Daily diurnal temperature range associated with emergency ambulance calls: a nine-year time-series study.

Background: Diurnal temperature range (DTR) is associated with the increased risk of morbidity and mortality. However, the relationship between DTR and emergency ambulance calls (EACs), which more accurately and immediately reflect the health impacts of temperature changes, remains underexplored in China. Methods: We collected daily data on EACs and meteorological factors from 2009 to 2017 in Guangzhou, China. DTR, representing the temperature range within a day, was calculated by subtracting the minimum temperature from the maximum temperature for each day. Generalized additive models were used to estimate the association between DTR and EACs for all-cause, cardiovascular diseases, and respiratory diseases. Additionally, subgroup and sensitivity analyses were conducted in our study. Results: We found significant associations between daily DTR and EACs. The excess risks (ERs) were 0.47% (95% CI: 0.14, 0.81%) for all-cause EACs, 0.94% (95% CI: 0.46, 1.43%) for cardiovascular-related EACs, and 1.31% (95% CI: 0.76, 1.86%) for respiratory -related EACs at lag01, respectively. Subgroup analyses indicated that these associations were notably stronger among the older, males, and during the warm season. Specifically, there was an increase of 1.16% (95% CI: 0.59, 1.74%) in cardiovascular-related EACs among the older adult, compared to 0.45% (95% CI: -0.21, 1.12%) among those younger than 65 years. Among males, the increase was 1.39% (95% CI: 0.79, 1.99%), compared to 0.13% (95% CI: -0.53, 0.79%) among females. During the warm season, the increase was 1.53% (95% CI: 0.74, 2.34%), compared to 0.75% (95% CI: 0.14, 1.37%) during the cold season. Conclusion: DTR might increase the risk of daily all-cause, cardiovascular-related, and respiratory-related EACs in Guangzhou, China. The associations were particularly strong among older adults, males, and during the warm season. Implementing public health policies is essential to mitigate the adverse health effects of DTR.

Safety and efficacy of a feed additive consisting of an essential oil derived from the leaves of Salvia officinalis ssp. lavandulifolia (Vahl) Gams (Spanish sage oil) for use in all animal species (FEFANA asbl).

Following a request from the European Commission, EFSA was asked to deliver a scientific opinion on the safety and efficacy of Spanish sage oil from the leaves of Salvia officinalis ssp. lavandulifolia (Vahl) Gams (Spanish sage oil) when used as a sensory additive in feed and in water for drinking for all animal species. The EFSA Panel on Additives and Products or Substances used in Animal Feed (FEEDAP) concluded that the additive under assessment is considered safe up to the maximum use level of 14 mg/kg complete feed for all animal species. The FEEDAP Panel considered that the use of Spanish sage oil in water for drinking is safe provided that the total daily intake of the additive does not exceed the daily amount that is considered safe when consumed via feed. The use of Spanish sage oil in animal feed under the proposed conditions of use is safe for the consumer and the environment. Regarding user safety, the essential oil under assessment should be considered as an irritant to skin and eyes and as a dermal and respiratory sensitiser. Since the oil of the leaves of S. officinalis ssp. lavandulifolia (Vahl) Gams is recognised to flavour food and its function in feed would be essentially the same as that in food, no further demonstration of efficacy was considered necessary.

Revisiting Factors Influencing Under-Five Mortality in India: The Application of a Generalised Additive Cox Proportional Hazards Model.

BACKGROUND: Despite the implementation of various preventive measures, India continues to experience an alarmingly high under-five mortality rate (U5MR). The most recent nationwide data on U5MRs has provided an opportunity to re-examine the associated factors of U5MRs using advanced techniques. This study attempted to identify the associated determinants of U5MRs via the generalised additive Cox proportional hazards method. METHODS: This study analysed the fifth round of unit-level data for 213,612 children from the National Family Health Survey (NFHS-5) to identify the risk factors associated with U5MRs, employing a generalised additive Cox proportional hazards regression analysis. RESULTS: The children who had a length of pregnancy of less than 9 months had a 2.621 (95% CI: 2.494, 2.755) times greater hazard of U5MRs than the children who had a gestational period of 9 months or more. The non-linear association with U5MRs was highest in the mother's age, followed by the mother's haemoglobin, the mother's education, and household wealth score. The relationships between the mother's age and the mother's haemoglobin level with the U5MR were found to be U-shaped. CONCLUSIONS: This study highlights the importance of addressing maternal and socioeconomic factors while improving access to healthcare services in order to reduce U5MRs in India. Furthermore, the findings underscore the necessity for more sophisticated approaches to healthcare delivery that consider the non-linear relationships between predictor variables and U5MRs.

Evaluating the relative importance of predictors in Generalized Additive Models using the gam.hp R package.

Generalized Additive Models (GAMs) are widely employed in ecological research, serving as a powerful tool for ecologists to explore complex nonlinear relationships between a response variable and predictors. Nevertheless, evaluating the relative importance of predictors with concurvity (analogous to collinearity) on response variables in GAMs remains a challenge. To address this challenge, we developed an R package named gam.hp. gam.hp calculates individual R 2 values for predictors, based on the concept of 'average shared variance', a method previously introduced for multiple regression and canonical analyses. Through these individual R 2s, which add up to the overall R 2, researchers can evaluate the relative importance of each predictor within GAMs. We illustrate the utility of the gam.hp package by evaluating the relative importance of emission sources and meteorological factors in explaining ozone concentration variability in air quality data from London, UK. We believe that the gam.hp package will improve the interpretation of results obtained from GAMs.

Advancing precision medicine in gliomas through single-cell sequencing: unveiling the complex tumor microenvironment.

Glioblastoma (GBM) displays an infiltrative growth characteristic that recruits neighboring normal cells to facilitate tumor growth, maintenance, and invasion into the brain. While the blood-brain barrier serves as a critical natural defense mechanism for the central nervous system, GBM disrupts this barrier, resulting in the infiltration of macrophages from the peripheral bone marrow and the activation of resident microglia. Recent advancements in single-cell transcriptomics and spatial transcriptomics have refined the categorization of cells within the tumor microenvironment for precise identification. The intricate interactions and influences on cell growth within the tumor microenvironment under multi-omics conditions are succinctly outlined. The factors and mechanisms involving microglia, macrophages, endothelial cells, and T cells that impact the growth of GBM are individually examined. The collaborative mechanisms of tumor cell-immune cell interactions within the tumor microenvironment synergistically promote the growth, infiltration, and metastasis of gliomas, while also influencing the immune status and therapeutic response of the tumor microenvironment. As immunotherapy continues to progress, targeting the cells within the inter-tumor microenvironment emerges as a promising novel therapeutic approach for GBM. By comprehensively understanding and intervening in the intricate cellular interactions within the tumor microenvironment, novel therapeutic modalities may be developed to enhance treatment outcomes for patients with GBM.

An R-based integrated method for producing river bathymetry and cross-sections from recreational-grade sonar sensor data.

Water bodies' bathymetry is a crucial information for understanding and sustainably managing water resources. Bathymetric surveys can be expensive due to sonar equipment cost, but low-cost alternatives options exist. We present a methodology that standardize the bathymetric data collection and processing of recreational-grade sonar data. The sonar data postprocessing if fully implemented in R, with ready to use functions able to produce bathymetric maps or extract river cross sections' metrics with minimal computing efforts. The method robustly produces a variety of outputs; the performance of the equipment adopted and of the interpolation technique allow for high accuracy and low-cost bathymetric reconstruction.•The method implemented allow for a robust and consistent processing of recreational-grade sonar water depth measures.•Through R-based functions the data are postprocessed to obtain bathymetry maps also for complex shape waterbodies.•Further metrics of rivers/channel cross sections can be extracted.

Crop water requirement and irrigation scheduling under climate change scenario, and optimal cropland allocation in lower kulfo catchment.

Crop water requirement and irrigation scheduling in Lower Kulfo Catchment of southern Ethiopia have not assessed under climate change scenarios, and the allocation of crop land also not optimal that signifcantly challenges to crop productivity.Therefore, this study was conducted to evaluate the effects of climate change on future crop water requirements, and irrigation scheduling, and to allocate cropland optimally. Bias of projected precipitation and temperature were corrected by utilizing Climate Model data with the hydrologic modeling tool (CMhyd). Alongside, crop water requirements and irrigation scheduling were assessed using Crop Water Assessment Tool. After estimating crop water requirement, crop land were allocated optimally using General Algebraic Modeling System programming with non-negativity constraints (scenario 1), and non-negativity constraints based on farmers adaptation (scenario 2). Average reference evapotranspiration from 2030 to 2050 and 2060 to 2080 was increased by 11.9 %, and 16.2 %, respectively compared with the reference period (2010-2022). The total seasonal crop water requirements were 4,529 mm, 4866.7 mm, and 5272.2 mm under 2010 to 2022, 2030 to 2050, and 2060 to 2080 climate change scenarios, respectively. The meean irrigation interval in 2010-2022, 2030 to 2050, and 2060 to 2080 climate change scenarios were 8 days, 7 days, and 5 days, respectively. This irrigation interval was decreased by 14 % (2030-2050), and 34 % (2060-2080) compared with the reference period. In 2030 to 2050 and 2026 to 2080 climate change scenarios, the required irrigation water at the inlet of main canal increased by 6.8 %, and 18 %, respectively. The optimal allocated area for tomato (60.4 %), maize (20.8 %), and watermelon (18.8 %) in scenario 1 with net benefit of 1.47*108 Ethiopian Birr. The allocated areas in scenario 2 were (48 %) for maize, (31.6 %) for tomato, and (20.4 %) for watermelon with 1.34*108 Ethiopian Birr net benefit it was reduced by 19.1 % compared with the net benefit in scenario 1. Fruit crops alone may not suffice for local food needs and to address this, small farmers should grow maize, tomato, and watermelon. This research aids policymakers in encouraging climate-resilient agriculture and improving small-scale farmers' awareness through conducting workshops and training.

Phenylpropanoids Following Wounding and Infection of Sweet Sorghum Lines Differing in Responses to Stalk Pathogens.

Sweet sorghum (Sorghum bicolor) lines M81-E and Colman were previously shown to differ in responses to Fusarium thapsinum and Macrophomina phaseolina, stalk rot pathogens that can reduce the yields and quality of biomass and extracted sugars. Inoculated tissues were compared for transcriptomic, phenolic metabolite, and enzymatic activity during disease development 3 and 13 days after inoculation (DAI). At 13 DAI, M81-E had shorter mean lesion lengths than Colman when inoculated with either pathogen. Transcripts encoding monolignol biosynthetic and modification enzymes were associated with transcriptional wound (control) responses of both lines at 3 DAI. Monolignol biosynthetic genes were differentially coexpressed with transcriptional activator SbMyb76 in all Colman inoculations, but only following M. phaseolina inoculation in M81-E, suggesting that SbMyb76 is associated with lignin biosynthesis during pathogen responses. In control inoculations, defense-related genes were expressed at higher levels in M81-E than Colman. Line, treatment, and timepoint differences observed in phenolic metabolite and enzyme activities did not account for observed differences in lesions. However, generalized additive models were able to relate metabolites, but not enzyme activities, to lesion length for quantitatively modeling disease progression: in M81-E, but not Colman, sinapic acid levels positively predicted lesion length at 3 DAI when cell wall-bound syringic acid was low, soluble caffeic acid was high, and lactic acid was high, suggesting that sinapic acid may contribute to responses at 3 DAI. These results provide potential gene targets for development of sweet sorghum varieties with increased stalk rot resistance to ensure biomass and sugar quality.

Optimum utilization of hub energy micro-grids with micro-networking strategy of local biogas productions.

In this paper, the optimal utilization of local biogas production in multi-energy systems is investigated. The micro-grid policy of local biogas production, to supply the variable demand of natural gas and also to convert it into electrical energy required by an energy hub micro-grid, along with other sources of scattered energy production and national electricity and gas networks, is one of the main goals of this paper. The multi-factor intelligent optimization method is considered to investigate the non-linear and heterogeneous optimization problem in the IEEE standard 33-bus energy hub microgrid. Most of the main indicators of interest are economic savings and minimization of operating costs. The multi-objective optimization method has been chosen as a method to solve the aforementioned non-linear and heterogeneous problem. Simulation has been done in GAMS software and the results of optimal load distribution of electric energy and natural gas supply for selected energy microhub have been explained and compared. The final results show a significant reduction of 46 % (2.8 thousand dollars per day) in operating costs. Significant reduction of energy flow between local network microhubs by 39.4 % is also one of the optimization results studied. Also, the Island performance of the local energy microhub has been simulated with the desired strategy. The cost of local energy microhub energy supply in island mode has decreased by 34 % (1.4 thousand dollars per day) compared to normal mode. © 2022 Published by.

A comparison among optimization software to solve bi-objective sectorization problem.

In this study, we compare the performance of optimization software to solve the bi-objective sectorization problem. The used solution method is based on an approach that has not been used before in the literature on sectorization, in which, the bi-objective model is transformed into single-objective ones, whose results are regarded as ideal points for the objective functions in the bi-objective model. Anti-ideal points are also searched similarly. Then, using the ideal and anti-ideal points, the bi-objective model is redefined as a single-objective one and solved. The difficulties of solving the models, which are basically non-linear, are discussed. Furthermore, the models are linearized, in which case how the number of variables and constraints changes is discussed. Mathematical models are implemented in Python's Pulp library, Lingo, IBM ILOG CPLEX Optimization Studio, and GAMS software, and the obtained results are presented. Furthermore, metaheuristics available in Python's Pymoo library are utilized to solve the models' single- and bi-objective versions. In the experimental results section, benchmarks of different sizes are derived for the problem, and the results are presented. It is observed that the solvers do not perform satisfactorily in solving models; of all of them, GAMS achieves the best results. The utilized metaheuristics from the Pymoo library gain feasible results in reasonable times. In the conclusion section, suggestions are given for solving similar problems. Furthermore, this article summarizes the managerial applications of the sectorization problems.

In situ transient transfection of 3D cell cultures and tissues, a promising tool for tissue engineering and gene therapy.

Various research fields use the transfection of mammalian cells with genetic material to induce the expression of a target transgene or gene silencing. It is a tool widely used in biological research, bioproduction, and therapy. Current transfection protocols are usually performed on 2D adherent cells or suspension cultures. The important rise of new gene therapies and regenerative medicine in the last decade raises the need for new tools to empower the in situ transfection of tissues and 3D cell cultures. This review will present novel in situ transfection methods based on a chemical or physical non-viral transfection of cells in tissues and 3D cultures, discuss the advantages and remaining gaps, and propose future developments and applications.

Established and Emerging Methods for Protecting Linear DNA in Cell-Free Expression Systems.

Cell-free protein synthesis (CFPS) is a method utilized for producing proteins without the limits of cell viability. The plug-and-play utility of CFPS is a key advantage over traditional plasmid-based expression systems and is foundational to the potential of this biotechnology. A key limitation of CFPS is the varying stability of DNA types, limiting the effectiveness of cell-free protein synthesis reactions. Researchers generally rely on plasmid DNA for its ability to support robust protein expression in vitro. However, the overhead required to clone, propagate, and purify plasmids reduces the potential of CFPS for rapid prototyping. While linear templates overcome the limits of plasmid DNA preparation, linear expression templates (LETs) were under-utilized due to their rapid degradation in extract based CFPS systems, limiting protein synthesis. To reach the potential of CFPS using LETs, researchers have made notable progress toward protection and stabilization of linear templates throughout the reaction. The current advancements range from modular solutions, such as supplementing nuclease inhibitors and genome engineering to produce strains lacking nuclease activity. Effective application of LET protection techniques improves expression yields of target proteins to match that of plasmid-based expression. The outcome of LET utilization in CFPS is rapid design-build-test-learn cycles to support synthetic biology applications. This review describes the various protection mechanisms for linear expression templates, methodological insights for implementation, and proposals for continued efforts that may further advance the field.

Precision recruitment for high-risk participants in a COVID-19 cohort study.

Background: Studies for developing diagnostics and treatments for infectious diseases usually require observing the onset of infection during the study period. However, when the infection base rate incidence is low, the cohort size required to measure an effect becomes large, and recruitment becomes costly and prolonged. We developed a model for reducing recruiting time and resources in a COVID-19 detection study by targeting recruitment to high-risk individuals. Methods: We conducted an observational longitudinal cohort study at individual sites throughout the U.S., enrolling adults who were members of an online health and research platform. Through direct and longitudinal connection with research participants, we applied machine learning techniques to compute individual risk scores from individually permissioned data about socioeconomic and behavioral data, in combination with predicted local prevalence data. The modeled risk scores were then used to target candidates for enrollment in a hypothetical COVID-19 detection study. The main outcome measure was the incidence rate of COVID-19 according to the risk model compared with incidence rates in actual vaccine trials. Results: When we used risk scores from 66,040 participants to recruit a balanced cohort of participants for a COVID-19 detection study, we obtained a 4- to 7-fold greater COVID-19 infection incidence rate compared with similar real-world study cohorts. Conclusion: This risk model offers the possibility of reducing costs, increasing the power of analyses, and shortening study periods by targeting for recruitment participants at higher risk.

A Video Segmentation Pipeline for Assessing changes in Pupil Response to Light After Cannabis Consumption.

Due to long-standing federal restrictions on cannabis-related research, the implications of cannabis legalization on traffic and occupational safety are understudied. Accordingly, there is a need for objective and validated measures of acute cannabis impairment that may be applied in public safety and occupational settings. Pupillary response to light may offer an avenue for detection that outperforms typical sobriety tests and THC concentrations. We developed a video processing and analysis pipeline that extracts pupil sizes during a light stimulus test administered with goggles utilizing infrared videography. The analysis compared pupil size trajectories in response to a light for those with occasional, daily, and no cannabis use before and after smoking. Pupils were segmented using a combination of image pre-processing techniques and segmentation algorithms which were validated using manually segmented data and found to achieve 99% precision and 94% F-score. Features extracted from the pupil size trajectories captured pupil constriction and rebound dilation and were analyzed using generalized estimating equations. We find that acute cannabis use results in less pupil constriction and slower pupil rebound dilation in the light stimulus test.

Spatial association between green space and COPD mortality: a township-level ecological study in Chongqing, China.

BACKGROUND: There are regional differences in the effect of green space on mortality of Chronic obstructive pulmonary disease (COPD). We conduct an ecological study, using the administrative divisions of Chongqing townships in China as the basic unit, to investigate the association between COPD mortality and green space based on data of 313,013 COPD deaths in Chongqing from 2012 to 2020. Green space is defined by Fractional vegetation cover (FVC), which is further calculated based on the normalised vegetation index (NDVI) from satellite remote sensing imagery maps. METHODS: After processing the data, the non-linear relationship between green space and COPD mortality is revealed by generalised additive models; the spatial differences between green space and COPD mortality is described by geographically weighted regression models; and finally, the interpretive power and interaction of each factor on the spatial distribution of COPD mortality is examined by a geographic probe. RESULTS: The results show that the FVC local regression coefficients ranged from - 0.0397 to 0.0478, 63.0% of the regions in Chongqing have a positive correlation between green space and COPD mortality while 37.0% of the regions mainly in the northeast and west have a negative correlation. The interpretive power of the FVC factor on the spatial distribution of COPD mortality is 0.08. CONCLUSIONS: Green space may be a potential risk factor for increased COPD mortality in some regions of Chongqing. This study is the first to reveal the relationship between COPD mortality and green space in Chongqing at the township scale, providing a basis for public health policy formulation in Chongqing.

Probabilistic simulation of big climate data for robust quantification of changes in compound hazard events.

Understanding changes in extreme compound hazard events is important for climate mitigation and policy. By definition, such events are rare so robust quantification of their future changes is challenging. An approach is presented, for probabilistic modelling and simulation of climate model data, which is invariant to the event definition since it models the underlying weather variables. The approach is based on the idea of a 'moving window' in conjunction with Generalised Additive Models (GAMs) and Bayesian inference. As such, it is robust to the data size and completely parallelizable, while it fully quantifies uncertainty allowing also for comprehensive model checking. Lastly, Gaussian anamorphosis is used to capture dependency across weather variables. The approach results in probabilistic simulations to enable extrapolation beyond the original data range and thus robust quantification of future changes of rare events. We illustrate by application to daily temperature, humidity and precipitation from a regional climate model.

Osteometabolism: Metabolic Alterations in Bone Pathologies.

Renewing interest in the study of intermediate metabolism and cellular bioenergetics is brought on by the global increase in the prevalence of metabolic illnesses. Understanding of the mechanisms that integrate energy metabolism in the entire organism has significantly improved with the application of contemporary biochemical tools for quantifying the fuel substrate metabolism with cutting-edge mouse genetic procedures. Several unexpected findings in genetically altered mice have prompted research into the direction of intermediate metabolism of skeletal cells. These findings point to the possibility of novel endocrine connections through which bone cells can convey their energy status to other metabolic control centers. Understanding the expanded function of skeleton system has in turn inspired new lines of research aimed at characterizing the energy needs and bioenergetic characteristics of these bone cells. Bone-forming osteoblast and bone-resorbing osteoclast cells require a constant and large supply of energy substrates such as glucose, fatty acids, glutamine, etc., for their differentiation and functional activity. According to latest research, important developmental signaling pathways in bone cells are connected to bioenergetic programs, which may accommodate variations in energy requirements during their life cycle. The present review article provides a unique perspective of the past and present research in the metabolic characteristics of bone cells along with mechanisms governing energy substrate utilization and bioenergetics. In addition, we discussed the therapeutic inventions which are currently being utilized for the treatment and management of bone-related diseases such as osteoporosis, rheumatoid arthritis (RA), osteogenesis imperfecta (OIM), etc., by modulating the energetics of bone cells. We further emphasized on the role of GUT-associated metabolites (GAMs) such as short-chain fatty acids (SCFAs), medium-chain fatty acids (MCFAs), indole derivates, bile acids, etc., in regulating the energetics of bone cells and their plausible role in maintaining bone health. Emphasis is importantly placed on highlighting knowledge gaps in this novel field of skeletal biology, i.e., "Osteometabolism" (proposed by our group) that need to be further explored to characterize the physiological importance of skeletal cell bioenergetics in the context of human health and bone related metabolic diseases.

Spatial disparities of HIV prevalence in South Africa. Do sociodemographic, behavioral, and biological factors explain this spatial variability?

Background: In 2021, an estimated 38 million people were living with human immunodeficiency virus (HIV) globally, with over two-thirds living in African regions. In South Africa, ~20% of South African adults are living with HIV. Accurate estimation of the risk factors and spatial patterns of HIV risk using individual-level data from a nationally representative sample is invaluable for designing geographically targeted intervention and control programs. Methods: Data were obtained from the 2016 South Africa Demographic and Health Survey (SDHS16). The study involved all men and women aged 15 years and older, who responded to questions and tested for HIV in the SDHS. Generalized additive models (GAMs) were fitted to our data with a nonparametric bivariate smooth term of spatial location parameters (X and Y coordinates). The GAMs were used to assess the spatial disparities and the potential contribution of sociodemographic, biological, and behavioral factors to the spatial patterns of HIV prevalence in South Africa. Results: A significantly highest risk of HIV was observed in east coast, central and north-eastern regions. South African men and women who are widowed and divorced had higher odds of HIV as compared to their counterparts. Additionally, men and women who are unemployed had higher odds of HIV as compared to the employed. Surprisingly, the odds of HIV infection among men residing in rural areas were 1.60 times higher (AOR 1.60, 95% CI 1.12, 2.29) as compared to those in urban areas. But men who were circumcised had lower odds of HIV (AOR 0.73, 95% CI 0.52, 0.98), while those who had STI in the last 12 months prior to the survey had higher odds of HIV (AOR 1.76, 95% CI 1.44, 3.68). Conclusion: Spatial heterogeneity in HIV risk persisted even after covariate adjustment but differed by sex, suggesting that there are plausible unobserved influencing factors contributing to HIV uneven variation. This study's findings could guide geographically targeted public health policy and effective HIV intervention in South Africa.

Origin, activation, and targeted therapy of glioma-associated macrophages.

The glioma tumor microenvironment plays a crucial role in the development, occurrence, and treatment of gliomas. Glioma-associated macrophages (GAMs) are the most widely infiltrated immune cells in the tumor microenvironment (TME) and one of the major cell populations that exert immune functions. GAMs typically originate from two cell types-brain-resident microglia (BRM) and bone marrow-derived monocytes (BMDM), depending on a variety of cytokines for recruitment and activation. GAMs mainly contain two functionally and morphologically distinct activation types- classically activated M1 macrophages (antitumor/immunostimulatory) and alternatively activated M2 macrophages (protumor/immunosuppressive). GAMs have been shown to affect multiple biological functions of gliomas, including promoting tumor growth and invasion, angiogenesis, energy metabolism, and treatment resistance. Both M1 and M2 macrophages are highly plastic and can polarize or interconvert under various malignant conditions. As the relationship between GAMs and gliomas has become more apparent, GAMs have long been one of the promising targets for glioma therapy, and many studies have demonstrated the therapeutic potential of this target. Here, we review the origin and activation of GAMs in gliomas, how they regulate tumor development and response to therapies, and current glioma therapeutic strategies targeting GAMs.

Benthic megafaunal biodiversity of the Charlie-Gibbs fracture zone: spatial variation, potential drivers, and conservation status.

The Charlie-Gibbs Fracture Zone (CGFZ) is a prominent geological feature offsetting the Mid-Atlantic Ridge (MAR), consisting of two parallel fractures, creating a highly variable seafloor bathymetry. It has been defined as the most important latitudinal biodiversity transitional zone on the MAR. Despite this recognition, the faunal communities living on the fracture zone have not been extensively described. A remotely operated vehicle (ROV) was utilised during the TOSCA (Tectonic Ocean Spreading at the Charlie-Gibbs Fracture Zone) survey. The survey included five ROV video transects at depths between 560 and 2900 m. The objectives of the study were to use this video footage to quantify benthic megafaunal density and biodiversity patterns on the CGFZ and their environmental drivers. Species accumulation curves and generalised additive modelling show that depth and the presence of hard substrates play an important role in explaining species richness and abundance at the CGFZ. Coral taxa showed highest abundance between depths of 1500 and 2000 m, while sponge taxa were more abundant between 1750 and 2250 m. A dense sponge aggregation was identified on a ridge feature at 2250 m depth. The high biodiversity and presence of dense sponge aggregations and coral gardens found in this study highlight the need for detailed surveys to help support decisions made by governing bodies on the protection status of the CGFZ. Supplementary Information: The online version contains supplementary material available at 10.1007/s12526-022-01285-1.