Haplotype-resolved genome of Prunus zhengheensis provides insight into its evolution and low temperature adaptation in apricot.

Prunus zhengheensis, an extremely rare population of apricots, originated in warm South-East China and is an excellent material for genetic breeding. However, most apricots and two related species (P. sibirica, P. mandshurica) are found in the cold northern regions in China and the mechanism of their distribution is still unclear. In addition, the classification status of P. zhengheensis is controversial. Thus, we generated a high-quality haplotype-resolved genome for P. zhengheensis, exploring key genetic variations in its adaptation and the causes of phylogenetic incongruence. We found extensive phylogenetic discordances between the nuclear and organelle phylogenies of P. zhengheensis, which could be explained by incomplete lineage sorting. A 242.22-Mb pan-genome of the Armeniaca section was developed with 13 chromosomal genomes. Importantly, we identified a 566-bp insertion in the promoter of the HSFA1d gene in apricot and showed that the activity of the HSFA1d promoter increased under low temperatures. In addition, HSFA1d overexpression in Arabidopsis thaliana indicated that HSFA1d positively regulated plant growth under chilling. Therefore, we hypothesized that the insertion in the promoter of HSFA1d in apricot improved its low-temperature adaptation, allowing it to thrive in relatively cold locations. The findings help explain the weather adaptability of Armeniaca plants.

Partial root-zone drying combined with nitrogen treatments mitigates drought responses in rice.

Drought is a major stress affecting rice yields. Combining partial root-zone drying (PRD) and different nitrogen fertilizers reduces the damage caused by water stress in rice. However, the underlying molecular mechanisms remain unclear. In this study, we combined treatments with PRD and ammonia:nitrate nitrogen at 0:100 (PRD0:100) and 50:50 (PRD50:50) ratios or PEG and nitrate nitrogen at 0:100 (PEG0:100) ratios in rice. Physiological, transcriptomic, and metabolomic analyses were performed on rice leaves to identify key genes involved in water stress tolerance under different nitrogen forms and PRD pretreatments. Our results indicated that, in contrast to PRD0:100, PRD50:50 elevated the superoxide dismutase activity in leaves to accelerate the scavenging of ROS accumulated by osmotic stress, attenuated the degree of membrane lipid peroxidation, stabilized photosynthesis, and elevated the relative water content of leaves to alleviate the drought-induced osmotic stress. Moreover, the alleviation ability was better under PRD50:50 treatment than under PRD0:100. Integrated transcriptome and metabolome analyses of PRD0:100 vs PRD50:50 revealed that the differences in PRD involvement in water stress tolerance under different nitrogen pretreatments were mainly in photosynthesis, oxidative stress, nitrogen metabolism process, phytohormone signaling, and biosynthesis of other secondary metabolites. Some key genes may play an important role in these pathways, including OsGRX4, OsNDPK2, OsGS1;1, OsNR1.2, OsSUS7, and YGL8. Thus, the osmotic stress tolerance mediated by PRD and nitrogen cotreatment is influenced by different nitrogen forms. Our results provide new insights into osmotic stress tolerance mediated by PRD and nitrogen cotreatment, demonstrate the essential role of nitrogen morphology in PRD-induced molecular regulation, and identify genes that contribute to further improving stress tolerance in rice.

Enhanced PSO feature selection with Runge-Kutta and Gaussian sampling for precise gastric cancer recurrence prediction.

Gastric cancer (GC), characterized by its inconspicuous initial symptoms and rapid invasiveness, presents a formidable challenge. Overlooking postoperative intervention opportunities may result in the dissemination of tumors to adjacent areas and distant organs, thereby substantially diminishing prospects for patient survival. Consequently, the prompt recognition and management of GC postoperative recurrence emerge as a matter of paramount urgency to mitigate the deleterious implications of the ailment. This study proposes an enhanced feature selection model, bRSPSO-FKNN, integrating boosted particle swarm optimization (RSPSO) with fuzzy k-nearest neighbor (FKNN), for predicting GC. It incorporates the Runge-Kutta search, for improved model accuracy, and Gaussian sampling, enhancing the search performance and helping to avoid locally optimal solutions. It outperforms the sophisticated variants of particle swarm optimization when evaluated in the CEC 2014 test suite. Furthermore, the bRSPSO-FKNN feature selection model was introduced for GC recurrence prediction analysis, achieving up to 82.082 % and 86.185 % accuracy and specificity, respectively. In summation, this model attains a notable level of precision, poised to ameliorate the early warning system for GC recurrence and, in turn, advance therapeutic options for afflicted patients.

SinCWIm: An imputation method for single-cell RNA sequence dropouts using weighted alternating least squares.

BACKGROUND AND OBJECTIVES: Single-cell RNA sequencing (scRNA-seq) provides a powerful tool for exploring cellular heterogeneity, discovering novel or rare cell types, distinguishing between tissue-specific cellular composition, and understanding cell differentiation during development. However, due to technological limitations, dropout events in scRNA-seq can mistakenly convert some entries in the real data to zero. This is equivalent to introducing noise into the data of cell gene expression entries. The data is contaminated, which affects the performance of downstream analyses, including clustering, cell annotation, differential gene expression analysis, and so on. Therefore, it is a crucial work to accurately determine which zeros are due to dropout events and perform imputation operations on them. METHODS: Considering the different confidence levels of different zeros in the gene expression matrix, this paper proposes a SinCWIm method for dropout events in scRNA-seq based on weighted alternating least squares (WALS). The method utilizes Pearson correlation coefficient and hierarchical clustering to quantify the confidence of zero entries. It is then combined with WALS for matrix decomposition. And the imputation result is made close to the actual number by outlier removal and data correction operations. RESULTS: A total of eight single-cell sequencing datasets were used for comparative experiments to demonstrate the overall superiority of SinCWIm over state-of-the-art models. SinCWIm was applied to cluster the data to obtain an adjusted RAND index evaluation, and the Usoskin, Pollen and Bladder datasets scored 94.46%, 96.48% and 76.74%, respectively. In addition, significant improvements were made in the retention of differential expression genes and visualization. CONCLUSIONS: SinCWIm provides a valuable imputation method for handling dropout events in single-cell sequencing data. In comparison to advanced methods, SinCWIm demonstrates excellent performance in clustering, visualization and other aspects. It is applicable to various single-cell sequencing datasets.

AGAMOUS-LIKE24 controls pistil number in Japanese apricot by targeting the KNOTTED1-LIKE gene KNAT2/6-a.

The formation of multi-pistil flowers reduces the yield and quality in Japanese apricot (Prunus mume). However, the molecular mechanism underlying the formation of multi-pistil flowers remains unknown. In the current study, overexpression of PmKNAT2/6-a, a class I KNOTTED1-like homeobox (KNOX) member, in Arabidopsis (Arabidopsis thaliana) resulted in a multi-pistil phenotype. Analysis of the upstream regulators of PmKNAT2/6-a showed that AGAMOUS-like 24 (PmAGL24) could directly bind to the PmKNAT2/6-a promoter and regulate its expression. PmAGL24 also interacted with Like Heterochromatin Protein 1 (PmLHP1) to recruit lysine trimethylation at position 27 on histone H3 (H3K27me3) to regulate PmKNAT2/6-a expression, which is indirectly involved in multiple pistils formation in Japanese apricot flowers. Our study reveals that the PmAGL24 transcription factor, an upstream regulator of PmKNAT2/6-a, regulates PmKNAT2/6-a expression via direct and indirect pathways and is involved in the formation of multiple pistils in Japanese apricot.

Two new terpenoids from the branches and leaves of Rhododendron dauricum L. with neuroprotective activity.

Two new terpenoids were isolated from the branches and leaves of Rhododendron dauricum L., named as rhodayritions A (1) and B (2), together with five known compounds which were identified litseachromolaevane A (3), 11-αH-dihydrodehydrocostus lactone (4), (+)-9ß-hydroxyeudesma-4,11(13)-dien-12-al (5), macrostachyoside B (6) and aglaiabbreviatin E (7), respectively. The structures of isolated compounds were determined by UV, HR-ESI-MS, NMR analysis and X-Ray. Their neuroprotective activity was studied on serum deprivation-induced PC12 cells by the MTT method, compounds 1, 6, and 7 exhibited significant neuroprotective activity at 20 µΜ.

Allelic variation of PmCBF03 contributes to the altitude and temperature adaptability in Japanese apricot (Prunus mume Sieb. et Zucc.).

Japanese apricot is an important subtropical deciduous fruit tree in China, widely distributed in different altitude areas. How does it adapt to the different temperature environments in these areas? In this study, we identified a low-temperature transcription factor PmCBF03 on chromosome 7 through adaptive analysis of populations at different altitudes, which has an early termination single nucleotide polymorphism mutation. There were two different types of variation, PmCBF03A type in high-altitude areas and PmCBF03T type in low-altitude areas. PmCBF03A gene increased the survival rate, Fv/Fm values, antioxidant enzyme activity, and expression levels of antioxidant enzyme genes, and reducing electrolyte leakage and accumulation of reactive oxygen species in transgenic Arabidopsis under low temperature and freezing stress. Simultaneously, PmCBF03A gene promoted the dormancy of transgenic Arabidopsis seeds than wild-type. Biochemical analysis demonstrated that PmCBF03A directly bound to the DRE/CRT element in the promoters of the PmCOR413, PmDAM6 and PmABI5 genes, promoting their transcription and enhanced the cold resistance and dormancy of the overexpressing PmCBF03A lines. While PmCBF03T gene is unable to bind to the promoters of PmDAM6 and PmABI5 genes, leading to early release of dormancy to adapt to the problem of insufficient chilling requirement in low-altitude areas.

Kinetic analysis and mechanism of nitrate, calcium, and cadmium removal using the newly isolated Pseudomonas sp. LYF26.

The co-existence of heavy metals and nitrate (NO3--N) pollutants in wastewater has been a persistent global concern for a long time. A strain LYF26, which can remove NO3--N, calcium (Ca(II)), and cadmium (Cd(II)) simultaneously, was isolated to explore the properties and mechanisms of synergistic contaminants removal. Different conditions (Cd(II) and Ca(II) concentrations and pH) were optimized by Zero-, Half-, and First-order kinetic analyses to explore the environmental parameters for the optimal effect of strain LYF26. Results of the kinetic analyses revealed that the optimal culture conditions for strain LYF26 were pH of 6.5, Cd(II) and Ca(II) concentrations of 3.00 and 180.00 mg L-1, accompanied by Ca(II), Cd(II), and NO3--N efficiencies of 53.10%, 90.03%, and 91.45%, respectively. The removal mechanisms of Cd(II) using strain LYF26 as a nucleation template were identified as biomineralization, lattice substitution, and co-precipitation. The differences and changes of dissolved organic matter during metabolism were analyzed and the results demonstrated that besides the involvement of extracellular polymeric substances in the precipitation of Cd(II) and Ca(II), the high content of humic acid-like species revealed a remarkable contribution to the denitrification process. This study is hopeful to contribute a theory for further developing microbially induced calcium precipitation used to treat complex polluted wastewater.

Development of a deep learning model for early gastric cancer diagnosis using preoperative computed tomography images.

Background: Gastric cancer is a highly prevalent and fatal disease. Accurate differentiation between early gastric cancer (EGC) and advanced gastric cancer (AGC) is essential for personalized treatment. Currently, the diagnostic accuracy of computerized tomography (CT) for gastric cancer staging is insufficient to meet clinical requirements. Many studies rely on manual marking of lesion areas, which is not suitable for clinical diagnosis. Methods: In this study, we retrospectively collected data from 341 patients with gastric cancer at the First Affiliated Hospital of Wenzhou Medical University. The dataset was randomly divided into a training set (n=273) and a validation set (n=68) using an 8:2 ratio. We developed a two-stage deep learning model that enables fully automated EGC screening based on CT images. In the first stage, an unsupervised domain adaptive segmentation model was employed to automatically segment the stomach on unlabeled portal phase CT images. Subsequently, based on the results of the stomach segmentation model, the image was cropped out of the stomach area and scaled to a uniform size, and then the EGC and AGC classification models were built based on these images. The segmentation accuracy of the model was evaluated using the dice index, while the classification performance was assessed using metrics such as the area under the curve (AUC) of the receiver operating characteristic (ROC), accuracy, sensitivity, specificity, and F1 score. Results: The segmentation model achieved an average dice accuracy of 0.94 on the hand-segmented validation set. On the training set, the EGC screening model demonstrated an AUC, accuracy, sensitivity, specificity, and F1 score of 0.98, 0.93, 0.92, 0.92, and 0.93, respectively. On the validation set, these metrics were 0.96, 0.92, 0.90, 0.89, and 0.93, respectively. After three rounds of data regrouping, the model consistently achieved an AUC above 0.9 on both the validation set and the validation set. Conclusion: The results of this study demonstrate that the proposed method can effectively screen for EGC in portal venous CT images. Furthermore, the model exhibits stability and holds promise for future clinical applications.

Berberine ameliorates renal interstitial inflammation and fibrosis in mice with unilateral ureteral obstruction.

Berberine acts via multiple pathways to alleviate fibrosis in various tissues and shows renoprotective effects. However, its role and underlying mechanisms in renal fibrosis remain unclear. Herein, we aimed to investigate the protective effects and molecular mechanisms of berberine against unilateral ureteric obstruction-induced renal fibrosis. The results indicated that berberine treatment (50 mg/kg/day) markedly alleviated histopathological alterations, collagen deposition and inflammatory cell infiltration in kidney tissue and restored mouse renal function. Mechanistically, berberine intervention inhibited NOD-like receptor family pyrin domain-containing 3 (NLRP3) inflammasome activation and the levels of the inflammatory cytokine IL-1ß in the kidneys of unilateral ureteric obstruction mice. In addition, berberine relieved unilateral ureteric obstruction-induced renal injury by activating adenosine monophosphate-activated protein kinase (AMPK) signalling and promoting fatty acid ß-oxidation. In vitro models showed that berberine treatment prevented the TGF-ß1-induced profibrotic phenotype of hexokinase 2 (HK-2) cells, characterized by loss of an epithelial phenotype (alpha smooth muscle actin [α-SMA]) and acquisition of mesenchymal marker expression (E-cadherin), by restoring abnormal fatty acid ß-oxidation and upregulating the expression of the fatty acid ß-oxidation related-key enzymes or regulators (phosphorylated-AMPK, peroxisome proliferator activated receptor alpha [PPARα] and carnitine palmitoyltransferase 1A [CPT1A]). Collectively, berberine alleviated renal fibrosis by inhibiting NLRP3 inflammasome activation and protected tubular epithelial cells by reversing defective fatty acid ß-oxidation. Our findings might be exploited clinically to provide a potential novel therapeutic strategy for renal fibrosis.

Integrated omic analysis provides insights into the molecular regulation of stress tolerance by partial root-zone drying in rice.

Partial root-zone drying (PRD) is an effective water-saving irrigation strategy that improves stress tolerance and facilitates efficient water use in several crops. It has long been considered that abscisic acid (ABA)-dependent drought resistance may be involved during partial root-zone drying. However, the molecular mechanisms underlying PRD-mediated stress tolerance remain unclear. It's hypothesized that other mechanisms might contribute to PRD-mediated drought tolerance. Here, rice seedlings were used as a research model and the complex transcriptomic and metabolic reprogramming processes were revealed during PRD, with several key genes involved in osmotic stress tolerance identified by using a combination of physiological, transcriptome, and metabolome analyses. Our results demonstrated that PRD induces transcriptomic alteration mainly in the roots but not in the leaves and adjusts several amino-acid and phytohormone metabolic pathways to maintain the balance between growth and stress response compared to the polyethylene glycol (PEG)-treated roots. Integrated analysis of the transcriptome and metabolome associated the co-expression modules with PRD-induced metabolic reprogramming. Several genes encoding the key transcription factors (TFs) were identified in these co-expression modules, highlighting several key TFs, including TCP19, WRI1a, ABF1, ABF2, DERF1, and TZF7, involved in nitrogen metabolism, lipid metabolism, ABA signaling, ethylene signaling, and stress regulation. Thus, our work presents the first evidence that molecular mechanisms other than ABA-mediated drought resistance are involved in PRD-mediated stress tolerance. Overall, our results provide new insights into PRD-mediated osmotic stress tolerance, clarify the molecular regulation induced by PRD, and identify genes useful for further improving water-use efficiency and/or stress tolerance in rice.

An enhanced grey wolf optimizer boosted machine learning prediction model for patient-flow prediction.

Large and medium-sized general hospitals have adopted artificial intelligence big data systems to optimize the management of medical resources to improve the quality of hospital outpatient services and decrease patient wait times in recent years as a result of the development of medical information technology and the rise of big medical data. However, owing to the impact of several elements, including the physical environment, patient, and physician behaviours, the real optimum treatment effect does not meet expectations. In order to promote orderly patient access, this work provides a patient-flow prediction model that takes into account shifting dynamics and objective rules of patient-flow to handle this issue and forecast patients' medical requirements. First, we propose a high-performance optimization method (SRXGWO) and integrate the Sobol sequence, Cauchy random replacement strategy, and directional mutation mechanism into the grey wolf optimization (GWO) algorithm. The patient-flow prediction model (SRXGWO-SVR) is then proposed using SRXGWO to optimize the parameters of support vector regression (SVR). Twelve high-performance algorithms are examined in the benchmark function experiments' ablation and peer algorithm comparison tests, which are intended to validate SRXGWO's optimization performance. In order to forecast independently in the patient-flow prediction trials, the data set is split into training and test sets. The findings demonstrated that SRXGWO-SVR outperformed the other seven peer models in terms of prediction accuracy and error. As a result, SRXGWO-SVR is anticipated to be a reliable and efficient patient-flow forecast system that may help hospitals manage medical resources as effectively as possible.

COVID-19 mortality prediction using ensemble learning and grey wolf optimization.

COVID-19 is now often moderate and self-recovering, but in a significant proportion of individuals, it is severe and deadly. Determining whether individuals are at high risk for serious disease or death is crucial for making appropriate treatment decisions. We propose a computational method to estimate the mortality risk for patients with COVID-19. To develop the model, 4,711 reported cases confirmed as SARS-CoV-2 infections were used for model development. Our computational method was developed using ensemble learning in combination with a genetic algorithm. The best-performing ensemble model achieves an AUCROC (area under the receiver operating characteristic curve) value of 0.7802. The best ensemble model was developed using only 10 features, which means it requires less medical information so that the diagnostic cost may be reduced while the prognostic time may be improved. The results demonstrate the robustness of the used method as well as the efficiency of the combination of machine learning and genetic algorithms in developing the ensemble model.

CdSe@CdS quantum dot-sensitized Au/α-Fe2O3 structure for photoelectrochemical detection of circulating tumor cells.

Circulating tumor cells (CTCs) are the important biomarker for cancer diagnosis and individualized treatment. However, due to the extreme rarity of CTCs (only 1-10 CTCs are found in every milliliter of peripheral blood) high sensitivity and selectivity are urgently needed for CTC detection. Here, a sandwich PEC cytosensor for the ultrasensitive detection of CTCs was developed using the photoactive material Au NP/-Fe2O3 and core-shell CdSe@CdS QD sensitizer. In the proposed  protocol, the CdSe@CdS QD/Au NP/α-Fe2O3-sensitized structure with cascade band-edge levels could evidently promote the photoelectric conversion efficiency due to suitable light absorption and efficient electron-hole pair recombination inhibition. Additionally, a dendritic aptamer-DNA concatemer was constructed for highly efficient capture of MCF-7 cells carrying CdSe@CdS QDs, a sensitive material. The linear range of this proposed signal-on PEC sensing method was 300 cell mL-1 to 6 × 105 cell mL-1 with a detection limit of 3 cell mL-1, and it demonstrated an ultrasensitive response to CTCs. Furthermore, this PEC sensor enabled accurate detection of  CTCs in serum samples. Hence, a promising strategy for CTC detection in clinical diagnosis was developed based on CdSe@CdS QD-sensitized Au NP/α-Fe2O3-based PEC cytosensor with dendritic aptamer-DNA concatemer.

Dual-scale similarity-guided cycle generative adversarial network for unsupervised low-dose CT denoising.

Removing the noise in low-dose CT (LDCT) is crucial to improving the diagnostic quality. Previously, many supervised or unsupervised deep learning-based LDCT denoising algorithms have been proposed. Unsupervised LDCT denoising algorithms are more practical than supervised ones since they do not need paired samples. However, unsupervised LDCT denoising algorithms are rarely used clinically due to their unsatisfactory denoising ability. In unsupervised LDCT denoising, the lack of paired samples makes the direction of gradient descent full of uncertainty. On the contrary, paired samples used in supervised denoising allow the parameters of networks to have a clear direction of gradient descent. To bridge the gap in performance between unsupervised and supervised LDCT denoising, we propose dual-scale similarity-guided cycle generative adversarial network (DSC-GAN). DSC-GAN uses similarity-based pseudo-pairing to better accomplish unsupervised LDCT denoising. We design a Vision Transformer-based global similarity descriptor and a residual neural network-based local similarity descriptor for DSC-GAN to effectively describe the similarity between two samples. During training, pseudo-pairs, i.e., similar LDCT samples and normal-dose CT (NDCT) samples, dominate parameter updates. Thus, the training can achieve equivalent effect as training with paired samples. Experiments on two datasets demonstrate that DSC-GAN beats the state-of-the-art unsupervised algorithms and reaches a level close to supervised LDCT denoising algorithms.

Ultrasensitive electrochemical biosensor for detection of circulating tumor cells based on a highly efficient enzymatic cascade reaction.

There has been great interest in the enzymatic cascade amplification strategy for the electrochemical detection of circulating tumor cells (CTCs). In this work, we designed a highly efficient enzymatic cascade reaction based on a multiwalled carbon nanotubes-chitosan (MWCNTs-CS) composite for detection of CTCs. A high electrochemical effective surface area was obtained for a MWCNTs-CS-modified glassy carbon electrode (GCE) for loading glucose oxidase (GOD), as well as a high loading rate and high electrical activity of the enzyme. As a 'power source', the MWCNTs-CS composites provided a strong driving power for horseradish peroxidase (HRP) on the surface of polystyrene (PS) microspheres, which acted as probes for capturing CTCs and allowed the reaction to proceed with further facilitation of electron transfer. Aptamer, CTCs, and PS microspheres with HRP and anti-epithelial cell adhesion molecule (anti-EpCAM) antibody were assembled on the MWCNTs-CS/GCE to allow for the modulation of enzyme distance at the micrometer level, and thus ultra-long-range signal transmission was made possible. An ultrasensitive response to CTCs was obtained via this proposed sensing strategy, with a linear range from 10 cell mL-1 to 6 × 106 cell mL-1 and a detection limit of 3 cell mL-1. Moreover, this electrochemical sensor possessed the capability to detect CTCs in serum samples with satisfactory accuracy, which indicated great potential for early diagnosis and clinical analysis of cancer.

PmAGAMOUS recruits polycomb protein PmLHP1 to regulate single-pistil morphogenesis in Japanese apricot.

Japanese apricot (Prunus mume Sieb. et Zucc.) is a traditional fruit tree with a long history. Multiple pistils (MP) lead to the formation of multiple fruits, decreasing fruit quality and yield. In this study, the morphology of flowers was observed at 4 stages of pistil development: undifferentiated stage (S1), predifferentiation stage (S2), differentiation stage (S3), and late differentiation stage (S4). In S2 and S3, the expression of PmWUSCHEL (PmWUS) in the MP cultivar was significantly higher than that in the single-pistil (SP) cultivar, and the gene expression of its inhibitor, PmAGAMOUS (PmAG), also showed the same trend, indicating that other regulators participate in the regulation of PmWUS during this period. Chromatin immunoprecipitation-qPCR (ChIP-qPCR) showed that PmAG could bind to the promoter and the locus of PmWUS, and H3K27me3 repressive marks were also detected at these sites. The SP cultivar exhibited an elevated level of DNA methylation in the promoter region of PmWUS, which partially overlapped with the region of histone methylation. This suggests that the regulation of PmWUS involves both transcription factors and epigenetic modifications. Also, the gene expression of Japanese apricot LIKE HETEROCHROMATIN PROTEIN (PmLHP1), an epigenetic regulator, in MP was significantly lower than that in SP in S2 to 3, contrary to the trend in expression of PmWUS. Our results showed that PmAG recruited sufficient PmLHP1 to maintain the level of H3K27me3 on PmWUS during the S2 of pistil development. This recruitment of PmLHP1 by PmAG inhibits the expression of PmWUS at the precise time, leading to the formation of 1 normal pistil primordium.

Attention guided neural ODE network for breast tumor segmentation in medical images.

Breast cancer is the most common cancer in women. Ultrasound is a widely used screening tool for its portability and easy operation, and DCE-MRI can highlight the lesions more clearly and reveal the characteristics of tumors. They are both noninvasive and nonradiative for assessment of breast cancer. Doctors make diagnoses and further instructions through the sizes, shapes and textures of the breast masses showed on medical images, so automatic tumor segmentation via deep neural networks can to some extent assist doctors. Compared to some challenges which the popular deep neural networks have faced, such as large amounts of parameters, lack of interpretability, overfitting problem, etc., we propose a segmentation network named Att-U-Node which uses attention modules to guide a neural ODE-based framework, trying to alleviate the problems mentioned above. Specifically, the network uses ODE blocks to make up an encoder-decoder structure, feature modeling by neural ODE is completed at each level. Besides, we propose to use an attention module to calculate the coefficient and generate a much refined attention feature for skip connection. Three public available breast ultrasound image datasets (i.e. BUSI, BUS and OASBUD) and a private breast DCE-MRI dataset are used to assess the efficiency of the proposed model, besides, we upgrade the model to 3D for tumor segmentation with the data selected from Public QIN Breast DCE-MRI. The experiments show that the proposed model achieves competitive results compared with the related methods while mitigates the common problems of deep neural networks.

Transcriptomic Analysis Reveals Genes Associated with the Regulation of Peach Fruit Softening and Senescence during Storage.

Peach (Prunus persica (L.) Batsch) is a highly desirable fruit that is consumed around the world. However, the peach fruit is highly perishable after harvest, a characteristic that limits the distribution and supply to the market and causes heavy economic losses. Thus, peach fruit softening and senescence after harvest urgently need to be addressed. In the current study, transcriptomic analysis was performed to identify candidate genes associated with peach fruit softening and senescence, comparing peach fruit from cultivars with different flesh textures, namely melting and stony hard (SH) flesh textures during storage at room temperature. The mitogen-activated protein kinase signaling pathway-plant and plant hormone signal transduction pathways were associated with peach fruit softening and senescence according to the Venn diagram analysis and weighted gene co-expression network analysis. The expression levels of seven genes, including Prupe.1G034300, Prupe.2G176900, Prupe.3G024700, Prupe.3G098100, Prupe.6G226100, Prupe.7G234800, and Prupe.7G247500, were higher in melting peach fruit than in SH peach fruit during storage. Furthermore, the SH peach fruit softened rapidly after 1-naphthylacetic acid treatment, during which the levels of expression of these seven genes, determined by a quantitative reverse transcription polymerase chain reaction, were strongly induced and upregulated. Thus, these seven genes may play essential roles in regulating peach fruit softening and senescence.

Genome-Wide Identification of the KNOX Gene Family in Japanese Apricot (Prunus mume Sieb. et Zucc.) and Functional Characterization of PmKNAT2 Genes.

The Knotted1-like Homeobox gene is crucial for plant morphological development and growth. Physicochemical characteristics, phylogenetic relationships, chromosomal localization, cis-acting elements, and tissue-specific expression patterns of the 11 PmKNOX genes found in the Japanese apricot genome in this study were examined. Proteins of 11 PmKNOX were soluble proteins with isoelectric points between 4.29 and 6.53, molecular masses between 15.732 and 44.011 kDa, and amino acid counts between 140 and 430. The identified PmKNOX gene family was split into three subfamilies by jointly constructing the phylogenetic tree of KNOX proteins in Japanese apricot and Arabidopsis thaliana. Combined outcomes of the analyzed conserved motifs and gene structures of the 11 PmKNOX genes from the same subfamily displayed comparable gene structure and motif patterns. The 11 PmKNOX members were distributed across six chromosomes, while two sets of PmKNOX genes were found to be collinear. Analysis of the 2000 bp promoter upstream of the coding region of the PmKNOX gene revealed that most PmKNOX genes might be involved in the physiological metabolism, growth and development processes of plants. The PmKNOX gene expression profile revealed that these genes were expressed at varying levels in different tissues, and most of them were linked to the meristems of leaf and flower buds, suggesting that PmKNOX may be involved in plants' apical meristems. In Arabidopsis thaliana, functional validation of PmKNAT2a and PmKNAT2b revealed that these two genes might be involved in regulating leaf and stem development. In addition to laying the groundwork for future research on the function of these genes, understanding the evolutionary relationships between members of the PmKNOX gene family provides opportunities for future breeding in Japanese apricots.