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Objective: An evaluation of the prognostic factors of patients with surgically treated high-grade neuroendocrine carcinoma of the cervix (NECC). Design: A multi-center, retrospective analysis Setting and Population: 58 cases Methods: 58 patients with stage IIA1-IIIC cervical cancer who had high-grade NECC and were initially treated with surgery between 2009 and 2022 was conducted. We divided them into two groups based on pathology: the pure cohort (only one histological type) and the mixed group (≥2 histological types). All clinicopathological characteristics of patients were reviewed retrospectively using electronic medical records. Main Outcome Measures: Survival outcomes of NECC, clinicopathological characteristics and various related factors were analysed. Results: The median overall survival (OS) was 32.5 months (range: 4.1–60.9). The mixed pathology was a protective factor impacting survival outcomes in NECC (HR,0.02; 95% CI, 0.00~0.45, P=0.014). In contrast, uterus invasion (HR, 24.5; 95%CI, 1.37~439.31, P=0.03) was a poor prognostic factor affecting the survival outcomes in NECC. In addition, among all postoperative adjuvant chemotherapy options, etoposide + platinum (EP) showed better survival outcome (HR,0.04; 95% CI, 0.00~0.89, P=0.041). According to the Kaplan-Meier survival curves, significant differences were observed with respect to mixed pathology (P=0.044), uterus invasion (P＜0.0001), parametrium invasion (P=0.025), surgical margin (P=0.035), and distant metastasis (P＜0.0001). Conclusions: Patients with mixed pathological subtypes who undergo surgery for NECC have a better prognosis. Meanwhile, uterus invasion is a poor predictor of prognosis. In terms of postoperative adjuvant chemotherapy, the EP regimen is superior to other regimens. (The trial registration number: ChiCTR2200063023)

In view of the unstable output voltage of the new double full bridge ZVZCS converter of beam power supply when it is disturbed by external disturbance or internal parameter change in practical application, combined with the characteristics of the beam power supply system model, this paper proposes a beam power supply control strategy based on linear active disturbance rejection control (LADRC). Firstly, the working principle of the new double full bridge ZVZCS converter of beam power supply is analyzed, and considering the parasitic parameters and effective duty cycle of circuit components, the small signal equivalent model under two working modes is established. Secondly, combined with the system model and linear active disturbance rejection control theory, a double closed-loop controller is designed, which takes the second-order LADRC as the outer voltage loop and PI control as the inner current loop. Finally, the simulation and experimental results show that the designed controller has stronger robustness, anti-interference and better dynamic response speed than the traditional double closed-loop PI controller in load switching and dual-mode switching, and has a good development prospect.

The burbot (Lota lota) is the only member of the order Gadiformes adapted solely to freshwater. This species has the widest longitudinal range among freshwater fish worldwide. Burbot serves as a good model for studies on adaptive genome evolution from marine to freshwater environments. However, a high-quality reference genome of burbot has not yet been released. Here, the first chromosome-level genome of burbot was constructed using PacBio long sequencing and Hi-C technology. A total of 95.24 Gb polished PacBio sequences were generated, and the preliminary genome assembly was 575.83 Mb in size with a contig N50 size of 2.15 Mb. The assembled sequences were anchored to 22 pseudochromosomes by using Hi-C data. The final assembled genome after Hi-C correction was 575.92 Mb, with a contig N50 of 2.01 Mb and a scaffold N50 of 22.10 Mb. A total of 22,067 protein-coding genes were predicted, 94.82% of which were functionally annotated. Phylogenetic analyses indicated that burbot diverged with the Atlantic cod approximately 43.8 million years ago. In addition, 377 putative genes that appear to be under positive selection in burbot were identified. These positively selected genes might be involved in the adaptation to the freshwater environment. These genome data provide an invaluable resource for the ecological and evolutionary study of the order Gadiformes.

The microRNA-based mechanisms underlying the antioxidant action(s) of co-existing flavonoids in response to oxidative stress are of high interest. This study aimed to extend the existing knowledge and provide insights into the potential regulatory network in response to oxidative stress and the co-presence of quercetin and catechin antioxidants, via a preclinical approach using H2O2-stimulated HepG2 cells. It was confirmed that BACH1 serves as an essential and direct negative regulator of the Keap1-Nrf2 signaling pathway and the antioxidant synergism between quercetin and catechin. BACH1 promoted reactive oxygen species (ROS) accumulation while inhibiting cell growth, which could be reversed by the synergistic action of let-7a-5p and miR-25-3p in the co-presence of quercetin and catechin. Both let-7a-5p and miR-25-3p could directly regulate the expression and function of BACH1 (e.g. upregulation of the two miRNAs could rescue largely overexpression of BACH1). Although these molecular interactions likely represented only some aspects of the overall regulatory network, this research confirms the feasibility of the combined uses of dietary flavonoids with chemopreventive properties in synergy during multiple-target interactions and multiple-pathway regulation.

Characterising the size-dependent behaviour of rock has been a significant challenge in rock engineering particularly during the design of structures on or within a rock mass. Generally, the mechanical characterisation of rock starts at the laboratory scale where the intact rock is tested and then the resulting data is extrapolated to the field conditions using a suitable size effect model. Despite extensive research on the size effect of intact rock, very few have included the weak rocks with low uniaxial compressive strength (UCS). Thus, in this study, the size-dependent behaviour of two different weak rocks namely, Gambier limestone and artificial rock with uniaxial compressive strength of less than 10 MPa were investigated experimentally and analytically. The diameter of the cylindrical Gambier limestone samples varied from 26 to 285 mm while the diameter of artificial rock samples ranged between 26 and 139 mm. For Gambier limestone, the uniaxial compressive, Brazilian and point load experiments were carried out while for artificial rock, only the uniaxial compressive tests were conducted. In both rock types, the ascending and then descending size effect trend was a pronounced behaviour for UCS and Young’s modulus data while the size effect behaviour of Poisson’s ratio was inconclusive. Also, the tensile strength and point load index data obtained from Gambier limestone revealed only descending size effect trends. The unified size effect law and its improved version were fitted to the UCS and Young’s modulus data leading to a very good agreement between the data and the model predictions. It was confirmed that an improved version of unified size effect law can predict a more realistic strength value for a sample with an infinite size. Finally, the applicability of two descending size effect models to the tensile strength and point load data was assessed and concluded that the multifractal scaling law is a suitable model for the point load data while the size effect law can better predict the tensile strength data.

A computation model is proposed for senior citizen health self-care through adaptive multi-level computation cycles according to the principles of slow intelligence. An experimental system, called the TDR system, is being implemented on a smart phone to serve as the test bed for the proposed approach. In this paper, we describe the computation model, the basic concepts and the prototype experimental system. The main theoretical concept is centered on adaptive multi-level computation cycles, events and event icons. Further experimental as well as theoretical investigations of the proposed computation model are discussed.

We proposed a design scheme to enable the monolithic integration between a silicon waveguide and a 1.3 μm wavelength band III–V quantum dot laser, which is epitaxially grown on silicon with an asymmetric structure. The III–V laser is grown in a deep trench of a silicon-on-insulator wafer by the selective area epitaxy technique, and a GaAs coupling layer is inserted into the lower cladding layer of the laser, which can make the optical field distribution of the laser shift down. Besides, a mode-size converter with a three-segment tapered structure is designed to couple the output laser into the standard single-mode silicon waveguide. For the laser, the composition and the thickness of AlGaAs cladding layers and AlGaAs transition layer are optimized based on the optical waveguide theory. When the upper cladding layer is 0.6 μm Al0.7Ga0.3As, the lower cladding layer is 1.2 μm Al0.25Ga0.75As, and the transition layer is 20 nm Al0.45Ga0.55As, the optical confinement factors of the active region and the coupling layer are 45.34% and 40.69%, respectively. Then the length of the mode-size converter with a three-segment tapered structure is further optimized by the mode-matching method. When the lengths of the three tapered structures of the mode-size converter are 50 μm, 53 μm and 10 μm respectively, a coupling efficiency of 65% can be obtained between the laser and the Si waveguide. This scheme is expected to realize the efficient optical coupling between the silicon integrated light source and the silicon waveguide, which will promote the development of silicon monolithic photonic integration.

BACKGROUND Gastric leiomyomas and gastric stromal tumors are the most common types of gastric tumors encountered. In recent years, the incidence of the two types of tumors has been increasing, but the differential diagnosis is still a challenge in clinical work. However, as there are many reports on stromal tumors and inflammation-related indicators are gradually being paid attention to as important factors in predicting tumor prognosis, the two main purposes of this study were to explore the inflammation-related differences between the two types of tumors and to develop a nomogram as a predictive model. AIM To explore the differences in platelet-lymphocyte ratio (PLR), neutrophil-lymphocyte ratio (NLR), lymphocyte mononuclear cell ratio (LMR), and SII between the two types of tumors, and simultaneously create the nomogram model. METHODS This study enrolled 88 patients in the gastric stromal tumor group and 56 patients in the gastric leiomyoma group, and the relevant data of the two groups were entered into the system for an integrated analysis. The primary objective of this study was to identify the differences in the inflammation index between the two types of tumors. RESULTS There were statistically significant differences between the two groups in sex, age, and tumor location. In comparison, gastric leiomyomas seem to be more common in women, young patients, and gastric cardia, which is in line with our previous research; the groups showed the following statistical differences: PLR (158.2% vs 134.3%, P = 0.028), NLR (2.35 vs 1.68, P = 0.000), LMR (5.75 vs 10.8, P = 0.004), and SII (546.2 vs 384.3, P = 0.003). The results of the multivariate logistic regression analysis showed that sex, age, tumor location, and LMR were independent risk factors for the identification of the two types of tumors. After considering the risk factors selected by the above analysis into the predictive model, a predictive model for distinguishing gastrointestinal stromal tumors from gastric leiomyomas was established as the nomogram. CONCLUSION Gastric leiomyomas and gastric stromal tumors are not only different in factors such as age of the patient, but also in inflammatory indicators such as LMR and PLR. We have established a predictive model related to the laboratory indicators and are looking forward to further research conducted in this clinical area.

In order to obtain high-quality lettuce seedlings, effects of photosynthetic photon flux density (PPFD), photoperiod and light quality at seedling stage on growth and quality of hydroponic lettuce were investigated at different growth stages. Hydroponic lettuce (Lactuca sativa L. cv. Frill ice) seedlings were grown under 12 combinations of two levels of PPFD (200 and 250 μmol m−2 s-1) and two photoperiods (14 and 16 h d-1) under three light quality: light-emitting diodes (LEDs) with red to blue ratio (R:B ratio) of 1.2 and 2.2, and fluorescent lamps with R:B ratio of 1.8 as control. After the 20-day treatment during seedling stage, lettuce seedlings were transplanted to a homogeneous lighting environment for another 20 days before harvest. Results showed that leaf length and ratio of leaf length to leaf width at 20 days after sowing decreased in a logarithmic way with increasing daily light integral and fluorescent lamps resulted in larger seedlings with bigger leaves and higher leaf and root dry weights compared to LEDs. Leaf and root fresh weights of harvested lettuces were higher in the seedlings grown under PPFD at 200 μmol m−2 s-1 with a 16 h d-1 photoperiod, regardless of light quality at seedling stage. Generally, vitamin C content of mature lettuce was lower in seedlings grown under LEDs with R:B ratio of 1.2 and no significant differences were observed in nitrate content among the three lighting sources. In consideration of mature yield and quality, lighting efficiency and electrical consumption of lighting sources, PPFD of 200 μmol m−2 s-1 with a photoperiod of 16 h d-1 provided by LEDs with R:B ratio of 2.2 was suitable for hydroponic lettuce (cv. Frill ice) seedling production.

In structural reliability engineering, designer may encounter twofold uncertainty that arises from the existence of the insufficient statistical data and the incorporated experts’ information. For this case, the randomness and fuzziness coexist in each uncertain element. This paper aims to develop a new approach to evaluating structural reliability and obtain the optimal structural design under twofold uncertainty. Specifically, we first introduce a metric termed equilibrium chance to formulate equilibrium reliability index (ERI) for modeling structural reliability under linear limit-state function and nonlinear limit-state function. Second, a new class of equilibrium reliability-based structural design (ERSD) optimization problem with equilibrium reliability constraint is developed. Under mild conditions, we provide the equivalent reformulation of the equilibrium reliability constraint and demonstrate its convexity. Finally, we introduce the fuzzy simulation (FS) scheme to obtain the feasible solution generated by equilibrium reliability constraint, and a hybrid heuristic algorithm by integrating the FS, genetic algorithm (GA) and local search (LS) is designed to solve the proposed ERSD optimization problem. Several numerical examples are carried out to illustrate the applicability and effectiveness of our proposed reliability modeling method.

Structural reliability design optimization under epistemic uncertainty has attracted the attention of many researchers, which plays a pivotal role both in theory and engineering application. However, many traditional fuzzy reliability indicators are formulated by fuzzy measure without self-duality. For this reason, we reconsider structural system with fuzzy parameters, and a new credibilistic failure indicator (CFI) is presented based on self-dual credibility measure, which provides the exact expression of structural failure degree under fuzzy environment. Then, for the structure with fuzzy trapezoidal parameters, the explicit expressions of the CFI formulations are presented under fuzzy linear limit-state function and nonlinear limit-state function. Furthermore, CFI-based design optimization is formulated to obtain the optimal structural design under the given reliability level. Meanwhile, one theorem on the reliability constraint is provided to facilitate us to obtain the equivalent deterministic constraint of the reliability constraint. Finally, two illustrative examples are performed to demonstrate the efficiency of the proposed CFI formulation and the corresponding computational methods.

We determined the whole mtDNA genome of the Common Pochard (Aythya ferina) in the Ningxia Hui Autonomous Region, China. The complete mitochondrial genome is 16,599 bp in length and consists of 13 protein-coding genes, 22 tRNA genes, 2 rRNA genes, and 1 control region (D-loop). The nucleotide composition is 29.34% A, 22.23% T, 15.66% G, and 32.77% C. Phylogenetic analysis results showed close genetic relationship between A. ferina and Aythya americana.

Serial section electron microscopy (ssEM) can provide comprehensive 3D ultrastructural information of the brain with exceptional computational cost. Targeted reconstruction of subcellular structures from ssEM datasets is less computationally demanding but still highly informative. We thus developed a region-CNN-based deep learning method to identify, segment, and reconstruct synapses and mitochondria to explore the structural plasticity of synapses and mitochondria in the auditory cortex of mice subjected to fear conditioning. Upon reconstructing over 135,000 mitochondria and 160,000 synapses, we find that fear conditioning significantly increases the number of mitochondria but decreases their size and promotes formation of multi-contact synapses, comprising a single axonal bouton and multiple postsynaptic sites from different dendrites. Modeling indicates that such multi-contact configuration increases the information storage capacity of new synapses by over 50%. With high accuracy and speed in reconstruction, our method yields structural and functional insight into cellular plasticity associated with fear learning.

Purpose The diagnosis of prostate transition zone cancer (PTZC) remains a clinical challenge due to their similarity to benign prostatic hyperplasia (BPH) on MRI. The Deep Convolutional Neural Networks (DCNNs) showed high efficacy in diagnosing PTZC on medical imaging but was limited by the small data size. A transfer learning (TL) method was combined with deep learning to overcome this challenge. Materials and methods A retrospective investigation was conducted on 217 patients enrolled from our hospital database (208 patients) and The Cancer Imaging Archive (nine patients). Using T2-weighted images (T2WIs) and apparent diffusion coefficient (ADC) maps, DCNN models were trained and compared between different TL databases (ImageNet vs. disease-related images) and protocols (from scratch, fine-tuning, or transductive transferring). Results PTZC and BPH can be classified through traditional DCNN. The efficacy of TL from natural images was limited but improved by transferring knowledge from the disease-related images. Furthermore, transductive TL from disease-related images had comparable efficacy to the fine-tuning method. Limitations include retrospective design and a relatively small sample size. Conclusion Deep TL from disease-related images is a powerful tool for an automated PTZC diagnostic system. In developing regions where only conventional MR scans are available, the accurate diagnosis of PTZC can be achieved via transductive deep TL from disease-related images.

Some traditional fuzzy reliability models quantify the system uncertainty based on possibility measure metric without self-duality, which leads to the undesired result that the sum of possibility of any one fuzzy set/event and its complementary set/event is not equal to one. To overcome this drawback, this paper formulates a new reliability metric tool based on self-dual credibility measure. Firstly, we introduce credilistic safety constraint to formulate credibilistic reliability optimization model on the basis of limit-state function. Then, several theorems are provided to show that the feasible domain generated by credibilistic safety constraint can be reformulated as the equivalent convex domain under mild assumptions, which enables credibilistic safety constraint to reformulate as the equivalent reliability constraint. Finally, one numerical example on the optimal design of three-bar truss structure is provided to show the applicability of the credibilistic reliability optimization model. By using out-of-sample back-testing, these comparison results are stated on the provided reliability level and the back-testing reliability level, which evidence the effectiveness of the optimal solution achieved by the proposed model for practical structural engineering.

Image information for single polarization parameters is weak, low contrast and the common visible light intensity image detail fuzzy problems, in order to further improve the target detection of polarization imaging detection system identification capability, put forward a kind of based on the sampling of shear wave transformation under visible light image fusion algorithms, intensity and polarization parameters can effectively improve the identification of targets in complex background. Polarization degree of the image and visible light intensity image using the sampling shear wave transformation under the decomposed high frequency subband and low-frequency subband, then low frequency subband image fusion rules design based on region distance energy weighted algorithm, and the high frequency subband image fusion rule is designed to combine guide take large filtering area of energy, will eventually high low frequency subband image by the NSST finally fused image is obtained by inverse transformation refactoring. By comparing the fusion results of this algorithm with those of other methods that adopt the same decomposition transformation method but choose different fusion rules, the experiment proves that this algorithm not only has the best visual effect, but also has the advantage in the objective evaluation index value.

Biologists often need to handle numerous video-based home-cage animal behavior analysis tasks that require massive workloads. Therefore, we develop an AI-based multi-species tracking and segmentation system, SiamBOMB, for real-time and automatic home-cage animal behavioral analysis. In this system, a background-enhanced Siamese-based network with replaceable modular design ensures the flexibility and generalizability of the system, and a user-friendly interface makes it convenient to use for biologists. This real-time AI system will effectively reduce the burden on biologists.

Multi-focus image fusion consists in the integration of the focus regions of multiple source images into a single image. At present, there are still some common problems in image fusion methods, such as block artifacts, artificial edges, halo effects, and contrast reduction. To address these problems, a novel, to the best of our knowledge, multi-focus image fusion method using energy of Laplacian and a deep neural network (DNN) is proposed in this paper. The DNN is composed of multiple denoising autoencoders and a classifier. The Laplacian energy operator can effectively extract the focus information of source images, and the trained DNN model can establish a valid mapping relationship between source images and a focus map according to the extracted focus information. First, the Laplacian energy operator is used to perform focus measurement for two source images to obtain the corresponding focus information maps. Then, the sliding window technology is used to sequentially obtain the windows from the corresponding focus information map, and all of the windows are fed back to the trained DNN model to obtain a focus map. After binary segmentation and small region filtering, a final decision map with good consistency is obtained. Finally, according to the weights provided by the final decision map, multiple source images are fused to obtain a final fusion image. Experimental results demonstrate that the proposed fusion method is superior to other existing ones in terms of subjective visual effects and objective quantitative evaluation.

The microRNA-based mechanisms underlying the antioxidant action(s) of co-existing flavonoids in response to oxidative stress are of high interest. This study aimed to extend the existing knowledge and provide insights into the potential regulatory network in response to oxidative stress and the co-presence of quercetin and catechin antioxidants, via a preclinical approach using H

Amyloid-β (Aβ) peptide and α-synuclein (α-syn) are major components of senile plaques in Alzheimer's disease (AD) and Lewy bodies in Parkinson's disease (PD), respectively. Co-occurrence of Aβ and α-syn in the senile brains of AD and LB diseases suggests interactions between the two proteins. However, the significance of the overlapping deposition, especially the effects of α-syn on the Aβ aggregation, still remains to be clarified. In the present study, we investigated the effects of α-syn pre-formed fibrils (PFFs) injection on the cognitive behaviors and Aβ deposition in the brain of APP/PS1 transgenic AD mice by using Morris water maze (MWM) test, immunohistochemistry and western blot techniques. We found that APP/PS1 transgenic mice exhibited an obvious elevation in the α-syn load, as well as Aβ deposition in the brain compared with wild type of C57 BL littermates. 5 months after cerebral injection of exogenous α-syn, MWM tests showed an alleviation in cognitive impairments in APP/PS1 mice; western blot and immunohistochemistry experiments also exhibited a significant reduction in Aβ level in the brain of APP/PS1 mice injected with α-syn. These results suggest that α-syn aggregated in the brain of AD may act as a protective factor and defend the brain tissue from early Aβ deposition and cognitive deficits.

The traditional reliability analysis methods based on probability theory and fuzzy set theory have been widely used in engineering practice. However, these methods are unable directly measure the uncertainty of mechanism reliability with uncertain variables, i.e., subjective random and fuzzy variables. In order to address this problem, a new quantification method for the mechanism reliability based on chance theory is presented to simultaneously satisfy the duality of randomness and the subadditivity of fuzziness in the reliability problem. Considering the fact that systems usually have multilevel performance and the components have multimode failures, this paper proposes a chance theory based multi-state performance reliability model. In the proposed method, the chance measure is adopted instead of probability and possibility measures to quantify the mechanism reliability for the subjective probability or fuzzy variables. The hybrid variables are utilized to represent the random and fuzzy parameters, based on which solutions are derived to analyze the chance theory based mechanism reliability with chance distributions. Since the input parameters of the model contain fuzziness and randomness simultaneously, an algorithm based on chance measure is designed. The experimental results on the case application demonstrate the validity of the proposed method.