Intergrative metabolomic and transcriptomic analyses reveal the potential regulatory mechanism of unique dihydroxy fatty acid biosynthesis in the seeds of an industrial oilseed crop Orychophragmus violaceus.

BACKGROUND: Orychophragmus violaceus is a potentially important industrial oilseed crop due to the two 24-carbon dihydroxy fatty acids (diOH-FA) that was newly identified from its seed oil via a 'discontinuous elongation' process. Although many research efforts have focused on the diOH-FA biosynthesis mechanism and identified the potential co-expressed diacylglycerol acyltranferase (DGAT) gene associated with triacylglycerol (TAG)-polyestolides biosynthesis, the dynamics of metabolic changes during seed development of O. violaceus as well as its associated regulatory network changes are poorly understood. RESULTS: In this study, by combining metabolome and transcriptome analysis, we identified that 1,003 metabolites and 22,479 genes were active across four stages of seed development, which were further divided into three main clusters based on the patterns of metabolite accumulation and/or gene expression. Among which, cluster2 was mostly related to diOH-FA biosynthesis pathway. We thus further constructed transcription factor (TF)-structural genes regulatory map for the genes associated with the flavonoids, fatty acids and diOH-FA biosynthesis pathway in this cluster. In particular, several TF families such as bHLH, B3, HD-ZIP, MYB were found to potentially regulate the metabolism associated with the diOH-FA pathway. Among which, multiple candidate TFs with promising potential for increasing the diOH-FA content were identified, and we further traced the evolutionary history of these key genes among species of Brassicaceae. CONCLUSION: Taken together, our study provides new insight into the gene resources and potential relevant regulatory mechanisms of diOH-FA biosynthesis uniquely in seeds of O. violaceus, which will help to promote the downstream breeding efforts of this potential oilseed crop and advance the bio-lubricant industry.

Hyperchaos of neuron under local active discrete memristor simulating electromagnetic radiation.

Memristor enables the coupling of magnetic flux to membrane voltage and is widely used to investigate the response characteristics of neurons to electromagnetic radiation. In this paper, a local active discrete memristor is constructed and used to study the effect of electromagnetic radiation on the dynamics of neurons. The research results demonstrate that increasing electromagnetic radiation intensity could induce hyperchaotic attractors. Furthermore, this neuron model generates hyperchaotic and three points coexistence attractors with the introduction of the memristor. A digital circuit is designed to implement the model and evaluate the randomness of its output sequence. Neuronal models exhibit a rich dynamic behavior with electrical radiation stimulation, which can provide new directions for exploring the production mechanisms of certain neurological diseases.

Demographic History and Natural Selection Shape Patterns of Deleterious Mutation Load and Barriers to Introgression across Populus Genome.

Hybridization and resulting introgression are important processes shaping the tree of life and appear to be far more common than previously thought. However, how the genome evolution was shaped by various genetic and evolutionary forces after hybridization remains unresolved. Here we used whole-genome resequencing data of 227 individuals from multiple widespread Populus species to characterize their contemporary patterns of hybridization and to quantify genomic signatures of past introgression. We observe a high frequency of contemporary hybridization and confirm that multiple previously ambiguous species are in fact F1 hybrids. Seven species were identified, which experienced different demographic histories that resulted in strikingly varied efficacy of selection and burdens of deleterious mutations. Frequent past introgression has been found to be a pervasive feature throughout the speciation of these Populus species. The retained introgressed regions, more generally, tend to contain reduced genetic load and to be located in regions of high recombination. We also find that in pairs of species with substantial differences in effective population size, introgressed regions are inferred to have undergone selective sweeps at greater than expected frequencies in the species with lower effective population size, suggesting that introgression likely have higher potential to provide beneficial variation for species with small populations. Our results, therefore, illustrate that demography and recombination have interplayed with both positive and negative selection in determining the genomic evolution after hybridization.

Complete chloroplast of four Sanicula taxa (Apiaceae) endemic to China: lights into genome structure, comparative analysis, and phylogenetic relationships.

BACKGROUND: The genus Sanicula comprises ca. 45 taxa, widely distributed from East Asia to North America, which is a taxonomically difficult genus with high medicinal value in Apiaceae. The systematic classification of the genus has been controversial for a long time due to varied characters in key morphological traits. China is one of the most important distributed centers, with ca. 18 species and two varieties. At present, chloroplast genomes are generally considered to be conservative and play an important role in evolutionary relationship study. To investigate the plastome evolution and phylogenetic relationships of Chinese Sanicula, we comprehensively analyzed the structural characteristics of 13 Chinese Sanicula chloroplasts and reconstructed their phylogenetic relationships. RESULTS: In present study, four newly complete chloroplast genome of Sanicula taxa by using Illumina sequencing were reported, with the typical quadripartite structure and 155,396-155,757 bp in size. They encoded 126 genes, including 86 protein-coding genes, 32 tRNA genes and 8 rRNA genes. Genome structure, distributions of SDRs and SSRs, gene content, among Sanicula taxa, were similar. The nineteen intergenic spacers regions, including atpH-atpI, ndhC-trnM, petB-petD, petD-rpoA, petN-psbM, psaJ-rpl33, rbcL-accD, rpoB-trnC, rps16-trnQ, trnE-psbD, trnF-ndhJ, trnH-psbA, trnN-ndhF, trnS-psbZ, trnS-trnR, trnT-trnF, trnV-rps12, ycf3-trnS and ycf4-cemA, and one coding region (ycf1 gene) were the most variable. Results of maximum likelihood analysis based on 79 unique coding genes of 13 Chinese Sanicula samples and two Eryngium (Apiaceae-Saniculoideae) species as outgroup taxa revealed that they divided into four subclades belonged to two clades, and one subclade was consistent with previously traditional Sanicula section of its system. The current classification based on morphology at sect. Sanicla and Sect. Tuberculatae in Chinese Sanicula was not supported by analysis of cp genome phylogeny. CONCLUSIONS: The chloroplast genome structure of Sanicula was similar to other angiosperms and possessed the typical quadripartite structure with the conserved genome arrangement and gene features. However, their size varied owing to expansion/contraction of IR/SC boundaries. The variation of non-coding regions was larger than coding regions of the chloroplast genome. Phylogenetic analysis within these Chinese Sanicula were determined using the 79 unique coding genes. These results could provide important data for systematic, phylogenomic and evolutionary research in the genus for the future studies.

Simple cyclic memristive neural networks with coexisting attractors and large-scale amplitude control.

The memristor's unique memory function and non-volatile nature make it an ideal electronic bionic device for artificial neural synapses. This paper aims to construct a class of memristive neural networks (MNNs) with a simple circular connection relationship and complex dynamics by introducing a generic memristor as synapse. For placing the memristive synapse in different coupling positions, three MNNs with the same coupling cyclic connection are yielded. One remarkable feature of the proposed MNNs is that they can yield complex dynamics, in particular, abundant coexisting attractors and large-scale parameter-relied amplitude control, by comparing with some existing MNNs. Taking one of the MNNs as an example, the complex dynamics (including chaos, period-doubling bifurcation, symmetric coexisting attractors, large-scale amplitude control) and circuit implementation are studied . The number of equilibria and their stabilities are discussed. The parameter-relied dynamic evolution and the coexisting attractors are numerically shown by using bifurcations and phase portraits. A microcontroller-based hardware circuit is given to realize the network, which verifies the correctness of the numerical results and experimental results.

Multi-scroll attractor and its broken coexisting attractors in cyclic memristive neural network.

This paper proposes a simple-structured memristive neural network, which incorporates self-connections of memristor synapses alongside both unidirectional and bidirectional connections. Different from other multi-scroll chaotic systems, this network structure has a more concise three-neuron structure. This simple memristive neural network can generate a number of multi-scroll attractors in manageable quantities and shows the characteristics of the coexisting attractors and amplitude control. In particular, when the parameters are changed, the coexisting attractors break up around the center of gravity into two centrosymmetric chaotic attractors. Abundant dynamic behaviors are studied through phase portraits, bifurcation diagrams, Lyapunov exponents, and attraction basins. The feasibility of the system is demonstrated by building a circuit realization platform.

Analysis of Significant Genes and Pathways in Esophageal Cancer Based on Gene Expression Omnibus Database.

Objective To screen antigen targets for immunotherapy by analyzing over-expressed genes, and to identify significant pathways and molecular mechanisms in esophageal cancer by using bioinformatic methods such as enrichment analysis, protein-protein interaction (PPI) network, and survival analysis based on the Gene Expression Omnibus (GEO) database.Methods By screening with highly expressed genes, we mainly analyzed proteins MUC13 and EPCAM with transmembrane domain and antigen epitope from TMHMM and IEDB websites. Significant genes and pathways associated with the pathogenesis of esophageal cancer were identified using enrichment analysis, PPI network, and survival analysis. Several software and platforms including Prism 8, R language, Cytoscape, DAVID, STRING, and GEPIA platform were used in the search and/or figure creation.Results Genes MUC13 and EPCAM were over-expressed with several antigen epitopes in esophageal squamous cell carcinoma (ESCC) tissue. Enrichment analysis revealed that the process of keratinization was focused and a series of genes were related with the development of esophageal cancer. Four genes including ALDH3A1, C2, SLC6A1,and ZBTB7C were screened with significant P value of survival curve.Conclusions Genes MUC13 and EPCAM may be promising antigen targets or biomarkers for esophageal cancer. Keratinization may greatly impact the pathogenesis of esophageal cancer. Genes ALDH3A1, C2, SLC6A1,and ZBTB7C may play important roles in the development of esophageal cancer.

Study on the monolayer dispersion behavior of SnO2 on ZSM-5 for NOx-SCR by C3H6: the remarkable promotional effects of air plasma treatment.

Aiming to fabricate more practical catalysts for NOx-SCR with C3H6, SnO2/ZSM-5 having different SnO2 loadings was prepared and treated with DBD air plasma. The dispersion of SnO2 on the H-ZSM-5 support and their interactions were investigated with both experimental methods and DFT calculations. SnO2 displays evident monolayer dispersion behavior, getting a threshold of 0.271 mmol 100 m-2 support. Plasma treatment improves significantly the SnO2 dispersion, hence amplifying the monolayer dispersion threshold to 0.380 mmol 100 m-2. XPS and DFT calculations have testified that plasma treatment strengthens strongly the SnO2-ZSM-5 support interaction, mainly through donating electrons from Sn4+ to Al3+ in the support, thus improving the dispersion of SnO2 at the same loadings. Consequently, the catalytic performance is remarkably improved because of the generation of more abundant surface acid sites and superoxide species devoted to the reaction. The sample having a SnO2 loading near the monolayer dispersion threshold shows the optimal activity in the corresponding catalyst series, demonstrating an evident threshold effect. Over SnO2/ZSM-5, the reaction goes through a Langmuir-Hinshelwood pathway, involving the adsorption and activation of both NO and C3H6 molecules. Surface mono-dentate/bridged-nitrate and carbonate species are the main reaction intermediates.

The ratio of red blood cell distribution width to serum calcium predicts severity of patients with acute pancreatitis.

BACKGROUND: Illness severity of patients with acute pancreatitis (AP) at early stage is crucial because the identified moderate and severe cases need early intensive care to reduce the risk of serious complications such as multi-organ failure. Although red blood cell distribution width(RDW)and serum calcium(Ca)alone can be used as predictors of the severity of AP, they have low sensitivity and specificity. Thus, this study is aimed at evaluating the value of the ratio of RDW to serum calcium, which can all be acquired on admission, in predicting the severity of AP. METHODS: This study was based on a retrospective cohort study on patients with AP at the emergency department (ED) of West China Hospital Hospital from January 2016 to June 2016. We divided the patients with AP into two groups, mild acute pancreatitis (MAP) and moderate severe acute pancreatitis (MSAP) + severe acute pancreatitis (SAP). A receiver-operating characteristic (ROC) curve analysis was used to valuate the predictive value of the ratio of RDW to serum calcium for the severity of AP patients and the cut-off value for the ratio of RDW to serum calcium was calculated with sensitivity and specificity. P < 0.05 was considered statistically significant. RESULTS: A total of 666 AP patients were enrolled in this study. These patients were divided into MAP (n = 518) and MSAP+SAP (n = 148) groups. The AUC of RDW/ Ca was 0.912 (95% CI 0.887 to 0.937, P < 0.001), larger than the AUCs of RDW (AUC = 0.768, 95% CI 0.723 to 0.812, P < 0.001) and Ca (AUC = 0.875, 95% CI 0.844 to 0.906, P < 0.001). The optimal cut-off value for RDW/ Ca to predict MSAP and SAP was 7.04 (sensitivity = 0.885, specificity = 0.834). CONCLUSION: The RDW/Ca might be a valuable predictor of the severity of patients with AP.

Tannin-Derived Hard Carbon for Stable Lithium-Ion Anode.

Graphite anodes are well established for commercial use in lithium-ion battery systems. However, the limited capacity of graphite limits the further development of lithium-ion batteries. Hard carbon obtained from biomass is a highly promising anode material, with the advantage of enriched microcrystalline structure characteristics for better lithium storage. Tannin, a secondary product of metabolism during plant growth, has a rich source on earth. But the mechanism of hard carbon obtained from its derivation in lithium-ion batteries has been little studied. This paper successfully applied the hard carbon obtained from tannin as anode and illustrated the relationship between its structure and lithium storage performance. Meanwhile, to further enhance the performance, graphene oxide is skillfully compounded. The contact with the electrolyte and the charge transfer capability are effectively enhanced, then the capacity of PVP-HC is 255.5 mAh g-1 after 200 cycles at a current density of 400 mA g-1, with a capacity retention rate of 91.25%. The present work lays the foundation and opens up ideas for the application of biomass-derived hard carbon in lithium anodes.

A 2D hyperchaotic map with conditional symmetry and attractor growth.

By introducing trigonometric functions, a 2D hyperchaotic map with conditional symmetric attractors is constructed, where a symmetric pair of hyperchaotic attractors and asymmetric hyperchaotic attractors is found. For the existence of periodic feedback, the newly proposed map also exhibits attractor growth under specific circumstances. The polarity balance of the discrete map can be restored from the applied sinusoidal functions, combined with an extra inversion of the constant term. To the best of our knowledge, the above properties are not found in other chaotic maps. Finally, the hardware implementation based on STM32 is conducted, and the corresponding results agree with the numerical simulation and the theoretical analysis.

Formation Tracking Control for Multi-Agent Networks with Fixed Time Convergence via Terminal Sliding Mode Control Approach.

This paper investigates formation tracking control for multi-agent networks with fixed time convergence. The control task is that the follower agents are required to form a prescribed formation within a fixed time and the geometric center of the formation moves in sync with the leader. First, an error system is designed by using the information of adjacent agents and a new control protocol is designed based on the error system and terminal sliding mode control (TSMC). Then, via employing the Lyapunov stability theorem and the fixed time stability theorem, the control task is proved to be possible within a fixed time and the convergence time can be calculated by parameters. Finally, numerical results illustrate the feasibility of the proposed control protocol.

A New Chaotic System with Multiple Attractors: Dynamic Analysis, Circuit Realization and S-Box Design.

This paper reports about a novel three-dimensional chaotic system with three nonlinearities. The system has one stable equilibrium, two stable equilibria and one saddle node, two saddle foci and one saddle node for different parameters. One salient feature of this novel system is its multiple attractors caused by different initial values. With the change of parameters, the system experiences mono-stability, bi-stability, mono-periodicity, bi-periodicity, one strange attractor, and two coexisting strange attractors. The complex dynamic behaviors of the system are revealed by analyzing the corresponding equilibria and using the numerical simulation method. In addition, an electronic circuit is given for implementing the chaotic attractors of the system. Using the new chaotic system, an S-Box is developed for cryptographic operations. Moreover, we test the performance of this produced S-Box and compare it to the existing S-Box studies.

Heterogeneous coexisting attractors, large-scale amplitude control and finite-time synchronization of central cyclic memristive neural networks.

Memristors are of great theoretical and practical significance for chaotic dynamics research of brain-like neural networks due to their excellent physical properties such as brain synapse-like memorability and nonlinearity, especially crucial for the promotion of AI big models, cloud computing, and intelligent systems in the artificial intelligence field. In this paper, we introduce memristors as self-connecting synapses into a four-dimensional Hopfield neural network, constructing a central cyclic memristive neural network (CCMNN), and achieving its effective control. The model adopts a central loop topology and exhibits a variety of complex dynamic behaviors such as chaos, bifurcation, and homogeneous and heterogeneous coexisting attractors. The complex dynamic behaviors of the CCMNN are investigated in depth numerically by equilibrium point stability analysis as well as phase trajectory maps, bifurcation maps, time-domain maps, and LEs. It is found that with the variation of the internal parameters of the memristor, asymmetric heterogeneous attractor coexistence phenomena appear under different initial conditions, including the multi-stable coexistence behaviors of periodic-periodic, periodic-stable point, periodic-chaotic, and stable point-chaotic. In addition, by adjusting the structural parameters, a wide range of amplitude control can be realized without changing the chaotic state of the system. Finally, based on the CCMNN model, an adaptive synchronization controller is designed to achieve finite-time synchronization control, and its application prospect in simple secure communication is discussed. A microcontroller-based hardware circuit and NIST test are conducted to verify the correctness of the numerical results and theoretical analysis.

Machine Learning Predictive Model for Septic Shock in Acute Pancreatitis with Sepsis.

Objective: Acute pancreatitis (AP) progresses to septic shock can be fatal. Early identification of high-risk patients and timely intervention can prevent and interrupt septic shock. By analyzing the clinical characteristics of AP with sepsis, this study uses machine learning (ML) to build a model for early prediction of septic shock within 28 days of admission, which guided emergency physicians in resource allocation and medical decision-making. Methods: This retrospective cohort study collected data from the emergency departments (EDs) of three tertiary care hospitals in China. The dataset was randomly divided into a training dataset (70%) and a testing dataset (30%). Ten ML classifiers were utilized to analyze characteristics of AP with sepsis in the training dataset upon admission. Results were evaluated through cross-validation analysis. The optimal model was then tested on the testing dataset without any parameter modifications. The ML model was evaluated using the receiver operating characteristic curve (ROC) and compared to scoring systems through the DeLong test. Results: A total of 604 AP patients with sepsis were included in this study. The auto-encoder (AE) model based on mean normalization, Pearson correlation coefficient (PCC), and recursive feature elimination (RFE) selection, achieved the highest Area Under the Curve (AUC) on the validation dataset (AUC 0.900, accuracy 0.868), with the AUC of 0.879 and accuracy of 0.790 on the testing dataset. Compared to the Sequential Organ Failure Assessment (AUC 0.741), quick Sequential Organ Failure Assessment (AUC 0.727), Acute Physiology and Chronic Health Evaluation II (AUC 0.778), and Bedside Index of Severity in Acute Pancreatitis (AUC 0.691), the AE model showed superior performance. Conclusion: The AE model outperforms traditional scoring systems in predicting septic shock in AP patients with sepsis within 28 days of admission. This assists emergency physicians in identifying high-risk patients early and making timely medical decisions.

Constructing Multiscroll Memristive Neural Network With Local Activity Memristor and Application in Image Encryption.

Memristor possesses synapse-like properties that can mimic excitation and inhibition between neurons. This article introduces the Sigmoid functions to the memristor and constructs a new memristive Hopfield neural network (HNN). Its most distinctive feature is the simple topology, which contains only unidirectional connections in neurons. The equilibrium points analysis reveals the mechanism of its multiscroll attractors generation. Homogeneous and heterogeneous coexisting attractors are observed with the variation of the network parameters. Note that the state equation of memristor can affect the number of coexisting attractors. A hardware implementation is designed for it, and the multiscroll attractors are captured in the oscilloscope. Finally, it is also applied to developing an image encryption algorithm with excellent performance.

The super-pangenome of Populus unveils genomic facets for its adaptation and diversification in widespread forest trees.

Understanding the underlying mechanisms and links between genome evolution and adaptive innovations stands as a key goal in evolutionary studies. Poplars, among the world's most widely distributed and cultivated trees, exhibit extensive phenotypic diversity and environmental adaptability. In this study, we present a genus-level super-pangenome comprising 19 Populus genomes, revealing the likely pivotal role of private genes in facilitating local environmental and climate adaptation. Through the integration of pangenomes with transcriptomes, methylomes, and chromatin accessibility mapping, we unveil that the evolutionary trajectories of pangenes and duplicated genes are closely linked to local genomic landscapes of regulatory and epigenetic architectures, notably CG methylation in gene-body regions. Further comparative genomic analyses have enabled the identification of 142 202 structural variants across species that intersect with a significant number of genes and contribute substantially to both phenotypic and adaptive divergence. We have experimentally validated a ∼180-bp presence/absence variant affecting the expression of the CUC2 gene, crucial for leaf serration formation. Finally, we developed a user-friendly web-based tool encompassing the multi-omics resources associated with the Populus super-pangenome (http://www.populus-superpangenome.com). Together, the present pioneering super-pangenome resource in forest trees not only aids in the advancement of breeding efforts of this globally important tree genus but also offers valuable insights into potential avenues for comprehending tree biology.

Proteomic analysis reveals that Adh1p is involved in a synergistic fluconazole and tetrandrine mechanism against Candida albicans.

We previously showed that Adh1p participates in fluconazole (FLC) resistance in Candida albicans through a mechanism that may involve efflux pumps. We also found that the concomitant use of tetrandrine (TET) and FLC provided a synergistic action against C. albicans and that the mechanism of action could be related to inhibition of a drug efflux system. To determine whether Adh1p participates in the synergistic antifungal activity of TET against C. albicans, we performed a comparative proteomic study comparing cells treated with FLC and/or TET in FLC-sensitive CA-3 and untreated control cells. Proteins were analyzed using two-dimensional polyacrylamide gel electrophoresis (2-D PAGE), and differentially expressed proteins were identified through matrix-assisted laser desorption/ionization time-of-flight (MALDI TOF/TOF) mass spectrometry. The resulting data were searched against a C. albicans protein database. Our analyses identified six differentially expressed proteins; four (Eno1p, Adh1p, Slb1p, and Tdh1p) were down-regulated, and two (Xyl2p, and Cdc19p) were up-regulated. The Adh1p mRNA levels were consistent with the Adh1p protein levels in all of the groups. The results suggest that Adh1p participates in the synergistic antifungal activity of TET against C. albicans.

Design and Analysis of Multiscroll Memristive Hopfield Neural Network With Adjustable Memductance and Application to Image Encryption.

Memristor is an ideal electronic device used as an artificial nerve synapse due to its unique memory function. This article presents a design of a new Hopfield neural network (HNN) that can generate multiscroll attractors by utilizing a new memristor as a synapse in the HNN. Differing from the others, this memristor is constructed with hyperbolic tangent functions. Taking the memristor as a self-feedback synapse of a neuron in the HNN, the memristive HNN can yield multidouble-scroll attractors, and its parameters can be used to effectively control the number of double scrolls contained in an attractor. Interestingly, the generation of multidouble-scroll attractors is independent of the memductance function but depends only on the internal state equation. Thus, the memductance function can be adjusted to yield various complex dynamical behaviors. Moreover, amplitude control effects and quantitatively controllable multistability are revealed by numerical analysis. The accurate reproduction of some dynamical behaviors by a designed circuit verifies the correctness of the numerical analysis. Finally, based on the proposed memristive HNN, a novel image encryption scheme in the 3-D setting is designed and evaluated, demonstrating its good encryption performances.

Development and Validation of a Rapid and Efficient Prognostic Scoring System for Sepsis Based on Oxygenation Index, Lactate and Glasgow Coma Scale.

Objective: To develop a concise scoring system for efficient and rapid assessment of sepsis prognosis applicable to emergency departments. Methods: This was a single-center retrospective cohort study of patients with sepsis. In this study, a new scoring system (oxygenation index, lactate, and Glasgow coma scale: GOL) was developed through a derivation group, and then the GOL was validated using a validation group. Multivariate logistic regression analysis was performed to investigate the relationship between GOL and 28-day adverse outcomes. The GOL was compared with the previous scoring system using receiver operating characteristic curves (ROC) and decision analysis curves. The endpoints of this study were mortality, mechanical ventilation (MV), and admission to the intensive care unit (AICU). Results: 608 patients were included in the derivation group and 213 patients in the validation group, with 131 and 42 deaths, respectively. In the validation group, lactate (Lac), oxygenation index (PaO2/FiO2), and Glasgow coma scale score (GCS), the three best performers in predicting 28-day mortality from receiver operating characteristic curves, were used to construct the GOL. The higher the GOL score, the higher the incidence of death, MV and AICU within 28 days. Multifactorial logistic regression analysis showed that when the GOL was greater than 1, it was an independent risk factor for 28-day mortality, MV, and AICU. In predicting 28-day mortality, GOL was superior to the quick Sequential Organ Failure Assessment (qSOFA), Mortality in Emergency Department Sepsis Score (MEDS), Systemic Inflammatory Response Syndrome Score (SIRS), and Modified Early Warning Score (MEWS), and was comparable to the Acute Physiology and Chronic Health Evaluation (APACHE) II and Sequential Organ Failure Assessment (SOFA). Conclusion: The GOL is a simple, rapid, and accurate method for early identification of patients at increased risk of in-hospital death from sepsis.