinical Health Act sets three stages of Meaningful Use requirements for the electronic health records incentive program. Health information exchange (HIE) technologies are critical in the meaningful use of electronic health records to support patient care coordination. However, HIE use trends and barriers remain unclear across hospitals in South Carolina (SC), a state with the earliest HIE implementation. OBJECTIVE: This study aims to explore changes in the proportion of HIE participation and factors associated with HIE participation, and barriers to exchange and interoperability across SC hospitals. METHODS: This study derived data from a longitudinal data set of the 2014-2020 American Hospital Association Information Technology Supplement for 69 SC hospitals. The primary outcome was whether a hospital participated in HIE in a year. A cross-sectional multivariable logistic regression model, clustered at the hospital level and weighted by bed size, was used to identify factors associated with HIE participation. The second outcome was barriers to sending, receiving, or finding patient health information to or from other organizations or hospital systems. The frequency of hospitals reporting each barrier related to exchange and interoperability were then calculated. RESULTS: Hospitals in SC have been increasingly participating in HIE, improving from 43% (24/56) in 2014 to 82% (54/66) in 2020. After controlling for other hospital factors, teaching hospitals (adjusted odds ratio [AOR] 3.7, 95% CI 1.0-13.3), system-affiliated hospitals (AOR 6.6, 95% CI 3.2-13.7), and rural referral hospitals (AOR 8.0, 95% CI 1.2-53.4) had higher odds to participate in HIE than their counterparts, whereas critical access hospitals (AOR 0.1, 95% CI 0.02-0.6) were less likely to participate in HIE than their counterparts reimbursed by the prospective payment system. Hospitals with greater ratios of Medicare or Medicaid inpatient days to total inpatient days also reported higher odds of HIE participation. Despite the majority of hospitals reporting HIE participation in 2020, barriers to exchange and interoperability remained, including lack of provider contacts (27/40, 68%), difficulty in finding patient health information (27/40, 68%), adapting different vendor platforms (26/40, 65%), difficulty matching or identifying same patients between systems (23/40, 58%), and providers that do not typically exchange patient data (23/40, 58%). CONCLUSIONS: HIE participation has been widely adopted in SC hospitals. Our findings highlight the need to incentivize optimization of HIE and seamless information exchange by facilitating and implementing standardization of health information across various HIE systems and by addressing other technical issues, including providing providers' addresses and training HIE stakeholders to find relevant information. Policies and efforts should include more collaboration with vendors to reduce platform compatibility issues and more user engagement and technical training and support to facilitate effective, accurate, and efficient exchange of provider contacts and patient health information.

High-Linearity Flexible Pressure Sensor Based on the Gaussian-Curve-Shaped Microstructure for Human Physiological Signal Monitoring.

Flexible pressure sensors with high-performance show broad application prospects in health monitoring, wearable electronic devices, intelligent robot sensing, and other fields. Although flexible pressure sensors have made significant progress in sensitivity and detection range, most of them still exhibit strong nonlinearity, which leads to significant troubles in signal acquisition and thus limits their popularity in practical applications. It remains a serious challenge for the flexible pressure sensor to achieve high linearity while maintaining high sensitivity. Herein, a doped sensing membrane with a uniformly distributed Gaussian-curve-shaped micropattern array was developed using the micro-electromechanical systems (MEMS) process, and a flexible sensor structure with the doped film as the core was designed and constructed. The prototype sensor has a high sensitivity of 1.77 kPa-1 and a linearity of 0.99 in the full detection range of 20 Pa to 30 kPa. In addition, its excellent performance also includes fast response/recovery times (∼25/50 ms) and long-term endurance (>10,000 cycles at 15 kPa). The prototype sensor has been successfully demonstrated in human pulse monitoring, speech recognition, and gesture recognition. The 2 × 6 sensor array can detect the spatial pressure distribution. Thus, such a microstructure shape design will open a new way to fabricate a high-linearity pressure sensor for potential applications in health monitoring, human-machine interaction, etc.

Issue of Data Imbalance on Low Birthweight Baby Outcomes Prediction and Associated Risk Factors Identification: Establishment of Benchmarking Key Machine Learning Models With Data Rebalancing Strategies.

BACKGROUND: Low birthweight (LBW) is a leading cause of neonatal mortality in the United States and a major causative factor of adverse health effects in newborns. Identifying high-risk patients early in prenatal care is crucial to preventing adverse outcomes. Previous studies have proposed various machine learning (ML) models for LBW prediction task, but they were limited by small and imbalanced data sets. Some authors attempted to address this through different data rebalancing methods. However, most of their reported performances did not reflect the models' actual performance in real-life scenarios. To date, few studies have successfully benchmarked the performance of ML models in maternal health; thus, it is critical to establish benchmarks to advance ML use to subsequently improve birth outcomes. OBJECTIVE: This study aimed to establish several key benchmarking ML models to predict LBW and systematically apply different rebalancing optimization methods to a large-scale and extremely imbalanced all-payer hospital record data set that connects mother and baby data at a state level in the United States. We also performed feature importance analysis to identify the most contributing features in the LBW classification task, which can aid in targeted intervention. METHODS: Our large data set consisted of 266,687 birth records across 6 years, and 8.63% (n=23,019) of records were labeled as LBW. To set up benchmarking ML models to predict LBW, we applied 7 classic ML models (ie, logistic regression, naive Bayes, random forest, extreme gradient boosting, adaptive boosting, multilayer perceptron, and sequential artificial neural network) while using 4 different data rebalancing methods: random undersampling, random oversampling, synthetic minority oversampling technique, and weight rebalancing. Owing to ethical considerations, in addition to ML evaluation metrics, we primarily used recall to evaluate model performance, indicating the number of correctly predicted LBW cases out of all actual LBW cases, as false negative health care outcomes could be fatal. We further analyzed feature importance to explore the degree to which each feature contributed to ML model prediction among our best-performing models. RESULTS: We found that extreme gradient boosting achieved the highest recall score-0.70-using the weight rebalancing method. Our results showed that various data rebalancing methods improved the prediction performance of the LBW group substantially. From the feature importance analysis, maternal race, age, payment source, sum of predelivery emergency department and inpatient hospitalizations, predelivery disease profile, and different social vulnerability index components were important risk factors associated with LBW. CONCLUSIONS: Our findings establish useful ML benchmarks to improve birth outcomes in the maternal health domain. They are informative to identify the minority class (ie, LBW) based on an extremely imbalanced data set, which may guide the development of personalized LBW early prevention, clinical interventions, and statewide maternal and infant health policy changes.

Metabolomic analysis reveals the molecular responses to copper toxicity in rice (Oryza sativa).

Copper (Cu) is one of the essential microelements and widely participates in various pathways in plants, but excess Cu in plant cells could induce oxidative stress and harm plant growth. Rice (Oryza sativa) is a main crop food worldwide. The molecular mechanisms of rice in response to copper toxicity are still not well understood. In this study, two-week-old seedlings of the rice cultivar Nipponbare were treated with 100 µM Cu2+ (CuSO4) in the external solution for 10 days. Physiological analysis showed that excess Cu significantly inhibited the growth and biomass of rice seedlings. After Cu treatment, the contents of Mn and Zn were significantly reduced in the roots and shoots, while the Fe content was significantly increased in the roots. Meanwhile, the activities of antioxidant enzymes including SOD and POD were dramatically enhanced after Cu treatment. Based on metabolomic analysis using liquid chromatography-tandem mass spectrometry (LC-MS/MS) methods, 695 metabolites were identified in rice roots. Among these metabolites, 123 metabolites were up-regulated and 297 were down-regulated, respectively. The differential metabolites (DMs) include carboxylic acids and derivatives, benzene and substituted derivatives, carbonyl compounds, cinnamic acids and derivatives, fatty acyls and organ nitrogen compounds. KEGG analysis showed that these DMs were mainly enriched in TCA cycle, purine metabolism and starch and sucrose metabolism pathways. Many intermediates in the TCA cycle and purine metabolism were down-regulated, indicating a perturbed carbohydrate and nucleic acid metabolism. Taken together, the present study provides new insights into the mechanism of rice roots to Cu toxicity.

One-step fabrication of high-performance graphene composites from graphite solution for bio-scaffolds and flexible strain sensors.

Graphene composites possess great application potential in various fields including flexible electrodes, wearable sensors and biomedical devices owing to their excellent mechanical and electrical properties. However, it remains challenging to fabricate graphene composites-based devices with high consistency due to the gradual aggression effect of graphene during fabrication process. Herein, we propose a method for one-step fabricating graphene/polymer composite-based devices from graphite/polymer solution by using electrohydrodynamic (EHD) printing with the Weissenberg effect (EPWE). Taylor-Couette flows with high shearing speed were generated to exfoliate high-quality graphene with a rotating steel microneedle coaxially set in a spinneret tube. The effects of the rotating speed of the needle, spinneret size and precursor ingredients on the graphene concentration were discussed. As a proof of concept, EPWE was used to successfully fabricate graphene/polycaprolactone (PCL) bio-scaffolds with good biocompatibility and graphene/thermoplastic polyurethane strain sensor for detecting human motions with a maximum gauge factor more than 2400 from 40% to 50% strain. As such, this method sheds a new light on one-stepin situfabrication of graphene/polymer composite-based devices from graphite solution with low cost.

Structural variations and environmental specificities of flowering time-related genes in Brassica napus.

KEY MESSAGE: We found that the flowering time order of accessions in a genetic population considerably varied across environments, and homolog copies of essential flowering time genes played different roles in different locations. Flowering time plays a critical role in determining the life cycle length, yield, and quality of a crop. However, the allelic polymorphism of flowering time-related genes (FTRGs) in Brassica napus, an important oil crop, remains unclear. Here, we provide high-resolution graphics of FTRGs in B. napus on a pangenome-wide scale based on single nucleotide polymorphism (SNP) and structural variation (SV) analyses. A total of 1337 FTRGs in B. napus were identified by aligning their coding sequences with Arabidopsis orthologs. Overall, 46.07% of FTRGs were core genes and 53.93% were variable genes. Moreover, 1.94%, 0.74%, and 4.49% FTRGs had significant presence-frequency differences (PFDs) between the spring and semi-winter, spring and winter, and winter and semi-winter ecotypes, respectively. SNPs and SVs across 1626 accessions of 39 FTRGs underlying numerous published qualitative trait loci were analyzed. Additionally, to identify FTRGs specific to an eco-condition, genome-wide association studies (GWASs) based on SNP, presence/absence variation (PAV), and SV were performed after growing and observing the flowering time order (FTO) of plants in a collection of 292 accessions at three locations in two successive years. It was discovered that the FTO of plants in a genetic population changed a lot across various environments, and homolog copies of some key FTRGs played different roles in different locations. This study revealed the molecular basis of the genotype-by-environment (G × E) effect on flowering and recommended a pool of candidate genes specific to locations for breeding selection.

Artificial Intelligence-Based Chatbots for Promoting Health Behavioral Changes: Systematic Review.

BACKGROUND: Artificial intelligence (AI)-based chatbots can offer personalized, engaging, and on-demand health promotion interventions. OBJECTIVE: The aim of this systematic review was to evaluate the feasibility, efficacy, and intervention characteristics of AI chatbots for promoting health behavior change. METHODS: A comprehensive search was conducted in 7 bibliographic databases (PubMed, IEEE Xplore, ACM Digital Library, PsycINFO, Web of Science, Embase, and JMIR publications) for empirical articles published from 1980 to 2022 that evaluated the feasibility or efficacy of AI chatbots for behavior change. The screening, extraction, and analysis of the identified articles were performed by following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. RESULTS: Of the 15 included studies, several demonstrated the high efficacy of AI chatbots in promoting healthy lifestyles (n=6, 40%), smoking cessation (n=4, 27%), treatment or medication adherence (n=2, 13%), and reduction in substance misuse (n=1, 7%). However, there were mixed results regarding feasibility, acceptability, and usability. Selected behavior change theories and expert consultation were used to develop the behavior change strategies of AI chatbots, including goal setting, monitoring, real-time reinforcement or feedback, and on-demand support. Real-time user-chatbot interaction data, such as user preferences and behavioral performance, were collected on the chatbot platform to identify ways of providing personalized services. The AI chatbots demonstrated potential for scalability by deployment through accessible devices and platforms (eg, smartphones and Facebook Messenger). The participants also reported that AI chatbots offered a nonjudgmental space for communicating sensitive information. However, the reported results need to be interpreted with caution because of the moderate to high risk of internal validity, insufficient description of AI techniques, and limitation for generalizability. CONCLUSIONS: AI chatbots have demonstrated the efficacy of health behavior change interventions among large and diverse populations; however, future studies need to adopt robust randomized control trials to establish definitive conclusions.

A flexible pressure sensor with highly customizable sensitivity and linearity via positive design of microhierarchical structures with a hyperelastic model.

The tactile pressure sensor is of great significance in flexible electronics, but sensitivity customization over the required working range with high linearity still remains a critical challenge. Despite numerous efforts to achieve high sensitivity and a wide working range, most sensitive microstructures tend to be obtained only by inverting naturally existing templates without rational design based on fundamental contact principles or models for piezoresistive pressure sensors. Here, a positive design strategy with a hyperelastic model and a Hertzian contact model for comparison was proposed to develop a flexible pressure sensor with highly customizable linear sensitivity and linearity, in which the microstructure distribution was precalculated according to the desired requirement prior to fabrication. As a proof of concept, three flexible pressure sensors exhibited sensitivities of 0.7, 1.0, and 1.3 kPa- 1 over a linear region of up to 200 kPa, with a low sensitivity error (<5%) and high linearity (~0.99), as expected. Based on the superior electromechanical performance of these sensors, potential applications in physiological signal recognition are demonstrated as well, and such a strategy could shed more light on demand-oriented scenarios, including designable working ranges and linear sensitivity for next-generation wearable devices.

Self-Patterning of Highly Stretchable and Electrically Conductive Liquid Metal Conductors by Direct-Write Super-Hydrophilic Laser-Induced Graphene and Electroless Copper Plating.

Stretchable electrodes are desirable in flexible electronics for the transmission and acquisition of electrical signals, but their fabrication process remains challenging. Herein, we report an approach based on patterned liquid metals (LMs) as stretchable electrodes using a super-hydrophilic laser-induced graphene (SHL-LIG) process with electroless plating copper on a polyimide (PI) film. The LMs/SHL-LIG structures are then transferred from the PI film to an Ecoflex substrate as stretchable electrodes with an ultralow sheet resistance of 3.54 mΩ per square and excellent stretchability up to 480% in elongation. Furthermore, these electrodes show outstanding performances of only 8% electrical resistance changes under a tensile strain of 300%, and strong immunity to temperature and pressure changes. As demonstration examples, these electrodes are integrated with a stretchable strain sensing system and a smart magnetic soft robot toward practical applications.

Molecular evidence for adaptive evolution of drought tolerance in wild cereals.

The considerable drought tolerance of wild cereal crop progenitors has diminished during domestication in the pursuit of higher productivity. Regaining this trait in cereal crops is essential for global food security but requires novel genetic insight. Here, we assessed the molecular evidence for natural variation of drought tolerance in wild barley (Hordeum spontaneum), wild emmer wheat (Triticum dicoccoides), and Brachypodium species collected from dry and moist habitats at Evolution Canyon, Israel (ECI). We report that prevailing moist vs dry conditions have differentially shaped the stomatal and photosynthetic traits of these wild cereals in their respective habitats. We present the genomic and transcriptomic evidence accounting for differences, including co-expression gene modules, correlated with physiological traits, and selective sweeps, driven by the xeric site conditions on the African Slope (AS) at ECI. Co-expression gene module 'circadian rhythm' was linked to significant drought-induced delay in flowering time in Brachypodium stacei genotypes. African Slope-specific differentially expressed genes are important in barley drought tolerance, verified by silencing Disease-Related Nonspecific Lipid Transfer 1 (DRN1), Nonphotochemical Quenching 4 (NPQ4), and Brassinosteroid-Responsive Ring-H1 (BRH1). Our results provide new genetic information for the breeding of resilient wheat and barley in a changing global climate with increasingly frequent drought events.

Topics and Sentiment Surrounding Vaping on Twitter and Reddit During the 2019 e-Cigarette and Vaping Use-Associated Lung Injury Outbreak: Comparative Study.

BACKGROUND: Vaping or e-cigarette use has become dramatically more popular in the United States in recent years. e-Cigarette and vaping use-associated lung injury (EVALI) cases caused an increase in hospitalizations and deaths in 2019, and many instances were later linked to unregulated products. Previous literature has leveraged social media data for surveillance of health topics. Individuals are willing to share mental health experiences and other personal stories on social media platforms where they feel a sense of community, reduced stigma, and empowerment. OBJECTIVE: This study aimed to compare vaping-related content on 2 popular social media platforms (ie, Twitter and Reddit) to explore the context surrounding vaping during the 2019 EVALI outbreak and to support the feasibility of using data from both social platforms to develop in-depth and intelligent vaping detection models on social media. METHODS: Data were extracted from both Twitter (316,620 tweets) and Reddit (17,320 posts) from July 2019 to September 2019 at the peak of the EVALI crisis. High-throughput computational analyses (sentiment analysis and topic analysis) were conducted. In addition, in-depth manual content analyses were performed and compared with computational analyses of content on both platforms (577 tweets and 613 posts). RESULTS: Vaping-related posts and unique users on Twitter and Reddit increased from July 2019 to September 2019, with the average post per user increasing from 1.68 to 1.81 on Twitter and 1.19 to 1.21 on Reddit. Computational analyses found the number of positive sentiment posts to be higher on Reddit (P<.001, 95% CI 0.4305-0.4475) and the number of negative posts to be higher on Twitter (P<.001, 95% CI -0.4289 to -0.4111). These results were consistent with the clinical content analyses results indicating that negative sentiment posts were higher on Twitter (273/577, 47.3%) than Reddit (184/613, 30%). Furthermore, topics prevalent on both platforms by keywords and based on manual post reviews included mentions of youth, marketing or regulation, marijuana, and interest in quitting. CONCLUSIONS: Post content and trending topics overlapped on both Twitter and Reddit during the EVALI period in 2019. However, crucial differences in user type and content keywords were also found, including more frequent mentions of health-related keywords on Twitter and more negative health outcomes from vaping mentioned on both Reddit and Twitter. Use of both computational and clinical content analyses is critical to not only identify signals of public health trends among vaping-related social media content but also to provide context for vaping risks and behaviors. By leveraging the strengths of both Twitter and Reddit as publicly available data sources, this research may provide technical and clinical insights to inform automatic detection of social media users who are vaping and may benefit from digital intervention and proactive outreach strategies on these platforms.

Understanding Alcohol Use Discourse and Stigma Patterns in Perinatal Care on Twitter.

(1) Background: perinatal alcohol use generates a variety of health risks. Social media platforms discuss fetal alcohol spectrum disorder (FASD) and other widespread outcomes, providing personalized user-generated content about the perceptions and behaviors related to alcohol use during pregnancy. Data collected from Twitter underscores various narrative structures and sentiments in tweets that reflect large-scale discourses and foster societal stigmas; (2) Methods: We extracted alcohol-related tweets from May 2019 to October 2021 using an official Twitter search API based on a set of keywords provided by our clinical team. Our exploratory study utilized thematic content analysis and inductive qualitative coding methods to analyze user content. Iterative line-by-line coding categorized dynamic descriptive themes from a random sample of 500 tweets; (3) Results: qualitative methods from content analysis revealed underlying patterns among inter-user engagements, outlining individual, interpersonal and population-level stigmas about perinatal alcohol use and negative sentiment towards drinking mothers. As a result, the overall silence surrounding personal experiences with alcohol use during pregnancy suggests an unwillingness and sense of reluctancy from pregnant adults to leverage the platform for support and assistance due to societal stigmas; (4) Conclusions: identifying these discursive factors will facilitate more effective public health programs that take into account specific challenges related to social media networks and develop prevention strategies to help Twitter users struggling with perinatal alcohol use.

Patient Trust in Physicians Matters-Understanding the Role of a Mobile Patient Education System and Patient-Physician Communication in Improving Patient Adherence Behavior: Field Study.

BACKGROUND: The ultimate goal of any prescribed medical therapy is to achieve desired outcomes of patient care. However, patient nonadherence has long been a major problem detrimental to patient health and, thus, is a concern for all health care providers. Moreover, nonadherence is extremely costly for global medical systems because of unnecessary complications and expenses. Traditional patient education programs often serve as an intervention tool to increase patients' self-care awareness, disease knowledge, and motivation to change patient behaviors for better adherence. Patient trust in physicians, patient-physician relationships, and quality of communication have also been identified as critical factors influencing patient adherence. However, little is known about how mobile patient education technologies help foster patient adherence. OBJECTIVE: This study aimed to empirically investigate whether and how a mobile patient education system (MPES) juxtaposed with patient trust can increase patient adherence to prescribed medical therapies. METHODS: This study was conducted based on a field survey of 125 patients in multiple states in the United States who have used an innovative mobile health care system for their health care education and information seeking. Partial least squares techniques were used to analyze the collected data. RESULTS: The results revealed that patient-physician communication and the use of an MPES significantly increase patients' trust in their physicians. Furthermore, patient trust has a prominent effect on patient attitude toward treatment adherence, which in turn influences patients' behavioral intention and actual adherence behavior. Based on the theory of planned behavior, the results also indicated that behavioral intention, response efficacy, and self-efficacy positively influenced patients' actual treatment adherence behavior, whereas descriptive norms and subjective norms do not play a role in this process. CONCLUSIONS: Our study is one of the first that examines the relationship between patients who actively use an MPES and their trust in their physicians. This study contributes to this context by enriching the trust literature, addressing the call to identify key patient-centered technology determinants of trust, advancing the understanding of patient adherence mechanisms, adding a new explanation of the influence of education mechanisms delivered via mobile devices on patient adherence, and confirming that the theory of planned behavior holds in this patient adherence context.

Population-level transcriptomes reveal gene expression and splicing underlying cadmium accumulation in barley.

Genetic variation is an important determinant of gene transcription, which in turn contributes to functional and phenotypic diversity. Identification of the genetic variants controlling gene expression and alternative splicing in crops responding to cadmium (Cd), an important issue for food safety and human health, is of great value to improve our understanding of Cd accumulation-related genes. Here we report an in-depth survey of population-level transcriptome variation of barley (Hordeum vulgare) core accessions under Cd exposure. We reveal marked transcriptomic changes in response to Cd exposure, and these are largely independent of tissues. A genome-wide association study (GWAS) revealed 59 498 expression quantitative trait loci (eQTLs) and 23 854 splicing quantitative trait loci (sQTLs), leading to a complex network that covers 66.6% of the expressed genes, including 68 metal transporter genes. On average, 41.0% of sQTLs overlapped with eQTLs across different tissues, indicating that these two dimensions of transcript variation are largely independent. Moreover, we found that 34.5% of GWAS QTLs that underlie 10 Cd accumulation traits in barley are co-localized with eQTLs and sQTLs, which could imply a mechanistic role of different genetic variants affecting gene expression and alternative splicing in these traits. This study highlights the role of distal and proximal genetic effects on gene expression, splicing, and phenotypic plasticity. We anticipate that our results on the genetic control of expression and splicing underlying Cd accumulation provide a bridge to better understand genetic variation and phenotypic diversity to elucidate the mechanisms underlying Cd accumulation in plants.

Stomatal morphological variation contributes to global ecological adaptation and diversification of Brassica napus.

MAIN CONCLUSION: Stomatal density and guard cell length of 274 global core germplasms of rapeseed reveal that the stomatal morphological variation contributes to global ecological adaptation and diversification of Brassica napus. Stomata are microscopic structures of plants for the regulation of CO2 assimilation and transpiration. Stomatal morphology has changed substantially in the adaptation to the external environment during land plant evolution. Brassica napus is a major crop to produce oil, livestock feed and biofuel in the world. However, there are few studies on the regulatory genes controlling stomatal development and their interaction with environmental factors as well as the genetic mechanism of adaptive variation in B. napus. Here, we characterized stomatal density (SD) and guard cell length (GL) of 274 global core germplasms at seedling stage. It was found that among the significant phenotypic variation, European germplasms are mostly winter rapeseed with high stomatal density and small guard cell length. However, the germplasms from Asia (especially China) are semi-winter rapeseed, which is characterized by low stomatal density and large guard cell length. Through selective sweep analysis and homology comparison, we identified several candidate genes related to stomatal density and guard cell length, including Epidermal Patterning Factor2 (EPF2; BnaA09g23140D), Epidermal Patterning Factor Like4 (EPFL4; BnaC01g22890D) and Suppressor of LLP1 (SOL1 BnaC01g22810D). Haplotype and phylogenetic analysis showed that natural variation in EPF2, EPFL4 and SOL1 is closely associated with the winter, spring, and semi-winter rapeseed ecotypes. In summary, this study demonstrated for the first time the relation between stomatal phenotypic variation and ecological adaptation in rapeseed, which is useful for future molecular breeding of rapeseed in the context of evolution and domestication of key stomatal traits and global climate change.

The genome and gene editing system of sea barleygrass provide a novel platform for cereal domestication and stress tolerance studies.

The tribe Triticeae provides important staple cereal crops and contains elite wild species with wide genetic diversity and high tolerance to abiotic stresses. Sea barleygrass (Hordeum marinum Huds.), a wild Triticeae species, thrives in saline marshlands and is well known for its high tolerance to salinity and waterlogging. Here, a 3.82-Gb high-quality reference genome of sea barleygrass is assembled de novo, with 3.69 Gb (96.8%) of its sequences anchored onto seven chromosomes. In total, 41 045 high-confidence (HC) genes are annotated by homology, de novo prediction, and transcriptome analysis. Phylogenetics, non-synonymous/synonymous mutation ratios (Ka/Ks), and transcriptomic and functional analyses provide genetic evidence for the divergence in morphology and salt tolerance among sea barleygrass, barley, and wheat. The large variation in post-domestication genes (e.g. IPA1 and MOC1) may cause interspecies differences in plant morphology. The extremely high salt tolerance of sea barleygrass is mainly attributed to low Na+ uptake and root-to-shoot translocation, which are mainly controlled by SOS1, HKT, and NHX transporters. Agrobacterium-mediated transformation and CRISPR/Cas9-mediated gene editing systems were developed for sea barleygrass to promote its utilization for exploration and functional studies of hub genes and for the genetic improvement of cereal crops.

Impact of Hospital Characteristics and Governance Structure on the Adoption of Tracking Technologies for Clinical and Supply Chain Use: Longitudinal Study of US Hospitals.

BACKGROUND: Despite the increasing adoption rate of tracking technologies in hospitals in the United States, few empirical studies have examined the factors involved in such adoption within different use contexts (eg, clinical and supply chain use contexts). To date, no study has systematically examined how governance structures impact technology adoption in different use contexts in hospitals. Given that the hospital governance structure fundamentally governs health care workflows and operations, understanding its critical role provides a solid foundation from which to explore factors involved in the adoption of tracking technologies in hospitals. OBJECTIVE: This study aims to compare critical factors associated with the adoption of tracking technologies for clinical and supply chain uses and examine how governance structure types affect the adoption of tracking technologies in hospitals. METHODS: This study was conducted based on a comprehensive and longitudinal national census data set comprising 3623 unique hospitals across 50 states in the United States from 2012 to 2015. Using mixed effects population logistic regression models to account for the effects within and between hospitals, we captured and examined the effects of hospital characteristics, locations, and governance structure on adjustments to the innate development of tracking technology over time. RESULTS: From 2012 to 2015, we discovered that the proportion of hospitals in which tracking technologies were fully implemented for clinical use increased from 36.34% (782/2152) to 54.63% (1316/2409), and that for supply chain use increased from 28.58% (615/2152) to 41.3% (995/2409). We also discovered that adoption factors impact the clinical and supply chain use contexts differently. In the clinical use context, compared with hospitals located in urban areas, hospitals in rural areas (odds ratio [OR] 0.68, 95% CI 0.56-0.80) are less likely to fully adopt tracking technologies. In the context of supply chain use, the type of governance structure influences tracking technology adoption. Compared with hospitals not affiliated with a health system, implementation rates increased as hospitals affiliated with a more centralized health system-1.9-fold increase (OR 1.87, 95% CI 1.60-2.13) for decentralized or independent hospitals, 2.4-fold increase (OR 2.40, 95% CI 2.07-2.80) for moderately centralized health systems, and 3.1-fold increase for centralized health systems (OR 3.07, 95% CI 2.67-3.53). CONCLUSIONS: As the first of such type of studies, we provided a longitudinal overview of how hospital characteristics and governance structure jointly affect adoption rates of tracking technology in both clinical and supply chain use contexts, which is essential for developing intelligent infrastructure for smart hospital systems. This study informs researchers, health care providers, and policy makers that hospital characteristics, locations, and governance structures have different impacts on the adoption of tracking technologies for clinical and supply chain use and on health resource disparities among hospitals of different sizes, locations, and governance structures.

A flexible tactile sensor that uses polyimide/graphene oxide nanofiber as dielectric membrane for vertical and lateral force detection.

Flexible force sensors are of great interest in the fields of healthcare, physiological signals, and aircraft smart skin applications because of their compatibility with curved surfaces. However, the simultaneous detection of multidirectional forces remains an engineering challenge, despite the great progress made in recent years. Herein, we present the development of a flexible capacitive force sensor capable of efficiently distinguishing normal and sliding shear forces. A two-layer electrospun polyimide/graphene oxide (PI/GO) nanofiber membrane is used as the dielectric layer, which is sandwiched between one top electrode and four symmetrically distributed bottom electrodes. This composite membrane has an improved dielectric constant, a reduced friction coefficient, and good compressibility, leading to superior performance that includes high sensitivity over a wide operational range with measured results of 3 MPa-1for 0-242 kPa (0-2.2 N) and 0.92 MPa-1for 242-550 kPa (2.2-5 N) in the normal direction; and better than 1 N-1for 0-3 N in thex- andy-axis directions. The system also has a low detection limit of 10 Pa, fast response and recovery times of 39 ms and 13 ms, respectively, a good cyclic stability of 10,000 cycles at a pressure of 176 kPa, and promising potential for use in high-temperature environments (200 °C). Moreover, a prototype 4 × 4 sensor array has been fabricated and successfully used in a robotic system to grasp objects and operate a wireless toy car. As such, the proposed system could offer superior capabilities in simultaneous multidirectional force sensing for applications such as intelligent robots, human-machine interaction, and smart skin.

Laser-Sculptured Hierarchical Spinous Structures for Ultra-High-Sensitivity Iontronic Sensors with a Broad Operation Range.

Tactile pressure sensing over a wide operation range (>1 MPa) is challenging for a variety of applications in fields such as aviation, oceanography, and biomedicine. Recently, innovative strategies have been utilized to improve the performances of tactile sensors using specially designed structures, dielectric layers, and electrodes. Here, a hierarchical structural design based on ionic gel films has been utilized to build iontronic pressure sensors with ultrahigh sensitivities and broad operation ranges. Sculptured patterns made by a controlled CO2 laser scanning process have been produced on polyimide films to achieve two kinds of protrusion structures for high specific surface areas and strength to withstand high pressure. The iontronic sensor has been constructed by adding two screen-printed electrodes of high surface areas to achieve an ultrahigh sensitivity of 2593 kPa-1 and a wide pressure range from 0 Pa to 3.36 MPa. The prototype device also has a fast response and recovery time of 26 and 13 ms, respectively, and an excellent mechanical durability in the endurance test of over 2700 repeated loading and unloading cycles under a pressure of 1 MPa. Several application examples have been demonstrated, including the detection of physiological signals on human volunteers, the feedback control of intelligent robots, the grasping operation of underwater soft grippers, and the environmental wind-speed monitoring. As such, this work demonstrates a versatile and economical methodology to produce high-performance flexible sensors for various potential applications.