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Abstract During extensive periods without rain, known as dry-downs, decreasing soil moisture (SM) induces plant water stress at the point when it limits evapotranspiration, defining a critical SM threshold (θcrit). Better quantification of θcrit is needed for improving future projections of climate and water resources, food production, and ecosystem vulnerability. Here, we combine systematic satellite observations of the diurnal amplitude of land surface temperature (dLST) and SM during dry-downs, corroborated by in-situ data from flux towers, to generate the observation-based global map of θcrit. We find an average global θcrit of 0.19 m3/m3, varying from 0.12 m3/m3 in arid ecosystems to 0.26 m3/m3 in humid ecosystems. θcrit simulated by Earth System Models is overestimated in dry areas and underestimated in wet areas. The global observed pattern of θcrit reflects plant adaptation to soil available water and atmospheric demand. Using explainable machine learning, we show that aridity index, leaf area and soil texture are the most influential drivers. Moreover, we show that the annual fraction of days with water stress, when SM stays below θcrit, has increased in the past four decades. Our results have important implications for understanding the inception of water stress in models and identifying SM tipping points.

Abstract Background Urban agglomerates play a crucial role in reaching global climate objectives. Many cities have committed to reducing their greenhouse gas emissions, but current emission trends remain unverifiable. Atmospheric monitoring of greenhouse gases offers an independent and transparent strategy to measure urban emissions. However, careful design of the monitoring network is crucial to be able to monitor the most important sectors as well as adjust to rapidly changing urban landscapes. Results Our study of Paris and Munich demonstrates how climate action plans, carbon emission inventories, and urban development plans can help design optimal atmospheric monitoring networks. We show that these two European cities display widely different trajectories in space and time, reflecting different emission reduction strategies and constraints due to administrative boundaries. The projected carbon emissions rely on future actions, hence uncertain, and we demonstrate how emission reductions vary significantly at the sub-city level. Conclusions We conclude that quantified individual cities’ climate actions are essential to construct more robust emissions trajectories at the city scale. Also, harmonization and compatibility of plans from various cities are necessary to make inter-comparisons of city climate targets possible. Furthermore, dense atmospheric networks extending beyond the city limits are needed to track emission trends over the coming decades.

In Europe, the heterogeneous features of crop systems with majority of small to medium sized agricultural holdings, and diversity of crop rotations, require high-resolution information to estimate cropland Net Ecosystem Exchange (NEE) and its two main components of Gross Ecosystem Exchange (GEE) and the Ecosystem Respiration (RECO). In this context, this paper presents an assimilation of high-resolution Sentinel-2 indices with eddy covariance measurements at selected European cropland flux sites in a new modified version of Vegetation Photosynthesis Respiration Model (VPRM). VRPM is a data-driven model simulating CO2 fluxes previously applied using satellite-derived vegetation indices from the Moderate Resolution Imaging Spectroradiometer (MODIS). This study proposes a modification of the VPRM by including an explicit soil moisture stress function to the GEE and changing the equation of RECO. It also compares the model results driven by S2 indices instead of MODIS. The parameters of the VPRM model are calibrated using eddy-covariance data. All possible parameters optimization scenarios include the use of the initial version vs. the proposed modified VPRM, S2, or MODIS vegetation indices, and finally the choice of calibrating a single set of parameters against observations from all crop types, a set of parameters per crop type, or one set of parameters per site. Then, we focus the analysis on the improvement of the model with distinct parameters for different crop types vs. parameters optimized without distinction of crop types. Our main findings are: (1) the superiority of S2 vegetation indices over MODIS for cropland CO2 fluxes simulations, leading to a root mean squared error (RMSE) for NEE of less than 3.5 μmolm-2s-1 with S2 compared to 5 μmolm-2s-1 with MODIS (2) better performances of the modified VPRM version leading to a significant improvement of RECO, and (3) better performances when the parameters are optimized per crop-type instead of for all crop types lumped together, with lower RMSE and Akaike information criterion (AIC), despite a larger number of parameters. Associated with the availability of crop-type land cover maps, the use of S2 data and crop-type modified VPRM parameterization presented in this study, provide a step forward for upscaling cropland carbon fluxes at European scale.

Despite their key role in the low-carbon transition, cities depend on methods with pressing data availability and quality issues for carbon monitoring. Origins.earth and partners have leveraged cutting-edge data and scientific knowledge to deploy an operational digital solution delivering high-quality climate data in under a month. The solution relies on networks of innovative CO2 monitoring stations, big data, and expert knowledge, and delivers historical and current emissions and associated indicators. We demonstrate the robustness of the technology and its applicability for informing policy decisions in Paris and explore its expansion into other mature markets, with a particular focus on Italy.

The quantification of CO2 emissions from cities using atmospheric measurements requires accurate knowledge of the atmospheric transport. Complex urban terrains significantly modify surface roughness, augment surface energy budgets, and create heat islands, all of which lead to lower horizontal winds and enhanced convection over urban areas. The question remains whether these processes should be included in atmospheric transport models that are used for city scale CO2 inversion, and whether they need to be tailored on a city basis. In this study, we use the WRF model over Paris to address the following research question: does WRF runs at a 3 km resolution, including urban effects and the assimilation of local weather data, perform better than ECMWF forecasts that give fields at 16 km resolution? The analysis of model performances focuses on three variables: air temperature, wind and the planetary boundary layer (PBL) height. The results show that the use of objective analysis and nudging tools are required to obtain good agreements between WRF simulated fields with observations. Surface temperature is well reproduced by both WRF and ECMWF forecasts, with correlation coefficients with hourly observations larger than 0.92 and MBEs within 1°C over one month. Wind speed correlations with hourly observations are similar for WRF (range 0.76~0.85 across stations) and ECMWF (0.79~0.84), but the associated RMSEs and MBEs are better for ECMWF. Conversely, WRF outperforms ECMWF forecasts for its description of wind direction, horizontal and vertical gradients. Sensitivity tests with different WRF physics schemes show that the wind speed and the PBL height are strongly influenced by PBL schemes. The marginal advantage of WRF over ECMWF for the desired application is sufficient to motive additional testing with prescribed CO2 flux maps for comparing modeled CO2 concentrations with available observations in an urban environment.

To effectively monitor the highly heterogeneous urban CO2 emissions using atmospheric observations, there is a need to deploy cost-effective CO2 sensors at multiple locations within the city with sufficient accuracy to capture the concentration gradients in urban environments. Its measurements could be used as input of an atmospheric inversion system for the quantification of emissions at the sub-city scale or separate specific sectors. Such quantification would offer valuable insights into the efficacy of local initiatives and could also identify unknown emission hotspots that require attention. Here we present the development and evaluation of a mid-cost CO2 instrument designed for continuous monitoring of atmospheric CO2 concentrations with a target accuracy of 1 ppm on hourly mean measurement. We assess the sensor sensitivity in relation to environmental factors such as humidity, pressure, temperature and CO2 signal, which leads to the development of an effective calibration algorithm. Since July 2020, eight mid-cost instruments have been installed within the city of Paris and its vicinity to provide continuous CO2 measurements, complementing the seven high-precision Cavity Ring-Down Spectroscopy (CRDS) stations that have been in operation since 2016. A data processing system, called CO2 calqual, has been implemented to automatically handle data quality control, calibration and storage, which enables the management of extensive real-time CO2 measurements from the monitoring network. Colocation assessments with the high-precision instrument show that the accuracies of the eight mid-cost instruments are within the range of 1.0 to 2.4 ppm for hourly afternoon (12–17 UTC) measurements. The long-term stability issues require manual data checks and instrument maintenance. The analyses show that CO2 measurements can provide evidence for underestimations of CO2 emissions in the Paris region and a lack of several emission point sources in the emission inventory. Our study demonstrates promising prospects in integrating mid-cost measurements along with high precision data into the subsequent atmospheric inverse modeling to improve the accuracy of quantifying the fine-scale CO2 emissions in the Paris metropolitan area. [ABSTRACT FROM AUTHOR]

ABSTRACTOvarian aging occurs prior to the aging of other organ systems and acts as the pacemaker of the aging process of multiple organs. As life expectancy has increased, preventing ovarian aging has become an essential goal for promoting extended reproductive function and improving bone and genitourinary conditions related to ovarian aging in women. An improved understanding of ovarian aging may ultimately provide tools for the prediction and mitigation of this process. Recent studies have suggested a connection between ovarian aging and the gut microbiota, and alterations in the composition and functional profile of the gut microbiota have profound consequences on ovarian function. The interaction between the gut microbiota and the ovaries is bidirectional. In this review, we examine current knowledge on ovary-gut microbiota crosstalk and further discuss the potential role of gut microbiota in anti-aging interventions. Microbiota-based manipulation is an appealing approach that may offer new therapeutic strategies to delay or reverse ovarian aging.

Existing CO2 emissions reported by city inventories usually lag real-time by a year or more and are prone to large uncertainties. This study responds to the growing need for timely and precise estimation of urban CO2 emissions to support the present and future mitigation measures and policies. We focus on the Paris metropolitan area, the largest urban region in the European Union and the city with the densest atmospheric CO2 observation network in Europe. We performed long-term atmospheric inversions to quantify the citywide CO2 emissions, both fossil fuel and biogenic sources and sinks, over six years (2016-2021) using a Bayesian inverse modeling system. Our inversion framework benefits from a novel near-real-time hourly fossil fuel CO2 emission inventory (Origins.earth) at 1 km spatial resolution. In addition to the mid-afternoon observations, we attempt to assimilate morning CO2 concentrations based on the ability of the WRF-Chem transport model to simulate atmospheric boundary layer dynamics constrained by observed layer heights. Our results show a long-term decreasing trend by around 2% per year in annual CO2 emissions over the Paris region. The impact of COVID-19 pandemic led to a 13%±1% reduction in annual fossil fuel CO2 emissions in 2020 with respect to 2019. Then, annual emissions increased by 5.2% from 32.6±2.2 MtCO2 in 2020 to 34.3±2.3 MtCO2 in 2021. Based on a combination of up-to-date inventories, high-resolution atmospheric modeling, and highprecision observations, our current capacity could deliver near real-time CO2 emission estimates at city scale in less than a month, and the results agree within 10% with independent estimates from multiple city-scale inventories. [ABSTRACT FROM AUTHOR]

Under the Copernicus programme, an operational CO2 monitoring system (CO2MVS) is being developed and will exploit data from future satellites monitoring the amount of CO2 within the atmosphere. Methods for estimating CO2 emissions from significant local emitters (hotspots, i.e. cities or power plants) can greatly benefit from the availability of such satellite images, displaying atmospheric plumes of CO2. Indeed, local emissions are strongly correlated to the size, shape and concentrations distribution of the corresponding plume, the visible consequence of the emission. The estimation of emissions from a given source can therefore directly benefit from the detection of its associated plumes in the satellite image. In this study, we address the problem of plume segmentation, i.e. the problem of finding all pixels in an image that constitute a city or power plant plume. This represents a significant challenge, as the signal from CO2 plumes induced by emissions from cities or power plants is inherently difficult to detect since it rarely exceeds values of a few ppm and is perturbed by variable regional CO2 background signals and observation errors. To address this key issue, we investigate the potential of deep learning methods and in particular convolutional neural networks to learn to distinguish plume-specific spatial features from background or instrument features. Specifically, a U-net algorithm, an image-to-image convolutional neural network, with a state-of-the-art encoder, is used to transform an XCO2 field into an image representing the positions of the targeted plume. Our models are trained on hourly 1 km simulated XCO2 fields in the regions of Paris, Berlin and several German power plants. Each field represents the plume of the hotspot, the background consisting of the signal of anthropogenic and biogenic CO2 surface fluxes near or far from the targeted source and the simulated satellite observation errors. The performance of the deep learning method is thereafter evaluated and compared with a plume segmentation technique based on thresholding in two contexts: the first where the model is trained and tested on data from the same region, and the second where the model is trained and tested in two different regions. In both contexts, our method outperforms the usual segmentation technique based on thresholding and demonstrates its ability to generalise in various cases: city plumes, power plant plumes, and areas with multiple plumes. Although less accurate than in the first context, the ability of the algorithm to extrapolate on new geographical data is conclusive, paving the way to a promising universal segmentation model, trained on a well-chosen sample of power plants and cities, and able to detect the majority of the plumes from all of them. Finally, the highly accurate results for segmentation suggest a significant potential of convolutional neural networks for estimating local emissions from spaceborne imagery. [ABSTRACT FROM AUTHOR]

Abstract Noninvasive pharmacological strategies like nicotinamide mononucleotide (NMN) supplementation can effectively address age‐related ovarian infertility by maintaining or enhancing oocyte quality and quantity. This study revealed that ovarian nicotinamide adenine dinucleotide levels decline with age, but NMN administration significantly restores these levels, preventing ovarian atrophy and enhancing the quality and quantity of ovulated oocytes. Improvements in serum hormone secretion and antioxidant factors, along with decreased expression of proinflammatory factors, were observed. Additionally, a significant increase in the number of ovarian follicles in aging individuals was noted. Scanning electron microscopy data indicated that NMN significantly alters the density and morphology of lipid droplets and mitochondria in granulosa cells, suggesting potential targets and mechanisms. Transcriptomic analysis and validation experiments collectively suggested that the beneficial effects of NMN on aging ovaries are mediated through enhanced mitochondrial function, improved energy metabolism, and reduced inflammation levels. Our results suggest that NMN supplementation could improve the health status of aging ovaries and enhance ovarian reserve, offering new insights into addressing fertility challenges in older women through assisted reproductive technology.

Abstract Nicotinamide adenine dinucleotide (NAD+), a crucial coenzyme in cellular redox reactions, is closely associated with age-related functional degeneration and metabolic diseases. NAD exerts direct and indirect influences on many crucial cellular functions, including metabolic pathways, DNA repair, chromatin remodeling, cellular senescence, and immune cell functionality. These cellular processes and functions are essential for maintaining tissue and metabolic homeostasis, as well as healthy aging. Causality has been elucidated between a decline in NAD levels and multiple age-related diseases, which has been confirmed by various strategies aimed at increasing NAD levels in the preclinical setting. Ovarian aging is recognized as a natural process characterized by a decline in follicle number and function, resulting in decreased estrogen production and menopause. In this regard, it is necessary to address the many factors involved in this complicated procedure, which could improve fertility in women of advanced maternal age. Concerning the decrease in NAD+ levels as ovarian aging progresses, promising and exciting results are presented for strategies using NAD+ precursors to promote NAD+ biosynthesis, which could substantially improve oocyte quality and alleviate ovarian aging. Hence, to acquire further insights into NAD+ metabolism and biology, this review aims to probe the factors affecting ovarian aging, the characteristics of NAD+ precursors, and the current research status of NAD+ supplementation in ovarian aging. Specifically, by gaining a comprehensive understanding of these aspects, we are optimistic about the prominent progress that will be made in both research and therapy related to ovarian aging.

In order to track progress towards the global climate targets, the parties that signed the Paris Climate Agreement will regularly report their anthropogenic carbon dioxide (CO2) emissions based on energy statistics and CO2 emission factors. Independent evaluation of this self-reporting system is a fast-growing research topic. Here, we study the value of satellite observations of the column CO2 concentrations to estimate CO2 anthropogenic emissions with 5 years of the Orbiting Carbon Observatory-2 (OCO-2) retrievals over and around China. With the detailed information of emission source locations and the local wind, we successfully observe CO2 plumes from 46 cities and industrial regions over China and quantify their CO2 emissions from the OCO-2 observations, which add up to a total of 1.3 Gt CO2 yr -1 that accounts for approximately 13 % of mainland China's annual emissions. The number of cities whose emissions are constrained by OCO-2 here is 3 to 10 times larger than in previous studies that only focused on large cities and power plants in different locations around the world. Our satellite-based emission estimates are broadly consistent with the independent values from China's detailed emission inventory MEIC but are more different from those of two widely used global gridded emission datasets (i.e., EDGAR and ODIAC), especially for the emission estimates for the individual cities. These results demonstrate some skill in the satellite-based emission quantification for isolated source clusters with the OCO-2, despite the sparse sampling of this instrument not designed for this purpose. This skill can be improved by future satellite missions that will have a denser spatial sampling of surface emitting areas, which will come soon in the early 2020s. [ABSTRACT FROM AUTHOR]

The top-down atmospheric inversion method that couples atmospheric CO2 observations with an atmospheric transport model has been used extensively to quantify CO2 emissions from cities. However, the potential of the method is limited by several sources of misfits between the measured and modeled CO2 that are of different origins than the targeted CO2 emissions. This study investigates the critical sources of errors that can compromise the estimates of the city-scale emissions and identifies the signal of emissions that has to be filtered when doing inversions. A set of one-year forward simulations is carried out using the WRF-Chem model at a horizontal resolution of 1 km focusing on the Paris area with different anthropogenic emission inventories, physical parameterizations and CO2 boundary conditions. The simulated CO2 concentrations are compared with in situ observations from six continuous monitoring stations located within Paris and its vicinity. Results highlight large nighttime observation-model misfits, especially in winter within the city, which are attributed to large uncertainties in the diurnal profile of anthropogenic emissions as well as to errors in the vertical mixing near the surface in the WRF-Chem model. The nighttime biogenic respiration to the CO2 concentration is a significant source of modeling errors during the growing season outside the city. When winds are from continental Europe and the CO2 concentration of incoming air masses is influenced by remote emissions and large-scale biogenic fluxes, differences in the simulated CO2 induced by the two different boundary conditions (CAMS and CarbonTracker) can be of up to 5 ppm. Our results suggest three selection criteria for the CO2 data to be assimilated for the inversion of CO2 emissions from Paris (i) discard data that appear as statistical outliers in the model-data misfits which are interpreted as model's deficiencies under complex meteorological conditions; (ii) use only afternoon urban measurements in winter and suburban ones in summer; (iii) test the influence of different boundary conditions in inversions. If possible, using additional observations to constrain the boundary inflow, or using CO2 gradients of upwind-downwind stations, rather than absolute CO2 concentration, as atmospheric inversion inputs. [ABSTRACT FROM AUTHOR]

Abstract Circular RNAs play important roles in many cancers, including esophageal squamous cell carcinoma (ESCC), but the precise functions of most circular RNAs are poorly understood. Here we detected significant downregulation of circTRPS1-2 in ESCC based on high-throughput sequencing of three pairs of ESCC tissue and adjacent normal tissue, followed by PCR validation with another 30 tissue pairs. Patients with ESCC whose circTRPS1-2 expression was below the median level for the sample showed significantly shorter median overall survival (13 months) than patients whose circTRPS1-2 expression was above the median (36 months). Overexpressing circTRPS1-2 in the human ESCC cell lines K150 and E109, which express low endogenous levels of circTRPS1-2, inhibited cell proliferation and migration. Conversely, knocking down circTRPS1-2 using short interfering RNA promoted cell proliferation and migration. Similar results were observed in mice bearing K150 xenografts in which circTRPS1-2 was overexpressed or knocked down. Several ribosomal proteins co-immunoprecipitated with circTRPS1-2 from K150 cells in culture, and K150 cells overexpressing circTRPS1-2 showed reduced numbers of ribosomes by A260 absorbance measure and electron microscopy. Our results suggest that circTRPS1-2 can inhibit ESCC proliferation and migration by reducing the production of ribosomes, establishing its potential usefulness in ESCC treatment and prediction of prognosis.

Abstract Cyclin-dependent kinases (CDKs) control the progression of the cell cycle. D-type cyclin (CYCD) is generally believed to form a complex with CDK and control the G1/S transition. In plants, CYCD and CDK gene families can be divided into 6 (D1–D7) and 7 (CDKA–CDKG) subclasses, respectively. Different subclasses in the CYCD and CDK families have different numbers, structures and functions. In some heterologous woody plants, the functions of these subclass family members remain unclear. In this study, 43 CYCD and 27 CDK gene family members were identified in the allodiploid Populus tomentosa Carr. Phylogenetic analysis suggested that these CYCDs and CDKs were divided into 6 and 7 subclasses, respectively, which were the same as other species. The analysis of protein properties, gene structure, motifs, domains, cis-acting elements and tissue-specific expression of all members of these CYCDs and CDKs showed that the differences between members of different subclasses varied widely, but members of the same subclass especially in the CDK gene family were very similar. These findings also demonstrated a strong correlation between CYCD and CDK gene family members in response to hormones and specific expression. The collinear analysis of P. tomentosa, Populus trichocarpa and Arabidopsis thaliana showed that the expansion patterns of CYCD and CDK gene families were predominantly whole genome duplications (WGD). The protein interaction prediction results of different subclasses of CYCD and CDKs showed that the interaction between different subclasses of CYCD and CDKs was significantly different. Our previous study found that transgenic PtoCYCD2;1 and PtoCYCD3;3 poplars exhibited opposite phenotypes. Y2H and BIFC results showed that the interaction between PtoCYCD2;1 and PtoCYCD3;3 was significantly different with CDKs. This finding might suggest that the functional differences of different CYCD subclasses in plant growth and development were closely related to the different interactions between CYCD and CDK. Our results provide a good idea and direction for the functional study of CYCD and CDK proteins in woody plants.

In order to track progress towards the global climate targets, the parties that signed the Paris Climate Agreement will regularly report their anthropogenic carbon dioxide (CO2) emissions based on energy statistics and CO2 emission factors. Independent evaluation of this self-reporting system is a fast-growing research topic. Here, we study the value of satellite observations of the column CO2 concentrations to estimate CO2 anthropogenic emissions with five years of the Orbiting Carbon Observatory-2 (OCO-2) retrievals over and around China. With the detailed information of emission source locations and the local wind, we successfully observe CO2 plumes from 60 cities and industrial regions over China and quantify their CO2 emissions from the OCO-2 observations, which add up to a total of 1.6 Gt CO2 yr−1 that account for 17 % of mainland China's annual emissions. The number of cities whose emissions are constrained by OCO-2 here is three to ten times larger than previous studies that only focused on large cities and power plants in different locations around the world. Our satellite-based emission estimates are broadly consistent with the independent values from the detailed China's emission inventory MEIC, but are more different from those of two widely used global gridded emission datasets (i.e., EDGAR and ODIAC), especially for the emission estimates for the individual cities. These results demonstrate some skill in the satellite-based emission quantification for isolated source clusters with the OCO-2, despite the sparse sampling of this instrument not designed for this purpose. This skill can be improved by future satellite missions that will have a denser spatial sampling of surface emitting areas, which will come soon in the early 2020s. [ABSTRACT FROM AUTHOR]

The Greenhouse gas Laser Imaging Tomography Experiment (GreenLITE™) trace gas measurement system, jointly designed and developed by Atmospheric and Environmental Research, Inc. and Spectral Sensor Solutions LLC, provides high-precision, long-path measurements of atmospheric trace gases including CO2 and CH4 over extended (0.04 km²-25 km²) areas of interest. In 2015, a prototype unit was deployed in Paris, France to demonstrate its ability to provide continuous observations of CO2 concentrations along horizontal air segments and two-dimensional (2-D) maps of time-varying CO2 concentrations over a complex urban environment. Subsequently, these data have been adapted to create a physically consistent set of horizontal segment mean concentrations for: 1) Comparisons to highly accurate in situ point measurements obtained for coincident times within the Greater Paris area, 2) Inter-comparisons with results from high-spatial and temporal regional carbon cycle model data, and 3) Potential assimilation of these data to constrain and inform regional carbon cycle modeling frameworks. To achieve these ends, the GreenLITE™ data are calibrated against precise in situ point measurements to reconcile constant systematic as well as slowly varying temporal differences that exist between in situ and GreenLITE™ measurements to provide unbiased comparisons, and the potential for long-term co-assimilation of both measurements into urban-scale emission models. While both the constant systematic biases and the slowly varying differences may have different impacts on the measurement accuracy and/or precisions, they are in part due to a number of potential common terms that include limitation in the instrument design, uncertainties in spectroscopy and imprecise knowledge of the atmospheric state. This work provides a brief overview of the system design and the current gas concentration retrieval and 2-D reconstruction approaches, a description of the bias correction approach, the results as applied to data collected in Paris, France, and an analysis of the inter-comparison between collocated in situ measurements and GreenLITE™ observations. [ABSTRACT FROM AUTHOR]

Abstract Background We compared the dosimetry, application, and acute toxicity of a 3D‐printed and a conventional bolus for postmastectomy radiotherapy (PMRT) with volumetric modulated arc therapy (VMAT). Materials and Methods Eligible patients (n = 75) with PMRT breast cancer were randomly selected to receive VMAT with a conventional bolus or a 3D‐printed bolus. The primary endpoint was a 10% decrease in the mean heart dose to left‐sided breast cancer patients. The secondary endpoint was a 5% decrease in the mean ipsilateral lung dose to all patients. A comparative analysis was carried out of the dosimetry, normal tissue complication probability (NTCP), acute skin toxicity, and radiation pneumonitis. Results Compared to a conventional bolus, the mean heart dose in left‐sided breast cancer was reduced by an average of 0.8 Gy (5.5 ± 1.3 Gy vs. 4.7 ± 0.8 Gy, p = 0.035) and the mean dose to the ipsilateral lung was also reduced by an average of 0.8 Gy (12.4 ± 1.0 Gy vs. 11.6 ± 0.8 Gy, p

Parageobacillus thermoglucosidasius is a thermophilic bacterium of interest for lignocellulosic biomass fermentation. However, carbon catabolite repression (CCR) hinders co-utilization of pentoses and hexoses in the biomass substrate. Hence, to optimize the fermentation process, it is critical to remove CCR in the fermentation strains with minimal fitness cost. In this study, we investigated whether CCR could be removed from P. thermoglucosidasius DSM 2542 by mutating the Ser46 regulatory sites on HPr and Crh to a non-reactive alanine residue. It was found that neither the ptsH1 (HPr-S46A) nor the crh1 (Crh-S46A) mutation individually eliminated CCR in P. thermoglucosidasius DSM 2542. However, it was not possible to generate a ptsH1 crh1 double mutant. While the Crh-S46A mutation had no obvious fitness effect in DSM 2542, the ptsH1 mutation had a negative impact on cell growth and sugar utilization under fermentative conditions. Under these conditions, the ptsH1 mutation was associated with the production of a brown pigment, believed to arise from methylglyoxal production, which is harmful to cells. Subsequently, a less directed adaptive evolution approach was employed, in which DSM 2542 was grown in a mixture of 2-deoxy-D-glucose(2-DG) and xylose. This successfully removed CCR from P. thermoglucosidasius DSM 2542. Two selection strategies were applied to optimize the phenotypes of evolved strains. Genome sequencing identified key mutations affecting the PTS components PtsI and PtsG, the ribose operon repressor RbsR and adenine phosphoribosyltransferase APRT. Genetic complementation and bioinformatics analysis revealed that the presence of wild type rbsR and apt inhibited xylose uptake or utilization, while ptsI and ptsG might play a role in the regulation of CCR in P. thermoglucosidasius DSM 2542.

Abstract Esophageal squamous cell carcinoma (ESCC) is a life-threatening thoracic tumor with a poor prognosis. The role of molecular alterations and the immune microenvironment in ESCC development has not been fully elucidated. The present study aimed to elucidate key candidate genes and immune cell infiltration characteristics in ESCC by integrated bioinformatics analysis. Nine gene expression datasets from the Gene Expression Omnibus (GEO) database were analysed to identify robust differentially expressed genes (DEGs) using the robust rank aggregation (RRA) algorithm. Functional enrichment analyses showed that the 152 robust DEGs are involved in multiple processes in the tumor microenvironment (TME). Immune cell infiltration analysis based on the 9 normalized GEO microarray datasets was conducted with the CIBERSORT algorithm. The changes in macrophages between ESCC and normal tissues were particularly obvious. In ESCC tissues, M0 and M1 macrophages were increased dramatically, while M2 macrophages were decreased. A robust DEG-based protein–protein interaction (PPI) network was used for hub gene selection with the CytoHubba plugin in Cytoscape. Nine hub genes (CDA, CXCL1, IGFBP3, MMP3, MMP11, PLAU, SERPINE1, SPP1 and VCAN) had high diagnostic efficiency for ESCC according to receiver operating characteristic (ROC) curve analysis. The expression of all hub genes except MMP3 and PLAU was significantly related to macrophage infiltration. Univariate and multivariate regression analyses showed that a 7-gene signature constructed from the robust DEGs was useful for predicting ESCC prognosis. Our results might facilitate the exploration of potential targeted TME therapies and prognostic evaluation in ESCC.

Abstract Background Although Mex3 RNA‐binding family member A (Mex3a) has demonstrated an important role in multiple cancers, its role and regulatory mechanism in CRC is unclear. In this study, we aimed to investigate the role and clinical significance of Mex3a in CRC and to explore its underlying mechanism. Methods Western blotting and quantitative real‐time polymerase chain reaction (qRT‐PCR) were performed to detect the expression levels of genes. 5‐Ethynyl‐2'‐deoxyuridine (EDU) and transwell assays were utilized to examine CRC cell proliferation and metastatic ability. The R software was used to do hierarchical clustering analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Overexpression and rescue experiments which included U0126, a specific mitogen activated protein kinase kinase/extracellular regulated protein kinase (MEK/ERK) inhibitor, and PX‐478, a hypoxia‐inducible factor 1 subunit alpha (HIF‐1α) inhibitor, were used to study the molecular mechanisms of Mex3a in CRC cells. Co‐immunoprecipitation (Co‐IP) assay was performed to detect the interaction between two proteins. Bioinformatics analysis including available public database and Starbase software (starbase.sysu.edu.cn) were used to evaluate the expression and prognostic significance of genes. TargetScan (www.targetscan.org) and the miRDB (mirdb.org) website were used to predict the combination site between microRNA and target mRNA. BALB/c nude mice were used to study the function of Mex3a and hsa‐miR‐6887‐3p in vivo. Results Clinicopathological and immunohistochemical (IHC) studies of 101 CRC tissues and 79 normal tissues demonstrated that Mex3a was a significant prognostic factor for overall survival (OS) in CRC patients. Mex3a knockdown substantially inhibited the migration, invasion, and proliferation of CRC cells. Transcriptome analysis and mechanism verification showed that Mex3a regulated the RAP1 GTPase activating protein (RAP1GAP)/MEK/ERK/HIF‐1α pathway. Furthermore, RAP1GAP was identified to interact with Mex3a in Co‐IP experiments. Bioinformatics and dual‐luciferase reporter experiments revealed that hsa‐miR‐6887‐3p could bind to the 3'‐untranslated regions (3'‐UTR) of the Mex3a mRNA. hsa‐miR‐6887‐3p downregulated Mex3a expression and inhibited the tumorigenesis of CRC both in vitro and in vivo. Conclusions Our study demonstrated that the hsa‐miR‐6887‐3p/Mex3a/RAP1GAP signaling axis was a key regulator of CRC and Mex3a has the potential to be a new diagnostic marker and treatment target for CRC.