OsCaM1-1 Is Responsible for Salt Tolerance by Regulating Na+/K+ Homoeostasis in Rice.

Calmodulin, a highly conserved calcium-binding protein, plays a crucial role in response to salt stress. Previous studies investigated sequence and function of calmodulin members in some plants, but their roles in rice have not been fully elucidated. Three OsCaM1 genes namely OsCaM1-1/2/3 encode the same OsCaM1 protein. Here, we found that OsCaM1-1 had significantly higher expression than the other two genes under salt stress. After 4 weeks of exposure to 75 mM NaCl, OsCaM1-1 overexpressed mutants showed higher salt tolerance, while knocked-out mutants exhibited lower salt tolerance, compared to the wild type. Moreover, the oscam1-1 mutants had higher Na+ concentration and Na+/K+ ratio in both shoots and roots, less instantaneous K+ and Ca2+ fluxes in roots, compared to wild type under salt stress, indicating the involvement of OsCaM1-1 in regulation of Na+ and K+ homoeostasis via Ca2+ signal. RNA-seq analysis identified 452 differentially expressed genes (DEGs) regulated by OsCaM1-1 and salt stress, and they were mainly enriched in nucleus DNA-binding activities, including ABI5, WRKY76, WRKY48 and bHLH120 transcription factors. Knockout of OsCaM1-1 also modulated the expression of Na+ transporters, including HKT1;1, HKT1;5, SOS1, NHX1 and NHX4. In conclusion, OsCaM1-1 positively regulates salt tolerance in rice through mediating ion homoeostasis.

Disclosure Patterns of Opioid Use Disorders in Perinatal Care During the Opioid Epidemic on X From 2019 to 2021: Thematic Analysis.

BACKGROUND: In 2021, the United States experienced a 14% rise in fatal drug overdoses totaling 106,699 deaths, driven by harmful opioid use, particularly among individuals in the perinatal period who face increased risks associated with opioid use disorders (OUDs). Increased concerns about the impacts of escalating harmful opioid use among pregnant and postpartum persons are rising. Most of the current limited perinatal OUD studies were conducted using traditional methods, such as interviews and randomized controlled trials to understand OUD treatment, risk factors, and associated adverse effects. However, little is known about how social media data, such as X, formerly known as Twitter, can be leveraged to explore and identify broad perinatal OUD trends, disclosure and communication patterns, and public health surveillance about OUD in the perinatal period. OBJECTIVE: The objective is 3-fold: first, we aim to identify key themes and trends in perinatal OUD discussions on platform X. Second, we explore user engagement patterns, including replying and retweeting behaviors. Third, we investigate computational methods that could potentially streamline and scale the labor-intensive manual annotation effort. METHODS: We extracted 6 million raw perinatal-themed tweets posted by global X users during the opioid epidemic from May 2019 to October 2021. After data cleaning and sampling, we used 500 tweets related to OUD in the perinatal period by US X users for a thematic analysis using NVivo (Lumivero) software. RESULTS: Seven major themes emerged from our thematic analysis: (1) political views related to harmful opioid and other substance use, (2) perceptions of others' substance use, (3) lived experiences of opioid and other substance use, (4) news reports or papers related to opioid and other substance use, (5) health care initiatives, (6) adverse effects on children's health due to parental substance use, and (7) topics related to nonopioid substance use. Among these 7 themes, our user engagement analysis revealed that themes 4 and 5 received the highest average retweet counts, and theme 3 received the highest average tweet reply count. We further found that different computational methods excel in analyzing different themes. CONCLUSIONS: Social media platforms such as X can serve as a valuable tool for analyzing real-time discourse and exploring public perceptions, opinions, and behaviors related to maternal substance use, particularly, harmful opioid use in the perinatal period. More health promotion strategies can be carried out on social media platforms to provide educational support for the OUD perinatal population.

Reinforced Magnetic-Responsive Electro-Ionic Artificial Muscles by 3D Laser-Induced Graphene Nano-Heterostructures.

Efficient ion transport and enriched responsive modals via modulating electrochemical properties of conductivity and capacitance are essential for soft electro-ionic actuators. However, cost-effective and straightforward approaches to achieve expedited fabrication of active electrode materials capable of multimodal-responsiveness remain limited. Herein, this work reports the one-step ultrafast laser direct patterning method, to readily synthesize electro- and magneto-active electrode material, derived from the unique cobalt-phosphorus co-doped core-shell heterostructures within 3D graphene frameworks, for fulfilling the dual-mode responsive electro-ionic actuators. The designed nanofiber-structured heterointerfaces across electrodes and electrolytes further promote highly efficient electron/ion transfer. The developed soft actuator exhibits superior actuation performance of peak-to-peak displacement to 13.08 mm under an ultra-low ±0.5 V, with doubled direct current deflection under 200 mT at 1 V, an ultrafast response of 1.38 s and long-term stability (>90% retention for ≈106 000 cycles), even detectable bending to ≈280 µm under exceptional ±10 mV. The promising demonstration of promoting differentiation and proliferation of stem cells under mechanical strain and electrical stimuli, sheds more light as well on the possibility of facilitating biomedical soft robotics with ultrahigh actuation performance.

Multi-omics analysis unveils early molecular responses to aluminum toxicity in barley root tip.

Barley (Hordeum vulgare L.) is widely cultivated across diverse soil types, including acidic soils where aluminum (Al) toxicity is the major limiting factor. The relative Al sensitivity of barley highlights the need for a deeper understanding of early molecular responses in root tip (the primary target of Al toxicity) to develop Al-tolerant cultivars. Integrative N6-methyladenosine (m6A) modification, transcriptomic, and metabolomic analyses revealed that elevated auxin and jasmonic acid (JA) levels modulated Al-induced root growth inhibition by repressing genes involved in cell elongation and proliferation. Additionally, these pathways promoted pectin demethylation via up-regulation of genes encoding pectin methylesterases (PMEs). The up-regulation of citrate efflux transporter genes including Al-activated citrate transporter 1 (HvAACT1), and ATP-binding cassette (ABC) transporters like HvABCB25, facilitated Al exclusion and vacuolar sequestration. Enhanced activity within the phenylpropanoid pathway supported antioxidant defenses and internal chelation through the production of specific flavonoids and altered cell wall composition via lignin unit modulation. Notably, several Al-responsive genes, including HvABCB25 and transcription factors (TFs), exhibited m6A modification changes, with two microtubule associated protein 65 (MAP65) members displaying opposing regulatory patterns at both transcriptional and m6A levels, underscoring the crucial role of m6A modification in gene expression regulation. This comprehensive study provides valuable insights into the epitranscriptomic regulation of gene expression and metabolite accumulation in barley root tip under Al stress.

Harnessing the Power of Complementarity Between Smart Tracking Technology and Associated Health Information Technologies: Longitudinal Study.

BACKGROUND: Smart tracking technology (STT) that was applied for clinical use has the potential to reduce 30-day all-cause readmission risk through streamlining clinical workflows with improved accuracy, mobility, and efficiency. However, previously published literature has inadequately addressed the joint effects of STT for clinical use and its complementary health ITs (HITs) in this context. Furthermore, while previous studies have discussed the symbiotic and pooled complementarity effects among different HITs, there is a lack of evidence-based research specifically examining the complementarity effects between STT for clinical use and other relevant HITs. OBJECTIVE: Through a complementarity theory lens, this study aims to examine the joint effects of STT for clinical use and 3 relevant HITs on 30-day all-cause readmission risk. These HITs are STT for supply chain management, mobile IT, and health information exchange (HIE). Specifically, this study examines whether the pooled complementarity effect exists between STT for clinical use and STT for supply chain management, and whether symbiotic complementarity effects exist between STT for clinical use and mobile IT and between STT for clinical use and HIE. METHODS: This study uses a longitudinal in-patient dataset, including 879,122 in-patient hospital admissions for 347,949 patients in 61 hospitals located in Florida and New York in the United States, from 2014 to 2015. Logistic regression was applied to assess the effect of HITs on readmission risks. Time and hospital fixed effects were controlled in the regression model. Robust standard errors (SEs) were used to account for potential heteroskedasticity. These errors were further clustered at the patient level to consider possible correlations within the patient groups. RESULTS: The interaction between STT for clinical use and STT for supply chain management, mobile IT, and HIE was negatively associated with 30-day readmission risk, with coefficients of -0.0352 (P=.003), -0.0520 (P<.001), and -0.0216 (P=.04), respectively. These results indicate that the pooled complementarity effect exists between STT for clinical use and STT for supply chain management, and symbiotic complementarity effects exist between STT for clinical use and mobile IT and between STT for clinical use and HIE. Furthermore, the joint effects of these HITs varied depending on the hospital affiliation and patients' disease types. CONCLUSIONS: Our results reveal that while individual HIT implementations have varying impacts on 30-day readmission risk, their joint effects are often associated with a reduction in 30-day readmission risk. This study substantially contributes to HIT value literature by quantifying the complementarity effects among 4 different types of HITs: STT for clinical use, STT for supply chain management, mobile IT, and HIE. It further offers practical implications for hospitals to maximize the benefits of their complementary HITs in reducing the 30-day readmission risk in their respective care scenarios.

JAZ2 Negatively Regulates Drought Tolerance in Barley by Modulating PLT2 Expression.

Drought is an important abiotic factor constricting crop production globally. Although the roles of JAZ proteins in regulating jasmonic acid signalling and plant responses to environmental stress are well documented, their specific functions and underlying mechanisms remain little known. In this study, JAZ proteins in barley were thoroughly analyzed, revealing a total of 11 members classified into three phylogenetic subgroups. HvJAZ2, based on its distinct expression patterns, is considered a key candidate gene for regulating drought tolerance in barley. Using the HvJAZ2 knockout mutants, we revealed that the gene negatively regulates drought tolerance by inhibiting barley root growth. Notably, the jaz2 mutants upregulated the expression of root development genes, including SHR1, PLT1, PLT2 and PLT6. plt2 and plt1/plt2 mutants exhibited suppressed root development and reduced drought tolerance. Analysis of interactions between HvJAZ2 and other proteins showed that HvJAZ2 does not directly interact with HvPLT1/2/6, but interacts with some other proteins. BIFC and LCA assays further confirmed the nuclear interaction between HvJAZ2 and HvMYC2. Y1H and Dual-Luciferase experiments demonstrated that HvMYC2 can bind to and activate the HvPLT2 promoter. In summary, HvJAZ2 negatively regulates root development and drought tolerance in barley by suppressing HvPLT2 expression through interacting with HvMYC2.

Uncovering the Complexity of Perinatal Polysubstance Use Disclosure Patterns on X: Mixed Methods Study.

BACKGROUND: According to the Morbidity and Mortality Weekly Report, polysubstance use among pregnant women is prevalent, with 38.2% of those who consume alcohol also engaging in the use of one or more additional substances. However, the underlying mechanisms, contexts, and experiences of polysubstance use are unclear. Organic information is abundant on social media such as X (formerly Twitter). Traditional quantitative and qualitative methods, as well as natural language processing techniques, can be jointly used to derive insights into public opinions, sentiments, and clinical and public health policy implications. OBJECTIVE: Based on perinatal polysubstance use (PPU) data that we extracted on X from May 1, 2019, to October 31, 2021, we proposed two primary research questions: (1) What is the overall trend and sentiment of PPU discussions on X? (2) Are there any distinct patterns in the discussion trends of PPU-related tweets? If so, what are the implications for perinatal care and associated public health policies? METHODS: We used X's application programming interface to extract >6 million raw tweets worldwide containing ≥2 prenatal health- and substance-related keywords provided by our clinical team. After removing all non-English-language tweets, non-US tweets, and US tweets without disclosed geolocations, we obtained 4848 PPU-related US tweets. We then evaluated them using a mixed methods approach. The quantitative analysis applied frequency, trend analysis, and several natural language processing techniques such as sentiment analysis to derive statistics to preview the corpus. To further understand semantics and clinical insights among these tweets, we conducted an in-depth thematic content analysis with a random sample of 500 PPU-related tweets with a satisfying κ score of 0.7748 for intercoder reliability. RESULTS: Our quantitative analysis indicates the overall trends, bigram and trigram patterns, and negative sentiments were more dominant in PPU tweets (2490/4848, 51.36%) than in the non-PPU sample (1323/4848, 27.29%). Paired polysubstance use (4134/4848, 85.27%) was the most common, with the combination alcohol and drugs identified as the most mentioned. From the qualitative analysis, we identified 3 main themes: nonsubstance, single substance, and polysubstance, and 4 subthemes to contextualize the rationale of underlying PPU behaviors: lifestyle, perceptions of others' drug use, legal implications, and public health. CONCLUSIONS: This study identified underexplored, emerging, and important topics related to perinatal PPU, with significant stigmas and legal ramifications discussed on X. Overall, public sentiments on PPU were mixed, encompassing negative (2490/4848, 51.36%), positive (1884/4848, 38.86%), and neutral (474/4848, 9.78%) sentiments. The leading substances in PPU were alcohol and drugs, and the normalization of PPU discussed on X is becoming more prevalent. Thus, this study provides valuable insights to further understand the complexity of PPU and its implications for public health practitioners and policy makers to provide proper access and support to individuals with PPU.

Response Surface Methodology to Explore the Influence Mechanism of Fiber Diameter in a New Multi-Needle Electrospinning Spinneret.

Multi-needle electrospinning is an efficient method for producing nanofiber membranes. However, fluctuations in the fluid flow rate during the process affect membrane quality and cause instability, an issue that remains unresolved. To address this, a multi-stage flow runner spinneret needs to be developed for large-scale nanofiber membrane production. This paper uses COMSOL finite element software to simulate polymer flow in the spinneret runner. From this, the velocity field distribution and velocity instability coefficient were obtained, providing theoretical guidance for optimal spinneret design. In addition, response surface analysis (RSM) was used to experimentally explore the process parameters, and then residual probability plots were used for reliability verification to evaluate the effect of each process parameter on fiber diameter. These process parameters can guide the controlled production of nanofibers during multi-needle electrospinning.

Laser Fabrication of Humidity Sensors on Ethanol-Soaked Polyimide for Fully Contactless Respiratory Monitoring.

Humidity-sensor-based fully contactless respiratory monitoring can eliminate the discomfort and infection risks associated with any wearable device. However, challenges in the facile fabrication of highly sensitive humidity sensors continue to hinder their widespread application for fully contactless respiratory monitoring. In this study, we introduce a simple method to fabricate highly sensitive humidity sensors. Our method employs laser-induced graphene (LIG) on an ethanol-soaked polyimide (PI) film as the electrode of the humidity sensor. The ethanol-soaked PI between adjacent LIG electrodes functions as the sensing material, enabling ion-conductive humidity sensing. Compared to the LIG humidity sensors fabricated on untreated PI films, LIG humidity sensors fabricated on ethanol-soaked PI films exhibit superior performance with higher linearity (R2 = 0.9936), reduced hysteresis (ΔH = 5.1% RH), and increased sensitivity (0.65%/RH). Notably, the LIG humidity sensor fabricated on the ethanol-soaked PI film can detect a person's breathing from a distance of 30 cm, a capability not achieved by sensors fabricated on untreated PI films. Moreover, incorporating these LIG humidity sensors into an array further enhances both the detection distance and the sensitivity for respiratory monitoring. Experimental results demonstrate that the LIG humidity sensor array can be employed for fully contactless on-bed respiration monitoring and for continuous, fully contactless monitoring of the respiratory rate during treadmill exercise. These results highlight the great potential of our LIG humidity sensors for various practical applications in medicine and sports.

Automatic uncovering of patient primary concerns in portal messages using a fusion framework of pretrained language models.

OBJECTIVES: The surge in patient portal messages (PPMs) with increasing needs and workloads for efficient PPM triage in healthcare settings has spurred the exploration of AI-driven solutions to streamline the healthcare workflow processes, ensuring timely responses to patients to satisfy their healthcare needs. However, there has been less focus on isolating and understanding patient primary concerns in PPMs-a practice which holds the potential to yield more nuanced insights and enhances the quality of healthcare delivery and patient-centered care. MATERIALS AND METHODS: We propose a fusion framework to leverage pretrained language models (LMs) with different language advantages via a Convolution Neural Network for precise identification of patient primary concerns via multi-class classification. We examined 3 traditional machine learning models, 9 BERT-based language models, 6 fusion models, and 2 ensemble models. RESULTS: The outcomes of our experimentation underscore the superior performance achieved by BERT-based models in comparison to traditional machine learning models. Remarkably, our fusion model emerges as the top-performing solution, delivering a notably improved accuracy score of 77.67 ± 2.74% and an F1 score of 74.37 ± 3.70% in macro-average. DISCUSSION: This study highlights the feasibility and effectiveness of multi-class classification for patient primary concern detection and the proposed fusion framework for enhancing primary concern detection. CONCLUSIONS: The use of multi-class classification enhanced by a fusion of multiple pretrained LMs not only improves the accuracy and efficiency of patient primary concern identification in PPMs but also aids in managing the rising volume of PPMs in healthcare, ensuring critical patient communications are addressed promptly and accurately.

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.

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.

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.

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.

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.

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.

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.