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Introduction: Gastric cancer is one of the most prevalent types of cancer worldwide. The World Health Organization (WHO), the International Agency for Research on Cancer (IARC), and the Global Cancer Statistics (GLOBOCAN) reported an age standardized global incidence rate of 9.2 per 100,000 individuals for gastric cancer in 2022, with a mortality rate of 6.1. Despite considerable progress in precision oncology through the efforts of international consortia, understanding the genomic features and their influence on the effectiveness of anti-cancer treatments across diverse ethnic groups remains essential. Methods: Our study aimed to address this need by conducting integrated in silico analyses to identify actionable genomic alterations in gastric cancer driver genes, assess their impact using deleteriousness scores, and determine allele frequencies across nine global populations: European Finnish, European non-Finnish, Latino, East Asian, South Asian, African, Middle Eastern, Ashkenazi Jewish, and Amish. Furthermore, our goal was to prioritize targeted therapeutic strategies based on pharmacogenomics clinical guidelines, in silico drug prescriptions, and clinical trial data. Results: Our comprehensive analysis examined 275,634 variants within 60 gastric cancer driver genes from 730,947 exome sequences and 76,215 whole-genome sequences from unrelated individuals, identifying 13,542 annotated and predicted oncogenic variants. We prioritized the most prevalent and deleterious oncogenic variants for subsequent pharmacogenomics testing. Additionally, we discovered actionable genomic alterations in the ARID1A, ATM, BCOR, ERBB2, ERBB3, CDKN2A, KIT, PIK3CA, PTEN, NTRK3, TP53, and CDKN2A genes that could enhance the efficacy of anti-cancer therapies, as suggested by in silico drug prescription analyses, reviews of current pharmacogenomics clinical guidelines, and evaluations of phase III and IV clinical trials targeting gastric cancer driver proteins. Discussion: These findings underline the urgency of consolidating efforts to devise effective prevention measures, invest in genomic profiling for underrepresented populations, and ensure the inclusion of ethnic minorities in future clinical trials and cancer research in developed countries. [ABSTRACT FROM AUTHOR]

Piscirickettsia salmonis is the pathogen that most affects the salmon industry in Chile. Large quantities of antibiotics have been used to control it. In search of alternatives, we have developed [Cu(NN1)2]ClO4 where NN1 = 6-((quinolin-2-ylmethylene)amino)-2H-chromen-2-one. The antibacterial capacity of [Cu(NN1)2]ClO4 was determined. Subsequently, the effect of the administration of [Cu(NN1)2]ClO4 on the growth of S. salar, modulation of the immune system and the intestinal microbiota was studied. Finally, the ability to protect against a challenge with P. salmonis was evaluated. The results obtained showed that the compound has an MIC between 15 and 33.9 μg/mL in four isolates. On the other hand, the compound did not affect the growth of the fish; however, an increase in the transcript levels of IFN-γ, IL-12, IL-1β, CD4, lysozyme and perforin was observed in fish treated with 40 μg/g of fish. Furthermore, modulation of the intestinal microbiota was observed, increasing the genera of beneficial bacteria such as Lactobacillus and Bacillus as well as potential pathogens such as Vibrio and Piscirickettsia. Finally, the treatment increased survival in fish challenged with P. salmonis by more than 60%. These results demonstrate that the compound is capable of protecting fish against P. salmonis, probably by modulating the immune system and the composition of the intestinal microbiota. [ABSTRACT FROM AUTHOR]

Aquaculture is a growing industry worldwide, but it faces challenges related to animal health. These challenges include infections by parasites, bacteria, and viral pathogens. These harmful pathogens have devastating effects on the industry, despite efforts to control them through vaccination and antimicrobial treatments. Unfortunately, these measures have proven insufficient to address the sanitary problems, resulting in greater environmental impact due to the excessive use of antimicrobials. In recent years, probiotics have emerged as a promising solution to enhance the performance of the immune system against parasitic, bacterial, and viral pathogens in various species, including mammals, birds, and fish. Some probiotics have been genetically engineered to express and deliver immunomodulatory molecules. These promote selective therapeutic effects and specific immunization against specific pathogens. This review aims to summarize recent research on the use of probiotics in fish aquaculture, with a particular emphasis on genetically modified probiotics. In particular, we focus on the advantages of using these microorganisms and highlight the main barriers hindering their widespread application in the aquaculture industry. [ABSTRACT FROM AUTHOR]

Soil respiration (Rs), the soil‐to‐atmosphere flux of CO2, is a dominant but uncertain part of the carbon cycle, even after decades of study. This review focuses on progress in understanding Rs from laboratory incubations to global estimates. We survey key developments of in situ ecosystem‐scale Rs observations and manipulations, synthesize Rs meta‐analyses and global flux estimates, and discuss the most compelling challenges and opportunities for the future. Increasingly sophisticated lab experiments have yielded insights into the interaction among heterotrophic respiration, substrate supply, and enzymatic kinetics, and extended incubation‐based analyses across space and time. Observational and manipulative field‐based experiments have used improved measurement approaches to deepen our understanding of the integrated effects of environmental change and disturbance on Rs. Freely‐available observational databases have enabled meta‐analyses and studies probing the magnitude of, and constraints on, the global Rs flux. Key challenges for the field include expanding Rs measurements, experiments, and opportunities to under‐represented communities and ecosystems; reconciling independent estimates of global respiration fluxes and trends; testing and leveraging the power of machine learning and process‐based models, both independently and in conjunction with each other; and continuing the field's tradition of using novel experiments to explore diverse mechanisms and ecosystems. Plain Language Summary: "Soil respiration" refers to the flow of carbon dioxide, mostly generated by plant roots and microbes, from the soil to the atmosphere. This flux is large and highly uncertain, and understanding it has significant implications for our ability to predict the effects of land use and climate change. This review summarizes advances, insights, and challenges in soil respiration science. It examines laboratory approaches and findings; documents changes in how researchers measure and understand soil respiration in the natural world; and describes efforts to estimate the global dynamics of soil respiration from openly‐available databases of observations. We conclude by discussing the most compelling challenges and opportunities for the future. Key Points: Recent decades have seen large advances in measurement methods, experimental approaches, and data availability in soil respiration scienceLab and field‐based experiments have improved our understanding of the integrated effect of environmental change on soil respirationKey challenges include broadening access and continuing to explore diverse mechanisms and ecosystems using novel model‐experiment approaches [ABSTRACT FROM AUTHOR]

Methane dynamics within salt marshes are complex because vegetation types, temperature, oscillating water levels, and changes in salinity and redox conditions influence CH4 production, consumption, oxidation, and emissions. These non‐linear and complex interactions among variables affect the traditionally expected functional relationships and present challenges for interpreting and developing process‐based models. We employed empirical dynamic modeling (EDM) and convergent cross mapping (CCM) as a novel approach for characterizing seasonal/multiday and diurnal CH4 dynamics by inferring causal variables, lags, and interconnections among multiple biophysical variables within a temperate salt marsh using 5 years of eddy covariance data. EDM/CCM is a nonparametric approach capable of quantifying the coupling between variables while determining time scales where variable interactions are the most relevant. We found that gross primary productivity, tidal creek dissolved oxygen, and temperature were important for seasonal/multiday dynamics (rho = 0.73–0.80), while water level was most important for diurnal dynamics during both the growing and dormancy phenoperiods (rho = 0.72 and 0.56, respectively). Lags for the top‐ranked variables (i.e., gross primary productivity, dissolved oxygen, temperature, water level) occurred between 1 and 5 weeks at the seasonal scale and 1–24 hr at the diurnal scale. The EDM had high prediction capabilities for intra‐/inter‐seasonal patterns and annual CH4 sums but had limitations in representing large, infrequent fluxes. Results highlight the importance of non‐linearity, drivers, lag times, and interconnections among multiple biophysical variables that regulate CH4 fluxes in tidal wetlands. This research introduces a novel approach to examining CH4 fluxes, which will aid in evaluating current paradigms in wetlands and other ecosystems. Plain Language Summary: The movement of methane gas in salt marshes is complex and influenced by various factors such as plant types, temperature, water level, and changes in water salinity and oxygen levels. These factors interact in complex ways, making predicting the outcome of methane production and movement difficult. We applied a new method of studying methane dynamics using Empirical Dynamic Modeling and Convergent Cross Mapping. After analyzing 5 years of ecosystem‐scale measurements of methane fluxes, we show that the amount of oxygen in the water, the temperature, and the amount of light received by plants are crucial for understanding regulating methane fluxes within days and across seasons. The interconnections among these variables are complex, and methane fluxes may have delayed responses (i.e., lag times), highlighting the importance of these interactions. This research improves our comprehension of how environmental factors interact to affect methane fluxes in wetlands. Key Points: Methane dynamics in salt marshes are complex and influenced by multiple variablesEmpirical dynamic modeling and convergent cross mapping are novel approaches to characterizing CH4 dynamicsResults highlight drivers, non‐linearities, lag times, and interconnections among multiple biophysical variables for CH4 dynamics [ABSTRACT FROM AUTHOR]

Application of the best available science to improve quantification of greenhouse gas (GHG) emissions at regional and national scales is key to climate action. Here, we present a two‐decade (2000–2019) GHG (CO2, CH4, and N2O) budget for Mexico derived from multiple products. Data from the National GHG Inventory, global observations, and the scientific literature were compared to identify knowledge gaps on GHG flux dynamics and discrepancies among estimates. Total mean annual GHG emissions were estimated at 695–910 TgCO2‐eq year−1 over these two decades, with 70% of the emissions attributable to CO2, 23% to CH4, and 5% to N2O (2% to other gases). When divided by sectors, we found agreement across emission estimates from various sources for fossil fuels, cattle, agriculture, and waste for all GHGs. However, considerable discrepancies were identified in the fluxes from terrestrial ecosystems. The disagreement was particularly large for the land CO2 sink, where net biome production estimations from the national inventory were double those from any other observational product. Extensive knowledge gaps exist, mainly related to aquatic systems (e.g., outgassing in rivers) and the lateral fluxes (e.g., wood trade). In addition, limited information is available on CH4 emissions from wetlands and soil CH4 consumption. We expect these results to guide future research to reduce estimation uncertainties and fill the information gaps across Mexico. Plain Language Summary: Mexico represents the 13th global emitter of greenhouse gases (GHGs) and the second among Latin American countries, releasing roughly 485 million tons of CO2, six million tons of CH4 (equivalent to 160 million tons of CO2), and 0.1 million tons of N2O (equivalent to 35 million tons of CO2). However, large uncertainties prevail in the estimation of several components of each flux; thus, the application of the best available science to account for and improve these estimations is essential for sound mitigation policies. Here, we used multiple available products (national inventories, satellites, flux towers, dynamic global vegetation models, inversion models, and other data sources) to advance our understanding of the GHG budget of Mexico over the last 20 years. We found that fluxes from fossil fuel combustion (including energy production, transportation, industry, etc.), agriculture, livestock, and waste management agree remarkably well across products. In contrast, we documented large differences among products for land‐based fluxes (e.g., CO2 capture) and scant available information on aquatic ecosystems (e.g., CH4 emission from wetlands) and on lateral fluxes (e.g., emissions from trade). We expect these results to help in guiding future measuring efforts and policies to mitigate GHG emissions from Mexico. Key Points: A greenhouse gas (GHG) budget for Mexico was calculated based on multiple products. We estimated fluxes for CO2, CH4, and N2O over the 2000–2019 periodTotal GHG emissions were 695 TgCO2‐eq year−1 in the national inventory and as much as a mean 910 TgCO2‐eq year−1 across productsGHG fluxes agreed well across products for emissions, but large discrepancies exist for the land CO2 and CH4 sinks [ABSTRACT FROM AUTHOR]

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Soils play a critical role in the global carbon cycle, yet the biophysical factors regulating soil CO2 dynamics remain unclear. We combined high-frequency in situ observations of fine roots and ectomycorrhizal (EM) fungi with data from multiple soil sensor arrays to examine the biophysical interactions influencing soil CO2 production for one year in a mixed conifer forest. Using structural equation modeling we constructed a hypothesized model to test for causal interactions among environmental factors, biotic factors, and soil CO2 dynamics throughout the soil profile. According to our model, seasonal variation in CO2 production was significantly influenced by EM rhizomorph production, soil temperature, and soil moisture. Fine root production, on the other hand, did not appear to significantly influence soil CO2 production. The relationship between EM rhizomorph production and soil CO2 production was also supported by a zero temporal lag between these two measurements in a cross-correlation analysis. In contrast, CO2 production increased before fine root production suggesting that these two measurements were decoupled in time. Results from this study highlight the need to better understand differences in carbon allocation between plant roots and EM fungi to improve our predictions of soil carbon dynamics under global climate change.