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Fruit spine density is an important commercial trait for cucumber (Cucumis sativus L.). Most North China-type cucumbers that are grown over large areas have a dense-spine phenotype, which directly affects the appearance quality, storage, and transportation of the fruits. Here, we isolated a novel few spines mutant (fs2) from the wild-type (WT) inbred line WD1, a North China-type cucumber with high density fruit spines, by an ethyl methanesulfonate (EMS) mutagenesis treatment. Genetic analysis revealed that the phenotype of fs2 is controlled by a single recessive nuclear gene. We fine-mapped the fs2 locus using F2 and BC1 populations (1,802 and 420 individuals, respectively), which showed that the candidate gene of FS2 (Csa4G652850) encodes an ARID-HMG transcription factor containing an AT-rich interaction domain (ARID) and a high mobility group box domain (HMG). One SNP (C to T) and one InDel (a 40-bp deletion) in the coding region of FS2 result in amino acid variation and premature translation termination in the fs2 mutant, respectively. FS2 was found to be highly expressed in the apical buds and young ovaries. In addition, experiments suggest that FS2 participates in the regulation of fruit spine initiation by activating the expression of the Tril gene in cucumber. This work provides not only an important reference for understanding the molecular mechanisms of fruit spine development but also an important resource for fruit appearance quality breeding in cucumber.

Objective: Identifying reliable prognostic markers for breast cancer is crucial for improving survival rates and reducing mortality. Recent studies highlight the AT-rich interactive domain-containing protein (ARID) family, particularly ARID3A, as influential in cancer progression, though its specific role in breast cancer remains unclear. This study investigates ARID3A's expression, prognostic relevance, clinicopathological correlations, co-expression profiles, and protein-protein interactions in breast cancer. Methods: ARID3A mRNA and protein expression levels were analyzed using UALCAN, GEPIA databases, and immunohistochemistry from our hospital samples. Clinical prognostic parameters and survival data were examined through bioinformatics tools, including GEPIA, Bc-GenExMiner, and BEST. Subtype-specific expression and co-expression, particularly with REXO1, were evaluated using LinkedOmics, TIMER, and bc-GenExMiner. Functional enrichment analysis was conducted via LinkedOmics. Protein-protein interactions (PPI) were established using GeneMANIA and STRING, with validation through molecular docking using Cluspro. Results: Elevated ARID3A expression was associated with poor prognosis in breast cancer, particularly in Luminal and HER2-positive subtypes. A positive correlation with REXO1 was identified, and enrichment analysis emphasized ARID3A's involvement in immune-related pathways, such as “interferon gamma production” and “primary immunodeficiency.” PPI network and docking studies identified TP53 as a potential binding partner, suggesting a novel interaction influencing tumor progression. Conclusion: These findings indicate that ARID3A may serve as a prognostic biomarker and therapeutic target in breast cancer, providing insights into its involvement in oncogenic pathways and interactions, particularly with TP53, that may drive cancer development and progression.

Gibberellin (GA) is one of the major plant hormones that promote parthenocarpy, a highly valuable agronomic trait. Here, we demonstrated that exogenous GA3 application triggered the formation of parthenocarpic fruits with smaller size but unchanged shape in tomato (Solanum lycopersicum). These fruits exhibited a thicker pericarp, undeveloped ovules, and few jelly tissues, leading to smaller locules with empty cavities. Histological investigation showed that GA treatment produced more cell layers with larger cells in the pericarp, suggesting its promotion in both cell division and expansion. Transcriptomic analyses between GA3- and mock-treated unpollinated ovaries/fruits identified a large number of differentially expressed genes related to hormones, cell division, cell expansion, and transcription factors, implying that they coordinately regulated parthenocarpy conferred by GA. In particular, the downregulation of five reported repressors of tomato parthenocarpy, including two auxin signaling components, AUXIN RESPONSE FACTOR5 (SlARF5) and SlARF7, and three MADS-box genes, TOMATO APETALA3 (TAP3), TOMATO PISTILLATA (TPI), and AGAMOUS-LIKE6 (SlAGL6), after GA treatment might play a key role in this process. Furthermore, we found that the knockdown of a GA signaling factor SlMYB33, which was depressed by GA treatment, induced parthenocarpic fruit set in tomato, an effect that might have been achieved by enhancing GA biosynthesis and decreasing the expression of some repressors of tomato parthenocarpy. Thus, our results provide a basis for understanding the regulatory mechanism of GA in tomato parthenocarpy.

In response to the challenge of improving the performance of deep learning models for earthquake detection in low signal-to-noise ratio environments, this article introduces a new earthquake detection model called ECPickNet. Drawing inspiration from the EQTransformer, this model leverages Convolution-Enhanced Transformer technology, Conformer architecture, and incorporates the Residual Stacking Block Unit with Channel-Skipping (RSBU-CS) module. The manuscript provides a detailed overview of the model’s network architecture, parameter settings used during the training process, and compares it with several similar methods through a series of experiments. The experimental results highlight ECPickNet’s well performance on both the STEAD and Gansu datasets, particularly performing exceptionally well in the processing of low signal-to-noise ratio data. Interested readers can access and download the proposed method from the following website address: https://github.com/20041170036/EcPick.

BackgroundEarly identification of response to neoadjuvant chemotherapy (NAC) is instrumental in predicting patients prognosis. However, since a fixed criterion with high accuracy cannot be generalized to molecular subtypes, our study first aimed to redefine grades of clinical response to NAC in invasive breast cancer patients (IBC). And then developed a prognostic model based on clinical features and ultrasound semantics.MethodsA total of 480 IBC patients were enrolled who underwent anthracycline and taxane-based NAC between 2018 and 2020. The decrease rate of the largest diameter was calculated by ultrasound after NAC and their cut-off points were determined among subtypes. Thereafter, a nomogram was constructed based on clinicopathological and ultrasound-related data, and validated using the calibration curve, receiver operating characteristic (ROC) curve, decision curve analysis (DCA), and clinical impact curve (CIC).ResultsThe optimal cut-off points for predicting pCR were 53.23%, 51.56%, 41.89%, and 53.52% in luminal B-like (HER2 negative), luminal B-like (HER2 positive), HER2 positive, and triple-negative, respectively. In addition, time interval, tumor size, molecular subtypes, largest diameter decrease rate, and change of blood perfusion were significantly associated with pCR (all p < 0.05). The prediction model based on the above variables has great predictive power and clinical value.ConclusionTaken together, our data demonstrated that calculated cut-off points of tumor reduction rates could be reliable in predicting pathological response to NAC and developed nomogram predicting prognosis would help tailor systematic regimens with high precision.

The spines of cucumber fruit not only have important commercial value but are also a classical tissue to study cell division and differentiation modes of multicellular trichomes. It has been reported that CsTs (C-type Lectin receptor-like kinase) can influence the development of fruit spines. In this study, we took a pair of cucumber materials defined as hard (Ts, wild type) and tender spines (ts, mutant) and defined the developmental process of fruit spines as consisting of four stages (stage I to stage IV) by continuously observing by microscope and SEM. Comparisons of transcriptome profiles at different development stages of wild-type spines showed that 803 and 722 genes were upregulated in the stalk (stage II and stage III) and base (stage IV) development stages of fruit spines, respectively. The function analysis of DEGs showed that genes related to auxin polar transport and HD-ZIP transcription factor are significantly upregulated during the development of the stalk. bHLH transcription factors and cytoskeleton-related genes were significantly upregulated during the development of the base. In addition, stage III is the key point for the difference between wild-type and mutant spines. We detected 628 DEGs between wild type and mutant at stage III. These DEGs are mainly involved in the calcium signaling of the cytoskeleton and auxin polar transport. Coincidentally, we found that CsVTI11, a factor involved in auxin signal transmission, can interact with CsTs in vivo, but this interaction does not occur between CsVTI11 and Csts, further suggesting that CsTs may regulate the development of fruit spines by influencing cell polarity. These results provide useful tools to study the molecular networks associated with cucumber fruit spine development and elucidate the biological pathways that C-type Lectin receptor-like kinase plays in regulating the development of fruit spines.

In this paper, the time series classification frontier method MiniRocket was used to classify earthquakes, blasts, and background noise. From supervised to unsupervised classification, a comprehensive analysis was carried out, and finally, the supervised method achieved excellent results. The relatively simple model, MiniRocket, is only a one-dimensional convolutional neural network structure which has achieved the best comprehensive results, and its computational efficiency is far stronger than other supervised classification methods. Through our experimental results, we found that the MiniRocket model could well-extract the decisive features of the seismic sensing signal. In order to try to eliminate the tedious work of making data labels, we proposed a novel lightweight collaborative learning for seismic sensing signals (LCL-SSS) based on the method of feature extraction in MiniRocket combined with unsupervised classification. The new method gives new vitality to the unsupervised classification method that could not be used originally and opens up a new path for the unsupervised classification of seismic sensing signals.