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Exceptional alpine plant diversity exists in the Hengduan Mountains. Here, through genome assembly and population genomics studies, the authors find notable intraspecific divergence among Cushion willow populations isolated by the sky island-like habitats and consider it contributes to speciation and biodiversity.

BackgroundUnfractionated heparin is widely used in the intensive care unit as an anticoagulant. However, weight-based heparin dosing has been shown to be suboptimal and may place patients at unnecessary risk during their intensive care unit stay. ObjectiveIn this study, we intended to develop and validate a machine learning–based model to predict heparin treatment outcomes and to provide dosage recommendations to clinicians. MethodsA shallow neural network model was adopted in a retrospective cohort of patients from the Multiparameter Intelligent Monitoring in Intensive Care III (MIMIC III) database and patients admitted to the Peking Union Medical College Hospital (PUMCH). We modeled the subtherapeutic, normal, and supratherapeutic activated partial thromboplastin time (aPTT) as the outcomes of heparin treatment and used a group of clinical features for modeling. Our model classifies patients into 3 different therapeutic states. We tested the prediction ability of our model and evaluated its performance by using accuracy, the kappa coefficient, precision, recall, and the F1 score. Furthermore, a dosage recommendation module was designed and evaluated for clinical decision support. ResultsA total of 3607 patients selected from MIMIC III and 1549 patients admitted to the PUMCH who met our criteria were included in this study. The shallow neural network model showed results of F1 scores 0.887 (MIMIC III) and 0.925 (PUMCH). When compared with the actual dosage prescribed, our model recommended increasing the dosage for 72.2% (MIMIC III, 1240/1718) and 64.7% (PUMCH, 281/434) of the subtherapeutic patients and decreasing the dosage for 80.9% (MIMIC III, 504/623) and 76.7% (PUMCH, 277/361) of the supratherapeutic patients, suggesting that the recommendations can contribute to clinical improvements and that they may effectively reduce the time to optimal dosage in the clinical setting. ConclusionsThe evaluation of our model for predicting heparin treatment outcomes demonstrated that the developed model is potentially applicable for reducing the misdosage of heparin and for providing appropriate decision recommendations to clinicians.

BackgroundMonitoring critically ill patients in intensive care units (ICUs) in real time is vitally important. Although scoring systems are most often used in risk prediction of mortality, they are usually not highly precise, and the clinical data are often simply weighted. This method is inefficient and time-consuming in the clinical setting. ObjectiveThe objective of this study was to integrate all medical data and noninvasively predict the real-time mortality of ICU patients using a gradient boosting method. Specifically, our goal was to predict mortality using a noninvasive method to minimize the discomfort to patients. MethodsIn this study, we established five models to predict mortality in real time based on different features. According to the monitoring, laboratory, and scoring data, we constructed the feature engineering. The five real-time mortality prediction models were RMM (based on monitoring features), RMA (based on monitoring features and the Acute Physiology and Chronic Health Evaluation [APACHE]), RMS (based on monitoring features and Sequential Organ Failure Assessment [SOFA]), RMML (based on monitoring and laboratory features), and RM (based on all monitoring, laboratory, and scoring features). All models were built using LightGBM and tested with XGBoost. We then compared the performance of all models, with particular focus on the noninvasive method, the RMM model. ResultsAfter extensive experiments, the area under the curve of the RMM model was 0.8264, which was superior to that of the RMA and RMS models. Therefore, predicting mortality using the noninvasive method was both efficient and practical, as it eliminated the need for extra physical interventions on patients, such as the drawing of blood. In addition, we explored the top nine features relevant to real-time mortality prediction: invasive mean blood pressure, heart rate, invasive systolic blood pressure, oxygen concentration, oxygen saturation, balance of input and output, total input, invasive diastolic blood pressure, and noninvasive mean blood pressure. These nine features should be given more focus in routine clinical practice. ConclusionsThe results of this study may be helpful in real-time mortality prediction in patients in the ICU, especially the noninvasive method. It is efficient and favorable to patients, which offers a strong practical significance.

BackgroundWidespread adoption of electronic health records has enabled the secondary use of electronic health record data for clinical research and health care delivery. Natural language processing techniques have shown promise in their capability to extract the information embedded in unstructured clinical data, and information retrieval techniques provide flexible and scalable solutions that can augment natural language processing systems for retrieving and ranking relevant records. ObjectiveIn this paper, we present the implementation of a cohort retrieval system that can execute textual cohort selection queries on both structured data and unstructured text—Cohort Retrieval Enhanced by Analysis of Text from Electronic Health Records (CREATE). MethodsCREATE is a proof-of-concept system that leverages a combination of structured queries and information retrieval techniques on natural language processing results to improve cohort retrieval performance using the Observational Medical Outcomes Partnership Common Data Model to enhance model portability. The natural language processing component was used to extract common data model concepts from textual queries. We designed a hierarchical index to support the common data model concept search utilizing information retrieval techniques and frameworks. ResultsOur case study on 5 cohort identification queries, evaluated using the precision at 5 information retrieval metric at both the patient-level and document-level, demonstrates that CREATE achieves a mean precision at 5 of 0.90, which outperforms systems using only structured data or only unstructured text with mean precision at 5 values of 0.54 and 0.74, respectively. ConclusionsThe implementation and evaluation of Mayo Clinic Biobank data demonstrated that CREATE outperforms cohort retrieval systems that only use one of either structured data or unstructured text in complex textual cohort queries.

BackgroundHeparin is one of the most commonly used medications in intensive care units. In clinical practice, the use of a weight-based heparin dosing nomogram is standard practice for the treatment of thrombosis. Recently, machine learning techniques have dramatically improved the ability of computers to provide clinical decision support and have allowed for the possibility of computer generated, algorithm-based heparin dosing recommendations. ObjectiveThe objective of this study was to predict the effects of heparin treatment using machine learning methods to optimize heparin dosing in intensive care units based on the predictions. Patient state predictions were based upon activated partial thromboplastin time in 3 different ranges: subtherapeutic, normal therapeutic, and supratherapeutic, respectively. MethodsRetrospective data from 2 intensive care unit research databases (Multiparameter Intelligent Monitoring in Intensive Care III, MIMIC-III; e–Intensive Care Unit Collaborative Research Database, eICU) were used for the analysis. Candidate machine learning models (random forest, support vector machine, adaptive boosting, extreme gradient boosting, and shallow neural network) were compared in 3 patient groups to evaluate the classification performance for predicting the subtherapeutic, normal therapeutic, and supratherapeutic patient states. The model results were evaluated using precision, recall, F1 score, and accuracy. ResultsData from the MIMIC-III database (n=2789 patients) and from the eICU database (n=575 patients) were used. In 3-class classification, the shallow neural network algorithm performed the best (F1 scores of 87.26%, 85.98%, and 87.55% for data set 1, 2, and 3, respectively). The shallow neural network algorithm achieved the highest F1 scores within the patient therapeutic state groups: subtherapeutic (data set 1: 79.35%; data set 2: 83.67%; data set 3: 83.33%), normal therapeutic (data set 1: 93.15%; data set 2: 87.76%; data set 3: 84.62%), and supratherapeutic (data set 1: 88.00%; data set 2: 86.54%; data set 3: 95.45%) therapeutic ranges, respectively. ConclusionsThe most appropriate model for predicting the effects of heparin treatment was found by comparing multiple machine learning models and can be used to further guide optimal heparin dosing. Using multicenter intensive care unit data, our study demonstrates the feasibility of predicting the outcomes of heparin treatment using data-driven methods, and thus, how machine learning–based models can be used to optimize and personalize heparin dosing to improve patient safety. Manual analysis and validation suggested that the model outperformed standard practice heparin treatment dosing.

The ‘Two Oceans and One Sea’ area (West Pacific, Indian Ocean, and South China Sea; 15°S–60°N, 39°–178°E) is a core strategic area for the ‘21st Century Maritime Silk Road’ project, as well as national defense. With the increasing demand for disaster prevention and mitigation, the importance of 10–30-day extended range prediction, between the conventional short-term (around seven days) and the climate scale (longer than one month), is apparent. However, marine extended range prediction is still a ‘blank point’ in China, making the early warning of marine disasters almost impossible. Here, the authors introduce a recently launched Chinese national project on a numerical forecasting system for extended range prediction in the ‘Two Oceans and One Sea’ area based on a regional ultra-high resolution multi-layer coupled model, including the scientific aims, technical scheme, innovation, and expected achievements. The completion of this prediction system is of considerable significance for the economic development and national security of China.

AIM: To investigate a possible trigeminal proprioceptive-oculomotor neural pathway and explore possible synaptic connections between neurons in this pathway. Attempt to bring a new insight to mechanism of Marcus Gunn syndrome (MGS). METHODS: Anterograde and retrograde tract tracing was applied and combined with immunofluorescent stain in rats. After electrophysiological identifying mesencephalic trigeminal nucleus (Vme) neurons, intracellular injection of tracer was performed to trace axon trajectory. RESULTS: Following injections of anterograde tracers into the Vme, labeled terminals were observed ipsilateral in oculomotor and trochlear nuclei (III/IV), as well as in their premotor neurons in interstitial nucleus of Cajal and Darkschewitsch nucleus (INC/DN). Combining with choline acetyltransferase (ChAT) immunofluorescent stain, it showed that Vme projecting terminals contact upon ChAT positive III/IV motoneurons under confocal microscope. By retrograde labeling premotor neurons of the III, it showed that Vme neuronal terminals contact with retrogradely labeled pre-oculomotor neurons in the INC/DN. Axons of intracellularly labeled Vme neurons that respond to electric stimuli of the masseter nerve traveled into the ipsilateral III. CONCLUSION: There may exist a trigeminal proprioceptive- oculomotor system neural circuit in the rat, which is probably related to vertical-torsional eye movements. Possible association of this pathway with MGS etiology was discussed.

The synergization of 3D graphene with magnetic nanoparticles provides effective strategies to shield electromagnetic waves. However, the uniform distribution of magnetic nanoparticles within the 3D graphene network still remains a great challenge, owing to the agglomeration tendency of the magnetic nanoparticles. Herein, we report a facile method to in situ fabricate 3D graphene decorated with highly dispersed nickel nanoparticles (Ni NPs) by laser conversion from the polyimide/Ni(II) precursor film. In this process, 3D graphene was generated by CO2 laser scribing of the polyimide film, accompanied by carbothermal reduction of Ni(II) to form Ni NPs. Significantly, in the precursor film, carboxylate ions were introduced to complex with Ni(II), leading to a uniform dispersion of Ni(II), thus Ni NPs with high dispersibility in 3D graphene networks were achieved after laser fabrication. As a result, massive heterogeneous interfaces and dense magnetic coupling networks were realized in the graphene/Ni composite, which is conducive to improving polarization loss and magnetic loss for the dissipation of incident electromagnetic waves. Combined with the multiple electromagnetic wave dissipation paths provided by 3D graphene, the graphene/Ni composite exhibits absorption-dominated electromagnetic interference shielding effectiveness (EMI SE). At an Ni content of 5.3 wt%, the EMI SE of the double-layer graphene/Ni composite was 94 dB with an adsorption ratio of 91% in the X-band. Such a high EMI SE value will pave the way for practical applications of 3D graphene in EMI shielding. [ABSTRACT FROM AUTHOR]

The development of a small‐molecule probe designed to selectively target neurons would enhance the exploration of intricate neuronal structures and functions. Among such probes, NeuO stands out as the pioneer and has gained significant traction in the field of research. Nevertheless, neither the mechanism behind neuron‐selectivity nor the cellular localization has been determined. Here, we introduce NeuM, a derivative of NeuO, designed to target neuronal cell membranes. Furthermore, we elucidate the mechanism behind the selective neuronal membrane trafficking that distinguishes neurons. In an aqueous buffer, NeuM autonomously assembles into micellar structures, leading to the quenching of its fluorescence (Φ=0.001). Upon exposure to neurons, NeuM micelles were selectively internalized into neuronal endosomes via clathrin‐mediated endocytosis. Through the endocytic recycling pathway, NeuM micelles integrate into neuronal membrane, dispersing fluorescent NeuM molecules in the membrane (Φ=0.61). Molecular dynamics simulations demonstrated that NeuM, in comparison to NeuO, possesses optimal lipophilicity and molecular length, facilitating its stable incorporation into phospholipid layers. The stable integration of NeuM within neuronal membrane allows the prolonged monitoring of neurons, as well as the visualization of intricate neuronal structures. [ABSTRACT FROM AUTHOR]

Hybrid genomes usually harbor asymmetrical parental contributions. However, it is challenging to infer the functional significance of asymmetrical retention of parental alleles in hybrid populations of conifer trees. Here we investigated the diversity in the glutathione S‐transferase (GST) gene family in a hybrid pine Pinus densata and its parents (Pinus tabuliformis and Pinus yunnanensis). Plant GSTs play major roles in protecting plants against biotic and abiotic stresses. In this study, 19 orthologous groups of GST genes were identified and cloned from these three species. We examined their expression in different tissues, and then purified the corresponding proteins to characterize their enzymatic activities and specificities toward different substrates. We found that among the 19 GST orthologous groups, divergence in gene expression and in enzymatic activities toward different substrates was prevalent. P. densata preferentially retained P. yunnanensis‐like GSTs for 17 out of the 19 gene loci. We determined the first GST crystal structure from conifer species at a resolution of 2.19 Å. Based on this structure, we performed site‐directed mutagenesis to replace amino acid residuals in different wild‐types of GSTs to understand their functional impacts. Reciprocal replacement of amino acid residuals in native GSTs of P. densata and P. tabuliformis demonstrated significant changes in enzyme functions and identified key sites controlling GSTs activities. This study illustrates an approach to evaluating the functional significance of sequence variations in conifer genomes. Our study also sheds light on plausible mechanisms for controlling the selective retention of parental alleles in the P. densata genome. [ABSTRACT FROM AUTHOR]

AIM: To investigate the influence and mechanism of blue light on the proliferation of human retinal pigment epithelial cells. METHODS: Cells were divided into two groups, including blue light group and control group. The 35W white light lamp with blue filter was used to establish damaged RPE cell model in vitro. Blue ray wavelength ranged between 470nm and 520nm. And the light intensity was about 2 000Lx. After exposure to blue light, we tested the proliferation of human retinal pigment epithelial cells by CCK-8 kit. And then expression of miR-103 was measured by the real-time PCR. RESULTS: Exposure to blue light inhibited the proliferation of human retinal pigment epithelial cells and increased the expression of miR-103. Moreover, up-regulation of miR-103 inhibited the proliferation of human retinal pigment epithelial cells, and down-regulates miR-103 promoted the proliferation of human retinal pigment epithelial cells. CONCLUSION: Blue light inhibits the proliferation of human retinal pigment epithelial cells by the up-regulation of miR-103.

Abstract Background Severe fever with thrombocytopenia syndrome (SFTS) is a newly identified emerging infectious disease, which is caused by a novel bunyavirus (termed SFTSV) in Asia. Although mosquitoes have not been identified as the primary vectors, as revealed by epidemiological surveys, their role in transmitting this SFTSV as a suspicious vector has not been validated. Findings In this study, we conducted experimental infections of mosquitoes with SFTSV to examine the role of mosquitoes in the transmission of the virus. We did not detect viral replication in Culex pipiens pallens, Aedes aegyptis and Anopheles sinensis as revealed by qRT-PCR assay. In addition, we failed to isolate SFTSV from the Vero cells cultured with suspensions of SFTSV-infected mosquitoes. Conclusion The results of the present study demonstrate little possibility that mosquitoes act as vectors for the emerging pathogen SFTSV.