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Abstract The Wannan black pig is a superior local breed in Anhui province, renowned for its exceptional meat quality and remarkable adaptability to various environmental conditions. Semen, being a crucial indicator of male sexual maturity and fertility, significantly influences the performance of breeding boars. The molecular basis for comprehending the fecundity of boars in practical production lies in understanding the disparities in sperm proteins among boars of varying ages. In this investigation, spermatozoa from three one-year-old and three seven-year-old Wannan black pigs were individually chosen. Employing a tandem mass tag (TMT)-based quantitative proteomics approach, a total of 4050 proteins were identified, out of which 130 proteins exhibited significant differences between the two groups. GO enrichment analysis revealed that these proteins primarily participated in energy metabolism, spermatogenesis, fertilization, and reproduction. KEGG enrichment analysis demonstrated that the differential proteins predominantly resided within the ribosome pathway. A protein-protein interaction (PPI) network was constructed to identify core proteins such as Small ribosomal subunit protein uS7 (RPS5). Ultimately, parallel reaction monitoring (PRM) was conducted on the selected differential proteins to validate result accuracy. The findings of this study establish a foundation for elucidating the molecular mechanism underlying variations in spermatozoa protein levels among Wannan Black Pig with different age.

Thermal barrier coatings (TBCs), commonly used in industrial gas turbines and aero-engines, have become a vital technology for prolonging their lifespan. However, the spalling of TBCs from the substrates during service could compromise the safety and reliability of the equipment. This study presents a methodology for assessing the risk of interfacial spalling induced by thermal stress mismatches between TBCs and their underlying substrates. A life forecasting technique that integrates critical failure probability, probabilistic statistics, and finite element analysis (FEA) was employed in this approach. Initially, FEA was used to establish the relationship between residual stress and thermal cycle. Subsequently, a comprehensive analysis of the failure-related characteristics is conducted to formulate the failure criterion. Then, the probability of failure was computed. The prediction was made for the thermal cycles experienced by the thermal barrier coating (TBC) on disc specimens with varying thermally grown oxide (TGO) thicknesses. The theoretically predicted lifespan was compared to experimental results, indicating the overall life is higher than the predicted value, with a maximum deviation of 16.8 %. Finally, an extensive evaluation was performed to identify the factors that influence the reliability of the TBCs. The investigation revealed that the TGO thickness had the most significant influence on the failure probability of TBCs, while fracture strength is the next important factor. These results have direct guidance for predicting the service life of TBCs and ensuring the service safety of its application components.

Ammonium dihydrogen phosphate (ADP) is a nitrogen-phosphorus-based inorganic flame retardant that is environmentally friendly and non-toxic. Wood treated with ADP has enhanced thermal stability and flame retardancy. Compounding ADP with Acid Red 3R dye further improves the decorative effect of the wood on the basis of excellent flame retardancy, resulting in a kind of dyeing and flame retardant multifunctionalized wood. Single-factor tests were designed to investigate the effects of flame retardant concentration, dye concentration, temperature, and time on three evaluation indexes: dye-uptake, color difference, and oxygen index, respectively. Through SEM and FTIR analysis, it was found that after the wood was simultaneously treated with flame retardant and dye, the two additives were aggregated in the grain pores of the cell wall by a simple physical combination. They did not undergo a chemical reaction. TG analysis showed that fire-retardant dyed wood had good thermal stability, which can delay the thermal degradation of wood and increase the residual charcoal rate of wood. XRD showed that the crystallinity of fire-retardant dyed wood increased compared to untreated wood. Through cone calorimetric test, it is found that flame retardant dyed veneer had excellent flame retardancy.

Ammonium dihydrogen phosphate (ADP) is a nitrogen-phosphorus-based inorganic flame retardant that is environmentally friendly and non-toxic. Wood treated with ADP has enhanced thermal stability and flame retardancy. Compounding ADP with Acid Red 3R dye further improves the decorative effect of the wood on the basis of excellent flame retardancy, resulting in a kind of dyeing and flame retardant multifunctionalized wood. Single-factor tests were designed to investigate the effects of flame retardant concentration, dye concentration, temperature, and time on three evaluation indexes: dye-uptake, color difference, and oxygen index, respectively. Through SEM and FTIR analysis, it was found that after the wood was simultaneously treated with flame retardant and dye, the two additives were aggregated in the grain pores of the cell wall by a simple physical combination. They did not undergo a chemical reaction. TG analysis showed that fire-retardant dyed wood had good thermal stability, which can delay the thermal degradation of wood and increase the residual charcoal rate of wood. XRD showed that the crystallinity of fire-retardant dyed wood increased compared to untreated wood. Through cone calorimetric test, it is found that flame retardant dyed veneer had excellent flame retardancy.

To enhance the synergistic effect of phosphorus (P) and nitrogen (N) on flame retardant property, four different phytic acid-based NP flame retardants (FR-PAN) were manufactured using phytic acid and urea with various molar ratios, ranging from 1:3 to 1:12. The FR-PAN water solution was used to impregnate poplar wood under vacuum condition, and the thermal degradation performance of the FR-PAN treated wood were investigated. Compared to untreated wood, the PAN-6 (molar ratio is 1:6) group showed a reduction of 57.1% in total heat release and 80.0% in total smoke release. In the combustion, due to the introduction of P and N, FR-PAN generates O=P/C-O/C-P/C-N bonds, forming highly graphitized char residues, which effectively isolate the entry of oxygen and heat and play a good protective role in the condensed phase. Morphological and chemical analysis of the residual char layer revealed that the introduction of P and N elements formed a more stable hybrid structure, significantly improving the thermal stability of the char layer. Among them, the PAN-6 group exhibited the highest char layer stability, indicating optimal synergistic effects of nitrogen and phosphorus under these conditions.

To enhance the synergistic effect of phosphorus (P) and nitrogen (N) on flame retardant property, four different phytic acid-based NP flame retardants (FR-PAN) were manufactured using phytic acid and urea with various molar ratios, ranging from 1:3 to 1:12. The FR-PAN water solution was used to impregnate poplar wood under vacuum condition, and the thermal degradation performance of the FR-PAN treated wood were investigated. Compared to untreated wood, the PAN-6 (molar ratio is 1:6) group showed a reduction of 57.1% in total heat release and 80.0% in total smoke release. In the combustion, due to the introduction of P and N, FR-PAN generates O=P/C-O/C-P/C-N bonds, forming highly graphitized char residues, which effectively isolate the entry of oxygen and heat and play a good protective role in the condensed phase. Morphological and chemical analysis of the residual char layer revealed that the introduction of P and N elements formed a more stable hybrid structure, significantly improving the thermal stability of the char layer. Among them, the PAN-6 group exhibited the highest char layer stability, indicating optimal synergistic effects of nitrogen and phosphorus under these conditions.

Circular RNAs (circRNAs) are a class of circular non-coding RNAs that play essential roles in the intricate and dynamic networks governing cell growth, development, and apoptosis. Resveratrol (RSV), a non-flavonoid polyphenol, is known to participate in follicular development and ovulation. In our previous research, we established a model using porcine ovarian granulosa cells (POGCs) treated with resveratrol, which confirmed its regulatory effects on long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) within these cells. However, the influence of resveratrol on circRNA expression has not been thoroughly investigated. To explore how resveratrol affects circRNA levels in POGCs, we designed an experiment with three groups: a control group (CON, n = 3, 0 μM RSV), a low-dose RSV group (LOW, n = 3, 50 μM RSV), and a high-dose RSV group (HIGH, n = 3, 100 μM RSV) for circRNA sequencing. We identified a total of 10,045 candidate circRNAs from POGCs treated with different concentrations of resveratrol (0, 50, and 100 μM). Differential expression analysis indicated that 96 circRNAs were significantly altered in the LOW vs. CON group, while 109 circRNAs showed significant changes in the HIGH vs. CON group. These circRNAs were notably enriched in biological processes associated with cell metabolism, apoptosis, and oxidative stress. Functional enrichment analysis of the host genes revealed their involvement in critical signaling pathways, including mTOR, AMPK, and apoptosis pathways. Additionally, we identified potential miRNA sponge candidates among the differentially expressed circRNAs, particularly novel_circ_0012954 and novel_circ_0004762, which exhibited strong connectivity within miRNA-target networks. Our findings provide valuable insights into the regulatory mechanisms of circRNAs in the context of resveratrol-induced apoptosis in POGCs, highlighting their potential as innovative therapeutic targets in reproductive biology.

Change detection refers to revealing the surface changes from multitemporal images of a given scene, where changed regions of different size and shape may appear anywhere. Convolutional neural network, as one of the most widely used deep learning architectures, has shown good performance in change detection task recently. However, due to multiple convolution and pooling operators in deep architectures, it often happens that small changes are missed and sharp edges are blurred. In this article, an adaptive spatial and difference learning method is proposed to tackle this problem, which mainly contains a feature-adapted difference learning (FADL) module and a difference recalibration (DR) module. In FADL, a kernel-adapted spatial learning part is constructed to capture varying spatial information, which selects proper kernels to adapt different shape and size of changed regions; a joint feature refinement part is employed to learn comprehensive features between bitemporal data, which are further enhanced by difference information. In addition, DR with shallow features is designed to recalibrate spatial and difference details further. From the experiments on three public datasets, our proposed method can relieve the spatial ambiguity problem and obtains the optimal results among the compared change detection methods.

Pubertal genetic variations between the indigenous Chinese Wanyue Black pig breed and the imported Yorkshire breed significantly impact their reproductive capacity. In order to identify the differentially expressed genes, gene networks, and metabolic pathways in ovary transcriptome of gilts, the serum hormone levels were analyzed by ELISA, and RNA-seq was performed to analyze ovarian genes. Our results reveal higher estradiol (E2) levels in Wanyue black gilts compared to Yorkshire gilts, while Yorkshire gilts exhibit elevated progesterone (P4) and GnRH levels. We identified a total of 154 differentially expressed genes (DEGs), with 87 up-regulated and 67 down-regulated genes in the Wanyue black gilts ovaries compared to the Yorkshire gilts. GO enrichment analysis unveiled the participation of DEGs in processes such as “Reproduction”, “Reproductive system development”, and “Ovarian follicle development”. Moreover, KEGG enrichment analysis revealed the involvement of DEGs in multiple signaling pathways associated with hormone biosynthesis and puberty, encompassing “Steroid hormone biosynthesis”, “Estrogen signaling pathway”, and “Prolactin signaling pathway”. The subsequent bioinformatics analysis identified nine functional genes that potentially contribute to the disparity in ovaries between Wanyue black gilts and Yorkshire gilts. This study offers significant insights into the endocrine and genetic aspects of pubertal development in gilts.

Abstract Statistically significant pattern mining (SSPM) is to mine patterns with significance based on hypothesis test. Under the constraint of statistical significance, our study aims to introduce a new preference relation into high utility patterns and to discover high utility and significant patterns (HUSPs) from transaction datasets, which has never been considered in existing SSPM problems. Our approach can be divided into two parts, HUSP-Mining and HUSP-Test. HUSP-Mining looks for HUSP candidates and HUSP-Test tests their significance. HUSP-Mining is not outputting all high utility itemsets (HUIs) as HUSP candidates; it is established based on candidate length and testable support requirements which can remove many insignificant HUIs early in the mining process; compared with the traditional HUIs mining algorithm, it can get candidates in a short time without losing the real HUSPs. HUSP-Test is to draw significant patterns from the results of HUSP-Mining based on Fisher’s test. We propose an iterative multiple testing procedure, which can alternately and efficiently reject a hypothesis and safely ignore the hypotheses that have less utility than the rejected hypothesis. HUSP-Test controls Family-wise Error Rate (FWER) under a user-defined threshold by correcting the test level which can find more HUSPs than standard Bonferroni’s control. Substantial experiments on real datasets show that our algorithm can draw HUSPs efficiently from transaction datasets with strong mathematical guarantee.

MicroRNAs (miRNAs) are small, noncoding RNAs that play a crucial role in the complex and dynamic network that regulates the apoptosis of porcine ovarian granulosa cells (POGCs). Resveratrol (RSV) is a nonflavonoid polyphenol compound that is involved in follicular development and ovulation. In previous study, we established a model of RSV treatment of POGCs, confirming the regulatory effect of RSV in POGCs. To investigate the miRNA-level effects of RSV on POGCs to reveal differentially expressed miRNAs, a control group (n = 3, 0 μM RSV group), a low RSV group (n = 3, 50 μM RSV group), and a high RSV group (n = 3, 100 μM RSV group) were created for small RNA-seq. In total, 113 differentially expressed miRNAs (DE-miRNAs) were identified, and a RT-qPCR analysis showed a correlation with the sequencing data. Functional annotation analysis revealed that DE-miRNAs in the LOW vs. CON group may be involved in cell development, proliferation, and apoptosis. In the HIGH vs. CON group, RSV functions were associated with metabolic processes and responses to stimuli, while the pathways were related to PI3K24, Akt, Wnt, and apoptosis. In addition, we constructed miRNA-mRNA networks related to Apoptosis and Metabolism. Then, ssc-miR-34a and ssc-miR-143-5p were selected as key miRNAs. In conclusion, this study provided an improved understanding of effects of RSV on POGCs apoptosis through the miRNA modulations. The results suggest that RSV may promote POGCs apoptosis by stimulating the miRNA expressions and provided a better understanding of the role of miRNAs combined with RSV in ovarian granulosa cell development in pigs.

Resveratrol (RES) is a non-flavonoid polyphenol compound that can be involved in follicular development and ovulation. However, the mechanism by which resveratrol regulates the apoptosis of porcine ovarian granulosa cells (POGCs) through long non-coding RNA (lncRNA) is poorly understood. We generated POGCs models of different doses of RES (0, 25, 50, 75, and 100 μM). It was observed that the cell viability was the highest in the 50 μM group, and the highest apoptosis rates were recorded in the 100 μM group. Therefore, a control group (n = 3, 0 μM RES group), a low RES group (n = 3, 50 μM RES group), and a high RES group (n = 3, 100 μM RES group) of POGCs were created for next RNA sequencing. Gene Ontology (GO) indicated that differentially expressed lncRNAs associated with apoptotic process were highly enriched. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of lncRNA target genes found that the Wnt signaling pathway and PI3K-Akt signaling pathway were both enriched. Furthermore, we constructed lncRNA-mRNA networks related to Metabolic and Cell Apoptosis, respectively. In the networks, five key-lncRNAs were screened, which may play a significant role in the process of POGCs metabolism and apoptosis. Furthermore, we focused on the function of a lnc-GAM (lncRNA associated with Granulosa cells Apoptosis and Metabolism) and verified that lnc-GAM could influence cell apoptosis in POGCs development by affecting the mRNA expression of apoptosis-related markers, and also affects the secretion of steroid hormones and related genes expression in POGCs cultured in vitro. Our study provides seminal data and important new insights into the regulation of reproductive mechanisms in porcine and other female mammals.

N-hexane, a common industrial organic solvent, causes multiple organ damage owing to its metabolite, 2,5-hexanedione (2,5-HD). To identify and evaluate the effects of 2,5-HD on sows’ reproductive performance, we used porcine ovarian granulosa cells (pGCs) as a vehicle and carried out cell morphology and transcriptome analyses. 2,5-HD has the potential to inhibit the proliferation of pGCs and induce morphological changes and apoptosis depending on the dose. RNA-seq analyses identified 4817 differentially expressed genes (DEGs), with 2394 down-regulated and 2423 up-regulated following 2,5-HD exposure treatment. The DEG, cyclin-dependent kinase inhibitor 1A (CDKN1A), according to the Kyoto Encyclopedia of Genes and Genomes enrichment analysis, was significantly enriched in the p53 signaling pathway. Thus, we evaluated its function in pGC apoptosis in vitro. Then, we knocked down the CDKN1A gene in the pGCs to identify its effects on pGCs. Its knockdown decreased pGC apoptosis, with significantly fewer cells in the G1 phase (p < 0.05) and very significantly more cells in the S phase (p < 0.01). Herein, we revealed novel candidate genes that influence pGCs apoptosis and cell cycle and provided new insights into the role of CDKN1A in pGCs during apoptosis and cell cycle arrest.

Mining high-utility pattern (HUP) on transactional datasets has been widely discussed, and various algorithms have been introduced to settle this problem. However, the time-space efficiency of the algorithms is still limited, and the mining system cannot provide timely feedback on relevant information. In addition, when mining HUP from taxonomy transactional datasets, a large portion of the quantitative results are just accidental responses to the user-defined utility constraints, and they may have no statistical significance. To address these two problems, we propose two corresponding approaches named Sampling HUP-Miner and Significant HUP-Miner. Sampling HUP-Miner pursues a sample size of a transitional dataset based on a theoretical guarantee; the mining results based on such a sample size can be an effective approximation to the results on the whole datasets. Significant HUP-Miner proposes the concept of testable support, and significant HUPs could be drawn timely based on the constraint of testable support. Experiments show that the designed two algorithms can discover approximate and significant HUPs smoothly and perform well according to the runtime, pattern numbers, memory usage, and average utility.

This paper presents a new scheme of a cost-effective tunable millimeter-wave (MMW) frequency synthesizer based on an ultra-wideband electro-optic frequency comb. The architecture for the quasi-tunable millimeter-wave frequency synthesizer mainly consists of a compact ultra-wide flat electro-optic frequency comb and a multi-tone frequency generator, which only includes a quantum dot mode-locked laser, a LiNbO3 dual-driving Mach–Zehnder modulator (DD-MZM) and Uni-traveling-carrier photodiode (UTC-PD). MMW signals generated with a quasi-tunable frequency are experimentally demonstrated. The difference in power is obtained for the different frequencies. The linewidth of the quasi-tunable frequency signals is less than 273 Hz. In addition, the single side band (SSB) phase noise of the 25, 37.5, 50 and 75 GHz is measured as −115, −106, −102 and −95 dBc/Hz at an offset of 1 kHz, respectively. The proposed frequency synthesizer has ultra-low phase noise, quasi-tunable frequency and simple structure. The research results of the frequency synthesizer are applied for 5G+ transmission with radio wave working at K-band and V-band. The flexible, compact and robust MMW frequency synthesizer is suitable for the future of ultra-high capacity 5G+ communication.

High utility sequential pattern (HUSP) mining has emerged as a novel topic in data mining, its computational complexity increases compared to frequent sequences mining and high utility itemsets mining. A number of algorithms have been proposed to solve such problem, but they mainly focus on mining HUSP in static databases and do not take streaming data into account, where unbounded data come continuously and often at a high speed. The efficiency of mining algorithms is still the main research topic in this field. In view of this, this paper proposes an efficient HUSP mining algorithm named HUSP-UT (utility on Tail Tree) based on tree structure over data stream. Substantial experiments on real datasets show that HUSP-UT identifies high utility sequences efficiently. Comparing with the state-of-the-art algorithm HUSP-Stream (HUSP mining over data streams) in our experiments, the proposed HUSP-UT outperformed its counterpart significantly, especially for time efficiency, which was up to 1 order of magnitude faster on some datasets.

Androgen receptor (AR) plays a critical role in the development and progression of prostate cancer (PCa). Current antiandrogen therapies induce resistant mutations at the hormone binding pocket (HBP) that convert the activity of these agents from antagonist to agonist. Thus, there is a high unmet medical need for the development of novel antiandrogens which circumvent mutation-based resistance. Herein, through the analysis of AR structures with ligands binding to the activation function-2 (AF2) site, we built a combined pharmacophore model. In silico screening and the subsequent biological evaluation lead to the discovery of the novel lead compound IMB-A6 that binds to the AF2 site, which inhibits the activity of either wild-type (WT) or resistance mutated ARs. Our work demonstrates structure-based drug design is an efficient strategy to discover new antiandrogens, and provides a new class of small molecular antiandrogens for the development of novel treatment agents against PCa.

The construction of a similarity matrix is one significant step for the spectral clustering algorithm; while the Gaussian kernel function is one of the most common measures for constructing the similarity matrix. However, with a fixed scaling parameter, the similarity between two data points is not adaptive and appropriate for multi-scale datasets. In this paper, through quantitating the value of the importance for each vertex of the similarity graph, the Gaussian kernel function is scaled, and an adaptive Gaussian kernel similarity measure is proposed. Then, an adaptive spectral clustering algorithm is gotten based on the importance of shared nearest neighbors. The idea is that the greater the importance of the shared neighbors between two vertexes, the more possible it is that these two vertexes belong to the same cluster; and the importance value of the shared neighbors is obtained with an iterative method, which considers both the local structural information and the distance similarity information, so as to improve the algorithm’s performance. Experimental results on different datasets show that our spectral clustering algorithm outperforms the other spectral clustering algorithms, such as the self-tuning spectral clustering and the adaptive spectral clustering based on shared nearest neighbors in clustering accuracy on most datasets.

The paper discusses the effects of B _4 C particle (B _4 C _p ) content on the microstructure and high-temperature oxidation resistance of B _4 C _p /Ti-5.4Al-4.03Mo-3.93V-2.37Cr-0.01Zr composite prepared by powder metallurgy. The results demonstrate that grain size of the composites decreases by increasing reinforcement content from 1 to 3 wt.%, suggesting that B _4 C _p can refine grain of the materials. For the composites with increasing B _4 C _p content from 0 to 3 wt.%, the apparent porosity increase from 3.23 to 6.89%, indicating that the density of materials decreased with the increase of B _4 C _P content. In addition, the hardness of the materials have been greatly improved. Comparing to the substrate 434.02 HV, the 1, 2, and 3 wt.% B _4 C _P specimens are increased by 37.2%, 57.5%, and 84.7%, respectively. The weight of the oxidation product is reduced and the oxidation rate is slowed down, as the B _4 C _P content increases. Moreover, the thickness of the oxide layer is greatly reduced, and the thickness of oxidation layer in composites with 0, 1, 2 and 3 wt.% B4CP specimens were 60, 55, 47 and 40 μ m, indicating that B _4 C _P can improve the high-temperature oxidation resistance.