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Cathepsin B(CTSB) is a key protease within the lysosomal protease family and is recognized as a tumor-promoting factor that exerts a substantial impact on cancer progression. It plays a critical role in the initiation, proliferation, metastasis, and angiogenesis of cancer, significantly advancing the disease. This review offers a concise overview of the structure and biological functions of CTSB, clarifying its relationship with cancer and the role it plays in the disease's progression. Additionally, we discuss the association between CTSB and several common malignant tumors of the urinary system, highlighting its potential role and clinical significance within these tumors, as well as the challenges that remain.

RNA modification is a post-transcriptional level of regulation that is widely distributed in all types of RNAs, including mRNA, tRNA, rRNA, miRNA, and lncRNA, where N6-methyladenine (m 6 A) is the most abundant mRNA methylation modification. Significant evidence has depicted that m 6 A modifications are closely related to human diseases, especially cancer, and play pivotal roles in RNA transcription, splicing, stabilization, and translation processes. The most common urological cancers include prostate, bladder, kidney, and testicular cancers, accounting for a certain proportion of human cancers, with an ever-increasing incidence and mortality. The recurrence, systemic metastasis, poor prognosis, and drug resistance of urologic tumors have prompted the identification of new therapeutic targets and mechanisms. Research on m 6 A modifications may provide new solutions to the current puzzles. In this review, we provide a comprehensive overview of the key roles played by RNA modifications, especially m 6 A modifications, in urologic cancers, as well as recent research advances in diagnostics and molecularly targeted therapies.

Programmed self-assembly of nucleic acids is a powerful approach for nano-constructions. The assembled nanostructures have been explored for various applications. However, nucleic acid assembly often requires chemical or in vitro enzymatical synthesis of DNA or RNA, which is not a cost-effective production method on a large scale. In addition, the difficulty of cellular delivery limits the in vivo applications. Herein we report a strategy that mimics protein production. Gene-encoded DNA duplexes are transcribed into single-stranded RNAs, which self-fold into well-defined RNA nanostructures in the same way as polypeptide chains fold into proteins. The resulting nanostructure contains only one component RNA molecule. This approach allows both in vitro and in vivo production of RNA nanostructures. In vivo synthesized RNA strands can fold into designed nanostructures inside cells. This work not only suggests a way to synthesize RNA nanostructures on a large scale and at a low cost but also facilitates the in vivo applications. RNA nanostructures have been demonstrated in a range of biological applications, but their assembly and delivery to cells is difficult. Here the authors demonstrate the in vivo assembly of a RNA nanostructure from a single transcript inside the cellular environment.

The precise characterization of interfacial structure for polymer-bonded explosive (PBX) modification is challenging due to the complexity of the interface. The inherent properties between explosive and binders affect interface bonding, lowering the interfacial strength in unpredicted ways. Surface modification is an effective method to balance multi-utility in materials engineering, which has been carried out to design of high-performance composites with improved interfacial properties. Experimental methods may determine the coating shell for capturing the PBX structures. Various approaches were applied to characterize the structure and properties of PBX interface, including molecular dynamics-based computational models to predict bonding properties. In this review, systematic organization were provided and summarized with detective methods on the surface and interface of explosives. Meanwhile, the usage scenarios and limitations of each measurement were proposed. Conclusions from the review yield useful guidelines and references for systematical characterization on the modification of explosive and can be extended to other materials. [Display omitted] •Surface characterization of modified explosive was systematically summarized firstly.•Four types of measurements with typical applied examples were demonstrated in detail.•Conducive to mastering the surface characterization methods of energetic materials.

Background and aims: Asthma–chronic obstructive pulmonary disease (COPD) overlap (ACO) is poorly recognized in China. Our study determined the distribution of ACO and its clinical characteristics among patients (aged ⩾40 years) with airflow limitation at Chinese tertiary hospitals. Methods: This cross-sectional, non-interventional study (NCT02600221), conducted between December 2015 and October 2016 in 20 Tier-3 Chinese hospitals, included patients aged ⩾40 years with post-bronchodilator (BD) FEV1/FVC <0.7. The primary variable was distribution of ACO in adults with post-BD forced expiratory volume /forced vital capacity (FEV1/FVC) <0.7 based on Global Initiative for Chronic Obstructive Lung Disease (GOLD) 2015 and 2017 reports. Other variables included determination of characteristics of ACO and its clinical recognition rate. Results: In 2003 patients (mean age 62.30 ± 9.86 years), distribution of ACO, COPD and asthma were 37.40%, 48.50% and 14.10%, respectively. Proportions of patients with A, B, C and D grouping were 11.70%, 31.00%, 6.90% and 50.30% as per GOLD 2017, whereas they were 15.10%, 51.10%, 3.60% and 30.20% as per GOLD 2015. Similar clinical symptoms were reported in all three groups. A higher percentage of ACO patients presented with dyspnea, wheezing and chest tightness. Compared with the COPD group, a greater proportion of ACO patients reported wheezing (74.6% and 65.40%), while a lower proportion in the ACO group reported cough (79.40% versus 82.70%) and expectoration (76.50% versus 81.60%). Blood eosinophil count ⩾0.3 × 109/L was observed in 34.6% of ACO patients. The clinical recognition rate of ACO was 31.4%. Conclusion: Despite ACO affecting two-fifths of the study population, the initial diagnosis rate was low at 6% in China, thus warranting concerted efforts to improve ACO diagnosis. ClinicalTrials.gov: [ClinicalTrials.gov identifier: NCT02600221] registered 22 October 2015, https://clinicaltrials.gov/ct2/show/NCT02600221 The reviews of this paper are available via the supplemental material section.

Rubus ellipticus Sm. var. obcordatus Focke is an important species in the phylogeny and evolution of genus Rubus L. in the family Rosaceae. Its chloroplast genome, as reported in this study, is 155,656 bp in size, and it has an average GC content of 37.14%. The chloroplast genome showed a typical quadripartite structure comprising a large single copy (LSC) region (85,388 bp) and a small single copy (SSC) region (18,730 bp), which were separated by a pair of inverted repeats (IRs, 25,769 bp). In total, this plastome was found to contain 129 different genes, including 85 protein-coding genes, 36 tRNA genes, and eight rRNA genes. The completed chloroplast genome of R. ellipticus var. obcordatus will set a new insight into clarifying the phylogeny and genomic studies in genus Rubus of the family Rosaceae.

One key challenge in Out-of-Distribution (OOD) detection is the absence of ground-truth OOD samples during training. One principled approach to address this issue is to use samples from external datasets as outliers (i.e., pseudo OOD samples) to train OOD detectors. However, we find empirically that the outlier samples often present a distribution shift compared to the true OOD samples, especially in Long-Tailed Recognition (LTR) scenarios, where ID classes are heavily imbalanced, \\ie, the true OOD samples exhibit very different probability distribution to the head and tailed ID classes from the outliers. In this work, we propose a novel approach, namely normalized outlier distribution adaptation (AdaptOD), to tackle this distribution shift problem. One of its key components is dynamic outlier distribution adaptation that effectively adapts a vanilla outlier distribution based on the outlier samples to the true OOD distribution by utilizing the OOD knowledge in the predicted OOD samples during inference. Further, to obtain a more reliable set of predicted OOD samples on long-tailed ID data, a novel dual-normalized energy loss is introduced in AdaptOD, which leverages class- and sample-wise normalized energy to enforce a more balanced prediction energy on imbalanced ID samples. This helps avoid bias toward the head samples and learn a substantially better vanilla outlier distribution than existing energy losses during training. It also eliminates the need of manually tuning the sensitive margin hyperparameters in energy losses. Empirical results on three popular benchmarks for OOD detection in LTR show the superior performance of AdaptOD over state-of-the-art methods. Code is available at https://github.com/mala-lab/AdaptOD.

Existing prompt learning methods have shown certain capabilities in Out-of-Distribution (OOD) detection, but the lack of OOD images in the target dataset in their training can lead to mismatches between OOD images and In-Distribution (ID) categories, resulting in a high false positive rate. To address this issue, we introduce a novel OOD detection method, named 'NegPrompt', to learn a set of negative prompts, each representing a negative connotation of a given class label, for delineating the boundaries between ID and OOD images. It learns such negative prompts with ID data only, without any reliance on external outlier data. Further, current methods assume the availability of samples of all ID classes, rendering them ineffective in open-vocabulary learning scenarios where the inference stage can contain novel ID classes not present during training. In contrast, our learned negative prompts are transferable to novel class labels. Experiments on various ImageNet benchmarks show that NegPrompt surpasses state-of-the-art prompt-learning-based OOD detection methods and maintains a consistent lead in hard OOD detection in closed- and open-vocabulary classification scenarios. Code is available at https://github.com/mala-lab/negprompt.

Existing out-of-distribution (OOD) methods have shown great success on balanced datasets but become ineffective in long-tailed recognition (LTR) scenarios where 1) OOD samples are often wrongly classified into head classes and/or 2) tail-class samples are treated as OOD samples. To address these issues, current studies fit a prior distribution of auxiliary/pseudo OOD data to the long-tailed in-distribution (ID) data. However, it is difficult to obtain such an accurate prior distribution given the unknowingness of real OOD samples and heavy class imbalance in LTR. A straightforward solution to avoid the requirement of this prior is to learn an outlier class to encapsulate the OOD samples. The main challenge is then to tackle the aforementioned confusion between OOD samples and head/tail-class samples when learning the outlier class. To this end, we introduce a novel calibrated outlier class learning (COCL) approach, in which 1) a debiased large margin learning method is introduced in the outlier class learning to distinguish OOD samples from both head and tail classes in the representation space and 2) an outlier-class-aware logit calibration method is defined to enhance the long-tailed classification confidence. Extensive empirical results on three popular benchmarks CIFAR10-LT, CIFAR100-LT, and ImageNet-LT demonstrate that COCL substantially outperforms state-of-the-art OOD detection methods in LTR while being able to improve the classification accuracy on ID data. Code is available at https://github.com/mala-lab/COCL.

Open-World Object Detection (OWOD) extends object detection problem to a realistic and dynamic scenario, where a detection model is required to be capable of detecting both known and unknown objects and incrementally learning newly introduced knowledge. Current OWOD models, such as ORE and OW-DETR, focus on pseudo-labeling regions with high objectness scores as unknowns, whose performance relies heavily on the supervision of known objects. While they can detect the unknowns that exhibit similar features to the known objects, they suffer from a severe label bias problem that they tend to detect all regions (including unknown object regions) that are dissimilar to the known objects as part of the background. To eliminate the label bias, this paper proposes a novel approach that learns an unsupervised discriminative model to recognize true unknown objects from raw pseudo labels generated by unsupervised region proposal methods. The resulting model can be further refined by a classification-free self-training method which iteratively extends pseudo unknown objects to the unlabeled regions. Experimental results show that our method 1) significantly outperforms the prior SOTA in detecting unknown objects while maintaining competitive performance of detecting known object classes on the MS COCO dataset, and 2) achieves better generalization ability on the LVIS and Objects365 datasets.