The purpose of this study was to evaluate a portable tool for use by first responders in documenting triage of victims in a mass casualty incident (MCI) more effectively. The tool presented in this study allows first responders to gather patients vital signs, injuries, and triage status in a prompt and accurate way, and enables first responders to wirelessly communicate vital health information throughout the entire care continuum. The architecture infrastructure for the portable device is called Triage and Casualty Informatics Technology (TACIT) and can expedite triage, transport and treatment procedures within an MCI. TACIT was developed by integrating handheld devices, wireless networks, global positioning system (GPS), digital cameras, and bar code scanners with customized triage software. Two MCI initial field trials verified that the TACIT software, battery life, data accuracy, and wireless transmission met the emergency response system requirements. Initial field trials also demonstrated robustness of operation, reduced triage collection time and improved collection accuracy. The TACIT system could work as an efficient prehospital response tool and platform.

The data presented in this study support the feasibility for mentoring and consultation to a remote audience with visual transmission of the surgical field, which is otherwise very difficult to share. Additionally, validation of technical protocols as teaching tools for robotic instrumentation and computer imaging of surgical fields was documented.

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The floral transition of the maize (Zea mays ssp. mays) shoot apical meristem determines leaf number and flowering time, which are key traits influencing local adaptation and yield potential. dlf1 (delayed flowering1) encodes a basic leucine zipper protein that interacts with the florigen ZCN8 to mediate floral induction in the shoot apex. However, the mechanism of how dlf1 promotes floral transition remains largely unknown. We demonstrate that dlf1 underlies qLB7-1, a quantitative trait locus controlling leaf number and flowering time that was identified in a BC 2 S 3 population derived from a cross between maize and its wild ancestor, teosinte (Zea mays ssp. parviglumis). Transcriptome sequencing and chromatin immunoprecipitation sequencing demonstrated that DLF1 binds the core promoter of two AP1/FUL subfamily MADS-box genes, ZmMADS4 and ZmMADS67, to activate their expression. Knocking out ZmMADS4 and ZmMADS67 both increased leaf number and delayed flowering, indicating that they promote the floral transition. Nucleotide diversity analysis revealed that dlf1 and ZmMADS67 were targeted by selection, suggesting that they may have played important roles in maize flowering time adaptation. We show that dlf1 promotes maize floral transition by directly activating ZmMADS4 and ZmMADS67 in the shoot apex, providing novel insights into the mechanism of maize floral transition.

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Predicting RNA-binding protein (RBP) specificity is important for understanding gene expression regulation and RNA-mediated enzymatic processes. It is widely believed that RBP binding specificity is determined by both the sequence and structural contexts of RNAs. Existing approaches, including traditional machine learning algorithms and more recently, deep learning models, have been extensively applied to integrate RNA sequence and its predicted or experimental RNA structural probabilities for improving the accuracy of RBP binding prediction. Such models were trained mostly on the large-scale in vitro datasets, such as the RNAcompete dataset. However, in RNAcompete, most synthetic RNAs are unstructured, which makes machine learning methods not effectively extract RBP-binding structural preferences. Furthermore, RNA structure may be variable or multi-modal according to both theoretical and experimental evidence. In this work, we propose ThermoNet, a thermodynamic prediction model by integrating a new sequence-embedding convolutional neural network model over a thermodynamic ensemble of RNA secondary structures. First, the sequence-embedding convolutional neural network generalizes the existing k-mer based methods by jointly learning convolutional filters and k-mer embeddings to represent RNA sequence contexts. Second, the thermodynamic average of deep-learning predictions is able to explore structural variability and improves the prediction, especially for the structured RNAs. Extensive experiments demonstrate that our method significantly outperforms existing approaches, including RCK, DeepBind and several other recent state-of-the-art methods for predictions on both in vitro and in vivo data. The implementation of ThermoNet is available at https://github.com/suyufeng/ThermoNet.

Background The Clinical Frailty Scale (CFS) is frequently used to measure frailty in critically ill adults. There is wide variation in the approach to analysing the relationship between the CFS score and mortality after admission to the ICU. This study aimed to evaluate the influence of modelling approach on the association between the CFS score and short-term mortality and quantify the prognostic value of frailty in this context. Methods We analysed data from two multicentre prospective cohort studies which enrolled intensive care unit patients ≥ 80 years old in 26 countries. The primary outcome was mortality within 30-days from admission to the ICU. Logistic regression models for both ICU and 30-day mortality included the CFS score as either a categorical, continuous or dichotomous variable and were adjusted for patient’s age, sex, reason for admission to the ICU, and admission Sequential Organ Failure Assessment score. Results The median age in the sample of 7487 consecutive patients was 84 years (IQR 81–87). The highest fraction of new prognostic information from frailty in the context of 30-day mortality was observed when the CFS score was treated as either a categorical variable using all original levels of frailty or a nonlinear continuous variable and was equal to 9% using these modelling approaches (p < 0.001). The relationship between the CFS score and mortality was nonlinear (p < 0.01). Conclusion Knowledge about a patient’s frailty status adds a substantial amount of new prognostic information at the moment of admission to the ICU. Arbitrary simplification of the CFS score into fewer groups than originally intended leads to a loss of information and should be avoided. Trial registration NCT03134807 (VIP1), NCT03370692 (VIP2)

+These two authors contributed equally to this paper.Abstract -Platycodon grandifl orus is an important medicinal plant in China and its root has been used as medicine or food for centuries. Polyploidy may increase the amounts of functional compounds in vegetative organs for medicinal plants. So polyploid manipulation for medicinal plants is an effective approach of germplasm development. This research focused on tetraploid induction of P. grandifl orus by modifi ed colchicine method. The diploid seedlings were induced under three treatments (24h, 48h and 72h) to test the best treatment time. Morphology and cytology identifi cations between obtained mutants and diploid controls were also conducted. The seedling growth and development of all mutants was more stunted than controls. According to preliminary morphological characteristics, mutant rates in different treatment times were statistically estimated and the highest mutant rate was 50% under the treatment of 72h. The chromosome number of most mutants was 36 (2n=4x), while the chromosome number of diploid controls was 18 (2n=2x) by cytology observation of root tip cells. Chimeras and octoploids were also identifi ed from obtained mutants. By microscope observation of low leaf epidermis, there were signifi cant differences for stoma area between tetraploid mutants and diploid controls. As a result, tetraploid mutants of P. grandifl orus were successfully obtained by modifi ed colchicine method and their desirable traits would be further evaluated to incorporate into next breeding and pharmacy production program.