Key Regions Research Articles

Investigating concentrations and sources of polycyclic aromatic hydrocarbons in South and Central Texas bays and estuaries along the Gulf of Mexico, USA

Polycyclic aromatic hydrocarbons (PAHs) are among the most widespread organic contaminants in the environment, and anthropogenic activities can produce PAHs through a variety of pyrogenic or petrogenic means. Knowing the concentrations and sources of PAHs helps evaluate ecosystem health and manage natural resources. In this study, 16 US Environmental Protection Agency priority PAHs were analyzed in water and sediment samples collected from September 2021 to September 2023 in four bay systems along the south and central Texas coast, which are a hotpot of crude oil transportation in the United States. Our results indicated that the total concentration of PAHs ranged from 1.9 to 8.3 ng/mL in surface waters (< 0.5 m) and from 520 to 1257 ng/g in surface sediments (top 5 cm). Grain size analysis revealed that the sediment was dominated by silt (4 - 63 μm), followed by clay (< 4 μm) and sand (> 63 μm) fractions. Both organic carbon and clay content were shown to play a significant role in controlling the PAH content in sediments. Diagnostic ratios indicated that PAHs were primarily sourced via pyrolytic processes, such as the combustion of fossil fuels. Additional sampling at Port Bay, a shallow, secondary bay in the Mission-Aransas National Estuarine Research Reserve, implicated a strong role of resuspension in the distribution and composition of PAHs in the bay systems studied. Overall, these data offer insights into the concentration levels and sources of PAHs in this key region housing oil production and transportation in the United States.

Contribution of prefrontal cortex and ventral hippocampus to anxiety in young epileptic mice

Children with epilepsy are particularly vulnerable to anxiety disorders, where these disorders are frequently underdiagnosed and untreated. Despite the high prevalence of anxiety in epilepsy, the underlying neurobiological mechanisms are not fully understood. The medial prefrontal cortex (mPFC) and ventral hippocampus (vHPC) are key brain regions implicated in the genesis and modulation of anxiety, and their interactions play a crucial role in emotional processing including anxiety. We utilized a pilocarpine-induced epilepsy model in young mice (7 weeks old) to assess anxiety-like behaviors using the open field test (OFT), light/dark box, and elevated plus maze (EPM). Local field potential (LFP) recordings were conducted to examine theta power and coherence between the mPFC and vHPC. LFP recordings revealed significantly altered theta power variation in both the mPFC and vHPC during exposure to anxiogenic contexts, suggesting the involvement of these regions in anxiety in the young epileptic mice. Notably, theta-frequency synchrony between the mPFC and vHPC was not significantly altered in the young epileptic mice, indicating that altered theta power rather than inter-regional synchrony may underlie anxiety behaviors in young epileptic mice. Furthermore, we demonstrated that chemogenetic inhibition of excitatory neurons in the mPFC and vHPC reduced anxiety levels in young epileptic mice. Altogether, our findings highlight the critical contributions of mPFC and vHPC to the pathogenesis of comorbid anxiety in epilepsy. These findings underscore the potential therapeutic significance of modulating the activity in these two regions as means to alleviate anxiety in a youth epilepsy population.

Nighttime-specific differential gene expression in suprachiasmatic nucleus and habenula is associated with resilience to chronic social stress

The molecular mechanisms that link stress and biological rhythms still remain unclear. The habenula (Hb) is a key brain region involved in regulating diverse types of emotion-related behaviours while the suprachiasmatic nucleus (SCN) is the body’s central clock. To investigate the effects of chronic social stress on transcription patterns, we performed gene expression analysis in the Hb and SCN of stress-naïve and stress-exposed mice. Our analysis revealed a large number of differentially expressed genes and enrichment of synaptic and cell signalling pathways between resilient and stress-naïve mice at zeitgeber 16 (ZT16) in both the Hb and SCN. This transcriptomic signature was nighttime-specific and observed only in stress-resilient mice. In contrast, there were relatively few differences between the stress-susceptible and stress-naïve groups across time points. Our results reinforce the functional link between circadian gene expression patterns and differential responses to stress, thereby highlighting the importance of temporal expression patterns in homoeostatic stress responses.

Generation of affinity maps for thiazolidinediones with human serum albumin using affinity microcolumns. II. Effects of advanced glycation end products on multisite drug binding

Multisite protein interactions by the thiazolidinedione-class drugs pioglitazone and rosiglitazone were examined by using high-performance affinity microcolumns that contained normal human serum albumin (HSA) vs HSA that had been modified to form advanced glycation end products by glyoxal (Go) or methylglyoxal (MGo). The results were used to generate an affinity map for these drugs at several key regions on HSA. Strong binding (∼105 M−1) by these drugs was seen at both Sudlow sites I and II. About a 50 % decrease in the affinities at Sudlow site II was observed for pioglitazone for Go-modified HSA, while either a 47 % decrease or 1.6-fold increase in affinity was seen for MGo-modified HSA, depending on the extent of modification. The binding affinity for rosiglitazone at Sudlow site II had a 40–83 % decrease for Go-modified HSA and either a non-significant change or 1.4-fold increase for MGo-modified HSA. At Sudlow site I, pioglitazone gave a 41 % decrease in affinity for either Go or MGo-modified HSA, and for rosiglitazone up to a 55 % decrease or 1.3-fold increase in affinity was noted. Positive allosteric effects were seen by these drugs with the tamoxifen site of HSA, and neither drug had any notable binding at the digitoxin site for the normal or modified forms of HSA. Rosiglitazone also had weak interactions at a site in subdomain IB, which increased in affinity by up to 5.0-fold with the Go- or MGo-modified HSA. This study illustrated how affinity microcolumns can be used to provide a detailed analysis of solute-protein systems that involve complex interactions. The data obtained should also be valuable in providing a better understanding of how drug interactions with HSA and other proteins can be altered by modifications of these binding agents in diseases such as diabetes.

Big Data Analysis and Digital Sharing Research on Innovation and Entrepreneurship Education in the Digital Economy Era

Within the rapidly evolving panorama of the virtual financial system, the function of training in fostering innovation and entrepreneurship has become more and more vital. This study aims to explore how big records analysis and digital sharing techniques can be leveraged to complement innovation and entrepreneurship schooling. The observation is grounded within the context of the virtual economy generation, characterised with the aid of the proliferation of virtual technologies and the exponential boom of facts. The middle objective is to research how instructional techniques can be more desirable thru the utility of large records analytics and digital sharing, thereby preparing college students extra effectively for entrepreneurial roles inside the digital age. The studies employ a deep learning technique, combining quantitative information evaluation with qualitative insights. Primary statistics could be accumulated through surveys and interviews with educators and marketers, even as secondary statistics will be sourced from existing educational literature and case research. The observe will awareness on key regions which includes the effect of huge facts on expertise marketplace developments and client conduct, the function of virtual sharing in fostering collaborative learning and innovation, and the integration of these technologies into curriculum design and pedagogical practices. Anticipated results include a set of recommendations for academic institutions on integrating large statistics and digital sharing tools into entrepreneurship training. The study targets to offer insights into how this technology can enhance college students’ analytical and innovative skills, put together them for the challenges of the digital economic system, and foster a tradition of innovation and entrepreneurial attitude.

Astrocytic CREB in nucleus accumbens promotes susceptibility to chronic stress

BackgroundIncreasing evidence implicates astrocytes in stress and depression in both rodent models and human Major Depressive Disorder (MDD). Despite this, little is known about the transcriptional responses to stress of astrocytes within the nucleus accumbens (NAc), a key brain reward region, and their influence on behavioral outcomes. MethodsWe used whole cell sorting, RNA-sequencing, and bioinformatic analyses to investigate the NAc astrocyte transcriptome in male mice in response to chronic social defeat stress (CSDS). Immunohistochemistry was used to determine stress-induced changes in astrocytic CREB within the NAc. Finally, astrocytic regulation of depression-like behavior was investigated using viral-mediated manipulation of CREB in combination with CSDS. ResultsWe found a robust transcriptional response in NAc astrocytes to CSDS in stressed mice, with changes seen in both stress-susceptible and stress-resilient animals. Bioinformatic analysis revealed CREB, a transcription factor widely studied in neurons, as one of the top-predicted upstream regulators of the NAc astrocyte transcriptome, with opposite activation states implicated in resilient vs. susceptible mice. This bioinformatic deduction was confirmed at the protein level with immunohistochemistry. Moreover, NAc astrocyte morphological complexity correlated with CREB activation and was reduced selectively in astrocytes of resilient mice. Viral overexpression of CREB selectively in NAc astrocytes promoted susceptibility to chronic stress. ConclusionsTogether, our data demonstrate that the astrocyte transcriptome responds robustly to CSDS and that transcriptional regulation in astrocytes contributes to depressive-like behaviors. A better understanding of transcriptional regulation in astrocytes may reveal unknown molecular mechanisms underlying neuropsychiatric disorders.

Cross-jurisdictional analysis and forecasting of North American nuclear fuel inventory using a standardized unit

This study fills the noted gap in comparative analyses of spent nuclear fuel (SNF) by assessing inventories from two key nuclear power regions, the USA and Canada, using a comprehensive analytical framework and standardized data from 2009 to 2021. In the USA, SNF inventory increased by 14.7 % in fuel assembly weight and 47 % in residual heavy metal content compared to Canada, in line with their use of light water reactors. Canada's SNF production is directly correlated to its nuclear power output, influenced by the lower burnup of natural uranium fuel used in CANDU reactors (R2 = 0.57; p-value < 0.05) while the USA shows insignificant correlation, likely due to a variety of reactor types and higher burnup rates (R2 = 0.008; p-value > 0.05). Further, the study identifies a strong negative correlation between uranium mine production and SNF inventory in the USA, indicating a reliance on imports amidst negligible domestic mining. In contrast, Canada also exhibits moderate negative dependency due to its position as a major uranium exporting jurisdiction. The obtained negative correlations with coal rents in both countries indicate a shift towards more nuclear energy use, impacting economic growth and energy patterns. The developed predictive models indicate a higher future SNF increase in Canada than in the USA. These findings are essential for planning the transition from temporary to permanent SNF disposal, ensuring safe long term management of radioactive waste.

680P Developing effective RNA editing approaches to target the nonsense mutation in the key functional region of DMD

680P Developing effective RNA editing approaches to target the nonsense mutation in the key functional region of DMD

Genome-wide profiling the epigenetic landscape of histone variant TH2B in murine oocytes and pre-implantation embryos.

The histone variant TH2B, enriched in oocytes, sperm, and early embryos, decreases as embryos differentiate into pre-gastrula stages. Despite its presence, the role of TH2B in epigenetic reprogramming during early embryonic development remains largely under-researched. Our study employed ultra-low-input ChIP-seq (ULI-ChIP) to analyze the genome-wide distribution of TH2B in MII oocytes and early embryos. We found that TH2B is enriched in the chromatin of oocytes and 2-cell stage embryos but becomes less prevalent after the 2-cell stage. Correlation analysis revealed that the TH2B chromatin patterns in sperm and preimplantation embryos are more similar to each other than to those in MII oocytes. Gene ontology (GO) analysis of TH2B-occupied loci linked them to various developmental processes, including oogenesis, fertilization, chromatin modification, and transcription regulation. The study also identified a strong association of TH2B with specific transposable elements (TEs), particularly long terminal repeats (LTRs), which are known to regulate preimplantation development. Additionally, early embryos showed H3K9me3 marks at TH2B-bound loci. TH2B exhibited strong correlations with H2A.Z and H3.3 in the 2-cell and 8-cell stages, a positive association with H3K27Ac and H3K4me3, and a negative correlation with H3K27me3. Allelic reprogramming analysis of TH2B in embryos from C57BL/6J and DBA/2J crosses revealed differential dynamics between maternal and paternal alleles, with a notable paternal bias at the promoter in 2-cell embryos. Thus, TH2B's enrichment in early embryonic stages and its association with key regulatory regions and histone modifications underscore its importance in ZGA and subsequent developmental processes.

Comparison and integration of physical and interpretable AI-driven models for rainfall-runoff simulation

Precise streamflow forecasting in river systems is crucial for water resources management and flood risk assessment. The Tagus Headwaters River Basin (THRB) in Spain is a key hydrological hub, providing regulated flow for agricultural, urban, and energy sectors, and facilitating water transfer to the Segura River Basin, a key semi-arid region. Given the basin's near-total allocation of water resources, accurate streamflow simulations are essential to optimize the socio-economic distribution and ensure sustainable management across both interconnected basins. This study conducts a comprehensive evaluation of the Soil and Water Assessment Tool (SWAT+), support vector regression (SVR), feed forward neural network (FFNN), and long short-term memory (LSTM) models in simulating the rainfall-runoff process at four gauging stations within the THRB. An ensemble machine learning technique is then applied to assess improvements in streamflow estimation. Results revealed that AI-based models significantly surpassed the SWAT+ model in performance. Furthermore, the application of an ensemble technique enhanced the precision of rainfall-runoff modeling by 18 to 26% during the calibration period and 4.1 to 9.2% during the validation period, compared to individual AI-based models. Additionally, the SWAT+ model's precision improved by 44 to 74% and 40 to 55% for the respective periods. The use of Shapley Additive Explanations (SHAP) methodology allowed the results of the ensemble with machine learning to be more interpretable by explaining how each model contributes to the prediction. This research offers significant contributions to hydrological modeling, highlighting the importance of ensemble techniques in elevating predictive accuracy for various river basins.

Abnormal individualized functional connectivity: A potential stimulation target for pediatric tourette syndrome

ObjectiveIn order to examine whether individualized peak functional connectivity could potentially serve as a target for repetitive transcranial magnetic stimulation (rTMS) therapy, we investigated the location of peak functional connectivity (FC) between the cortical motor area and the key brain region, the globus pallidus internus (GPi), in Tourette syndrome, and explored the relationship between the severity of the disease and these aberrant functional connections. MethodsThe study involved a cohort of 103 children diagnosed with Tourette syndrome and 66 age-matched typically developing children. The GPi was served as the seed, and the study compared individualized peak FC strength in the supplementary motor area (SMA) and premotor area between the two groups. Spatial distribution of peak FC in the motor area and GPi-based voxel-wise FC were also analyzed. ResultsChildren with Tourette syndrome exhibited lower peak FC in the left SMA when using left GPi as the seed. This reduction in peak FC demonstrated a significant and negative correlation with the Yale Global Tic Severity Scale scores. ConclusionsSMA-GPi FC is one of the key pathological circuit in Tourette syndrome. SignificanceThe individual peak FC location in the left SMA potentially serve as stimulation targets for rTMS treatment of TS.

Statistical Study of Energy Transport and Conversion in Electron Diffusion Regions at Earth's Dayside Magnetopause

AbstractThe electron diffusion region (EDR) is a key region for magnetic reconnection, but the typical energy transport and conversion in EDRs is still not well understood. In this work, we perform a statistical study of 80 previously published near X‐line events identified at the dayside magnetopause in Magnetospheric Multiscale data. We find 44 events that clearly present all commonly accepted EDR signatures and use this database to investigate energy flux partition and energy conversion. We find that energy partition is changed inside EDRs, with a 71%–29% allocation of particle energy flux density between electrons and ions respectively. The electron enthalpy flux density is found to dominate locally at all EDRs and is predominantly oriented in the out‐of‐plane direction, perpendicular to the reconnecting magnetic field. We also examine the transition from electron‐ to ion‐dominated energy flux partition further from the EDR, finding this typically occurs at scales of the order of the ion inertial length, larger than the typical EDR size. We then investigate energy conversion and transport and highlight complex processes, with potential non‐steady‐state energy accumulation and release near the EDR. We discuss the implications of our results for reconnection energy conversion, and for magnetopause dynamics in general.

Crops Feed Rain to Drylands in Northwest China

AbstractAs a key region supplementing China's limited croplands, Northwest China has undergone rapid cropland expansion over the past decades to satisfy rising food demand from population growth and socio‐economic development. Although cropland expansion may overconsume local water resources in general, in Northwest China, increased precipitation and enhanced glacier melt have increased the water available for croplands. Counterintuitively, the enhanced evapotranspiration (ET) resulting from this cropland expansion could benefit remote ecologically vulnerable natural vegetation through atmospheric moisture recycling. In this study, we used a moisture tracking model to quantify contributions of croplands and cropland expansion to local precipitation and the consequent precipitation supply to natural vegetation in Northwest China. We found that the croplands contributed 27.69 billion m3/year (2.13%) of regional total precipitation and supplied 17.30 billion m3/year (2.39%) of precipitation over natural vegetation, and the cropland expansion resulted in a net increase of 80.25 million m3/year (1.07% of the total increase) in regional precipitation and 36.23 million m3/year (4.56% of the total increase) in precipitation supply for natural vegetation. Among different types of natural vegetation, grasslands received the most precipitation supply due to its vast area, followed by forests and shrublands. The more arid regions experienced not only faster rates of cropland expansion but also obtained a greater increase in regional precipitation and precipitation supply to natural vegetation. Our study quantifies the ecological impacts of cropland expansion through moisture recycling in Northwest China. This shows the complexities of water competition between agricultural development and ecological conservation in drylands and elsewhere.

Assessing anthropogenic influences on the water quality of Gomati River using an innovative weighted fuzzy soft set based water pollution rating system

The increasing pollution of river systems poses a significant challenge to water quality management and public health. This study was conducted to evaluate the water quality of the Gomati River in India, focusing on the impact of anthropogenic activities on pollution levels across different seasons. The primary objective was to develop a comprehensive assessment model using an effective weighted fuzzy soft expert system (WFSES) to derive a weighted water pollution score (WWP-score) for rating water pollution levels based on various water quality parameters. To achieve this, we employed a fuzzy soft set (FSS) methodology integrated with a weighted fuzzy soft set (WFSS) model to analyze water quality indices at six sampling stations along the Gomati River, the largest river in Tripura, India. Originating from the Raima and Sarma streams, the river flows westward through key regions, providing 249.39 million cubic meters of water annually, essential for drinking, agriculture, fishing, and transportation. The study utilized crisp data summaries of key water quality parameters, including pH, total dissolved solids, total suspended solids, electrical conductivity, biochemical oxygen demand, dissolved oxygen, total hardness, chloride, total alkalinity, and total coliform, across the pre-monsoon, monsoon, and post-monsoon seasons from May 2022 to April 2023, guided by the distribution of waste discharge points. Fuzzy membership functions were defined for these parameters, and the root mean square (RMSQR) operation was used to combine multiple FSSs. The results revealed that the WWP-scores, a measure of water quality, ranged from 0.744 to 0.872 across the sampling stations, with Srimantapur exhibiting the highest level of pollution and Udaipur showing comparatively better quality. These scores indicate that water quality at all sites falls into the poor category, with Srimantapur experiencing the most severe contamination due to high levels of coliform bacteria and organic pollutants. The analysis highlights the impact of domestic wastewater and agricultural runoff on the river's health. This research offers a novel application of FSS and WFSS theory in environmental management, providing a robust framework for evaluating and ranking water quality across multiple parameters. The findings are valuable to the scientific community as they offer detailed insights into the pollution dynamics of the Gomati River and propose a methodological approach for assessing water quality in other river systems facing similar challenges. The study's results underscore the need for targeted pollution control measures and continuous monitoring to safeguard water resources and public health.

Determining the Effective Meteorological Parameters on Potato Yield in Niğde Province and Yield Prediction with Machine Learning and Deep Learning Models

In this study, we explore the impact of meteorological parameters on potato yield in Nigde province, a key agricultural region in Turkey for potato production. Understanding the relationship between weather conditions and crop yield is crucial for optimizing agricultural practices and ensuring food security, especially in regions susceptible to climate variability. The study includes all meteorological parameters observed and recorded in Nigde Directorate of Meteorology, covering the period between 1990 and 2023. Through the analysis of historical weather data and potato yield records, we aim to identify the most influential meteorological factors affecting potato production. Then, artificial intelligence techniques such as Random Forest, Gradient Boost, Convolutional Neural Networks and Recurrent Neural Networks are utilized to predict the potato yield in order to provide valuable insights into how different weather patterns influence crop performance. The findings suggest that potato yield is correlated with meteorological parameters to some degree and AI techniques can predict the potato yield but lacks the precision. The reason is that potato production in Nigde is mostly done with irrigation. However, this further increases the risk that could result from the changes in groundwater levels and pollution in irrigation ponds for agricultural practices in Nigde.

1H-MRS reveals abnormal energy metabolism and excitatory-inhibitory imbalance in a chronic migraine-like state induced by nitroglycerin in mice

BackgroundChronic migraine is closely related to the dysregulation of neurochemical substances in the brain, with metabolic imbalance being one of the proposed causes of chronic migraine. This study aims to evaluate the metabolic changes between energy metabolism and excitatory and inhibitory neurotransmitters in key brain regions of mice with chronic migraine-like state and to uncover the dysfunctional pathways of migraine.MethodsA chronic migraine-like state mouse model was established by repeated administration of nitroglycerin (NTG). We used von Frey filaments to assess the mechanical thresholds of the hind paw and periorbital in wild-type and familial hemiplegic migraine type 2 mice. After the experiments, tissue was collected from five brain regions: the somatosensory cortex (SSP), hippocampus, thalamus (TH), hypothalamus, and the spinal trigeminal nucleus caudalis (TNC). Proton magnetic resonance spectroscopy (1H-MRS) was employed to study the changes in brain metabolites associated with migraine, aiming to explore the mechanisms underlying metabolic imbalance in chronic migraine-like state.ResultsIn NTG-induced chronic migraine-like state model, we observed a significant reduction in energy metabolism during central sensitization, an increase in excitatory neurotransmitters such as glutamate, and a tendency for inhibitory neurotransmitters like GABA to decrease. The TNC and thalamus were the most affected regions. Furthermore, the consistency of N-acetylaspartate levels highlighted the importance of the TNC-TH-SSP pathway in the ascending nociceptive transmission of migraine.ConclusionAbnormal energy metabolism and neurotransmitter imbalance in the brain region of NTG-induced chronic migraine-like state model are crucial mechanisms contributing to the chronicity of migraine.

Enhancing chronic low back pain management: an initial neuroimaging study of a mobile interoceptive attention training.

Chronic low back pain (cLBP) poses significant challenges, often addressed through avoidance or distraction. Emerging evidence suggests that mind-body interventions, like our novel Mind Your Pain (MyP) smartphone mobile application, may offer relief. We conducted a single-arm, mixed-methods neuroimaging study to assess the degree to which treatment response to our 8-week intervention, as measured by the reduction in the Pain, Enjoyment of Life and General Activity Scale (PEG), was associated with enhanced pain-related insula activation over time. Twenty-nine individuals with cLBP completed patient-reported assessments, qualitative sensory testing (QST) measures, and neuroimaging pre- and post-MyP. Functional MRI data during experimental heat pain on the left forearm were collected and analyzed, comparing responders (≥50% reduction in PEG scores) and non-responders. MyP led to significant decreases in PEG scores overall. Furthermore, MyP responders exhibited increased pain-related activation in key brain regions, including the contralateral posterior insula, bilateral ventral anterior insula, ventral anterior cingulate, dorsolateral prefrontal cortex, and nucleus accumbens. Although baseline behavioral and sensory measures did not differ between the two responder groups, baseline neural differences related to the impact of the endogenous back pain were observed. MyP appears to modify pain response and underlying neural circuitry, suggesting neural changes in interoception may serve as biomarkers for mind-body interventions in cLBP. This study highlights the potential of MyP as a novel approach for cLBP management, warranting further investigation.

Satellite‐Based Surveys Reveal Substantial Methane Point‐Source Emissions in Major Oil &amp; Gas Basins of North America During 2022–2023

AbstractUtilizing imaging spectroscopy technology to identify methane super‐emitters plays a vital role in mitigating methane emissions in the Oil &amp; Gas (O&amp;G) sector. While earlier research has uncovered significant point‐source methane emissions from O&amp;G production in the US and Canada, which are key regions with large methane emissions, a comprehensive post‐COVID‐19 survey has been notably absent. Here, we perform a detailed survey of methane super‐emitters across multiple basins of North America (Marcellus Shale, Haynesville/Bossier Shale, Permian Basin and Montney Shale) using the new Chinese Gaofen5‐01A/02 (GF5‐01A/02) satellite measurements during 2022–2023. We detect 139 individual methane plumes emanating from 122 point sources, with flux rates ranging from 519 to 16,071 kg hr−1. These emissions exhibit a highly skewed and heavy‐tailed distribution, constituting approximately 23% of the flux inversion with TROPOMI in the sample region, with a range of 13%–40%. Moreover, we observe a 66.7% reduction in methane emissions in Permian Basin during COVID‐19, followed by fluctuations until spring 2023. By summer 2023, methane emissions rebound to twice their previous magnitude (1.68 ± 0.58 Tg a−1). Using these point‐source surveys, we further quantify a regional methane emission of 2.69 ± 0.86 Tg a−1 in Permian Basin. This estimation closely aligns with top‐down inversions (2.22 ± 0.40 Tg a−1) from TROPOMI. The upscale estimation underscores the effectiveness of high‐resolution remote sensing measurements in improving bottom‐up emissions inventories and refining regional methane emission assessments. Our results highlight the potential climate benefits derived from regular monitoring and specific remediation efforts focused on relatively few strong point‐source emissions.

Identification of target genes co-regulated by four key histone modifications of five key regions in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a cancer with high morbidity and mortality. Studies have shown that histone modification plays an important regulatory role in the occurrence and development of HCC. However, the specific regulatory effects of histone modifications on gene expression in HCC are still unclear. This study focuses on HepG2 cell lines and hepatocyte cell lines. First, the distribution of histone modification signals in the two cell lines was calculated and analyzed. Then, using the random forest algorithm, we analyzed the effects of different histone modifications and their modified regions on gene expression in the two cell lines, four key histone modifications (H3K36me3, H3K4me3, H3K79me2, and H3K9ac) and five key regions that co-regulate gene expression were obtained. Subsequently, target genes regulated by key histone modifications in key regions were screened. Combined with clinical data, Cox regression analysis and Kaplan-Meier survival analysis were performed on the target genes, and four key target genes (CBX2, CEBPZOS, LDHA, and UMPS) related to prognosis were identified. Finally, through immune infiltration analysis and drug sensitivity analysis of key target genes, the potential role of key target genes in HCC was confirmed. Our results provide a theoretical basis for exploring the occurrence of HCC and propose potential biomarkers associated with histone modifications, which may be potential drug targets for the clinical treatment of HCC.

Tracing post-domestication historical events and screening pre-breeding germplasm from large gene pools in wheat in the absence of phenotype data.

Wheat, particularly common wheat (Triticum aestivum L.), is a major crop accounting for 25% of the world cereal production and thriving indiverse ecogeographic regions. Its adaptation to diverse environments arises from its three distinct genomes adapted to different environments and post-domestication anthropogenic interventions. In search of key genomic regions revealing historic events and breeding significance to common wheat, we performed genome scan and genome-environment association (GEA) analyses using high-marker density genotype datasets. Whole-genome scans revealed highly differentiated regions on chromosomes 2A, 3B, and 4A. In-depth analyses corroborated our previous prediction of the 4A differentiated region signifying the separation between Spelt/Macha and other wheat types. Individual chromosome scans captured key introgressions, includingone from T. timopheevii and one fromThinopyrum ponticum on 2B and 3D, respectively, as well as known genes such as Vrn-A1 on 5A. GEA highlighted loci linked to latitude-induced environmental variations, influencing traits such as photoperiodism and responses to abiotic stress. Variation at theVrn-A1 locus on 5A assigned accessions to two haplotypes (6% and 94%). Further analysis on Vrn-A1 coding gene revealed four subgroups of the major haplotype, while the minor haplotype remained undifferentiated. Analyses at differentiated loci mostly dichotomized the population, illustrating the possibility of isolating pre-breeding materials with desirable traits from large gene pools in the absence of phenotype data. Given the current availability of broad genetic data, the genome-scan-GEA hybrid can be anefficient and cost-effective approach for pinpointing environmentally resilient pre-breeding germplasm from vast gene pools, including gene banks regardless of trait characterization.