Homogeneous Regions Research Articles

Indian summer monsoon rainfall response to two distinct evolutions of La Niña events

AbstractThis study examines the asymmetry in the Indian summer monsoon rainfall (ISMR) response over India and its four homogeneous regions to two distinct types of temporal evolution in La Niña. We have shown this uneven response by analysing the large‐scale dynamics over tropical Indo‐Pacific region for the period 1951–2022. We have identified two types of La Niña events during monsoon season (June–September) based on whether they evolved from El Niño or La Niña from preceding boreal winter season (December–February). India receives significantly more (less) rainfall during La Niña years, when it was preceded by El Niño (La Niña) in the preceding winter. We further observed the spatial diversity of rainfall over India with a northeast–southwest dipole pattern. When La Niña years were preceded by El Niño, positive surface pressure anomaly over west‐north Pacific, low‐level westerlies and moisture transport favoured the rainfall over south peninsula and west‐central India. Whereas moisture divergence associated with anomalous lower‐tropospheric anticyclone over west‐north Pacific suppressed the rainfall over Indo‐Gangetic plains. However, when La Niña years were preceded by La Niña in winter, the absence of westerlies and weak moisture transport subdued the rainfall over south peninsula and west‐central India. At the same time, moisture convergence and a greater number of monsoon depressions favoured rainfall over north‐west India. This study also looked at how well eight Copernicus Climate Change Service (C3S) models predicted ISMR and SST for two types of La Niña with April initial conditions during the period 1993–2016. Models were able to capture the spatial pattern of SST anomalies over Indo‐Pacific Ocean, but all models could not capture the spatial pattern of ISMR. However, in terms of intensity, six out of eight models could predict more (less) ISMR when it was preceded by El Niño (La Niña), coinciding with the observed anomaly.

Comparison of Brain Function and Structure in Patients with Major Depression: A Systematic Review and Meta-Analysis of MRI-Based Data.

Depression is a common mental illness worldwide. Neuroimaging techniques, such as magnetic resonance imaging and functional magnetic resonance imaging, play an essential role in diagnosing and evaluating depression. This study is based on magnetic resonance imaging (MRI)-related research to explore the comparison of brain function and structure between patients with severe depression and normal individuals, and to conduct meta-analysis. We conducted searches in various databases such as PubMed, Web of Science, Embase, and Cochrane Library to obtain research data on comparing brain function and structure between patients with severe depression and healthy individuals. The search keywords included "Major Depressive Disorder", "Brain Function", "Brain Structure", "Depression", "MRI", and "Magnetic Resonance". The quality assessment was conducted using the bias risk assessment tool recommended by the Cochrane Collaborative Network. Literature was screened following the predetermined inclusion and exclusion criteria, and Anisotropic Effect-Size Seed-Based Differential Mapping (AES-SDM) was used for systematic meta-analysis. Regression analysis was performed on age, gender, disease duration, years of education, and treatment status. After a thorough screening process, 10 documents were selected for subsequent analysis. These studies consisted of 477 study subjects, including 231 depression patients and 246 healthy individuals. The proportion of women was 36%-75%, and the disease duration was 3-60 months. The patients in 4 documents had first attacks, and the patients in the other 6 documents had multiple attacks. The baseline conditions of the 10 included documents were consistent and comparable. None of the studies reported blinding methods, and none of the results had incomplete data. The Regional homogeneity (ReHo) levels in the left precuneus (BA7), lentiform nucleus (BA48), and left prefrontal lobe (BA32) were significantly increased in the depression group, with voxel numbers of 358, 116, and 181, respectively. Conversely, the left postcentral gyrus (BA4), left cerebellar area (hemispheric lobule I, IV/V, lingual gyrus, fusiform gyrus), left fusiform gyrus (BA30), and right cingulate gyrus (BA23) were significantly reduced, with voxel numbers of 17, 50, and 124, respectively. Furthermore, regression analysis showed that gender, age, disease duration, years of education, and disease severity were potential influencing factors, and the disease duration demonstrated the most significant impact on the left cingulate gyrus (SDM = 2.777). There are significant differences in brain function and structure between patients with major depression and healthy individuals. Furthermore, our findings reveal a substantial correlation between the severity of depressive symptoms and brain function and structure indicators. These findings provide novel research directions and ideas for the diagnosis and treatment of depression.

Regional brain activity and connectivity associated with childhood trauma in drug-naive patients with obsessive–compulsive disorder

Obsessive–compulsive disorder (OCD) is characterized by intrusive thoughts and repetitive, compulsive behaviors, with childhood trauma recognized as a contributing factor to its pathophysiology. This study aimed to delineate brain functional aberrations in OCD patients and explore the association between these abnormalities and childhood trauma, to gain insights into the neural underpinnings of OCD. Forty-eight drug-naive OCD patients and forty-two healthy controls (HC) underwent resting-state functional magnetic resonance imaging and clinical assessments, including the Yale–Brown Obsessive Compulsive Scale (Y–BOCS) and Childhood Trauma Questionnaire-Short Form (CTQ-SF). Compared to HCs, OCD patients exhibited significantly decreased amplitude of low-frequency fluctuations (ALFF) in the right cerebellum, decreased regional homogeneity (ReHo) in the right cerebellum and right superior occipital lobes (FWE-corrected p < 0.05), which negatively correlated with Y–BOCS scores (p < 0.05). Furthermore, cerebellar ALFF negatively correlated with the CTQ emotional abuse subscale (r = − 0.514, p < 0.01). Mediation analysis revealed that cerebellar ALFF mediated the relationship between CTQ-emotional abuse and Y–BOCS (good model fit: R2 = 0.231, MSE = 14.311, F = 5.721, p < 0.01; direct effect, c′ = 0.153, indirect effect, a*b = 0.191). Findings indicated abnormal spontaneous and regional cerebellar activity in OCD, suggesting childhood trauma impacts OCD symptoms through cerebellar neural remodeling, highlighting its importance for clinical treatment selection.

Atmospheric Dust Forced Changes in the Precipitation Distribution Over Indian Homogeneous Regions

Atmospheric Dust Forced Changes in the Precipitation Distribution Over Indian Homogeneous Regions

Repetitive mild closed-head injury induced synapse loss and increased local BOLD-fMRI signal homogeneity.

Repeated mild head injuries due to sports, or domestic violence and military service are increasingly linked to debilitating symptoms in the long term. Although symptoms may take decades to manifest, potentially treatable neurobiological alterations must begin shortly after injury. Better means to diagnose and treat traumatic brain injuries, requires an improved understanding of the mechanisms underlying progression and means through which they can be measured. Here, we employ a repetitive mild closed-head injury (rmTBI) and chronic variable stress (CVS) mouse model to investigate emergent structural and functional brain abnormalities. Brain imaging is achieved with [18F]SynVesT-1 positron emission tomography, with the synaptic vesicle glycoprotein 2A ligand marking synapse density and BOLD (blood-oxygen-level-dependent) functional magnetic resonance imaging (fMRI). Animals were scanned six weeks after concluding rmTBI/Stress procedures. Injured mice showed widespread decreases in synaptic density coupled with an increase in local BOLD-fMRI synchrony detected as regional homogeneity. Injury-affected regions with higher synapse density showed a greater increase in fMRI regional homogeneity. Taken together, these observations may reflect compensatory mechanisms following injury. Multimodal studies are needed to provide deeper insights into these observations.

Novel Deep CNNs Explore Regions, Boundaries, and Residual Learning for COVID-19 Infection Analysis in Lung CT.

COVID-19 poses a global health crisis, necessitating precise diagnostic methods for timely containment. However, accurately delineating COVID-19-affected regions in lung CT scans is challenging due to contrast variations and significant texture diversity. In this regard, this study introduces a novel two-stage classification and segmentation CNN approach for COVID-19 lung radiological pattern analysis. A novel Residual-BRNet is developed to integrate boundary and regional operations with residual learning, capturing key COVID-19 radiological homogeneous regions, texture variations, and structural contrast patterns in the classification stage. Subsequently, infectious CT images undergo lesion segmentation using the newly proposed RESeg segmentation CNN in the second stage. The RESeg leverages both average and max-pooling implementations to simultaneously learn region homogeneity and boundary-related patterns. Furthermore, novel pixel attention (PA) blocks are integrated into RESeg to effectively address mildly COVID-19-infected regions. The evaluation of the proposed Residual-BRNet CNN in the classification stage demonstrates promising performance metrics, achieving an accuracy of 97.97%, F1-score of 98.01%, sensitivity of 98.42%, and MCC of 96.81%. Meanwhile, PA-RESeg in the segmentation phase achieves an optimal segmentation performance with an IoU score of 98.43% and a dice similarity score of 95.96% of the lesion region. The framework's effectiveness in detecting and segmenting COVID-19 lesions highlights its potential for clinical applications.

Alteration in early resting‑state functional MRI activity in comatose survivors of cardiac arrest: a prospective cohort study

BackgroundThis study aimed to explore the characteristics of abnormal regional resting-state functional magnetic resonance imaging (rs-fMRI) activity in comatose patients in the early period after cardiac arrest (CA), and to investigate their relationships with neurological outcomes. We also explored the correlations between jugular venous oxygen saturation (SjvO2) and rs-fMRI activity in resuscitated comatose patients. We also examined the relationship between the amplitude of the N20-baseline and the rs-fMRI activity within the intracranial conduction pathway of somatosensory evoked potentials (SSEPs).MethodsBetween January 2021 and January 2024, eligible post-resuscitated patients were screened to undergo fMRI examination. The amplitude of low-frequency fluctuation (ALFF), fractional ALFF (fALFF), and regional homogeneity (ReHo) of rs-fMRI blood oxygenation level-dependent (BOLD) signals were used to characterize regional neural activity. Neurological outcomes were evaluated using the Glasgow–Pittsburgh cerebral performance category (CPC) scale at 3 months after CA.ResultsIn total, 20 healthy controls and 31 post-resuscitated patients were enrolled in this study. The rs-fMRI activity of resuscitated patients revealed complex changes, characterized by increased activity in some local brain regions and reduced activity in others compared to healthy controls (P < 0.05). However, the mean ALFF values of the whole brain were significantly greater in CA patients (P = 0.011). Among the clusters of abnormal rs-fMRI activity, the cluster values of ALFF in the left middle temporal gyrus and inferior temporal gyrus and the cluster values of ReHo in the right precentral gyrus, superior frontal gyrus and middle frontal gyrus were strongly correlated with the CPC score (P < 0.001). There was a strong correlation between the mean ALFF and SjvO2 in CA patients (r = 0.910, P < 0.001). The SSEP N20-baseline amplitudes in CA patients were negatively correlated with thalamic rs-fMRI activity (all P < 0.001).ConclusionsThis study revealed that abnormal rs-fMRI BOLD signals in resuscitated patients showed complex changes, characterized by increased activity in some local brain regions and reduced activity in others. Abnormal BOLD signals were associated with neurological outcomes in resuscitated patients. The mean ALFF values of the whole brain were closely related to SjvO2 levels, and changes in the thalamic BOLD signals correlated with the N20-baseline amplitudes of SSEP responses.Trial registrationNCT05966389 (Registered July 27, 2023).

Unraveling multi-scale neuroimaging biomarkers and molecular foundations for schizophrenia: A combined multivariate pattern analysis and transcriptome-neuroimaging association study.

Schizophrenia is characterized by alterations in resting-state spontaneous brain activity; however, it remains uncertain whether variations at diverse spatial scales are capable of effectively distinguishing patients from healthy controls. Additionally, the genetic underpinnings of these alterations remain poorly elucidated. We aimed to address these questions in this study to gain better understanding of brain alterations and their underlying genetic factors in schizophrenia. A cohort of 103 individuals with diagnosed schizophrenia and 110 healthy controls underwent resting-state functional MRI scans. Spontaneous brain activity was assessed using the regional homogeneity (ReHo) metric at four spatial scales: voxel-level (Scale 1) and regional-level (Scales 2-4: 272, 53, 17 regions, respectively). For each spatial scale, multivariate pattern analysis was performed to classify schizophrenia patients from healthy controls, and a transcriptome-neuroimaging association analysis was performed to establish connections between gene expression data and ReHo alterations in schizophrenia. The ReHo metrics at all spatial scales effectively discriminated schizophrenia from healthy controls. Scale 2 showed the highest classification accuracy at 84.6%, followed by Scale 1 (83.1%) and Scale 3 (78.5%), while Scale 4 exhibited the lowest accuracy (74.2%). Furthermore, the transcriptome-neuroimaging association analysis showed that there were not only shared but also unique enriched biological processes across the four spatial scales. These related biological processes were mainly linked to immune responses, inflammation, synaptic signaling, ion channels, cellular development, myelination, and transporter activity. This study highlights the potential of multi-scale ReHo as a valuable neuroimaging biomarker in the diagnosis of schizophrenia. By elucidating the complex molecular basis underlying the ReHo alterations of this disorder, this study not only enhances our understanding of its pathophysiology, but also pave the way for future advancements in genetic diagnosis and treatment of schizophrenia.

The abnormalities of brain function in females with primary insomnia: a resting-state functional magnetic resonance imaging study.

The neuropathologic mechanism of primary insomnia (PI) of females remains unclear. This study aims to investigate the features of amplitude of low-frequency fluctuations (ALFF) and regional homogeneity (ReHo) in females with PI using functional magnetic resonance imaging (fMRI), and then explore the abnormalities of functional connectivity (FC). A total of 39 female PI patients and 31 female healthy controls (HC) were enrolled in the study. The sleep condition was assessed using the Pittsburgh Sleep Quality Index (PSQI), and Insomnia Severity Index (ISI), and their depressive symptom was evaluated using the Hamilton Depression Scale (HAMD-24). The rs-fMRI was once conducted for every subject. ReHo, ALFF, and ROI-based FC were used to analyze the changes of brain function. ALFF values were increased in the Cerebelum_4_5_L, as well as decreased ALFF in the bilateral Frontal_Sup_Medial (SFGmed), Angular_L (ANG.L), Parietal_Inf_R (IPL.R), SupraMarginal_R (SMG.R), and Postcentral_R (PoCG.R). ReHo values were increased in the Temporal_Pole_Mid_R (TPOsup.R), as well as decreased ReHo in the Insula_R (INS.R), Frontal_Inf_Oper_R (ORBinf.R), Putamen_R (PUT.R), Rolandic_Oper_R (ROL.R), bilateral Cingulum_Post (PCG), bilateral Frontal_Sup_Medial (SFGmed), bilateral anterior cingulate and paracingulate gyri (ACG), and the bilateral precuneus (PCUN). Across the entire brain, there was a decline in the FC between Angular_R and Frontal_Sup_Medial_L. Alterations in brain regions of female patients with PI involved multiple functional networks, including the default mode network, the salience network, the central executive network, and the limbic network. Reduced coordination between functional networks may be an important mechanism for insomnia and may lead to reduced cognitive function and decision-making ability.

Connectivity and climate influence diversity–stability relationships across spatial scales in European butterfly metacommunities

AbstractAimAnthropogenic‐driven biodiversity loss can impact ecosystem stability. However, most studies have only evaluated the diversity–stability relationship at the local scale and we do not fully understand which factors stabilize animal populations and communities across scales. Here, we investigate the role of species dispersal ability, climate, spatial distance and different facets of biodiversity on the stability of butterfly populations and communities across multiple spatial scales.LocationPrimarily Western Europe.Time Period2005–2016.Major Taxa StudiedButterflies (Rhopalocera) of Europe.MethodsWe assembled a continent‐wide database of European butterflies' abundance and used Structural Equation Modelling to evaluate the direct and indirect effects of multiple stabilizing mechanisms. In parallel, we tested the effect of dispersal ability on the stability at multiple spatial scales, using a butterfly mobility index as an indicator of dispersal capacity.ResultsRegional stability strongly reflected local stability, which in turn was driven by both taxonomic and functional α‐diversity. Spatial asynchrony was also important for regional stability and it was driven by both functional β‐diversity and metapopulation asynchrony, which in turn increased with spatial distance among communities. We observed a positive effect of temperature on functional α‐diversity and on local stability, whereas precipitation negatively influenced local diversity. Finally, spatial asynchrony contributed more to the regional stability of less mobile species compared to highly mobile ones, indicating that both extrinsic and intrinsic determinants of connectivity impact regional stability indirectly.Main ConclusionsOur results demonstrate the importance of local and regional processes for regional stability. However, the relative contribution of spatial asynchrony and metapopulation asynchrony increases with connectivity loss, especially for less mobile species, indicating that landscape management should be tailored depending on the dispersal capacity of organisms. Both local biodiversity loss and regional biotic homogenization destabilize metacommunities, with potential implications for the reliable provision of ecosystem functions.

Altered Resting-State Brain Entropy in Cerebral Small Vessel Disease Patients with Cognitive Impairment.

Objective: Cerebral small vessel disease (CSVD) is a primary vascular disease of cognitive impairment. Previous studies have predominantly focused on brain linear features. However, the nonlinear measure, brain entropy (BEN), has not been elaborated. Thus, this study aims to investigate if BEN abnormalities could manifest in CSVD patients with cognitive impairment. Methods: Thirty-four CSVD patients with cognitive impairment and 37 healthy controls (HCs) were recruited. Analysis of gray matter approximate entropy (ApEn) and sample entropy (SampEn) which are two indices of BEN was calculated. To explore whether BEN can provide unique information, we further performed brain linear methods, namely, amplitude of low frequency fluctuation (ALFF) and regional homogeneity (ReHo), to observe their differences. The ratios of BEN/ALFF and BEN/ReHo which represent the coupling of nonlinear and linear features were introduced. Correlation analysis was conducted between imaging indices and cognition. Subsequently, the linear support vector machine (SVM) was used to assess their discriminative ability. Results: CSVD patients exhibited lower ApEn and SamEn values in sensorimotor areas, which were correlated with worse memory and executive function. In addition, the results of BEN showed little overlap with ALFF and ReHo in brain regions. Correlation analysis also revealed a relationship between the two ratios and cognition. SVM analysis using BEN and its ratios as features achieved an accuracy of 74.64% (sensitivity: 86.49%, specificity: 61.76%, and AUC: 0.82439). Conclusion: Our study reveals that the reduction of sensorimotor system complexity is correlated with cognition. BEN exhibits distinctive characteristics in brain activity. Combining BEN and the ratios can be new biomarkers to diagnose CSVD with cognitive impairment. Impact Statement Cerebral small vessel disease (CSVD) is regarded as the most important vascular disease of cognitive impairment. However, conventional brain imaging fails to adequately elucidate the pathogenesis of cognitive disorder related to CSVD. In this regard, exploring brain entropy (BEN) based on resting-state functional magnetic resonance imaging (rs-fMRI) represents a relatively novel and unexplored approach in the context of CSVD. This approach provides novel insights into the pathogenesis, diagnosis, and rehabilitation of cognitive disorder associated with CSVD.

Decreased resting-state brain function in older adults predicts enlarged representational momentum.

Representational momentum (RM) refers to the phenomenon in which an observer's judgment of the final location of a previously viewed moving target is often displaced forward in the direction of motion. This phenomenon is an adaptive mechanism that compensates for neural processing delays and is closely associated with visual cortex function. However, the impact of age-related decline of visual cortex function on the manifestations of RM remains unclear. The present study examined differences in the RM effect between older (N = 82) and younger adults (N = 74) using a cursor-positioning task. Additionally, resting-state functional magnetic resonance imaging was used to explore the potential neural substrates that underlie these differences, employing amplitude of low-frequency fluctuation (ALFF, reflecting the intensity of neural activity) and regional homogeneity (ReHo, reflecting the synchronization of neural activity) as indicators. Our findings indicate a significant increase in RM among older adults compared with younger adults. Neuroimaging data revealed a significant decrease in ALFF and ReHo within extensive regions of the visual cortex in older adults, validating age-related differences in this cortical area. More importantly, ALFF values in the bilateral visual area 3 and ReHo values in the bilateral visual area 2 in older adults exhibited a strong negative correlation with their RM effects. These results suggest that larger RM in older adults may be functional compensation for aging of the visual cortex. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

A radiomics approach for predicting gait freezing in Parkinson's disease based on resting-state functional magnetic resonance imaging indices: a cross-sectional study.

Freezing of gait is a significant and debilitating motor symptom often observed in individuals with Parkinson's disease. Resting-state functional magnetic resonance imaging, along with its multi-level feature indices, has provided a fresh perspective and valuable insight into the study of freezing of gait in Parkinson's disease. It has been revealed that Parkinson's disease is accompanied by widespread irregularities in inherent brain network activity. However, the effective integration of the multi-level indices of resting-state functional magnetic resonance imaging into clinical settings for the diagnosis of freezing of gait in Parkinson's disease remains a challenge. Although previous studies have demonstrated that radiomics can extract optimal features as biomarkers to identify or predict diseases, a knowledge gap still exists in the field of freezing of gait in Parkinson's disease. This cross-sectional study aimed to evaluate the ability of radiomics features based on multi-level indices of resting-state functional magnetic resonance imaging, along with clinical features, to distinguish between Parkinson's disease patients with and without freezing of gait. We recruited 28 patients with Parkinson's disease who had freezing of gait (15 men and 13 women, average age 63 years) and 30 patients with Parkinson's disease who had no freezing of gait (16 men and 14 women, average age 64 years). Magnetic resonance imaging scans were obtained using a 3.0T scanner to extract the mean amplitude of low-frequency fluctuations, mean regional homogeneity, and degree centrality. Neurological and clinical characteristics were also evaluated. We used the least absolute shrinkage and selection operator algorithm to extract features and established feedforward neural network models based solely on resting-state functional magnetic resonance imaging indicators. We then performed predictive analysis of three distinct groups based on resting-state functional magnetic resonance imaging indicators indicators combined with clinical features. Subsequently, we conducted 100 additional five-fold cross-validations to determine the most effective model for each classification task and evaluated the performance of the model using the area under the receiver operating characteristic curve. The results showed that when differentiating patients with Parkinson's disease who had freezing of gait from those who did not have freezing of gait, or from healthy controls, the models using only the mean regional homogeneity values achieved the highest area under the receiver operating characteristic curve values of 0.750 (with an accuracy of 70.9%) and 0.759 (with an accuracy of 65.3%), respectively. When classifying patients with Parkinson's disease who had freezing of gait from those who had no freezing of gait, the model using the mean amplitude of low-frequency fluctuation values combined with two clinical features achieved the highest area under the receiver operating characteristic curve of 0.847 (with an accuracy of 74.3%). The most significant features for patients with Parkinson's disease who had freezing of gait were amplitude of low-frequency fluctuation alterations in the left parahippocampal gyrus and two clinical characteristics: Montreal Cognitive Assessment and Hamilton Depression Scale scores. Our findings suggest that radiomics features derived from resting-state functional magnetic resonance imaging indices and clinical information can serve as valuable indices for the identification of freezing of gait in Parkinson's disease.

Drought trend and its association with land surface temperature (LST) over homogeneous drought regions of India (2001–2019)

Droughts pose significant challenges to food security, affecting millions of people and hectares of land in India. Despite their widespread impact, assessing drought patterns at a high spatial resolution remains a challenge, particularly in regions with poor data availability. Standardised Precipitation Evapotranspiration Index (SPEI) is one of the most widely accepted drought indices. The publicly available SPEI reanalysis dataset has a poor spatial resolution for regional drought studies. Land Surface Temperature (LST) has the potential to act as an effective proxy for SPEI and can be used for high-resolution drought studies. The study considered six homogeneous drought regions (NWH, CPR, NEI, NCP, NWI, and SPR) over India, and the recent trend in LST and SPEI over these regions was estimated using modified Mann-Kendell (MMK) trend and Sen’s slope estimator at monthly, seasonal, and annual time scales. The correlation between SPEI and LST was also checked at a 95% confidence level. Results indicate statistically significant decreasing annual LST trends in NWH (slope = −0.10), CPR (slope = −0.09), and NWI (slope = −0.11) regions, while increasing SPEI trends were observed in NWI (slope = 0.06) and NCP (slope = 0.01) regions during 2001–2019. Additionally, NCP (p = 0.025), CPR (p = 0.002), and NWI (p = 0.020) exhibited significantly decreasing LST trends during winter, with a notable post-monsoon decrease observed in NWI. Significant correlations between annual LST and SPEI were observed only in CPR (r =−0.527) and NWI (r = −0.601), with seasonal associations found predominantly in winter across CPR, NEI, NCP, and NWI regions. Notably, a significant correlation was observed in all months except July over NWI, with consistent correlations during most of the winter months in NWH, CPR, NCP, and NWI regions. The observed trends and correlations can provide valuable insights for policymakers and stakeholders in formulating effective drought mitigation strategies.

Meteorological Drought Analysis and Regional Frequency Analysis in the Kızılırmak Basin: Creating a Framework for Sustainable Water Resources Management

Drought research is needed to understand the complex nature of drought phenomena and to develop effective management and mitigation strategies accordingly. This study presents a comprehensive regional frequency analysis (RFA) of 12-month meteorological droughts in the Kızılırmak Basin of Turkey using the L-moments approach. For this purpose, monthly precipitation data from 1960 to 2020 obtained from 22 meteorological stations in the basin are used. In the drought analysis, the Standard Precipitation Index (SPI), Z-Score Index (ZSI), China-Z Index (CZI) and Modified China-Z Index (MCZI), which are widely used precipitation-based indices in the literature, are employed. Here, the main objectives of this study are (i) to determine homogeneous regions based on drought, (ii) to identify the best-fit regional frequency distributions, (iii) to estimate the maximum drought intensities for return periods ranging from 5 to 1000 years, and (iv) to obtain drought maps for the selected return periods. The homogeneity test results show that the basin consists of a single homogeneous region according to the drought indices considered here. The best-fit regional frequency distributions for the selected drought indices are identified using L-moment ratio diagrams and ZDIST goodness-of-fit tests. According to the results, the best-fit regional distributions are the Pearson-Type 3 (PE3) for the SPI and ZSI, generalized extreme value (GEV) for the CZI, and generalized logistic distribution (GLO) for the MCZI. The drought maps obtained here can be utilized as a useful tool for estimating the probability of drought at any location across the basin, even without enough data for hydrological research.

Major depressive disorder and perceived social support: Moderated mediation model of security and brain dysfunction

Low social support increases the risk of Major depressive disorder (MDD), yet its effects on brain function are unclear. Thirty-two MDD patients with low social support, 52 with high social support, and 54 healthy controls were recruited. We investigated regional brain activity in MDD patients with low social support using resting-state functional Magnetic Resonance Imaging, employing measures such as degree centrality (DC), regional homogeneity, amplitude of low-frequency fluctuations, and fractional amplitude of low-frequency fluctuations. Abnormal regions identified in these analyses were selected as regions of interest for functional connectivity (FC) analysis. We then explored relationships among social support, brain dysfunction, MDD severity, and insecurity using partial correlation and moderated mediation models. Our findings reveal that MDD patients with low social support show decreased DC in the right superior temporal pole and right medial geniculate nucleus, coupled with increased FC between the right superior temporal pole and right inferior temporal gyrus, and the right supramarginal gyrus compared to those with high social support. Furthermore, the DC of the right medial geniculate nucleus positively correlates with social support, while the FC between the right superior temporal pole and right supramarginal gyrus negatively correlates with both social support and subjective support. Additionally, a moderated mediation model demonstrates that the FC between the right superior temporal pole and right supramarginal gyrus mediates the relationship between social support and depression severity, with security moderating this mediation. These findings underscore the impact of low social support on brain function and depression severity in MDD patients.

Interaction of aluminum alloys with MKPC and Portland-based cements on the metal-matrix interface

Cementitious matrices are considered for the immobilisation of radioactive aluminium. The processes occurring at the interface between both materials are relevant for the long-term stability of the repository. The present study compares Ordinary Portland cement (OPC), CEM I-type, with alternative Portland blended cement (CEM IV and CEM I + 50% silica fume) and magnesium potassium phosphate cement (MKPC). Al A1050 and Al AA5754, with 3.5% Mg, have been used as reactive metal alloys. Two exposure conditions were employed: (1) water immersion and (2) isolated in sealed plastic films. Long-term corrosion monitoring (Ecorr, icorr and Vcorr) was evaluated to understand the effect of Al reactivity in the matrix. The associated H2 release was quantified to understand the changes at the metal/matrix interface. Furthermore, pore ion concentrations and the pore microstructure were evaluated. The metal/matrix interface alterations were analysed at the end of the tests. The study revealed that after 300 days of water immersion, the CEM I + 50% of silica fume matrix had reduced the pore solution pH down to 10.5 compared to CEM I, which remained high alkaline (pH 12.9), and CEM IV with no significant decreases (pH 12.3). In contrast, MKPC showed the lowest pH (7–9.8). Low Al corrosion rates were found with MKPC, followed by CEM I + 50% SF according to their lower pH. A corrosion product layer of 90-50 μm thickness was observed at the Al/CEM I matrix interface constituted of aluminium and oxygen. Furthermore, enrichment in Al was detected in the matrix (around 1 mm depth), causing the formation of ettringite nodules. Voids were detected at the interface level associated with the high volume of H2 released. The MKPC matrix showed no alteration at the metal/matrix interface due to the lower reactivity of the matrix and lower H2 release. A homogeneous and dense region (30 μm) rich in phosphorous, potassium and magnesium was observed near the metal surface.

Deep learning image reconstruction for low-kiloelectron volt virtual monoenergetic images in abdominal dual-energy CT: medium strength provides higher lesion conspicuity.

The best settings of deep learning image reconstruction (DLIR) algorithm for abdominal low-kiloelectron volt (keV) virtual monoenergetic imaging (VMI) have not been determined. To determine the optimal settings of the DLIR algorithm for abdominal low-keV VMI. The portal-venous phase computed tomography (CT) scans of 109 participants with 152 lesions were reconstructed into four image series: VMI at 50 keV using adaptive statistical iterative reconstruction (Asir-V) at 50% blending (AV-50); and VMI at 40 keV using AV-50 and DLIR at medium (DLIR-M) and high strength (DLIR-H). The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of nine anatomical sites were calculated. Noise power spectrum (NPS) using homogenous region of liver, and edge rise slope (ERS) at five edges were measured. Five radiologists rated image quality and diagnostic acceptability, and evaluated the lesion conspicuity. The SNR and CNR values, and noise and noise peak in NPS measurements, were significantly lower in DLIR images than AV-50 images in all anatomical sites (all P < 0.001). The ERS values were significantly higher in 40-keV images than 50-keV images at all edges (all P < 0.001). The differences of the peak and average spatial frequency among the four reconstruction algorithms were significant but relatively small. The 40-keV images were rated higher with DLIR-M than DLIR-H for diagnostic acceptance (P < 0.001) and lesion conspicuity (P = 0.010). DLIR provides lower noise, higher sharpness, and more natural texture to allow 40 keV to be a new standard for routine VMI reconstruction for the abdomen and DLIR-M gains higher diagnostic acceptance and lesion conspicuity rating than DLIR-H.

Increasing monsoon precipitation extremes in relation to large-scale climatic patterns in Pakistan

This study comprehensively evaluates monsoon variability and its large-scale drivers in Pakistan, a country often affected by extreme monsoon events leading to destructive flooding. Utilizing the Monotonic Mann-Kendall and Sen's Slope test, we analyzed trends in seven indicators of extreme precipitation events (EPEs) from 1961 to 2017 across Pakistan and its homogeneous climatic regions. The wavelet coherence method further allowed us to probe the connection between EPEs and key climate indices such as the Atlantic Multidecadal Oscillation (AMO), North Atlantic Oscillation (NAO), El Niño–Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), Pacific Decadal Oscillation (PDO), Indian summer monsoon (ISM), and Southern Oscillation Index (SOI) in the time-frequency domain. Our findings indicate a significant increase in all EPEs across Pakistan, particularly in the monsoon region (R-II) from 1961 to 2017, with a noticeable decline post-1980s. The findings revealed a significant increase in EPEs in the whole country from 1961 to 1989 to 1990–2017, with a dominant change in the monsoon region during 1990–2017. In terms of climate indices combinations, IOD + ISM showed the highest coherences (>0.65), while ENSO+IOD + ISM and ENSO+IOD + PDO displayed coherences >0.81 and > 0.79, respectively, across the entire country. The multi-linear regression (MLR) model analysis drivers indicate that IOD and ENSO are the leading drivers of changes in monsoon EPEs in the whole country, whereas the core monsoon region of Pakistan also showed a strong influence from AMO and NAO in tandem with ENSO and IOD. The overall increase in EPEs over Pakistan are mainly associated with the indices from the Pacific Ocean, especially IOD, ENSO and PDO and with their various combinations. Our study reveals the variations in EPEs across Pakistan, yielding insights that could inform water resource management strategies and disaster prevention measures.

Post-COVID-19 Changes in Appetite-An Exploratory Study.

In this analysis, we aimed to investigate the effect of COVID-19 disease on eating behavior. A total of 55 right-handed adults, <50 years of age, without overweight or obesity, from two cross-sectional studies were included. The first one enrolled subjects between September 2018 and December 2019 (non-COVID-19 group). The second one included subjects enrolled between March 2022 and May 2023; for this analysis, 28 with a history of COVID-19 (COVID-19 group) were retained. Hunger, TFEQ-18, plasma ghrelin, neuropeptide Y (NPY) and resting-state fMRI were assessed during fasting. Intraregional neuronal synchronicity and connectivity were assessed by voxel-based regional homogeneity (ReHo) and degree of centrality (DC). Significantly higher ghrelin and NPY levels were observed in the COVID-19 group than in the non-COVID-19 group (ghrelin 197.5 pg/mL vs. 67.1 pg/mL, p < 0.001; NPY 128.0 pg/mL vs. 84.5 pg/mL, p = 0.005). The NPY levels positively correlated with the DC and ReHo in the left lingual (r = 0.67785 and r = 0.73604, respectively). Similar scores were noted for cognitive restraint, uncontrolled eating and emotional eating in both groups according to the TFEQ-18 questionnaire results (p > 0.05 for all). Our data showed increased levels of appetite-related hormones, correlated with activity in brain regions involved in appetite regulation, persisting long after COVID-19 infection.