Maize yield prediction with trait-missing data via bipartite graph neural network.

The timely and accurate prediction of maize (Zea mays L.) yields prior to harvest is critical for food security and agricultural policy development. Currently, many researchers are using machine learning and deep learning to predict maize yields in specific regions with high accuracy. However, existing methods typically have two limitations. One is that they ignore the extensive correlation in maize planting data, such as the association of maize yields between adjacent planting locations and the combined effect of meteorological features and maize traits on maize yields. The other issue is that the performance of existing models may suffer significantly when some data in maize planting records is missing, or the samples are unbalanced. Therefore, this paper proposes an end-to-end bipartite graph neural network-based model for trait data imputation and yield prediction. The maize planting data is initially converted to a bipartite graph data structure. Then, a yield prediction model based on a bipartite graph neural network is developed to impute missing trait data and predict maize yield. This model can mine correlations between different samples of data, correlations between different meteorological features and traits, and correlations between different traits. Finally, to address the issue of unbalanced sample size at each planting location, we propose a loss function based on the gradient balancing mechanism that effectively reduces the impact of data imbalance on the prediction model. When compared to other data imputation and prediction models, our method achieves the best yield prediction result even when missing data is not pre-processed.

Needs Assessment for the Development of a Sustainability Curriculum for Surgical Residents.

INTRODUCTION: The healthcare sector accounts for 8.5% of United States (U.S.) greenhouse gas emissions, of which one-third comes from operating rooms (ORs). As a result, there is great interest in decarbonizing the OR and surgical care. However, surgical residents are not routinely educated on the negative environmental impact of surgery or how to reduce it. In this paper, we present a formal needs assessment for a sustainability curriculum geared towards surgical residents. METHODS: Using Kern's Six-Step Framework for curriculum development, we conducted focus groups with surgical residents to perform a targeted needs assessment on 3 main topics: 1) the current state of surgical sustainability curricula; 2) resident knowledge regarding the environmental impact of surgery and barriers to sustainable practice; and 3) preferred educational methods and topics within sustainability education. We audio-recorded all focus groups and performed thematic analysis using anonymized transcripts. RESULTS: Fourteen residents participated in 3 focus groups, from which a qualitative analysis revealed 4 themes. First, surgery residents receive limited formal teaching on the negative environmental impact of surgical care or how to reduce this impact. Second, surgery residents have variable levels of prior education about and interest in sustainability in surgery. Third, several barriers prevent the implementation of sustainable changes in surgical practice, including a lack of institutional initiative, cultural inertia, concerns about workflow efficiency, and limited formal education. Finally, residents prefer to learn about practical ways to reduce waste, specifically through interactive approaches such as quality improvement initiatives. CONCLUSIONS: Given the increasing importance of sustainability in surgery, there is an urgent need for formal resident education on this topic. This needs assessment provides a valuable foundation for future sustainability curriculum development.

Inhibitory fear memory engram in the mouse central lateral amygdala.

Engrams, which are cellular substrates of memory traces, have been identified in various brain areas, including the amygdala. While most identified engrams are composed of excitatory, glutamatergic neurons, GABAergic inhibitory engrams have been relatively overlooked. Here, we report the identification of an inhibitory engram in the central lateral amygdala (CeL), a key area for auditory fear conditioning. This engram is primarily composed of GABAergic somatostatin-expressing (SST(+)) and, to a lesser extent, protein kinase C-δ-expressing (PKC-δ(+)) neurons. Fear memory is accompanied by a preferential enhancement of synaptic inhibition onto PKC-δ(+) neurons. Silencing this CeL GABAergic engram disinhibits the activity of targeted extra-amygdaloid areas, selectively increasing the expression of fear. Our findings define the behavioral function of an engram formed exclusively by GABAergic inhibitory neurons in the mammalian brain.

Plasma Chemical Looping: Unlocking High-Efficiency CO2 Conversion to Clean CO at Mild Temperatures.

We propose a plasma chemical looping CO2 splitting (PCLCS) approach that enables highly efficient CO2 conversion into O2-free CO at mild temperatures. PCLCS achieves an impressive 84% CO2 conversion and a 1.3 mmol g-1 CO yield, with no O2 detected. Crucially, this strategy significantly lowers the temperature required for conventional chemical looping processes from 650 to 1000 °C to only 320 °C, demonstrating a robust synergy between plasma and the Ce0.7Zr0.3O2 oxygen carrier (OC). Systematic experiments and density functional theory (DFT) calculations unveil the pivotal role of plasma in activating and partially decomposing CO2, yielding a mixture of CO, O2/O, and electronically/vibrationally excited CO2*. Notably, these excited CO2* species then efficiently decompose over the oxygen vacancies of the OCs, with a substantially reduced activation barrier (0.86 eV) compared to ground-state CO2 (1.63 eV), contributing to the synergy. This work offers a promising and energy-efficient pathway for producing O2-free CO from inert CO2 through the tailored interplay of plasma and OCs.

Advanced Glycation End Products and Health: A Systematic Review.

Advanced glycation end products (AGEs) have garnered significant attention due to their association with chronic diseases and the aging process. The prevalence of geriatric diseases among young individuals has witnessed a notable surge in recent years, potentially attributed to the accelerated pace of modern life. The accumulation of AGEs is primarily attributed to their inherent difficulty in metabolism, which makes them promising biomarkers for chronic disease detection. This review aims to provide a comprehensive overview of the recent advancements and findings in AGE research. The discussion is divided into two main sections: endogenous AGEs (formed within the body) and exogenous AGEs (derived from external sources). Various aspects of AGEs are subsequently summarized, including their production pathways, pathogenic mechanisms, and detection methods. Moreover, this review delves into the future research prospects concerning AGEs. Overall, this comprehensive review underscores the importance of AGEs in the detection of chronic diseases and provides a thorough understanding of their significance. It emphasizes the necessity for further research endeavors to deepen our comprehension of AGEs and their implications for human health.

[Consistency evaluation of multidimensional quality attributes of Atractylodis Macrocephalae Rhizoma by fusion of multi-source information].

The quality control of Chinese medicinal decoction pieces is one of the key tasks in the traditional Chinese medicine industry. In this study, multi-source information fusion was employed to fuse the data from near-infrared spectroscopy, electronic tongues, and other tests and establish an overall quality consistency evaluation method for Atractylodis Macrocephalae Rhizoma, which provided methodological support for the overall quality evaluation of Atractylodis Macrocephalae Rhizoma. The near-infrared spectroscopy information was measured in both static and dynamic states for 23 batches of Atractylodis Macrocephalae Rhizoma samples from different sources, and the electronic tongue sensory information, moisture content, and leachate content were measured. The overall quality of Atractylodis Macrocephalae Rhizoma was evaluated by multi-source information fusion. The results showed that the near-infrared spectroscopy information of 16122103, 801000509, 801000352, 701003656, HX21L01, and 160956 was different from that of other batches of Atractylodis Macrocephalae Rhizoma powder in the static state, and 701003298, 16122103, 701003656, 701003107, 801000229, and 18090404 were the different batches in the dynamic state. The moisture content showed no significant difference between batches. The leachate content in the batch 801000509 was different from that in other batches. The electronic tongue sensory information of 150721004, 151237, 160703004, HX21M01, HX21K04, HX21K01, and 601003516 was different from that of other batches. Furthermore, data layer fusion was employed to analyze the overall quality of Atractylodis Macrocephalae Rhizoma. Four batches, 150721004, HX21M01, HX21K04, and HX21K01, showed the parameters exceeding the 95% control limits and differed from the other samples in terms of the overall quality. This study integrated the information of moisture, near-infrared spectroscopy, and other sources to evaluate the quality consistency among 23 batches of Atractylodis Macrocephalae Rhizoma samples, which provides a reference for the quality consistency evaluation of Chinese medicinal decoction pieces.

TrG2P: A transfer-learning-based tool integrating multi-trait data for accurate prediction of crop yield.

Yield prediction is the primary goal of genomic selection (GS)-assisted crop breeding. Because yield is a complex quantitative trait, making predictions from genotypic data is challenging. Transfer learning can produce an effective model for a target task by leveraging knowledge from a different, but related, source domain and is considered a great potential method for improving yield prediction by integrating multi-trait data. However, it has not previously been applied to genotype-to-phenotype prediction owing to the lack of an efficient implementation framework. We therefore developed TrG2P, a transfer-learning-based framework. TrG2P first employs convolutional neural networks (CNN) to train models using non-yield-trait phenotypic and genotypic data, thus obtaining pre-trained models. Subsequently, the convolutional layer parameters from these pre-trained models are transferred to the yield prediction task, and the fully connected layers are retrained, thus obtaining fine-tuned models. Finally, the convolutional layer and the first fully connected layer of the fine-tuned models are fused, and the last fully connected layer is trained to enhance prediction performance. We applied TrG2P to five sets of genotypic and phenotypic data from maize (Zea mays), rice (Oryza sativa), and wheat (Triticum aestivum) and compared its model precision to that of seven other popular GS tools: ridge regression best linear unbiased prediction (rrBLUP), random forest, support vector regression, light gradient boosting machine (LightGBM), CNN, DeepGS, and deep neural network for genomic prediction (DNNGP). TrG2P improved the accuracy of yield prediction by 39.9%, 6.8%, and 1.8% in rice, maize, and wheat, respectively, compared with predictions generated by the best-performing comparison model. Our work therefore demonstrates that transfer learning is an effective strategy for improving yield prediction by integrating information from non-yield-trait data. We attribute its enhanced prediction accuracy to the valuable information available from traits associated with yield and to training dataset augmentation. The Python implementation of TrG2P is available at https://github.com/lijinlong1991/TrG2P. The web-based tool is available at http://trg2p.ebreed.cn:81.

Loss of mGlu5 receptors in somatostatin-expressing neurons alters negative emotional states.

Subtype 5 metabotropic glutamate receptors (mGlu5) are known to play an important role in regulating cognitive, social and valence systems. However, it remains largely unknown at which circuits and neuronal types mGlu5 act to influence these behavioral domains. Altered tissue- or cell-specific expression or function of mGlu5 has been proposed to contribute to the exacerbation of neuropsychiatric disorders. Here, we examined how these receptors regulate the activity of somatostatin-expressing (SST+) neurons, as well as their influence on behavior and brain rhythmic activity. Loss of mGlu5 in SST+ neurons elicited excitatory synaptic dysfunction in a region and sex-specific manner together with a range of emotional imbalances including diminished social novelty preference, reduced anxiety-like behavior and decreased freezing during retrieval of fear memories. In addition, the absence of mGlu5 in SST+ neurons during fear processing impaired theta frequency oscillatory activity in the medial prefrontal cortex and ventral hippocampus. These findings reveal a critical role of mGlu5 in controlling SST+ neurons excitability necessary for regulating negative emotional states.

SALVIANOLIC ACID A ATTENUATES ANGIOTENSIN II-INDUCED CARDIAC FIBROSIS THROUGH REGULATING THE TXNIP SIGNALING PATHWAY.

ABSTRACT: Cardiac fibrosis, characterized by excessive collagen accumulation in heart tissues, poses a significant clinical challenge in various heart diseases and complications. Although salvianolic acid A (Sal A) from Danshen ( Salvia miltiorrhiza ) has shown promise in the treatment of ischemic heart disease, myocardial infarction, and atherosclerosis, its effects on cardiac fibrosis remain unexplored. Our study investigated the efficacy of Sal A in reducing cardiac fibrosis and elucidated its underlying molecular mechanisms. We observed that Sal A demonstrated significant cardioprotective effects against Angiotensin II (Ang II)-induced cardiac remodeling and fibrosis, showing a dose-dependent reduction in fibrosis in mice and suppression of cardiac fibroblast proliferation and fibrotic protein expression in vitro . RNA sequencing revealed that Sal A counteracted Ang II-induced upregulation of Txnip, and subsequent experiments indicated that it acts through the inflammasome and ROS pathways. These findings establish the antifibrotic effects of Sal A, notably attenuated by Txnip overexpression, and highlight its significant role in modulating inflammation and oxidative stress pathways. This underscores the importance of further research on Sal A and similar compounds, especially regarding their effects on inflammation and oxidative stress, which are key factors in various cardiovascular diseases.

Breast Milk Excretion of Dinalbuphine Sebacate Injection Administered After Cesarean Section.

Ensuring the safety of analgesics during lactation is crucial for women of childbearing potential. Available data regarding the transfer of nalbuphine for postoperative acute pain via breast milk are limited to the postmarketing experience. This lactation study aimed to assess nalbuphine and dinalbuphine sebacate concentrations in breast milk from lactating women with postoperative pain treated with dinalbuphine sebacate extended-release injection (150 mg dinalbuphine sebacate/2 mL Naldebain). Breast milk was collected throughout the 5-day posthospitalization interval from 20 mothers injected with one dose of extended-release dinalbuphine sebacate intramuscularly. Maternal safety was assessed during the study period. Nalbuphine was detectable in 71% of milk samples collected from all mothers, whereas dinalbuphine sebacate was undetectable or below the quantitation limit (0.1 ng/mL). The mean nalbuphine concentration in milk was approximately 10.55 ng/mL, with the peak concentration reaching up to 12.7 ng/mL. The mean relative infant dose was 0.39% (coefficient of variation, 65%). The mean pain intensity at rest was reduced to mild pain from Day 2 morning to discharge. Overall, the maternal safety profile was tolerable. The breast milk of women who receive one dose of dinalbuphine sebacate injection postpartum contains low nalbuphine concentration. In addition, dinalbuphine sebacate injection potentially reduces maternal pain intensity during the first postpartum week and offers low toxicity risk among breastfed infants.

Instrument and Supply Variability: An Opportunity to Reduce the Carbon Footprint of the Operating Room.

INTRODUCTION: Reducing costs and carbon footprints are important, parallel priorities for the US health-care system. Within surgery, reducing the number of instruments that are sterilized and disposable supplies that are used for each operation may help achieve both goals. We wanted to measure the existing variability in surgical instrument and supply choices and assess whether standardization could have a meaningful cost and environmental impact. METHODS: We analyzed surgeon preference cards for common general surgery operations at our hospital to measure the number of sterilizable instrument trays and supplies used by each surgeon for each operation. From this data, we calculated supply costs, carbon footprint, and median operative time and studied the variability in each of these metrics. RESULTS: Among the ten operations studied, variability in sterilizable instrument trays requested on surgeon preference cards ranged from one to eight. Variability in disposable supplies requested ranged from 17 to 45. Variability in open supply costs ranged from $104 to $4184. Variability in carbon footprint ranged from 17 to 708 kg CO2e. If the highest-cost surgeon for each operation switched their preference card to that of the median-cost surgeon, $245,343 in open supply costs and 41,708 kg CO2e could be saved. CONCLUSIONS: There is significant variability in the instrument and supply choices of surgeons performing common general surgery operations. Standardizing this variability may lead to meaningful cost savings and carbon footprint reduction, especially if scaled across the entire health system.

Bicycle Data-Driven Application Framework: A Dutch Case Study on Machine Learning-Based Bicycle Delay Estimation at Signalized Intersections Using Nationwide Sparse GPS Data.

Data-driven approaches are helpful for quantitative justification and performance evaluation. The Netherlands has made notable strides in establishing a national protocol for bicycle traffic counting and collecting GPS cycling data through initiatives such as the Talking Bikes program. This article addresses the need for a generic framework to harness cycling data and extract relevant insights. Specifically, it focuses on the application of estimating average bicycle delays at signalized intersections, as this is an essential variable in assessing the performance of the transportation system. This study evaluates machine learning (ML)-based approaches using GPS cycling data. The dataset provides comprehensive yet incomplete information regarding one million bicycle rides annually across The Netherlands. These ML models, including random forest, k-nearest neighbor, support vector regression, extreme gradient boosting, and neural networks, are developed to estimate bicycle delays. The study demonstrates the feasibility of estimating bicycle delays using sparse GPS cycling data combined with publicly accessible information, such as weather information and intersection complexity, leveraging the burden of understanding local traffic conditions. It emphasizes the potential of data-driven approaches to inform traffic management, bicycle policy, and infrastructure development.

Transition to Reusable Surgical Gowns at a Hospital System.

This quality improvement study investigates the feasibility of transitioning back to reusable surgical gowns at a US tertiary hospital system and projected corresponding cost savings and solid waste reduction.

Knowledge Gaps in Anesthetic Gas Utilization in a Large Academic Hospital System: A Multicenter Survey.

Inhaled anesthetics account for a significant portion of the greenhouse gases generated by perioperative services within the healthcare systems. This cross-sectional study aimed to identify knowledge gaps and practice patterns related to carbon dioxide (CO2) absorbents and intraoperative delivery of fresh gas flows (FGF) for future sustainability endeavors. Secondary aims focused on differences in these knowledge gaps based on the level of training. Surveys were distributed at five large academic medical centers. In addition to site-specific CO2 absorbent use and practice volume and experience, respondents at each institution were queried about individual practice with FGF rates during anesthetic maintenance as well as the cost-effectiveness and environmental impact of different volatile anesthetics. Results were stratified and analyzed by the level of training. In total, 368 (44% physicians, 30% residents, and 26% nurse anesthetists) respondents completed surveys. Seventy-six percent of respondents were unaware or unsure about which type of CO2 absorbent was in use at their hospital. Fifty-nine percent and 48% of respondents used sevoflurane and desflurane with FGF ≥1 L/min, respectively. Most participants identified desflurane as the agent with the greatest environmental impact (89.9%) and a greater proportion of anesthesiologists correctly identified isoflurane as a cost-effective anesthetic (78.3%, p=0.02). Knowledge gaps about in-use CO2 absorbent and optimal FGF usage were identified within the anesthesia care team. Educational initiatives to increase awareness about the carbon emissions from anesthesia and newer CO2 absorbents will impact the environmental and economic cost per case and align anesthesia providers toward healthcare decarbonization.

A variety test platform for the standardization and data quality improvement of crop variety tests.

Variety testing is an indispensable and essential step in the process of creating new improved varieties from breeding to adoption. The performance of the varieties can be compared and evaluated based on multi-trait data from multi-location variety tests in multiple years. Although high-throughput phenotypic platforms have been used for observing some specific traits, manual phenotyping is still widely used. The efficient management of large amounts of data is still a significant problem for crop variety testing. This study reports a variety test platform (VTP) that was created to manage the whole workflow for the standardization and data quality improvement of crop variety testing. Through the VTP, the phenotype data of varieties can be integrated and reused based on standardized data elements and datasets. Moreover, the information support and automated functions for the whole testing workflow help users conduct tests efficiently through a series of functions such as test design, data acquisition and processing, and statistical analyses. The VTP has been applied to regional variety tests covering more than seven thousand locations across the whole country, and then a standardized and authoritative phenotypic database covering five crops has been generated. In addition, the VTP can be deployed on either privately or publicly available high-performance computing nodes so that test management and data analysis can be conveniently done using a web-based interface or mobile application. In this way, the system can provide variety test management services to more small and medium-sized breeding organizations, and ensures the mutual independence and security of test data. The application of VTP shows that the platform can make variety testing more efficient and can be used to generate a reliable database suitable for meta-analysis in multi-omics breeding and variety development projects.

A mixed-methods study on end-user perceptions of transitioning to reusable surgical gowns.

Background: Perioperative services contribute up to 70% of the US hospitals' solid waste generation. While surgical textiles are more environmentally friendly than their disposable counterparts, many US institutions have converted to disposable surgical wear in the last few decades. End-users' perception surrounding reusable textiles is currently unknown. Methods: Perioperative staff at the University of California San Francisco (UCSF) were surveyed to assess perceptions of reusable surgical gowns to guide potential implementation. The instrument included eight close-ended questions drawn from prior studies and a free-response section. The survey was piloted before dissemination. Descriptive statistics and qualitative inductive theme analysis were applied. Results: 205 participants or 19.8% of the workforce responded. 77.6% perceived reusable surgical gowns as better for the environment, while 34.1% were unsure or believed that switching to reusable surgical gowns would increase surgical site infections. If given an option, 39.8% preferred reusable gowns, 30.7% preferred disposable gowns, and 25.4% had no preference. Qualitatively, four themes were identified concerning reusable gowns' 1) functionality and safety, 2) user comfort, 3) environmental concern, and 4) cost, which hindered end-user buy-in. Laundering water utilization in a drought-prone area was of particular concern. Conclusions: While most perioperative staff in a US tertiary hospital believed reusable surgical gowns were environmentally friendly, ambivalence towards transitioning to reusable gowns stemmed from uncertainty in reusable textiles' environmental benefits, safety profile, and cost savings. These perceptions may prevent successful implementation of reusable surgical gowns and suggest a need for staff education and context-specific environmental impact analyses. Key message: End-user perceptions on transitioning to reusable surgical gowns are mixed and revolve around uncertainty in their environmental benefits, cost, and functionality, which may hinder their successful implementation.

Salicylic Acid Enhances Cadmium Tolerance and Reduces Its Shoot Accumulation in Fagopyrum tataricum Seedlings by Promoting Root Cadmium Retention and Mitigating Oxidative Stress.

Soil cadmium (Cd) contamination seriously reduces the production and product quality of Tartary buckwheat (Fagopyrum tataricum), and strategies are urgently needed to mitigate these adverse influences. Herein, we investigated the effect of salicylic acid (SA) on Tartary buckwheat seedlings grown in Cd-contaminated soil in terms of Cd tolerance and accumulation. The results showed that 75-100 µmol L-1 SA treatment enhanced the Cd tolerance of Tartary buckwheat, as reflected by the significant increase in plant height and root and shoot biomass, as well as largely mitigated oxidative stress. Moreover, 100 µmol L-1 SA considerably reduced the stem and leaf Cd concentration by 60% and 47%, respectively, which is a consequence of increased root biomass and root Cd retention with promoted Cd partitioning into cell wall and immobile chemical forms. Transcriptome analysis also revealed the upregulation of the genes responsible for cell wall biosynthesis and antioxidative activities in roots, especially secondary cell wall synthesis. The present study determines that 100 µmol L-1 is the best SA concentration for reducing Cd accumulation and toxicity in Tartary buckwheat and indicates the important role of root in Cd stress in this species.

An Accessible Clinical Decision Support System to Curtail Anesthetic Greenhouse Gases in a Large Health Network: Implementation Study.

BACKGROUND: Inhaled anesthetics in the operating room are potent greenhouse gases and are a key contributor to carbon emissions from health care facilities. Real-time clinical decision support (CDS) systems lower anesthetic gas waste by prompting anesthesia professionals to reduce fresh gas flow (FGF) when a set threshold is exceeded. However, previous CDS systems have relied on proprietary or highly customized anesthesia information management systems, significantly reducing other institutions' accessibility to the technology and thus limiting overall environmental benefit. OBJECTIVE: In 2018, a CDS system that lowers anesthetic gas waste using methods that can be easily adopted by other institutions was developed at the University of California San Francisco (UCSF). This study aims to facilitate wider uptake of our CDS system and further reduce gas waste by describing the implementation of the FGF CDS toolkit at UCSF and the subsequent implementation at other medical campuses within the University of California Health network. METHODS: We developed a noninterruptive active CDS system to alert anesthesia professionals when FGF rates exceeded 0.7 L per minute for common volatile anesthetics. The implementation process at UCSF was documented and assembled into an informational toolkit to aid in the integration of the CDS system at other health care institutions. Before implementation, presentation-based education initiatives were used to disseminate information regarding the safety of low FGF use and its relationship to environmental sustainability. Our FGF CDS toolkit consisted of 4 main components for implementation: sustainability-focused education of anesthesia professionals, hardware integration of the CDS technology, software build of the CDS system, and data reporting of measured outcomes. RESULTS: The FGF CDS system was successfully deployed at 5 University of California Health network campuses. Four of the institutions are independent from the institution that created the CDS system. The CDS system was deployed at each facility using the FGF CDS toolkit, which describes the main components of the technology and implementation. Each campus made modifications to the CDS tool to best suit their institution, emphasizing the versatility and adoptability of the technology and implementation framework. CONCLUSIONS: It has previously been shown that the FGF CDS system reduces anesthetic gas waste, leading to environmental and fiscal benefits. Here, we demonstrate that the CDS system can be transferred to other medical facilities using our toolkit for implementation, making the technology and associated benefits globally accessible to advance mitigation of health care-related emissions.