Hyperspectral Image (HSI) is used widely in many areas, especially in the remote sensing field. Compared with the traditional remote sensing HSI, the large-scale and high-resolution HSI (LHHSI) which has big data and large size is high-resolution both in spatial domain and spectral domain. However, traditional methods of automatic target detection do not apply to LHHSI. Therefore, this paper proposes a novel framework of automatic target detection for LHHSI based on spatial-spectral interest point (SSIP). It contains five key steps. Firstly, bands selection of LHHSI is used to reduce spectral dimension of LHHSIs. Second, we extract candidate SSIPs from the LHHSIs. Third, we need to determine whether there exist potential target regions by using spectral curves of many selected key SSIPs. And next, the image which contains the potential target regions is divided into image blocks by using quad-tree segmentation, and then every image block is represented by a vector with BoW model based on the selected SSIPs. Finally, these image blocks are classified with SVM. During the classification, if the result is what we need, the quad-tree segmentation of the current block will be ended. The experimental results show that the proposed algorithm has a better performance than traditional algorithms.
Depression, a widespread and highly heritable mental health condition, profoundly affects millions of individuals worldwide. Neuroimaging studies have consistently revealed volumetric abnormalities in subcortical structures associated with depression. However, the genetic underpinnings shared between depression and subcortical volumes remain inadequately understood. Here, we investigate the extent of polygenic overlap using the bivariate causal mixture model (MiXeR), leveraging summary statistics from the largest genome-wide association studies for depression (N = 674,452) and 14 subcortical volumetric phenotypes (N = 33,224). Additionally, we identify shared genomic loci through conditional/conjunctional FDR analyses. MiXeR shows that subcortical volumetric traits share a substantial proportion of genetic variants with depression, with 44 distinct shared loci identified by subsequent conjunctional FDR analysis. These shared loci are predominantly located in intronic regions (58.7%) and non-coding RNA intronic regions (25.4%). The 269 protein-coding genes mapped by these shared loci exhibit specific developmental trajectories, with the expression level of 55 genes linked to both depression and subcortical volumes, and 30 genes linked to cognitive abilities and behavioral symptoms. These findings highlight a shared genetic architecture between depression and subcortical volumetric phenotypes, enriching our understanding of the neurobiological underpinnings of depression. Depression affects millions of people worldwide. Here, the authors show a substantial polygenic overlap between depression and brain subcortical volumes, identifying 44 shared loci.
Abstract The pursuit of happiness is a lifelong endeavor for everyone; nevertheless, elucidating its etiology, neurobiological substrates, and implications for mental health continues to pose significant challenges in contemporary research. This study sought to delineate the causal relationships among subjective well-being (SWB), urbanization, brain, and mental health, and to explore the protective role of SWB against prevalent psychiatric disorders. Utilizing data from 198,823 adults in the UK Biobank, including SWB questionnaires (five items), urban living environments (121 variables), neuroimaging data (2,413 measures), mental health assessments (39 indicators), and ICD-10 psychiatric diagnoses (10 disorders), we initially identified two robust SWB components using ten-fold cross-validated factor analysis: internal subjective well-being (ISWB) and social subjective well-being (SSWB). Phenome-wide association studies (PheWAS) revealed significant associations between urbanization variables and both ISWB (78/121) and SSWB (59/121); between neuroimaging indicators and both ISWB (416/2,413 measures) and SSWB (77/2,413); and between mental health assessments and both ISWB (38/39 indicators) and SSWB (37/39) (P < 0.05, Bonferroni corrected). Sequential mediation analysis uncovered 28 causal pathways from urbanization to brain to SWB to mental health (ISWB: 16 pathways, SSWB: 12 pathways), while the moderated mediation analysis revealed 19 pathways where SWB significantly moderated the urbanization → brain → mental health pathways (14 for ISWB, 5 for SSWB). Finally, Cox proportional hazards survival analysis demonstrated that individuals in the highest ISWB sextile had a 76% reduction in the overall risk of developing 10 mental disorders compared with those in the lowest sextile (Z = -29.49, Hazard Ratio [HR] = 0.24, P = 3.93e-191), and SSWB showed a 36% risk reduction (Z = -9.42, HR = 0.64, P = 4.50e-2). Moreover, both SWB components demonstrated the highest protective effects against depression (ISWB: HR = 0.13, SSWB: HR = 0.39). By systematically uncovering the causal pathways through which SWB components differentially participate in the regulation of urban living environments on the human brain, thereby affecting mental health, this study thus provides biological evidence and modifiable SWB indicators for the prevention of common psychiatric disorders.
Abstract Previous observational investigations suggest that structural and diffusion imaging-derived phenotypes (IDPs) are associated with major neurodegenerative diseases; however, whether these associations are causal remains largely uncertain. Herein we conducted bidirectional two-sample Mendelian randomization analyses to infer the causal relationships between structural and diffusion IDPs and major neurodegenerative diseases using common genetic variants-single nucleotide polymorphism (SNPs) as instrumental variables. Summary statistics of genome-wide association study (GWAS) for structural and diffusion IDPs were obtained from 33,224 individuals in the UK Biobank cohort. Summary statistics of GWAS for seven major neurodegenerative diseases were obtained from the largest GWAS for each disease to date. The forward MR analyses identified significant or suggestively statistical causal effects of genetically predicted three structural IDPs on Alzheimer’s disease (AD), frontotemporal dementia (FTD), and multiple sclerosis. For example, the reduction in the surface area of the left superior temporal gyrus was associated with a higher risk of AD. The reverse MR analyses identified significantly or suggestively statistical causal effects of genetically predicted AD, Lewy body dementia (LBD), and FTD on nine structural and diffusion IDPs. For example, LBD was associated with increased mean diffusivity in the right superior longitudinal fasciculus and AD was associated with decreased gray matter volume in the right ventral striatum. Our findings might contribute to shedding light on the prediction and therapeutic intervention for the major neurodegenerative diseases at the neuroimaging level.
Neuroimaging studies have revealed that patients with schizophrenia exhibit disrupted resting-state functional connectivity. However, the inconsistent findings across these studies have hindered our comprehensive understanding of the functional connectivity changes associated with schizophrenia, and the molecular mechanisms associated with these alterations remain largely unclear. A quantitative meta-analysis was first conducted on 21 datasets, involving 1057 patients and 1186 healthy controls, to examine disrupted resting-state functional connectivity in schizophrenia, as measured by whole-brain voxel-wise functional network centrality (FNC). Subsequently, partial least squares regression analysis was employed to investigate the relationship between FNC changes and gene expression profiles obtained from the Allen Human Brain Atlas database. Finally, gene enrichment analysis was performed to unveil the biological significance of the altered FNC-related genes. Compared with healthy controls, patients with schizophrenia show consistently increased FNC in the right inferior parietal cortex extending to the supramarginal gyrus, angular gyrus, bilateral medial prefrontal cortex, and right dorsolateral prefrontal cortex, while decreased FNC in the bilateral insula, bilateral postcentral gyrus, and right inferior temporal gyrus. Meta-regression analysis revealed that increased FNC in the right inferior parietal cortex was positively correlated with clinical score. In addition, these observed functional connectivity changes were found to be spatially associated with the brain-wide expression of specific genes, which were enriched in diverse biological pathways and cell types. These findings highlight the aberrant functional connectivity observed in schizophrenia and its potential molecular underpinnings, providing valuable insights into the neuropathology of dysconnectivity associated with this disorder.
Survival prediction of esophageal cancer is an essential task for doctors to make personalized cancer treatment plans. However, handcrafted features from medical images need prior medical knowledge, which is usually limited and not complete, yielding unsatisfying survival predictions. To address these challenges, we propose a novel and efficient deep learning-based survival prediction framework for evaluating clinical outcomes before concurrent chemoradiotherapy. The proposed model consists of two key components: a 3D Coordinate Attention Convolutional Autoencoder (CACA) and an uncertainty-based jointly Optimizing Cox Model (UOCM). The CACA is built upon an autoencoder structure with 3D coordinate attention layers, capturing latent representations and encoding 3D spatial characteristics with precise positional information. Additionally, we designed an Uncertainty-based jointly Optimizing Cox Model, which jointly optimizes the CACA and survival prediction task. The survival prediction task models the interactions between a patient's feature signatures and clinical outcome to predict a reliable hazard ratio of patients. To verify the effectiveness of our model, we conducted extensive experiments on a dataset including computed tomography of 285 patients with esophageal cancer. Experimental results demonstrated that the proposed method achieved a C-index of 0.72, outperforming the state-of-the-art method.
Exogenous application of the plant hormone methyl jasmonate (MeJA) can trigger induced plant defenses against herbivores, and has been shown to provide protection against insect herbivory in conifer seedlings. Other methods, such as mechanical damage to seedlings, can also induce plant defenses, yet few have been compared to MeJA and most studies lack subsequent herbivory feeding tests. We conducted two lab experiments to: (1) compare the efficacy of MeJA to mechanical damage treatments that could also induce seedling resistance, (2) examine if subsequent insect damage differs depending on the time since induction treatments occurred, and (3) assess if these induction methods affect plant growth. We compared Scots pine ( Pinus sylvestris ) seedlings sprayed with MeJA (10 or 15 mM) to seedlings subjected to four different mechanical bark damage treatments (two different bark wound sizes, needle-piercing damage, root damage) and previous pine weevil ( Hylobius abietis ) damage as a reference treatment. The seedlings were exposed to pine weevils 12 or 32 days after treatments (early and late exposure, hereafter), and resistance was measured as the amount of damage received by plants. At early exposure, seedlings treated with needle-piercing damage received significantly more subsequent pine weevil feeding damage than those treated with MeJA. Seedlings treated with MeJA and needle-piercing damage received 84% less and 250% more pine weevil feeding, respectively, relative to control seedlings. The other treatments did not differ statistically from control or MeJA in terms of subsequent pine weevil damage. For the late exposure group, plants in all induction treatments tended to receive less pine weevil feeding (yet this was not statistically significant) compared to control seedlings. On the other hand, MeJA significantly slowed down seedling growth relative to control and all other induction treatments. Overall, the mechanical damage treatments appeared to have no or variable effects on seedling resistance. One of the treatments, needle-piercing damage, actually increased pine weevil feeding at early exposure. These results therefore suggest that mechanical damage shows little potential as a plant protection measure to reduce feeding by a bark-chewing insect.
Abstract Alzheimer’s disease, a common and progressive neurodegenerative disorder, is associated with alterations in hippocampal volume, as revealed by neuroimaging research. However, the causal links between the volumes of the hippocampus and its subfield structures with Alzheimer’s disease remain unknown. A genetic correlation analysis using linkage disequilibrium score regression was conducted to identify hippocampal volumetric traits linked to Alzheimer’s disease. Following this, to examine the causal links between Alzheimer’s disease and hippocampal volumetric traits, we applied a two-sample Mendelian randomization approach, utilizing a bidirectional framework. Seven hippocampal volumetric traits were found as genetically correlated with Alzheimer’s disease in the genetic correlation analysis and were then included in the Mendelian randomization analyses. Inverse variance weighted Mendelian randomization analyses revealed that increased volumes in the left whole hippocampus, left hippocampal body, right presubiculum head and right cornu ammonis 1 head were causally related to higher risks of Alzheimer’s disease. Conversely, a higher risk of Alzheimer’s disease was causally associated with decreased volumes of the left hippocampal body and left whole hippocampus. These results were validated through other Mendelian randomization approaches and sensitivity analysis. Our findings uncover bidirectional causal relationships between Alzheimer’s disease and hippocampal volumetric traits, suggesting not only the potential significance of these traits in predicting Alzheimer’s disease but also the reciprocal influence of Alzheimer’s disease on hippocampal volumes.
Schizophrenia, a multifaceted psychiatric disorder characterized by functional dysconnectivity, poses significant challenges in clinical practice. This study explores the potential of functional connectivity (FC)-based searchlight multivariate pattern analysis (CBS-MVPA) to discriminate between schizophrenia patients and healthy controls while also predicting clinical variables.
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