Homogeneous Regions Research Articles

Análise de cluster e regionalização hidrológica para Estados brasileiros

ABSTRACT Streamflow data from gauging stations are essential for effective water resources management. However, some regions in Brazil lack the necessary data. Hydrological regionalization is an alternative technique for obtaining data such regions. However, not all regions in Brazil have defined hydrological regionalization models, including the state of Goiás and the Brazilian Federal District. The objective of this study was to develop a hydrological regionalization methodology based on the separation of hydrologically homogeneous regions and multiple linear regression, using a Geographic Information System (GIS) program. Historical series data were used to calculate reference flows with 90 or 95% duration over time in the watercourse (Q90 and Q95) and the mean flow (Ǭ). Rain gauge station data were used to calculate the mean annual rainfall in each watershed through spatial interpolation by ordinary kriging. Subsequently, the physiographic characteristics of each watershed were calculated. The hydrologically homogeneous regions were delimited based on these data using cluster analysis, which identified seven hydrologically homogeneous regions in Goiás, two of them belonging to the Federal District. Multiple regression allowed the development of seven regionalization models. Models for regions 1, 3, 4, 5, and 7 showed better performance.

Regional frequency analysis of extreme wind in Pakistan using robust estimation methods

The quantile estimates of extreme wind speed are needed for various areas of interest using regional frequency analysis (RFA) and extreme value theory. These calculations are crucial for the coding of wind speed. The data was taken from the NASA official website at a 10-meter distance and measured in meters per second (m/s). RFA of annual maximum wind speed (AMWS) using L-moments is performed utilizing annual maximum wind speed data from sixteen sites (16) in Pakistan’s Khyber Pakhtunkhwa province. There are no sites that are found to be discordant. The wards method is used to construct a homogenous region and make two homogenous regions from 16 sites. The heterogeneity test justifies that both clusters are homogeneous. The most appropriate probability distribution from the Generalized Normal (GNO), Generalized Logistic (GLO), Pearson Type-3 (P3), Generalized Pareto (GPA), and Generalized Extreme Value (GEV) distributions is chosen to calculate regional quantiles. According to the L-moments diagram and Z statistics, the GEV for Cluster- Ι and GLO for Cluster- ΙΙ are the best suggestions from the others. Both clusters’ robustness is measured utilizing relative bias (RB) and relative root mean square error (RRMSE). Overall, GEV distribution is fit for cluster-Ι, and the GLO distribution is fit for cluster-ΙΙ. Utilizing the site mean and median as index parameters, we can also find at-site quantiles from regional quantiles. The study’s quantile estimates can be employed in codified structural designs with policy consequences.

Integrated Analysis of Gut microbiome, Inflammation and Neuroimaging Features Supports the Role of Microbiome-Gut-Brain Crosstalk in Schizophrenia

Abstract Background and Hypothesis Gut microbiota has been implicated in the pathogenesis of schizophrenia (SZ) and relevant changes in the brain, but the underlying mechanism remains elusive. This study aims to investigate the microbiota-gut-brain crosstalk centered on peripheral inflammation in SZ patients. Study Design We recruited a cohort of 182 SZ patients and 120 healthy controls (HC). Multi-omics data, including fecal 16S rRNA, cytokine data, and neuroimaging data, were collected and synthesized for analysis. Multi-omics correlations and mediation analyses were utilized to determine the associations of gut microbiome with inflammatory cytokines and neuroimaging characteristics. Additionally, machine learning models for effective SZ diagnosis were separately generated based on gut microbial and neuroimaging data. Study Results Gut microbial dysbiosis, characterized by a decrease in butyrate-producing bacteria and an increase in pro-inflammatory bacteria, has been identified in SZ patients. These key microbial taxa were associated with increased inflammatory cytokines, potentially through mediating lipid metabolic pathways such as steroid biosynthesis and linoleic acid metabolism. Further analysis revealed altered microbial genera to be correlated with disrupted gray matter volume and regional homogeneity in SZ patients. Importantly, certain inflammatory cytokines mediated the relationship between the SZ-enriched genus Succinivibrio and aberrant activity of anterior cingulate cortex and left inferior temporal gyrus in the SZ group. Moreover, the classification model based on gut microbial data showed comparable efficacy to the model based on brain functional signatures in SZ diagnosis. Conclusions This study presents evidence for the dysregulated microbiota-gut-brain axis in SZ and emphasizes the central role of peripheral inflammation.

EXPLORING CLIMATIC PATTERNS AMONG BRAZILIAN CAPITALS

This study aims to examine the patterns of climatic variables and delineate climatically homogeneous regions among Brazilian capitals using Principal Component Analysis (PCA) and clustering techniques. Annual climate data from 26 state capitals and the Federal District of Brazil were analyzed, covering the period from 1958 to 2022, representing different regions of the country. The PCA results revealed that the first three principal components accounted for 91.8% of the total data variability. Four climatically homogeneous regions were identified: the East region, the Northeast region, the North region and another region that comprises the rest of the country. Cluster Analysis (CA) also confirmed the formation of four homogeneous groups among municipalities based on climatic variables. The analysis of spatial distribution showed significant variations in annual climate variables between Brazilian capitals, with maximum values observed for maximum and minimum temperatures, precipitation, wind speed, relative humidity, evapotranspiration and soil moisture. These findings contribute to a deeper understanding of climate variability in Brazilian capitals and provide a basis for future studies on climate dynamics and regional impacts. Keywords: Principal Component Analysis, Cluster Analysis, Multivariate Analysis; Biplot

Predictive and Explainable Artificial Intelligence for Neuroimaging Applications

Background: The aim of this review is to highlight the new advance of predictive and explainable artificial intelligence for neuroimaging applications. Methods: Data came from 30 original studies in PubMed with the following search terms: “neuroimaging” (title) together with “machine learning” (title) or ”deep learning” (title). The 30 original studies were eligible according to the following criteria: the participants with the dependent variable of brain image or associated disease; the interventions/comparisons of artificial intelligence; the outcomes of accuracy, the area under the curve (AUC), and/or variable importance; the publication year of 2019 or later; and the publication language of English. Results: The performance outcomes reported were within 58–96 for accuracy (%), 66–97 for sensitivity (%), 76–98 for specificity (%), and 70–98 for the AUC (%). The support vector machine and the convolutional neural network registered the best performance (AUC 98%) for the classifications of low- vs. high-grade glioma and brain conditions, respectively. Likewise, the random forest delivered the best performance (root mean square error 1) for the regression of brain conditions. The following factors were discovered to be major predictors of brain image or associated disease: (demographic) age, education, sex; (health-related) alpha desynchronization, Alzheimer’s disease stage, CD4, depression, distress, mild behavioral impairment, RNA sequencing; (neuroimaging) abnormal amyloid-β, amplitude of low-frequency fluctuation, cortical thickness, functional connectivity, fractal dimension measure, gray matter volume, left amygdala activity, left hippocampal volume, plasma neurofilament light, right cerebellum, regional homogeneity, right middle occipital gyrus, surface area, sub-cortical volume. Conclusion: Predictive and explainable artificial intelligence provide an effective, non-invasive decision support system for neuroimaging applications.

Multimodal and quantitative analysis of the epileptogenic zone network in the pre-surgical evaluation of drug-resistant focal epilepsy

Surgical resection for epilepsy often fails due to incomplete Epileptogenic Zone Network (EZN) localization from scalp electroencephalography (EEG), stereo-EEG (SEEG), and Magnetic Resonance Imaging (MRI). Subjective interpretation based on interictal, or ictal recordings limits conventional EZN localization. This study employs multimodal analysis using high-density-EEG (HDEEG), Magnetoencephalography (MEG), functional-MRI (fMRI), and SEEG to overcome these limitations in a patient with drug-resistant MRI-negative focal epilepsy. A 17-year-old with drug-resistant epilepsy underwent evaluation. HDEEG, MEG, fMRI, and SEEG were used, with a novel HDEEG-cap facilitating simultaneous EEG-MEG and EEG-fMRI recordings. Electrical and magnetic source imaging were performed, and fMRI data were analysed for homogenous regions. SEEG analysis involved spike detection, spike timing analysis, ictal fast activity quantification, and Granger-based connectivity analysis. Non-invasive sessions revealed consistent interictal source imaging results identifying the EZN in the right anterior cingulate cortex. EEG-fMRI highlighted broader activation in the right cingulate cortex. SEEG analysis localized spikes and fast activity in the right anterior and posterior cingulate gyri. Multi-modal analysis suggested the EZN in the right frontal lobe, primarily involving the anterior and mid-cingulate cortices. Multi-modal non-invasive analyses can optimise SEEG implantation and surgical decision-making. Invasive analyses corroborated non-invasive findings, emphasising the importance of individual-case quantitative analysis across modalities in complex epilepsy cases.

Local heterogenisation and regional homogenisation linked to habitat loss induced by dams in riparian forests of the Brazilian Atlantic Forest

Riparian forests are crucial for biodiversity, but dam construction for hydroelectric power disrupts these ecosystems, causing habitat loss and altering river dynamics. Our study investigates the impacts of dams on tree diversity in the southern Brazilian Atlantic Forest. We sampled trees along riverbanks and uplands across 15 dam-affected fragments, analysing the relationship between habitat loss (i.e. loss of riparian zones by permanent flooding due to dam filling), elevation difference, fragment size, and dam implementation time with alpha and beta diversity using mixed models and redundancy analyses. Habitat loss had a more significant impact on beta diversity, leading to shifts in species composition and reduced uniqueness of communities as the impact’s intensity, spatial extent, and duration increased. Alpha diversity only increased in response to local elevation differences between plots located on uplands and riverbanks. Our sampling design can be applied to other inadequately monitored systems to provide insights into beta diversity, a component often neglected in dam licensing and mitigation processes. Our findings reveal a transient local heterogenisation, transitioning into regional homogenisation due to dam-induced habitat loss in riparian forests of the Brazilian Atlantic Forest.

Functional magnetic resonance imaging of depression: a bibliometrics and meta-analysis

ObjectivesThis study aims to reveal the current knowledge map, research hotspots of functional magnetic resonance imaging (fMRI) studies on depression, as well as identify the brain regions associated with depression.MethodsCiteSpace was conducted to analyze the publication outputs, country, institution, cited journals, author and cited author, references, keyword cocurrence and burst keywords of fMRI studies in depression from 2010 to 2024. And a meta-analysis of fMRI was used to identify brain regions associated with depression using Neurosynth.ResultsA total of 4,049 publications were included, and Gong Qiyong was the most prolific authors. Neuroimage, Biological Psychiatry, and Human Brain Mapping were prominent journals. Default mode network (DMN), prefrontal cortex, amygdala, and anterior cingulate cortex were the popular keywords. The fMRI studies on depression have mainly focused on major depression, especially the DMN. Functional connectivity and regional homogeneity of brain regions were research hotspots. The meta-analysis revealed significant differences in brain regions between patients with depression and healthy controls, including the Amygdala_L, Insula_R, Frontal_Inf_Oper_R, Cingulum_Post_L, Putamen_L, Thalamus_R, Angular_L, Precuneus_R, Frontal_Sup_R, Occipital_Inf_L.ConclusionsThis study sheds light on key issues and future directions in fMRI research on depression, elucidating the brain regions related to depression.Graphical

Enhancing the accessibility of regionalization techniques through large language models: a case study in conversational agent guidance

The concept of regions has long been crucial for understanding and managing Earth’s phenomena, leading to regionalization, aggregating smaller areas into larger, contiguous, and homogeneous regions to achieve specific goals. Open-source regionalization is gaining traction because it reduces dependence on commercial software and fosters wider adoption in analysis and decision-making. However, these packages, often designed by experts for specialized tasks, can be challenging to understand and utilize due to domain-specific jargon and functionalities, especially for unfamiliar users. A prevalent disconnect must be addressed: How can we make a complex optimization approach available to a broad audience with various backgrounds? This study introduces RegionDefiner, a Large Language Modeling (LLM)-powered conversational agent, to comprehensively understand the functionality, inputs, outputs, and potential applications of regionalization problems. We selected it as an illustrative example due to its wide-ranging potential for delineating study regions in various applications. RegionDefiner is designed to guide users in framing their problems, collecting necessary data, and implementing solution approaches in a straightforward and user-centric manner. The experiments demonstrate that RegionDefiner interprets and presents the results in an understandable way for all audiences, thus bridging the gap between intricate computations and practical problem-solving needs.

Reduced neurovascular coupling of the visual network in migraine patients with aura as revealed with arterial spin labeling MRI: is there a demand-supply mismatch behind the scenes?

BackgroundAlthough neuroimaging investigations have consistently demonstrated that “hyperresponsive” and “hyperconnected” visual cortices may represent the functional substrate of cortical spreading depolarization in patients with migraine with aura, the mechanisms which underpin the brain “tendency” to ignite the cortical spreading depolarization and, consequently, aura phenomenon are still matter of debate. Considering that triggers able to induce aura phenomenon constrain brain to increase global (such as physical activity, stressors and sleep abnormalities) or local (such as bright light visual stimulations) energy demand, a vascular supply unable to satisfy the increased energy requirement could be hypothesized in these patients.MethodsTwenty-three patients with migraine with aura, 25 patients with migraine without aura and 20 healthy controls underwent a 3-Tesla MRI study. Cerebral blood flow and local functional connectivity (regional homogeneity) maps were obtained and registered to the MNI space where 100 cortical regions were derived using a functional local-global normative parcellation. A surrogate estimate of the regional neurovascular coupling for each subject was obtained at each parcel from the correlation coefficient between the z-scored ReHo map and the z-scored cerebral blood flow maps.ResultsA significantly higher regional cerebral blood flow across the visual cortex of both hemispheres (i.e. fusiform and lingual gyri) was detected in migraine with aura patients when compared to patients with migraine without aura (p < 0.05, corrected for multiple comparisons). Concomitantly, a significantly reduced neurovascular coupling (p < 0.05, false discovery rate corrected) in the primary visual cortex parcel (VIS-4) of the large-scale visual network was observed in the left hemisphere of patients with migraine with aura (0.23±0.03), compared to both patients with migraine without aura (0.32±0.05) and healthy controls (0.29±0.05).ConclusionsVisual cortex neurovascular “decoupling” might represent the “link” between the exposure to trigger factors and aura phenomenon ignition. While physiological vascular oversupply may compensate neurovascular demand-supply at rest, it becomes inadequate in case of increased energy demand (e.g. when patients face with trigger factors) paving the way to the aura phenomenon ignition in patients with migraine with aura. Whether preventive treatments may exert their therapeutic activity on migraine with aura restoring the energy demands and cerebral blood flow trade-off within the visual network should be further investigated.

On the present and future changes in Indian summer monsoon precipitation characteristics under different SSP scenarios from CMIP6 models

AbstractMonsoons are a vital part of the agriculture and economy of India which most of its population rely on for their livelihoods. It still is not clear how climate change will impact precipitation events over India due to the complexity of accurately modelling precipitation. Using twelve Coupled Model Intercomparison Project Six (CMIP6) models, we compared their performance to observed data taken from CRU as well as looking at the future changes in precipitation until the end of the twenty first century for the six precipitation homogenous regions over India. The individual models showed varying degrees of wet and dry biases and the ensemble mean of these models showed relatively lesser bias and improved spatial correlation. Out of 12 models, NorESM and MIROC6 models outperform other models in terms of capturing the spatial variability of precipitation over the Indian region. It is also found that due to lesser moisture transport from the adjoining seas represented through vertically integrated moisture transport (VIMT) analysis, there is consistent dry bias across the models. Further a comprehensive analysis of model performance across six homogeneous precipitation regions indicates that NorESM demonstrates better performance in the CNE and HR regions, EC-Earth excels in the PR, WC, and NE regions, while CMCC shows better performance specifically in the NW region compared to other models. Shared Socioeconomic Pathways (SSPs) were used for future projections and a slight increase in June, July, August, and September (JJAS) precipitation until the end of the century with SSP5-8.5 showing the largest increase. We found an increase in precipitation of 0.49, 0.74 and 1.4 mm/day under SSP1-2.6, SSP2-4.5 and SSP5-8.5 in the far future. The northeast region was shown to receive the largest increase in precipitation (2.9 mm/day) compared to other precipitation homogenous regions and northwest will experience largest shift in precipitation. Interestingly, the number of wet days is expected to increase in the northwest region implying more VIMT towards the region. Our results indicate that monsoon precipitation extremes across all the homogenous regions will increase into the future with a higher severity under fossil-fuelled development, although the models still show large biases lowering confidence in our results.

The different impacts of pain-related negative emotion and trait negative emotion on brain function in patients with inflammatory bowel disease

Inflammatory bowel diseases (IBD) are a group of chronic, non-specific intestinal diseases that could comorbid with varieties of negative emotional constructs, including pain-related negative emotions and trait negative emotions; however, the link between brain functions and different dimensions of negative emotions remains largely unknown. Ninety-eight patients with IBD and forty-six healthy subjects were scanned using a 3.0-T functional magnetic resonance imaging scanner. The amplitudes of low-frequency fluctuation (ALFF), regional homogeneity (ReHo), and degree centrality (DC) were used to assess resting-state brain activity. Partial least squares (PLS) correlation was employed to assess the relationship among abnormal brain activities, pain-related and trait negative emotions. Compared to controls, patients with IBD exhibited higher values of ALFF in the right anterior cingulate cortex (ACC), lower values of ALFF in the left postcentral gyrus, and higher values of DC in the bilateral ACC. Multivariate PLS correlation analysis revealed the brain scores of the ACC were correlated with pain-related negative emotions, the brain salience in the left postcentral gyrus was associated with the higher-order trait depression. These findings can enhance our comprehension of how pain-related negative emotion and trait negative emotion affect the brains of patients with IBD in distinct ways.

Comparison of resting-state brain activity between insomnia and generalized anxiety disorder: A coordinate-based meta-analysis.

Patients with insomnia disorder (ID) usually experience a greater burden of comorbid anxiety symptoms. However, the neural mechanism under the mutual relationship between ID and anxiety remains largely unclear. The meta-analysis aimed to explore the concordance and distinction of regional brain functional activity in patients with ID and those with generalized anxiety disorder (GAD) using coordinate-based activation likelihood estimation approach. Studies using resting-state regional homogeneity, amplitude of low-frequency fluctuations (ALFF), or fractional ALFF in patients with ID or GAD were included by searching multiple databases up to May 24, 2024. Using meta-analytic approach, 21 studies of ID vs. healthy controls (HC) and 16 studies of GAD vs. HC were included to illuminate the common and distinct patterns between the two disorders. Results showed that ID and GAD shared increased brain activities in the left posterior cingulate cortex and left precuneus, as well as decreased brain activity in the left medial prefrontal cortex. Additionally, compared with ID, GAD showed greater increased activities in the left superior frontal gyrus. Our study reveals both common and different activation patterns between ID and GAD, which may provide novel insights for understanding the neural basis of the two disorders and enlighten the possibility of the development of more targeted treatment strategies for ID and GAD.

Graph theory-driven structural and functional connectivity analyses revealing regulatory mechanisms of brain network in patients with classic trigeminal neuralgia.

A specific regulatory mechanism underlying classical trigeminal neuralgia (cTN) remains unknown. The present study posits that the initiation and advancement of cTN may be attributed to a self-regulatory and compensatory mechanism within the brain's limbic system. A sample size of thirty-three patients diagnosed with cTN and twenty-one normal controls were recruited for this investigation. Functional magnetic resonance imaging data were collected from all participants. Graph-theoretic analysis was employed to identify abnormal nodes induced by cTN in the brain atlas, followed by determining the brain network function in conjunction with the outcomes of regional homogeneity (ReHo) and functional connectivity (FC). During data processing, relatively strict thresholds were set for all corrections. The findings indicated that the discrepancy in small-worldness characteristics between the two cohorts primarily stemmed from the characteristic path length. Additionally, there was an overlap between brain regions exhibiting markedly reduced node efficiency in cTN patients and those exhibiting markedly reduced ReHo signal. The FC analysis of the whole brain revealed nine brain regions with reduced connectivity in the cTN group, corresponding to brain regions with diminished node efficiency. Notably, most of these abnormal brain regions were located in the limbic system, providing evidence of the compensatory mechanism of the limbic system.

Structural and functional whole brain changes in autism spectrum disorder at different age stages.

Autism spectrum disorder (ASD) is a developmental disorder involving regional changes and local neural disturbances. However, few studies have investigated the dysfunctional phenomenon across different age stages. This study explores the structural and functional brain changes across different developmental stages in individuals with ASD, focusing on childhood (6-12 years), adolescence (12-18 years), and adulthood (18 + years). Using a comprehensive set of neuroimaging metrics, including modulated and non-modulated voxel-based morphometry (VBM), regional homogeneity (ReHo), amplitude of low-frequency fluctuation (ALFF), and fractional ALFF (fALFF), we identified significant stage-specific alterations in both VBM and functional measurements. Our results reveal that ASD is associated with progressive and stage-specific abnormalities in brain structure and function, with distinct patterns emerging at each developmental stage. Specifically, we observed significant modulated VBM reductions in the precuneus, lentiform nucleus, and inferior parietal lobule, accompanied by increases in the midbrain and sub-gyral regions. Moreover, we observed unmodulated VBM increment in regions including lentiform nucleus and thalamus. Functionally, ReHo analyses demonstrated disrupted local synchronization in the medial frontal gyrus, while ALFF and fALFF metrics highlighted altered spontaneous brain activity in the sub-gyral and sub-lobar. Finally, correlation analyses revealed that stage-specific findings are closely linked to clinical social- and behavior-related scores, with VBM in the inferior parietal lobule and putamen as well as ReHo in supplemental motor area being significantly associated with restrictive repetitive behaviors in childhood. These findings underscore the importance of considering age-specific brain changes when studying ASD and suggest that targeted interventions may be necessary at different developmental stages.

Hyperbolic map unravels eight regions in temperature volatility regionalization of Mainland China

Abstract Abrupt temperature volatility has detrimental effects on daily activities, macroeconomic growth, and human health. Predicting abrupt temperature volatility and thus diminishing its negative impacts can be achieved by exploring homogeneous regions of temperature volatility and analyzing the driving factors. To investigate the regionalization of temperature volatility in Mainland China, a network constructed by the cosine similarity of temperature volatility series from Mainland China was embedded in hyperbolic space. Subsequently, we partitioned the network on the hyperbolic map using the critical gap method and then found eight regions in all. Ultimately, a network of communities was constructed while the interaction among communities was quantified. This yields a perspective of temperature volatility regionalization that can accurately reflect factors including altitude, climate type, and the geographic location of mountains. Further analysis demonstrates that the regionalization in the hyperbolic map is distinct from provinces and has a realistic basis: communities in southwest China show strong correlations due to the temperature sensitivity to altitude, and communities in northern China show a convergence in the area of Dingxi, Gansu, mainly owing to the strong temperature sensitivity to climate types. As a consequence, node distributions and community divisions in the hyperbolic map can offer new insights into the regionalization of temperature volatility in Mainland China. The results demonstrate the potential of hyperbolic embedding of complex networks in forecasting future node associations in real-world data.

Performance of the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) Model in the Northeast Region of Brazil

Study is presented on the potential for using the tools contained in the Google Earth Engine (GEE) geospatial data processing and analysis platform in climatological studies in the Northeast Region of Brazil (NEB), in particular the validation of estimates of total annual rainfall obtained by the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) model in seven homogeneous regions of the NEB. This data is made up of various sources of meteorological information, including information from satellites, numerical models and observations at weather stations in operational and research centers across the globe. The performance of the CHIRPS model was assessed using climatological data obtained from the National Meteorological Institute's (INMET) meteorological database, metrics and statistical tests. The results showed that the CHIRPS model has potential for use in six of the seven regions studied, with low errors and strong statistical correlation, and limitations in one of these regions. The conclusion is that this technological tool, which has a low operating cost and is free of charge, can be used to assist in climatological studies, planning and the development of strategies for mitigation, adaptation and coexistence with the dry periods observed in the sub-regions of the NEB, especially in regions that are in a situation of socio-economic vulnerability, as is the case in the semi-arid region of the NEB.

Coastline target detection based on UAV hyperspectral remote sensing images

Timely and accurate monitoring of typical coastal targets using remote sensing technology is crucial for maintaining marine ecological stability. Hyperspectral target detection technology proves to be an effective tool in extracting various typical materials along the coastline. Traditional target detection methods using spectral domain information can effectively retain the intrinsic properties of the material. However, it is difficult to effectively recognize targets in homogeneous regions by using only spectral domain information, which may lead to insufficient utilization of spatial information. In this study, a detector based on signal-to-noise ratio fusion constrained energy minimization with low-rank sparse decomposition (SFLRSD) is proposed. This detector improves the separability of background and target by obtaining spatial domain information from hyperspectral images and fusing spectral domain information. First, total variation regularization and fractional Fourier transform are applied to process spatial and spectral domain information, respectively. The constrained energy minimization (CEM) detector is used to improve the separability between the target and background of the processed data. Then, the background and anomalies are represented as low-rank and sparse components, respectively, using low-rank sparse matrix factorization. This transforms the model solution into a covariance matrix problem, which is then solved using marginal distance difference (MDD) to isolate anomalous parts. Subsequently, the anomaly parts are fused with CEM detector results, weighted by their respective signal-to-noise ratios. This detection model leverages unified hyperspectral image features, enhancing spectral discreteness of anomalous targets and backgrounds. Finally, experiments on custom created hyperspectral dataset show that the proposed method outperforms other baseline methods in terms of visualization and quantitative performance. In this paper, we not only propose a new hyperspectral target detection method, but we also collect three typical marine litter of different materials by means of airborne hyperspectral remote sensing and construct four hyperspectral datasets in a real environment. All the simulation experiments in this paper are conducted in these four datasets.

Mapping brain activity and neurotransmitters pre-cigarette smoking evolution: A study of male subjects

BackgroundThe impact of tobacco smoking on global health persists and it is essential to understand the progression of addiction and the involvement of neurotransmitters. MethodsThis study assessed 47 participants with tobacco use disorder (TUD) categorized based on changes in Fagerström Test for Nicotine Dependence (FTND) scores over 6 years: progressive TUD (pTUD), regressive TUD (rTUD), and stable TUD (sTUD). Additionally, 35 healthy controls were included. Resting-state functional magnetic resonance imaging was used to evaluate brain regional homogeneity (ReHo) and correlations with neurotransmitter distributions using JuSpace. ResultsSignificant differences in ReHo were observed among pTUD, rTUD, sTUD, and controls. After strict Bonferroni correction, rTUD exhibited increased ReHo in the dorsolateral superior frontal gyrus compared to sTUD (p < 0.001) and controls (p < 0.001). Both pTUD (p < 0.001) and rTUD (p < 0.001) showed decreased ReHo in the superior temporal gyrus compared to sTUD. sTUD had increased ReHo in the supramarginal gyrus compared to all other groups (p < 0.001, p < 0.001, p = 0.002, separately). The strongest association, which survived rigorous Bonferroni correction, was between the ReHo changes in rTUD compared to sTUD and neurotransmitter distribution. This includes 5-hydroxytryptamine receptor 2A (p = 0.001), gamma-aminobutyric acid type A receptor (p < 0.001), norepinephrine transporter (p < 0.001), and N-Methyl-D-Aspartate (p = 0.002). ConclusionsThis study provides insights into how smoking behaviors correlate with alterations in brain activity and neurotransmitter function. By elucidating these neural links to tobacco use disorder progression, our findings contribute to a deeper understanding of smoking's neurological impact and potentially inform more targeted therapeutic strategies.

Structural and functional alterations in MRI-negative drug-resistant epilepsy and associated gene expression features

Neuroimaging techniques have been widely used in the study of epilepsy. However, structural and functional changes in the MRI-negative drug-resistant epilepsy (DRE) and the genetic mechanisms behind the structural alterations remain poorly understood. Using structural and functional MRI, we analyzed gray matter volume (GMV) and regional homogeneity (ReHo) in DRE, drug-sensitive epilepsy (DSE) and healthy controls. Gene expression data from Allen human brain atlas and GMV/ReHo were evaluated to obtain drug resistance-related and epilepsy-associated gene expression and compared with real transcriptional data in blood. We found structural and functional alterations in the cerebellum of DRE patients, which may be related to the mechanisms of drug resistance in DRE. Our study confirms that changes in brain morphology and regional activity in DRE patients may be associated with abnormal gene expression related to nervous system development. And SP1, as an important transcription factor, plays an important role in the mechanism of drug resistance.