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"Objective To analyze the expression of circular ribonucleic acid (circRNA) Hsa circ0016599 in patients with triple-negative breast cancer (TNBC) and its effect and predictive value for the efficacy of neoadjuvant chemotherapy (NACT). Methods A total of 124 patients with TNBC who received NACT treatment before operation in Cangzhou Peoples Hospital from April 2022 to February 2024 were selected retrospectively as the research objects. The expression of Hsa circ0016599 was measured in the resected cancer tissues and adjacent tissues, and they were divided into low expression groups (≤15.12, n=59) and high expression group (＞15.12, n=65)according to the median relative expression value of Hsa circ0016599 (2-ΔΔCt=15.12). The expression levels of Hsa circ0016599 in cancer tissues and adjacent tissues of 124 TNBC patients and the total effective rates of NACT in low expression group and high expression group were compared. The risk factors affecting the efficacy of NACT in TNBC patients were screened to construct a nomogram model. Results In 124 TNBC patients, the expression level of Hsa circ0016599 in cancer tissues was higher than that in adjacent tissues (19.76±4.31 vs 14.15±2.74, t=12.232, P＜0.01). Compared with the low expression group, the high expression group had a higher proportion of patients with regional lymph node metastasis, vascular tumor thrombus, and nerve invasion (P＜0.05), also had a lower total effective rate of NACT (35.38% vs 62.71%, χ2=9.248, P＜0.01）. High expression of Hsa circ0016599, presence of regional lymph node metastasis, vascular tumor thrombus, and nerve invasion were all risk factors affecting NACT efficacy in TNBC patients (OR=1.264, 1.239, 1.257, 1.247, P＜0.05). The calibration curve of Hsa circ0016599 expression level prediction model showed a good predictive value for NACT efficacy in TNBC patients. Conclusion Hsa circ0016599 is highly expressed in the cancer tissues of TNBC patients, and the total NACT effective rate of patients with high expression of Hsa circ0016599 is relatively low, indicating that Hsa circ0016599 has a certain predictive value for NACT efficacy."

Achieving full‐color emission with just two emitters presents a significant challenge. Two N‐heterocyclic carbene metallacycles (NHC‐M; M ═ Ag, Au) featuring a tetraphenylethene core, combining covalent and coordination bonds are synthesized to restrict rotation within the NHC‐M‐BC (BC = bicyclic) metallacycles and greatly enhanced their quantum yields. The enhancement is accomplished by adjusting the CH3CN/H2O solvent mixture, allowing emission tuning from blue to green for NHC‐Ag‐BC. Further diversification of the emission spectrum, including access to high‐quality white light (CIE coordinates 0.33, 0.34), is facilitated through the addition of sulforhodamine B via fluorescence resonance energy transfer (FRET). The compatibility of NHC‐Ag‐BC with agarose gel extends its applicability to UV‐LEDs chromic coatings, as well as information encryption and anti‐counterfeiting materials. The results underscore the viability of dual‐fluorophore systems for achieving full‐color emission and highlight the potential for developing versatile, multi‐colored functional materials. [ABSTRACT FROM AUTHOR]

BACKGROUND: Odorant‐binding proteins (OBPs) in insects are key to detection and recognition of external chemical signals associated with survival. OBP7 in Spodoptera frugiperda's larval stage (SfruOBP7) may search for host plants by sensing plant volatiles, which are important sources of pest attractants and repellents. However, the atomic‐level basis of binding modes remains elusive. RESULTS: SfruOBP7 structure was constructed through homology modeling, and complex models of six plant volatiles ((E)‐2‐hexenol, α‐pinene, (Z)‐3‐hexenyl acetate, lauric acid, O‐cymene and 1‐octanol) and SfruOBP7 were obtained through molecular docking. To study the detailed interactions between the six plant volatile molecules and SfruOBP7, we conducted three 300 ns molecular dynamics simulations for each study object. The correlation coefficients between binding free energy obtained by molecular mechanics/generalized Born surface area together with solvated interaction energy methods and experimental values are 0.90 and 0.88, respectively, showing a good correlation. By comparing binding free energy along with interaction patterns between SfruOBP7 and the six volatile molecules, hotspot residues of SfruOBP7 when binding with different volatile molecules were determined. Hydrophobic interactions stemming from van der Waals interactions play a significant role in SfruOBP7 and these plant volatile systems. CONCLUSION: The optimized three‐dimensional structure of SfruOBP7 and its binding modes with six plant volatiles revealed their interactions, thus providing a means for estimating the binding energies of other plant volatiles. Our study will help to guide the rational design of effective and selective insect attractants. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Ecosystem stability arises from the interplay of species diversity, environmental conditions, and external disturbances. Understanding the structure of plant communities, interspecific relationships, and community stability in urban ecosystems is fundamental to ecological restoration and community development. This study utilized the karst city of Guiyang as a case study and employed the α diversity index, variance ratio method (VR), χ2‐test, Pearson correlation test, Spearman rank correlation test, M. Godron stability, and canonical correspondence analysis (CCA). The research focused on analyzing the species diversity, interspecific associations, community stability, and environmental factors of lithophytic moss at various elevations (989–1398 m). The findings revealed the presence of 58 species belonging to 27 genera and 13 families of lithophytic moss in the study area. Notably, the Brachytheciaceae and Pottiaceae emerged as dominant, exhibiting a broad ecological range and adaptation mechanisms, thereby playing a crucial role in the ecological environment of rocky desertification. The study observed that the highest species richness and dominance values of lithophytic moss were recorded at the N4 (1296–1398 m) elevation gradient, while the highest species diversity and uniformity values were observed at the N3 (1194–1295 m) elevation gradient, indicating a significant impact of altitude on lithobryophyte species diversity, particularly at middle and high altitudes. The analysis of interspecific associations and stability indicated a predominantly negative overall association within the lithophytic moss community, suggesting an early stage of succession, with weak interspecific associations and correlations among dominant pairs, tending towards relative independence. Only the communities at N2 (1092–1193 m) elevation exhibited stability, while the other communities were in an unstable stage, showing no significant correlation with species diversity. Furthermore, light intensity (182–129300 lux) exerted the greatest influence on community stability. Additionally, air humidity (36.5–52.3%) and altitude (998–1327 m) emerged as the primary environmental factors influencing community distribution, with a close and positive correlation between the two. These results hold significant reference value for promoting the succession and steady development of vegetation in rocky desertification areas and enhancing the conservation and restoration of vegetation community diversity in karst urban ecosystems. [ABSTRACT FROM AUTHOR]

N-Heterocyclic carbene (NHC)-based cages have emerged as a prominent and dynamic research area within the research field of chemistry. Leveraging the distinctive electronic and steric properties of NHC ligands, the design, synthesis, and application of these corresponding cages have garnered substantial scholarly interest. In recent years, we have witnessed the successful fabrication of diverse NHC-based cages through a range of synthetic methodologies, which hold significant potential for applications in molecular recognition and catalysis. In this review, we delve into the foundational synthetic strategies that underlie the creation of NHC-based cages, employing approaches encompassing metal–NHC chelation, coordination assembly, and covalent bonding. Additionally, we compile the diverse applications of these cages within catalytic processes and molecular recognition. Lastly, we shed light on the current limitations of synthesis and outline future trends in the development of NHC-based cages. [ABSTRACT FROM AUTHOR]

Semantic segmentation, which aims to acquire pixel-level understanding about images, is among the key components in computer vision. To train a good segmentation model for real-world images, it usually requires a huge amount of time and labor effort to obtain sufficient pixel-level annotations of real-world images beforehand. To get rid of such a nontrivial burden, one can use simulators to automatically generate synthetic images that inherently contain full pixel-level annotations and use them to train a segmentation model for the real-world images. However, training with synthetic images usually cannot lead to good performance due to the domain difference between the synthetic images (i.e., source domain) and the real-world images (i.e., target domain). To deal with this issue, a number of unsupervised domain adaptation (UDA) approaches have been proposed, where no labeled real-world images are available. Different from those methods, in this work, we conduct a pioneer attempt by using easy-to-collect image-level annotations for target images to improve the performance of cross-domain segmentation. Specifically, we leverage those image-level annotations to construct curriculums for the domain adaptation problem. The curriculums describe multi-level properties of the target domain, including label distributions over full images, local regions and single pixels. Since image annotations are “weak” labels compared to pixel annotations for segmentation, we coin this new problem as weakly-supervised cross-domain segmentation. Comprehensive experiments on the $\texttt {GTA5}\rightarrow \texttt {Cityscapes}$ and $\texttt {SYNTHIA}\rightarrow \texttt {Cityscapes}$ settings demonstrate the effectiveness of our method over the existing state-of-the-art baselines. [ABSTRACT FROM AUTHOR]

Cross-domain object detection in images has attracted increasing attention in the past few years, which aims at adapting the detection model learned from existing labeled images (source domain) to newly collected unlabeled ones (target domain). Existing methods usually deal with the cross-domain object detection problem through direct feature alignment between the source and target domains at the image level, the instance level (i.e., region proposals) or both. However, we have observed that directly aligning features of all object instances from the two domains often results in the problem of negative transfer, due to the existence of (1) outlier target instances that contain confusing objects not belonging to any category of the source domain and thus are hard to be captured by detectors and (2) low-relevance source instances that are considerably statistically different from target instances although their contained objects are from the same category. With this in mind, we propose a reinforcement learning based method, coined as sequential instance refinement, where two agents are learned to progressively refine both source and target instances by taking sequential actions to remove both outlier target instances and low-relevance source instances step by step. Extensive experiments on several benchmark datasets demonstrate the superior performance of our method over existing state-of-the-art baselines for cross-domain object detection. [ABSTRACT FROM AUTHOR]

Andrographolide, which is enriched in the leaves of Andrographis paniculata , has been known as "natural antibiotic" due to its pharmacological activities such as anti-inflammatory, antimicrobial and antioxidant effects. Several key enzymes in andrographolide biosynthetic pathway have been studied since the genome sequences were released, but its regulatory mechanism remains unknown. WRKY transcription factors proteins have been reported to regulate plant secondary metabolism, development as well as biotic and abiotic stresses. Here, WRKY transcription factors related to andrographolide biosynthesis were systematically identified, including sequences alignment, phylogenetic analysis, chromosomal distribution, gene structure, conserved motifs, synteny, alternative splicing event and Gene ontology (GO) annotation. A total of 58 WRKYs were identified in Chuanxinlian genome and phylogenetically classified into three groups. Moreover, nine WRKY genes underwent alternative splicing events. Furthermore, the combination of binding site prediction, gene-specific expression patterns, and phylogenetic analysis suggested that 7 WRKYs (ApWRKY01, ApWRKY08, ApWRKY12, ApWRKY14, ApWRKY19, ApWRKY20 , and ApWRKY50) might regulate andrographolide biosynthesis. This study laid a foundation for understanding the regulatory mechanism of andrographolide biosynthesis and the improvement and breeding of Andrographis paniculata varieties. [ABSTRACT FROM AUTHOR]

Cold-start has being a critical issue in recommender systems with the explosion of data in e-commerce. Most existing studies proposed to alleviate the cold-start problem are also known as hybrid recommender systems that learn representations of users and items by combining user-item interactive and user/item content information. However, previous hybrid methods regularly suffered poor efficiency bottlenecking in online recommendations with large-scale items, because they were designed to project users and items into continuous latent space where the online recommendation is expensive. To this end, we propose a collaborative generated hashing (CGH) framework to improve the efficiency by denoting users and items as binary codes, then fast hashing search techniques can be used to speed up the online recommendation. In addition, the proposed CGH can generate potential users or items for marketing application where the generative network is designed with the principle of minimum description length, which is used to learn compact and informative binary codes. Extensive experiments on two public datasets show the advantages for recommendations in various settings over competing baselines and analyze its feasibility in marketing application. [ABSTRACT FROM AUTHOR]

Objective: The purpose of this paper is to propose a novel algorithm for joint optic disc and cup segmentation, which aids the glaucoma detection. Methods: By assuming the shapes of cup and disc regions to be elliptical, we proposed an end-to-end region-based convolutional neural network for joint optic disc and cup segmentation (referred to as JointRCNN). Atrous convolution is introduced to boost the performance of feature extraction module. In JointRCNN, disc proposal network (DPN) and cup proposal network (CPN) are proposed to generate bounding box proposals for the optic disc and cup, respectively. Given the prior knowledge that the optic cup is located in the optic disc, disc attention module is proposed to connect DPN and CPN, where a suitable bounding box of the optic disc is first selected and then continued to be propagated forward as the basis for optic cup detection in our proposed network. After obtaining the disc and cup regions, which are the inscribed ellipses of the corresponding detected bounding boxes, the vertical cup-to-disc ratio is computed and used as an indicator for glaucoma detection. Results: Comprehensive experiments clearly show that our JointRCNN model outperforms state-of-the-art methods for optic disc and cup segmentation task and glaucoma detection task. Conclusion: Joint optic disc and cup segmentation, which utilizes the connection between optic disc and cup, could improve the performance of optic disc and cup segmentation. Significance: The proposed method improves the accuracy of glaucoma detection. It is promising to be used for glaucoma screening. [ABSTRACT FROM AUTHOR]

Training deep models of video recognition usually requires sufficient labeled videos in order to achieve good performance without over-fitting. However, it is quite labor-intensive and time-consuming to collect and annotate a large amount of videos. Moreover, training deep neural networks on large-scale video datasets always demands huge computational resources which further hold back many researchers and practitioners. To resolve that, collecting and training on annotated images are much easier. However, thoughtlessly applying images to help recognize videos may result in noticeable performance degeneration due to the well-known domain shift and feature heterogeneity. This proposes a novel symmetric adversarial learning approach for heterogeneous image-to-video adaptation, which augments deep image and video features by learning domain-invariant representations of source images and target videos. Primarily focusing on an unsupervised scenario where the labeled source images are accompanied by unlabeled target videos in the training phrase, we present a data-driven approach to respectively learn the augmented features of images and videos with superior transformability and distinguishability. Starting with learning a common feature space (called image-frame feature space) between images and video frames, we then build new symmetric generative adversarial networks (Sym-GANs) where one GAN maps image-frame features to video features and the other maps video features to image-frame features. Using the Sym-GANs, the source image feature is augmented with the generated video-specific representation to capture the motion dynamics while the target video feature is augmented with the image-specific representation to take the static appearance information. Finally, the augmented features from the source domain are fed into a network with fully connected layers for classification. Thanks to an end-to-end training procedure of the Sym-GANs and the classification network, our approach achieves better results than other state-of-the-arts, which is clearly validated by experiments on two video datasets, i.e., the UCF101 and HMDB51 datasets. [ABSTRACT FROM AUTHOR]

Spine colour is an important fruit quality trait that influences the commercial value of cucumber (Cucumis sativus). However, little is known about the metabolites and the regulatory mechanisms of their biosynthesis in black spine varieties. In this study, we determined that the pigments of black spines are flavonoids, including flavonols and proanthocyanidins (PAs). We identified CsMYB60 as the best candidate for the previously identified B (Black spine) locus. Expression levels of CsMYB60 and the key genes involved in flavonoid biosynthesis were higher in black-spine inbred lines than that in white-spine lines at different developmental stages. The insertion of a Mutator -like element (CsMULE) in the second intron of CsMYB60 decreased its expression in a white-spine line. Transient overexpression assays indicated that CsMYB60 is a key regulatory gene and Cs4CL is a key structural gene in the pigmentation of black spines. In addition, the DNA methylation level in the CsMYB60 promoter was much lower in the black-spine line compared with white-spine line. The CsMULE insert may decrease the expression level of CsMYB60, causing hindered synthesis of flavonols and PAs in cucumber fruit spines. [ABSTRACT FROM AUTHOR]

The article discusses the use of liquid chromatography-mass spectrometry for the metabolomics analysis of true biological signals.

To study underlying possibilities for the successes of conventional image representation and deep neural networks (DNNs) in image representation, we propose a deep feature extraction, encoding, and pooling network (DEFEATnet) architecture, which is a marriage between conventional image representation approaches and DNNs. In particular, in DEFEATnet, each layer consists of three components: feature extraction, feature encoding, and pooling. The primary advantage of DEFEATnet is twofold. First, it consolidates the prior knowledge (e.g., translation invariance) from extracting, encoding, and pooling handcrafted features, as in the conventional feature representation approaches. Second, it represents the object parts at different granularities by gradually increasing the local receptive fields in different layers, as in DNNs. Moreover, DEFEATnet is a generalized framework that can readily incorporate all types of local features as well as all kinds of well-designed feature encoding and pooling methods. Since prior knowledge is preserved in DEFEATnet, it is especially useful for image representation on small/medium size data sets, where DNNs usually fail due to the lack of sufficient training data. Promising experimental results clearly show that DEFEATnets outperform shallow conventional image representation approaches by a large margin when the same type of features, feature encoding and pooling are used. The extensive experiments also demonstrate the effectiveness of the deep architecture of our DEFEATnet in improving the robustness for image presentation. [ABSTRACT FROM AUTHOR]