2-Arylbenzofurans from the stems and leaves of Artocarpus tonkinensis and their potential antiproliferative activities.

Phytochemical study on the stems and leaves of Artocarpus tonkinensis led to the isolation of a new 2-arylbenzofuran, artocartone (1), as well as seven known 2-arylbenzofurans (2-8). The chemical structure of 1 was established by means of comprehensive spectroscopic analyses and the known compounds were determined by comparing their MS and NMR data with those reported data in literature. The antiproliferative activities of all isolates 1-8 against five human cancer cell lines: HL-60, SMMC-7721, A-375, MCF-7 and SW480 in vitro were evaluated. As a result, compounds 1- 8 displayed notable antiproliferative activities against various human cancer cell lines with IC50 values in the range of 0.28 ± 0.05-26.89 ± 0.18 µM.

TFMKC: Tuning-Free Multiple Kernel Clustering Coupled With Diverse Partition Fusion.

Clustering is a popular research pipeline in unsupervised learning to find potential groupings. As a representative paradigm in multiple kernel clustering (MKC), late fusion-based models learn a consistent partition across multiple base kernels. Despite their promising performance, a common concern is the limited representation capacity caused by the inflexible fusion mechanism. Concretely, the representations are constrained by truncated- k Eigen-decomposition (EVD) without fully exploiting potential information. An intuitive idea to alleviate this concern is to generate a set of augmented partitions and then select the optimal partition by fine-tuning. However, this is overlimited by: 1) introducing undesired hyperparameters and dataset-related consequences; 2) neglecting rich information across diverse partitions; and 3) expensive parameter-tuning costs. To address these problems, we propose transforming the challenging problem of directly determining the optimal partition (optimal parameter) into a diverse partition fusion (parameter ensemble) problem. We design a novel flexible fusion mechanism called tuning-free multiple kernel clustering coupled with diverse partition fusion (TFMKC) by reweighting diverse partitions through optimization, achieving an optimal consensus partition by integrating diverse and complementary information rather than traditional fine-tuning, and distinguishing our work from existing methods. Extensive experiments verify that TFMKC achieves competitive effectiveness and efficiency over comparison baselines. The code can be accessed at https://github.com/ZJP/TFMKC.

Differentiated Anchor Quantity Assisted Incomplete Multiview Clustering Without Number-Tuning.

Incomplete multiview clustering (IMVC) generally requires the number of anchors to be the same in all views. Also, this number needs to be tuned with extra manual efforts. This not only degenerates the diversity of multiview data but also limits the model's scalability. For generating differentiated numbers of anchors without tuning, in this article we devise a novel framework named DAQINT. To be specific, the most perfect solution is to jointly find the optimal number of anchors that belongs to respective view. Regretfully, it is extremely time consuming. In view of this, we choose to first offer a set of anchor numbers for each view, and then integrate their contributions by adaptive weighting to approximate the optimal number. In particular, these offered numbers are all predefined and do not require any tuning. Through adaptively weighting them, we hold that this equivalently makes each view enjoy a different number of anchors. Accordingly, the bipartite graphs generated on all views are with diverse scales. Besides exploring multiview features more deeply, they also balance the importance between views. Then, to fuse these multiscale bipartite graphs, we design a combination strategy that owns linear computation and storage overheads. Afterward, to solve the resulting optimization problem, we also carefully develop a three-step iterative algorithm with linear complexities and demonstrated convergence. Experiments on the multiple public datasets validate the superiority of DAQINT against several advanced IMVC methods, such as on Mfeat, DAQINT surpasses the competitors like MKC, EEIMVC, FLSD, DSIMVC, IMVC-CBG, and DCP by 36.65%, 6.33%, 48.53%, 22.46%, 15.06%, and 32.04%, respectively, in ACC.

Scalable and Structural Multi-View Graph Clustering With Adaptive Anchor Fusion.

Anchor graph has been recently proposed to accelerate multi-view graph clustering and widely applied in various large-scale applications. Different from capturing full instance relationships, these methods choose small portion anchors among each view, construct single-view anchor graphs and combine them into the unified graph. Despite its efficiency, we observe that: (i) Existing mechanism adopts a separable two-step procedure-anchor graph construction and individual graph fusion, which may degrade the clustering performance. (ii)These methods determine the number of selected anchors to be equal among all the views, which may destruct the data distribution diversity. A more flexible multi-view anchor graph fusion framework with diverse magnitudes is desired to enhance the representation ability. (iii) During the latter fusion process, current anchor graph fusion framework follows simple linearly-combined style while the intrinsic clustering structures are ignored. To address these issues, we propose a novel scalable and flexible anchor graph fusion framework for multi-view graph clustering method in this paper. Specially, the anchor graph construction and graph alignment are jointly optimized in our unified framework to boost clustering quality. Moreover, we present a novel structural alignment regularization to adaptively fuse multiple anchor graphs with different magnitudes. In addition, our proposed method inherits the linear complexity of existing anchor strategies respecting to the sample number, which is time-economical for large-scale data. Experiments conducted on various benchmark datasets demonstrate the superiority and effectiveness of the newly proposed anchor graph fusion framework against the existing state-of-the-arts over the clustering performance promotion and time expenditure. Our code is publicly available at https://github.com/wangsiwei2010/SMVAGC-SF.

Regularized Instance Weighting Multiview Clustering via Late Fusion Alignment.

Multiview clustering has become a prominent research topic in data analysis, with wide-ranging applications across various fields. However, the existing late fusion multiview clustering (LFMVC) methods still exhibit some limitations, including variable importance and contributions and a heightened sensitivity to noise and outliers during the alignment process. To tackle these challenges, we propose a novel regularized instance weighting multiview clustering via late fusion alignment (R-IWLF-MVC), which considers the instance importance from various views, enabling information integration to be more effective. Specifically, we assign each sample an importance attribute to enable the learning process to focus more on the key sample nodes and avoid being influenced by noise or outliers, while laying the groundwork for the fusion of different views. In addition, we continue to employ late fusion alignment to integrate base clustering from various views and introduce a new regularization term with prior knowledge to ensure that the learning process does not deviate too much from the expected results. After that, we design a three-step alternating optimization strategy with proven convergence for the resultant problem. Our proposed approach has been extensively evaluated on multiple real-world datasets, demonstrating its superiority to state-of-the-art methods.

[Chemical constituents from stems and leaves of Artocarpus tonkinensis and their inhibitory effects on proliferation of synovioblasts in vitro].

The chemical constituents from the stems and leaves of Artocarpus tonkinensis in Artocarpus of Moraceae were systematically studied by means of silica gel, octadecylsilyl(ODS), and Sephadex LH-20 gel column chromatographies, as well as preparative high-performance liquid chromatography(Pre-HPLC) and a variety of chromatographic separation techniques. The spectral data and physicochemical properties of the compounds were obtained from separation and compared with those of the compounds reported in the literature. As a result, 11 compounds isolated from the 90% ethanol extract of the stems and leaves of A. tonkinensis were identified as artocatonkine(1), 5,6,7,4'-tetramethoxyflavone(2), apigenin-4'-O-ß-D-glucoside(3), rayalinol(4), psorachalcone A(5), 4-ketopinoresinol(6), ficusesquilignan B(7), pinnatifidanin AI(8), pinnatifidanin A(9), O-methylmellein(10), and trans-4-hydroxymellein(11). Among these compounds, compound 1 was a new prenylated flavone, and compounds 2-11 were isolated from the plants belonging to the genus Artocarpus for the first time. Furthermore, all compounds 1-11 were evaluated for their anti-rheumatoid arthritis activities, and the MTS method was used to measure their inhibitory effects on the proliferation of synovioblasts in vitro. The results of activity evaluation showed that flavonoid compounds 1-3, 5, and lignan compounds 8 and 9 displayed significant anti-rheumatoid arthritis activities, showing the IC_(50) values in inhibiting the proliferation of synovioblasts MH7A from(6.38±0.06) µmol·L~(-1) to(168.58±0.28)µmol·L~(-1).

Category Alignment Mechanism for Few-Shot Image Classification.

While humans can excel at image classification tasks by comparing a few images, existing metric-based few-shot classification methods are still not well adapted to novel tasks. Performance declines rapidly when encountering new patterns, as feature embeddings cannot effectively encode discriminative properties. Moreover, existing matching methods inadequately utilize support set samples, focusing only on comparing query samples to category prototypes without exploiting contrastive relationships across categories for discriminative features. In this work, we propose a method where query samples select their most category-representative features for matching, making feature embeddings adaptable and category-related. We introduce a category alignment mechanism (CAM) to align query image features with different categories. CAM ensures features chosen for matching are distinct and strongly correlated to intra-and inter-contrastive relationships within categories, making extracted features highly related to their respective categories. CAM is parameter-free, requires no extra training to adapt to new tasks, and adjusts features for matching when task categories change. We also implement a cross-validation-based feature selection technique for support samples, generating more discriminative category prototypes. We implement two versions of inductive and transductive inference and conduct extensive experiments on six datasets to demonstrate the effectiveness of our algorithm. The results indicate that our method consistently yields performance improvements on benchmark tasks and surpasses the current state-of-the-art methods.

One-Step Multi-View Clustering With Diverse Representation.

Multi-View clustering has attracted broad attention due to its capacity to utilize consistent and complementary information among views. Although tremendous progress has been made recently, most existing methods undergo high complexity, preventing them from being applied to large-scale tasks. Multi-View clustering via matrix factorization is a representative to address this issue. However, most of them map the data matrices into a fixed dimension, limiting the model's expressiveness. Moreover, a range of methods suffers from a two-step process, i.e., multimodal learning and the subsequent k -means, inevitably causing a suboptimal clustering result. In light of this, we propose a one-step multi-view clustering with diverse representation (OMVCDR) method, which incorporates multi-view learning and k -means into a unified framework. Specifically, we first project original data matrices into various latent spaces to attain comprehensive information and auto-weight them in a self-supervised manner. Then, we directly use the information matrices under diverse dimensions to obtain consensus discrete clustering labels. The unified work of representation learning and clustering boosts the quality of the final results. Furthermore, we develop an efficient optimization algorithm with proven convergence to solve the resultant problem. Comprehensive experiments on various datasets demonstrate the promising clustering performance of our proposed method. The code is publicly available at https://github.com/wanxinhang/OMVCDR.

On the Consistency and Large-Scale Extension of Multiple Kernel Clustering.

Existing multiple kernel clustering (MKC) algorithms have two ubiquitous problems. From the theoretical perspective, most MKC algorithms lack sufficient theoretical analysis, especially the consistency of learned parameters, such as the kernel weights. From the practical perspective, the high complexity makes MKC unable to handle large-scale datasets. This paper tries to address the above two issues. We first make a consistency analysis of an influential MKC method named Simple Multiple Kernel k-Means (SimpleMKKM). Specifically, suppose that ∧γn are the kernel weights learned by SimpleMKKM from the training samples. We also define the expected version of SimpleMKKM and denote its solution as γ*. We establish an upper bound of ||∧γn-γ*||∞ in the order of ~O(1/√n), where n is the sample number. Based on this result, we also derive its excess clustering risk calculated by a standard clustering loss function. For the large-scale extension, we replace the eigen decomposition of SimpleMKKM with singular value decomposition (SVD). Consequently, the complexity can be decreased to O(n) such that SimpleMKKM can be implemented on large-scale datasets. We then deduce several theoretical results to verify the approximation ability of the proposed SVD-based method. The results of comprehensive experiments demonstrate the superiority of the proposed method.

Fast Continual Multi-View Clustering With Incomplete Views.

Multi-view clustering (MVC) has attracted broad attention due to its capacity to exploit consistent and complementary information across views. This paper focuses on a challenging issue in MVC called the incomplete continual data problem (ICDP). Specifically, most existing algorithms assume that views are available in advance and overlook the scenarios where data observations of views are accumulated over time. Due to privacy considerations or memory limitations, previous views cannot be stored in these situations. Some works have proposed ways to handle this problem, but all of them fail to address incomplete views. Such an incomplete continual data problem (ICDP) in MVC is difficult to solve since incomplete information with continual data increases the difficulty of extracting consistent and complementary knowledge among views. We propose Fast Continual Multi-View Clustering with Incomplete Views (FCMVC-IV) to address this issue. Specifically, the method maintains a scalable consensus coefficient matrix and updates its knowledge with the incoming incomplete view rather than storing and recomputing all the data matrices. Considering that the given views are incomplete, the newly collected view might contain samples that have yet to appear; two indicator matrices and a rotation matrix are developed to match matrices with different dimensions. In addition, we design a three-step iterative algorithm to solve the resultant problem with linear complexity and proven convergence. Comprehensive experiments conducted on various datasets demonstrate the superiority of FCMVC-IV over the competing approaches. The code is publicly available at https://github.com/wanxinhang/FCMVC-IV.

Subgraph Propagation and Contrastive Calibration for Incomplete Multiview Data Clustering.

The success of multiview raw data mining relies on the integrity of attributes. However, each view faces various noises and collection failures, which leads to a condition that attributes are only partially available. To make matters worse, the attributes in multiview raw data are composed of multiple forms, which makes it more difficult to explore the structure of the data especially in multiview clustering task. Due to the missing data in some views, the clustering task on incomplete multiview data confronts the following challenges, namely: 1) mining the topology of missing data in multiview is an urgent problem to be solved; 2) most approaches do not calibrate the complemented representations with common information of multiple views; and 3) we discover that the cluster distributions obtained from incomplete views have a cluster distribution unaligned problem (CDUP) in the latent space. To solve the above issues, we propose a deep clustering framework based on subgraph propagation and contrastive calibration (SPCC) for incomplete multiview raw data. First, the global structural graph is reconstructed by propagating the subgraphs generated by the complete data of each view. Then, the missing views are completed and calibrated under the guidance of the global structural graph and contrast learning between views. In the latent space, we assume that different views have a common cluster representation in the same dimension. However, in the unsupervised condition, the fact that the cluster distributions of different views do not correspond affects the information completion process to use information from other views. Finally, the complemented cluster distributions for different views are aligned by contrastive learning (CL), thus solving the CDUP in the latent space. Our method achieves advanced performance on six benchmarks, which validates the effectiveness and superiority of our SPCC.

SAMCL: Subgraph-Aligned Multiview Contrastive Learning for Graph Anomaly Detection.

Graph anomaly detection (GAD) has gained increasing attention in various attribute graph applications, i.e., social communication and financial fraud transaction networks. Recently, graph contrastive learning (GCL)-based methods have been widely adopted as the mainstream for GAD with remarkable success. However, existing GCL strategies in GAD mainly focus on node-node and node-subgraph contrast and fail to explore subgraph-subgraph level comparison. Furthermore, the different sizes or component node indices of the sampled subgraph pairs may cause the "nonaligned" issue, making it difficult to accurately measure the similarity of subgraph pairs. In this article, we propose a novel subgraph-aligned multiview contrastive approach for graph anomaly detection, named SAMCL, which fills the subgraph-subgraph contrastive-level blank for GAD tasks. Specifically, we first generate the multiview augmented subgraphs by capturing different neighbors of target nodes forming contrasting subgraph pairs. Then, to fulfill the nonaligned subgraph pair contrast, we propose a subgraph-aligned strategy that estimates similarities with the Earth mover's distance (EMD) of both considering the node embedding distributions and typology awareness. With the newly established similarity measure for subgraphs, we conduct the interview subgraph-aligned contrastive learning module to better detect changes for nodes with different local subgraphs. Moreover, we conduct intraview node-subgraph contrastive learning to supplement richer information on abnormalities. Finally, we also employ the node reconstruction task for the masked subgraph to measure the local change of the target node. Finally, the anomaly score for each node is jointly calculated by these three modules. Extensive experiments conducted on benchmark datasets verify the effectiveness of our approach compared to existing state-of-the-art (SOTA) methods with significant performance gains (up to 6.36% improvement on ACM). Our code can be verified at https://github.com/hujingtao/SAMCL.

Sparse Low-Rank Multi-View Subspace Clustering With Consensus Anchors and Unified Bipartite Graph.

Anchor technology is popularly employed in multi-view subspace clustering (MVSC) to reduce the complexity cost. However, due to the sampling operation being performed on each individual view independently and not considering the distribution of samples in all views, the produced anchors are usually slightly distinguishable, failing to characterize the whole data. Moreover, it is necessary to fuse multiple separated graphs into one, which leads to the final clustering performance heavily subject to the fusion algorithm adopted. What is worse, existing MVSC methods generate dense bipartite graphs, where each sample is associated with all anchor candidates. We argue that this dense-connected mechanism will fail to capture the essential local structures and degrade the discrimination of samples belonging to the respective near anchor clusters. To alleviate these issues, we devise a clustering framework named SL-CAUBG. Specifically, we do not utilize sampling strategy but optimize to generate the consensus anchors within all views so as to explore the information between different views. Based on the consensus anchors, we skip the fusion stage and directly construct the unified bipartite graph across views. Most importantly, l1 norm and Laplacian-rank constraints employed on the unified bipartite graph make it capture both local and global structures simultaneously. l1 norm helps eliminate the scatters between anchors and samples by constructing sparse links and guarantees our graph to be with clear anchor-sample affinity relationship. Laplacian-rank helps extract the global characteristics by measuring the connectivity of unified bipartite graph. To deal with the nondifferentiable objective function caused by l1 norm, we adopt an iterative re-weighted method and the Newton's method. To handle the nonconvex Laplacian-rank, we equivalently transform it as a convex trace constraint. We also devise a four-step alternate method with linear complexity to solve the resultant problem. Substantial experiments show the superiority of our SL-CAUBG.

Graph Clustering with High-Order Contrastive Learning.

Graph clustering is a fundamental and challenging task in unsupervised learning. It has achieved great progress due to contrastive learning. However, we find that there are two problems that need to be addressed: (1) The augmentations in most graph contrastive clustering methods are manual, which can result in semantic drift. (2) Contrastive learning is usually implemented on the feature level, ignoring the structure level, which can lead to sub-optimal performance. In this work, we propose a method termed Graph Clustering with High-Order Contrastive Learning (GCHCL) to solve these problems. First, we construct two views by Laplacian smoothing raw features with different normalizations and design a structure alignment loss to force these two views to be mapped into the same space. Second, we build a contrastive similarity matrix with two structure-based similarity matrices and force it to align with an identity matrix. In this way, our designed contrastive learning encompasses a larger neighborhood, enabling our model to learn clustering-friendly embeddings without the need for an extra clustering module. In addition, our model can be trained on a large dataset. Extensive experiments on five datasets validate the effectiveness of our model. For example, compared to the second-best baselines on four small and medium datasets, our model achieved an average improvement of 3% in accuracy. For the largest dataset, our model achieved an accuracy score of 81.92%, whereas the compared baselines encountered out-of-memory issues.

Improving Embedding Generalization in Few-Shot Learning With Instance Neighbor Constraints.

Recently, metric-based meta-learning methods have been effectively applied to few-shot image classification. These methods classify images based on the relationship between samples in an embedding space, avoiding over-fitting that can occur when training classifiers with limited samples. However, finding an embedding space with good generalization properties remains a challenge. Our work highlights that having an initial manifold space that preserves sample neighbor relationships can prevent the metric model from reaching a suboptimal solution. We propose a feature learning method that leverages Instance Neighbor Constraints (INC). This theory is thoroughly evaluated and analyzed through experiments, demonstrating its effectiveness in improving the efficiency of learning and the overall performance of the model. We further integrate the INC into an alternate optimization training framework (AOT) that leverages both batch learning and episode learning to better optimize the metric-based model. We conduct extensive experiments on 5-way 1-shot and 5-way 5-shot settings on four popular few-shot image benchmarks: miniImageNet, tieredImageNet, Fewshot-CIFAR100 (FC100), and Caltech-UCSD Birds-200-2011(CUB). Results show that our method achieves consistent performance gains on benchmarks and state-of-the-art performance. Our findings suggest that initializing the embedding space appropriately and leveraging both batch and episode learning can significantly improve few-shot learning performance.

Carotid-cavernous fistula following mechanical thrombectomy of the tortuous internal carotid artery: A case report.

BACKGROUND: A carotid-cavernous fistula (CCF) is an abnormal connection between the internal carotid artery (ICA) and the cavernous sinus. Although direct CCFs typically result from trauma or as an iatrogenic complication of neuroendovascular procedures, they can occur as surgery-related complications after mechanical thrombectomy (MT). With the widespread use of MT in patients with acute ischemic stroke complicated with large vessel occlusion, it is important to document CCF following MT and how to avoid them. In this study, we present a case of a patient who developed a CCF following MT and describe in detail the characteristics of ICA tortuosity in this case. CASE SUMMARY: A 60-year-old woman experienced weakness in the left upper and lower limbs as well as difficulty speaking for 4 h. The neurological examination revealed left central facial paralysis and left hemiplegia, with a National Institutes of Health Stroke Scale score of 9. Head magnetic resonance imaging revealed an acute cerebral infarction in the right basal ganglia and radial crown. Magnetic resonance angiography demonstrated an occlusion of the right ICA and middle cerebral artery. Digital subtraction angiography demonstrated distal occlusion of the cervical segment of the right ICA. We performed suction combined with stent thrombectomy. Then, postoperative angiography was performed, which showed a right CCF. One month later, CCF embolization was performed, and the patient's clinical symptoms have significantly improved 5 mo after the operation. CONCLUSION: Although a CCF is a rare complication after MT, it should be considered. Understanding the tortuosity of the internal carotid-cavernous sinus may help predict the complexity of MT and avoid this complication.

Tri-comb generation with a dual-ring structure.

By introducing a third measurement comb with different repetition frequencies (Δ f r e p ), the tri-comb spectroscopy technique overcomes the ambiguity problem of the original dual-comb spectroscopy technique and eliminates physical delay stages in multidimensional coherent spectroscopy. Nowadays, tri-comb generation based on three frequency-stabilized comb lasers is overly complicated and costly for many potential applications. Previous research on single-cavity dual-combs inspired research on single-cavity tri-combs. However, the currently reported tri-comb structures cannot achieve independently controllable pulses. This paper shows a dual-ring tri-comb seed-source structure using wavelength-based multiplexing in one of the rings. The wavelength and power of the output pulse are independently controlled by using the dual-ring structure. The Δ f r e p of wavelength multiplexing-based dual-comb output can be tuned by adjusting the intra-ring polarization controller (PC). In the case of single-wavelength mode-locking, the PC can be adjusted to achieve a wavelength tuning range of nearly 20 nm. The tri-comb source could offer an attractive alternative solution as a low-complexity light source for field-deployable multi-comb metrology applications.

Iterative Deep Structural Graph Contrast Clustering for Multiview Raw Data.

Multiview clustering has attracted increasing attention to automatically divide instances into various groups without manual annotations. Traditional shadow methods discover the internal structure of data, while deep multiview clustering (DMVC) utilizes neural networks with clustering-friendly data embeddings. Although both of them achieve impressive performance in practical applications, we find that the former heavily relies on the quality of raw features, while the latter ignores the structure information of data. To address the above issue, we propose a novel method termed iterative deep structural graph contrast clustering (IDSGCC) for multiview raw data consisting of topology learning (TL), representation learning (RL), and graph structure contrastive learning to achieve better performance. The TL module aims to obtain a structured global graph with constraint structural information and then guides the RL to preserve the structural information. In the RL module, graph convolutional network (GCN) takes the global structural graph and raw features as inputs to aggregate the samples of the same cluster and keep the samples of different clusters away. Unlike previous methods performing contrastive learning at the representation level of the samples, in the graph contrastive learning module, we conduct contrastive learning at the graph structure level by imposing a regularization term on the similarity matrix. The credible neighbors of the samples are constructed as positive pairs through the credible graph, and other samples are constructed as negative pairs. The three modules promote each other and finally obtain clustering-friendly embedding. Also, we set up an iterative update mechanism to update the topology to obtain a more credible topology. Impressive clustering results are obtained through the iterative mechanism. Comparative experiments on eight multiview datasets show that our model outperforms the state-of-the-art traditional and deep clustering competitors.

Task-Related Saliency for Few-Shot Image Classification.

A weakness of the existing metric-based few-shot classification method is that task-unrelated objects or backgrounds may mislead the model since the small number of samples in the support set is insufficient to reveal the task-related targets. An essential cue of human wisdom in the few-shot classification task is that they can recognize the task-related targets by a glimpse of support images without being distracted by task-unrelated things. Thus, we propose to explicitly learn task-related saliency features and make use of them in the metric-based few-shot learning schema. We divide the tackling of the task into three phases, namely, the modeling, the analyzing, and the matching. In the modeling phase, we introduce a saliency sensitive module (SSM), which is an inexact supervision task jointly trained with a standard multiclass classification task. SSM not only enhances the fine-grained representation of feature embedding but also can locate the task-related saliency features. Meanwhile, we propose a self-training-based task-related saliency network (TRSN) which is a lightweight network to distill task-related salience produced by SSM. In the analyzing phase, we freeze TRSN and use it to handle novel tasks. TRSN extracts task-relevant features while suppressing the disturbing task-unrelated features. We, therefore, can discriminate samples accurately in the matching phase by strengthening the task-related features. We conduct extensive experiments on five-way 1-shot and 5-shot settings to evaluate the proposed method. Results show that our method achieves a consistent performance gain on benchmarks and achieves the state-of-the-art.

A single-cell atlas identifies pretreatment features of primary imatinib resistance in chronic myeloid leukemia.

Primary resistance to tyrosine kinase inhibitors (TKIs) is a significant barrier to optimal outcomes in chronic myeloid leukemia (CML), but factors contributing to response heterogeneity remain unclear. Using single-cell RNA (scRNA) sequencing, we identified 8 statistically significant features in pretreatment bone marrow, which correlated with either sensitivity (major molecular response or MMR) or extreme resistance to imatinib (eventual blast crisis [BC] transformation). Employing machine-learning, we identified leukemic stem cell (LSC) and natural killer (NK) cell gene expression profiles predicting imatinib response with >80% accuracy, including no false positives for predicting BC. A canonical erythroid-specifying (TAL1/KLF1/GATA1) regulon was a hallmark of LSCs from patients with MMR and was associated with erythroid progenitor [ERP] expansion in vivo (P < .05), and a 2- to 10-fold (6.3-fold in group A vs 1.09-fold in group C) erythroid over myeloid bias in vitro. Notably, ERPs demonstrated exquisite TKI sensitivity compared with myeloid progenitors (P < .001). These LSC features were lost with progressive resistance, and MYC- and IRF1-driven inflammatory regulons were evident in patients who progressed to transformation. Patients with MMR also exhibited a 56-fold expansion (P < .01) of a normally rare subset of hyperfunctional adaptive-like NK cells, which diminished with progressive resistance, whereas patients destined for BC accumulated inhibitory NKG2A+ NK cells favoring NK cell tolerance. Finally, we developed antibody panels to validate our scRNA-seq findings. These panels may be useful for prospective studies of primary resistance, and in assessing the contribution of predetermined vs acquired factors in TKI response heterogeneity.