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In recent years a vast amount of visual content has been generated and shared from many fields, such as social media platforms, medical imaging, and robotics. This abundance of content creation and sharing has introduced new challenges, particularly that of searching databases for similar content-Content Based Image Retrieval (CBIR)-a long-established research area in which improved efficiency and accuracy are needed for real-time retrieval. Artificial intelligence has made progress in CBIR and has significantly facilitated the process of instance search. In this survey we review recent instance retrieval works that are developed based on deep learning algorithms and techniques, with the survey organized by deep network architecture types, deep features, feature embedding and aggregation methods, and network fine-tuning strategies. Our survey considers a wide variety of recent methods, whereby we identify milestone work, reveal connections among various methods and present the commonly used benchmarks, evaluation results, common challenges, and propose promising future directions., IEEE Transactions on Pattern Analysis and Machine Intelligence

The community coupler (C-Coupler) for Earth system modelling is a coupler family that was developed in China in 2010. C-Coupler3.0, the latest version, is fully compatible with the previous version, C-Coupler2, and is an integrated infrastructure with new features, i.e. a series of parallel-optimization technologies for accelerating coupling initialization and reducing memory usage, a common halo-exchange library for developing a parallel version of a model, a common module-integration framework for integrating a software module (e.g. a flux algorithm, a parameterization scheme, and a data assimilation method), a common framework for conveniently developing a weakly coupled ensemble data assimilation system, and a common framework for flexibly inputting and outputting fields in parallel. Specifically, C-Coupler3.0 is able to handle coupling under much finer resolutions (e.g. more than 100 million horizontal grid cells) with fast coupling initialization and successful generation of remapping-weight files.

Background: The Boston Naming Test (BNT) is the most widely used measure to assess anomia. However, it has been criticized for failing to differentiate the underlying cognitive process of anomia. Objective: We validated the color-picture version of BNT (CP-BNT) in a sample with diverse neurodegenerative dementia diseases (NDDs). We also verified the differential ability of the composite indices of CP-BNT across NDDs groups. Methods: The present study included Alzheimer’s disease (n = 132), semantic variant primary progressive aphasia (svPPA, n = 53), non-svPPA (n = 33), posterior cortical atrophy (PCA, n = 35), and normal controls (n = 110). We evaluated psychometric properties of CP-BNT for the spontaneous naming (SN), the percentage of correct responses on semantic cuing and word recognition cuing (% SC, % WR). Receiver operating characteristic analysis was used to examine the discriminatory power of SN alone and the composite indices (SN, % SC, and % WR). Results: The CP-BNT had sufficient internal consistency, good convergent, divergent validity, and criterion validity. Different indices of CP-BNT demonstrated distinct cognitive underpinnings. Category fluency was the strongest predictor of SN (β= 0.46, p < 0.001). Auditory comprehension tests highly associated with % WR (Sentence comprehension: β= 0.22, p = 0.001; Word comprehension: β= 0.20, p = 0.001), whereas a lower visuospatial score predicted % SC (β= – 0.2, p = 0.001). Composite indices had better predictability than the SN alone when differentiating between NDDs, especially for PCA versus non-svPPA (area under the curve increased from 63.9% to 81.2%). Conclusion: The CP-BNT is a highly linguistically relevant test with sufficient reliability and validity. Composite indices could provide more differential information beyond SN and should be used in clinical practice.

The Pacific Decadal Oscillation (PDO) is the most dominant decadal climate variability over the North Pacific and has substantial global impacts. However, the interannual and decadal PDO prediction skills are not satisfactory, which may result from the failure of appropriately including the North Pacific midlatitude air-sea interaction (ASI) in the initialization for climate predictions. Here, we present a novel initialization method with a climate model to crack this nutshell and achieve successful PDO index predictions up to 10 years in advance. This approach incorporates oceanic observations under the constraint of ASI, thus obtaining atmospheric initial conditions (ICs) consistent with oceanic ICs. During predictions, positive atmospheric feedback to sea surface temperature changes and time-delayed negative ocean circulation feedback to the atmosphere over the North Pacific play essential roles in the high PDO index prediction skills. Our findings highlight a great potential of ASI constraints during initialization for skillful PDO predictions.

Background: The insula is the predominant brain region impaired in behavior variant frontotemporal dementia (bvFTD). However, structural and functional changes in the sub-insula in the asymptomatic stage of bvFTD are unknown. Objective: To describe structural and functional changes in insula subregions in asymptomatic carriers of the P301L mutation of the microtubule-associated protein tau (MAPT) gene and patients with bvFTD. Methods: Six asymptomatic MAPT P301L mutation carriers and 12 MAPT negative control subjects of the same pedigree were enrolled, along with 30 patients with a clinical diagnosis of bvFTD and 30 matched controls. All subjects underwent hybrid positron emission tomography/magnetic resonance imaging. Atlas-based parcellation using a fine-grained Brainnetome Atlas was conducted to assess gray matter (GM) volume, metabolism, and metabolic connectivity in the sub-insula (region of interest). Results: There was no significant GM atrophy or hypometabolism in insula subregions in asymptomatic MAPT P301L carriers, although decreased metabolic connectivity between vIa-middle temporal gyrus, vIa-temporal poles, dIa-middle temporal gyrus and dIa-temporal poles; and increased connectivity between vIa-orbitofrontal, vIa-dorsal lateral superior frontal gyrus, and dIa-orbitofrontal and dIa-dorsal lateral superior frontal gyrus were observed. Patients with bvFTD had significant atrophy and hypometabolism in all insula subregions and decreased metabolic connectivity in the whole brain, including vIa/dIa-middle temporal and vIa/dIa-temporal poles. The standardized uptake value ratios of vIa and dIa were negatively associated with behavioral disinhibition scale scores. Conclusion: Metabolic connectivity is altered in vIa and dIa subregions of the sub-insula in MAPT P301L mutation carriers before the occurrence of atrophy, hypometabolism, and clinical symptoms.

Background: Progranulin (GRN) mutations in frontotemporal dementia (FTD) have been less frequently reported in China than in Western countries. Objective: This study reports a novel GRN mutation and summarizes the genetic and clinical features of patients with GRN mutations in China. Methods: Comprehensive clinical, genetic, and neuroimaging examinations were conducted on a 58-year-old female patient diagnosed with semantic variant primary progressive aphasia. A literature review was also conducted and clinical and genetic features of patients with GRN mutations in China were summarized. Results: Neuroimaging revealed marked lateral atrophy and hypometabolism in the left frontal, temporal, and parietal lobes. The patient was negative for pathologic amyloid and tau deposition by positron emission tomography. A novel heterozygous 45-bp deletion (c.1414-14_1444delCCCTTCCCCGCCAGGCTGTGTGCTGCGAGGATCGCCAGCACTGCT) was detected by whole-exome sequencing of the patient’s genomic DNA. Nonsense-mediated mRNA decay was presumed to be involved in the degradation of the mutant gene transcript. The mutation was deemed pathogenic according to American College of Medical Genetics and Genomics criteria. The patient had a reduced plasma GRN level. In the literature, there were reports of 13 Chinese patients – mostly female – with GRN mutations; the prevalence was 1.2% –2.6% and patients mostly had early disease onset. Conclusion: Our findings expand the mutation profile of GRN in China, which can aid the diagnosis and treatment of FTD.

Background: Research on posterior cortical atrophy (PCA) has focused on cognitive decline, especially visual processing deficits. However, few studies have examined the impact of PCA on activities of daily living (ADL) and the neurofunctional and neuroanatomic bases of ADL. Objective: To identify brain regions associated with ADL in PCA patients. Methods: A total of 29 PCA patients, 35 typical Alzheimer’s disease (tAD) patients, and 26 healthy volunteers were recruited. Each subject completed an ADL questionnaire that included basic and instrumental subscales (BADL and IADL, respectively), and underwent hybrid magnetic resonance imaging and 18F fluorodeoxyglucose positron emission tomography. Voxel-wise regression multivariable analysis was conducted to identify specific brain regions associated with ADL. Results: General cognitive status was similar between PCA and tAD patients; however, the former had lower total ADL scores and BADL and IADL scores. All three scores were associated with hypometabolism in bilateral parietal lobes (especially bilateral superior parietal gyri) at the whole-brain level, PCA-related hypometabolism level, and PCA-specific hypometabolism level. A cluster that included the right superior parietal gyrus showed an ADL×group interaction effect that was correlated with the total ADL score in the PCA group (r = –0.6908, p = 9.3599e–5) but not in the tAD group (r = 0.1006, p = 0.5904). There was no significant association between gray matter density and ADL scores. Conclusion: Hypometabolism in bilateral superior parietal lobes contributes to a decline in ADL in patients with PCA and can potentially be targeted by noninvasive neuromodulatory interventions.

As subsystems of the Asian summer monsoon, summer precipitation variations in India and the northern part of eastern China (NEC) are physically connected. This study noted that the connection has been significantly enhanced after 1999 compared to 1979–98, which is due to the strengthened water vapor transportation connection between the two regions. That is associated with interdecadal variations of the combined effects of El Niño–Southern Oscillation (ENSO) and sea surface temperature anomalies (SSTAs) over the tropical Indian Ocean (TIO) on the northwest Pacific subtropical high (NWPSH) and the Indo-Pacific Walker cell. Against the background of La Niña, the strengthened NWPSH and Indo-Pacific Walker cell favor water vapor transport to India and the NEC since 1999. Accordingly, summer precipitation in the two regions increases simultaneously, leading to the enhancement of the summer precipitation teleconnection between them. In addition, the influence of TIO SSTAs and the Indian Ocean dipole (IOD) on Indo-Pacific circulations decreases, which further enhances the relative importance of ENSO on the summer precipitation in the two regions. However, during 1979–98, La Niña SSTAs has weak effects on the NWPSH and Indo-Pacific Walker cell, the negative TIO SSTAs significantly weaken NWPSH, and the negative IOD also obstructs the westward extension of the Indo-Pacific Walker cell. Circulations and water vapor transportation related to the Indian Ocean and NEC summer precipitation are inconsistent, resulting in a weak precipitation teleconnection between them. The above conclusions are also validated by extreme case analysis and CMIP6 models. Significance Statement This paper mainly studies the influences of different types of ENSO and Indian Ocean SSTAs on the interdecadal variations of the summer precipitation relationship between India and the northern part of eastern China (NEC). We find that the summer precipitation relationship between them is strengthened again after 1999, which deepens the understanding of summer precipitation in Asia and has great significance for improving dynamic models’ prediction skills. The interdecadal variations of the combined effects of the Indian and Pacific Oceans are the fundamental reasons for the interdecadal variations of precipitation relationships, which promotes the understanding of interactions of different oceans and their impacts on Asian climate.

Nous avons utilisé les données des cycles de 2020 et de 2021 de l’Enquête sur la COVID-19 et la santé mentale pour étudier les idées suicidaires chez les adultes de 18 à 34 ans au Canada. La prévalence des idées suicidaires chez les adultes de 18 à 34 ans était de 4,2 % à l’automne 2020 et de 8,0 % au printemps 2021. C’est le sous-groupe des adultes de 18 à 24 ans qui a connu la prévalence la plus élevée, avec 10,7 % au printemps 2021. La prévalence variait en fonction des caractéristiques sociodémographiques et tendait à être plus élevée chez les personnes vivant dans un milieu défavorisé sur le plan matériel. Les idées suicidaires se sont révélées fortement associées aux facteurs de stress liés à la pandémie subis par les répondants.

Well-annotated medical datasets enable deep neural networks (DNNs) to gain strong power in extracting lesion-related features. Building such large and well-designed medical datasets is costly due to the need for high-level expertise. Model pre-training based on ImageNet is a common practice to gain better generalization when the data amount is limited. However, it suffers from the domain gap between natural and medical images. In this work, we pre-train DNNs on ultrasound (US) domains instead of ImageNet to reduce the domain gap in medical US applications. To learn US image representations based on unlabeled US videos, we propose a novel meta-learning-based contrastive learning method, namely Meta Ultrasound Contrastive Learning (Meta-USCL). To tackle the key challenge of obtaining semantically consistent sample pairs for contrastive learning, we present a positive pair generation module along with an automatic sample weighting module based on meta-learning. Experimental results on multiple computer-aided diagnosis (CAD) problems, including pneumonia detection, breast cancer classification, and breast tumor segmentation, show that the proposed self-supervised method reaches state-of-the-art (SOTA). The codes are available at https://github.com/Schuture/Meta-USCL.