Evaluación de las estrategias de gestión alternativas para el atún patudo, Thunnus obesus, en el océano Índico

Bigeye tuna (Thunnus obesus) support a large commercial fishery in the Indian Ocean. However, explicit management strategies and harvest control rules are yet to be developed for the management of this fishery. We used a stochastic age-structured production model as an operating model to evaluate several potential management strategies under different assumptions of stock productivity. Five management strategies—constant fishing mortality, constant catch, quasi-constant catch, constant escapement, and status-dependent strategies—were evaluated and compared using the performance indicators including average catch, average spawning stock biomass, variation in catch, average fishing mortality and lowest biomass during the time period considered in the simulation. This study shows that (1) for the constant catch strategy, an annual catch of 90000 t would result in a low risk of stock being overfished while obtaining a stable catch; (2) for the constant fishing mortality strategy fishing mortality of 0.3 per year could yield a higher catch, but might have a high probability (64%) of stock dropping below the spawning stock biomass (SSB) that could achieve maximum sustainable yield (SSBmsy); and (3) for the quasi-constant catch strategy an annual catch of 110000 t was sustainable if the current SSB was higher than SSBmsy. Constant escapement and status-dependent strategies were robust with respect to different levels of virgin recruitment and steepness. This study suggests that it is important to incorporate uncertainties associated with key life history, fisheries and management processes in evaluating management strategies.El patudo (Thunnus obesus) soporta una pesquería comercial importante en el Océano Índico. Sin embargo, las estrategias de gestión de reglas de control de las capturas / explícitos aún no se han desarrollado para la gestión de esta pesquería. Se utilizó un modelo estocástico de producción estructurado por edad como un modelo operativo para evaluar varias estrategias de gestión de potenciales bajo diferentes supuestos de productividad de las poblaciones. Se evaluaron cinco estrategias de gestión, la mortalidad por pesca de captura constante, constante, captura casi constante, escape constante, y las estrategias de estado-dependientes, y se compararon con los indicadores de desempeño que incluye la captura promedio, promedio de biomasa de la población reproductora, la variación en la captura, la mortalidad por pesca media, y la biomasa más bajo durante el período de tiempo considerado en la simulación. Este estudio muestra que (1) la estrategia de captura constante, una captura anual de 90 mil toneladas daría lugar a un bajo riesgo de sobreexplotación, mientras se obtiene una captura estable, (2) para la constante de la mortalidad por pesca estrategia de la mortalidad por pesca de 0,3 años podría producir una captura superior, sino que tenga una alta probabilidad (64%) de las acciones caiga por debajo de la biomasa reproductora del stock (SSB), que podría alcanzar el rendimiento máximo sostenible (RMS; SSBRMS), y (3) para la estrategia de captura casi una constante anual captura de 110.000 toneladas era sostenible si SSB actual fue superior SSBRMS. Escape constante y estrategias de estado-dependientes fueron robustos con respecto a los diferentes niveles de reclutamiento virgen y escarpado. Este estudio sugiere que es importante incorporar las incertidumbres asociadas con ciclo de vida, la pesca y los procesos de gestión en la evaluación de las estrategias de gestión

Temporal-spatial Correlation Attention Network for Clinical Data Analysis in Intensive Care Unit

In recent years, medical information technology has made it possible for electronic health record (EHR) to store fairly complete clinical data. This has brought health care into the era of "big data". However, medical data are often sparse and strongly correlated, which means that medical problems cannot be solved effectively. With the rapid development of deep learning in recent years, it has provided opportunities for the use of big data in healthcare. In this paper, we propose a temporal-saptial correlation attention network (TSCAN) to handle some clinical characteristic prediction problems, such as predicting death, predicting length of stay, detecting physiologic decline, and classifying phenotypes. Based on the design of the attention mechanism model, our approach can effectively remove irrelevant items in clinical data and irrelevant nodes in time according to different tasks, so as to obtain more accurate prediction results. Our method can also find key clinical indicators of important outcomes that can be used to improve treatment options. Our experiments use information from the Medical Information Mart for Intensive Care (MIMIC-IV) database, which is open to the public. Finally, we have achieved significant performance benefits of 2.0\% (metric) compared to other SOTA prediction methods. We achieved a staggering 90.7\% on mortality rate, 45.1\% on length of stay. The source code can be find: \url{https://github.com/yuyuheintju/TSCAN}

Integrating UMLS Knowledge into Large Language Models for Medical Question Answering

Large language models (LLMs) have demonstrated powerful text generation capabilities, bringing unprecedented innovation to the healthcare field. While LLMs hold immense promise for applications in healthcare, applying them to real clinical scenarios presents significant challenges, as these models may generate content that deviates from established medical facts and even exhibit potential biases. In our research, we develop an augmented LLM framework based on the Unified Medical Language System (UMLS), aiming to better serve the healthcare community. We employ LLaMa2-13b-chat and ChatGPT-3.5 as our benchmark models, and conduct automatic evaluations using the ROUGE Score and BERTScore on 104 questions from the LiveQA test set. Additionally, we establish criteria for physician-evaluation based on four dimensions: Factuality, Completeness, Readability and Relevancy. ChatGPT-3.5 is used for physician evaluation with 20 questions on the LiveQA test set. Multiple resident physicians conducted blind reviews to evaluate the generated content, and the results indicate that this framework effectively enhances the factuality, completeness, and relevance of generated content. Our research demonstrates the effectiveness of using UMLS-augmented LLMs and highlights the potential application value of LLMs in in medical question-answering.Comment: 12 pages, 3 figure

Advances of MnO2 nanomaterials as novel agonists for the development of cGAS-STING-mediated therapeutics

As an essential micronutrient, manganese plays an important role in the physiological process and immune process. In recent decades, cGAS-STING pathway, which can congenitally recognize exogenous and endogenous DNA for activation, has been widely reported to play critical roles in the innate immunity against some important diseases, such as infections and tumor. Manganese ion (Mn2+) has been recently proved to specifically bind with cGAS and activate cGAS-STING pathway as a potential cGAS agonist, however, is significantly restricted by the low stability of Mn2+ for further medical application. As one of the most stable forms of manganese, manganese dioxide (MnO2) nanomaterials have been reported to show multiple promising functions, such as drug delivery, anti-tumor and anti-infection activities. More importantly, MnO2 nanomaterials are also found to be a potential candidate as cGAS agonist by transforming into Mn2+, which indicates their potential for cGAS-STING regulations in different diseased conditions. In this review, we introduced the methods for the preparation of MnO2 nanomaterials as well as their biological activities. Moreover, we emphatically introduced the cGAS-STING pathway and discussed the detailed mechanisms of MnO2 nanomaterials for cGAS activation by converting into Mn2+. And we also discussed the application of MnO2 nanomaterials for disease treatment by regulating cGAS-STING pathway, which might benefit the future development of novel cGAS-STING targeted treatments based on MnO2 nanoplatforms

Identification of key genes, pathways and potential therapeutic agents for liver fibrosis using an integrated bioinformatics analysis

Background Liver fibrosis is often a consequence of chronic liver injury, and has the potential to progress to cirrhosis and liver cancer. Despite being an important human disease, there are currently no approved anti-fibrotic drugs. In this study, we aim to identify the key genes and pathways governing the pathophysiological processes of liver fibrosis, and to screen therapeutic anti-fibrotic agents. Methods Expression profiles were downloaded from the Gene Expression Omnibus (GEO), and differentially expressed genes (DEGs) were identified by R packages (Affy and limma). Gene functional enrichments of each dataset were performed on the DAVID database. Protein–protein interaction (PPI) network was constructed by STRING database and visualized in Cytoscape software. The hub genes were explored by the CytoHubba plugin app and validated in another GEO dataset and in a liver fibrosis cell model by quantitative real-time PCR assay. The Connectivity Map L1000 platform was used to identify potential anti-fibrotic agents. Results We integrated three fibrosis datasets of different disease etiologies, incorporating a total of 70 severe (F3–F4) and 116 mild (F0–F1) fibrotic tissue samples. Gene functional enrichment analyses revealed that cell cycle was a pathway uniquely enriched in a dataset from those patients infected by hepatitis B virus (HBV), while the immune-inflammatory response was enriched in both the HBV and hepatitis C virus (HCV) datasets, but not in the nonalcoholic fatty liver disease (NAFLD) dataset. There was overlap between these three datasets; 185 total shared DEGs that were enriched for pathways associated with extracellular matrix constitution, platelet-derived growth-factor binding, protein digestion and absorption, focal adhesion, and PI3K-Akt signaling. In the PPI network, 25 hub genes were extracted and deemed to be essential genes for fibrogenesis, and the expression trends were consistent with GSE14323 (an additional dataset) and liver fibrosis cell model, confirming the relevance of our findings. Among the 10 best matching anti-fibrotic agents, Zosuquidar and its corresponding gene target ABCB1 might be a novel anti-fibrotic agent or therapeutic target, but further work will be needed to verify its utility. Conclusions Through this bioinformatics analysis, we identified that cell cycle is a pathway uniquely enriched in HBV related dataset and immune-inflammatory response is clearly enriched in the virus-related datasets. Zosuquidar and ABCB1 might be a novel anti-fibrotic agent or target

Signal Transduction Pathways in the Pentameric Ligand-Gated Ion Channels

The mechanisms of allosteric action within pentameric ligand-gated ion channels (pLGICs) remain to be determined. Using crystallography, site-directed mutagenesis, and two-electrode voltage clamp measurements, we identified two functionally relevant sites in the extracellular (EC) domain of the bacterial pLGIC from Gloeobacter violaceus (GLIC). One site is at the C-loop region, where the NQN mutation (D91N, E177Q, and D178N) eliminated inter-subunit salt bridges in the open-channel GLIC structure and thereby shifted the channel activation to a higher agonist concentration. The other site is below the C-loop, where binding of the anesthetic ketamine inhibited GLIC currents in a concentration dependent manner. To understand how a perturbation signal in the EC domain, either resulting from the NQN mutation or ketamine binding, is transduced to the channel gate, we have used the Perturbation-based Markovian Transmission (PMT) model to determine dynamic responses of the GLIC channel and signaling pathways upon initial perturbations in the EC domain of GLIC. Despite the existence of many possible routes for the initial perturbation signal to reach the channel gate, the PMT model in combination with Yen's algorithm revealed that perturbation signals with the highest probability flow travel either via the β1-β2 loop or through pre-TM1. The β1-β2 loop occurs in either intra- or inter-subunit pathways, while pre-TM1 occurs exclusively in inter-subunit pathways. Residues involved in both types of pathways are well supported by previous experimental data on nAChR. The direct coupling between pre-TM1 and TM2 of the adjacent subunit adds new insight into the allosteric signaling mechanism in pLGICs. © 2013 Mowrey et al

2D nanomaterial sensing array using machine learning for differential profiling of pathogenic microbial taxonomic identification

An integrated custom cross-response sensing array has been developed combining the algorithm module's visible machine learning approach for rapid and accurate pathogenic microbial taxonomic identification. The diversified cross-response sensing array consists of two-dimensional nanomaterial (2D-n) with fluorescently labeled single-stranded DNA (ssDNA) as sensing elements to extract a set of differential response profiles for each pathogenic microorganism. By altering the 2D-n and different ssDNA with different sequences, we can form multiple sensing elements. While interacting with microorganisms, the competition between ssDNA and 2D-n leads to the release of ssDNA from 2D-n. The signals are generated from binding force driven by the exfoliation of either ssDNA or 2D-n from the microorganisms. Thus, the signal is distinguished from different ssDNA and 2D-n combinations, differentiating the extracted information and visualizing the recognition process. Fluorescent signals collected from each sensing element at the wavelength around 520 nm are applied to generate a fingerprint. As a proof of concept, we demonstrate that a six-sensing array enables rapid and accurate pathogenic microbial taxonomic identification, including the drug-resistant microorganisms, under a data size of n=288. We precisely identify microbial with an overall accuracy of 97.9%, which overcomes the big data dependence for identifying recurrent patterns in conventional methods. For each microorganism, the detection concentration is 10(5) similar to 10(8) CFU/mL for Escherichia coli, 10(2) similar to 10(7) CFU/mL for E. coli beta, 10(3) similar to 10(8) CFU/mL for Staphylococcus aureus, 10(3) similar to 10(7) CFU/mL for MRSA, 10(2) similar to 10(8) CFU/ mL for Pseudomonas aeruginosa, 10(3) similar to 10(8) CFU/mL for Enterococcus faecalis, 10(2) similar to 10(8) CFU/mL for Klebsiella pneumoniae, and 10(3) similar to 10(8) CFU/mL for Candida albicans. Combining the visible machine learning approach, this sensing array provides strategies for precision pathogenic microbial taxonomic identification