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Breast cancer is one of the most common female diseases, posing a great threat to women’s health, and breast ultrasound imaging is a common method for breast cancer diagnosis. In recent years, U-Net and its variants have dominated the medical image segmentation field with their excellent performance. However, the existing U-type segmentation networks have the following problems: (1) the design of the feature extractor is complicated, and the calculation difficulty is increased; (2) the skip connection operation simply combines the features of the encoder and the decoder, without considering both spatial and channel dimensions; (3) during the downsampling phase, the pooling operation results in the loss of feature information. To address the above deficiencies, this paper proposes a breast tumor segmentation network, RMAU-Net, that combines residual depthwise separable convolution and a multi-scale channel attention gate. Specifically, we designed the RDw block, which has a simple structure and a larger sensory field, to overcome the localization problem of convolutional operations. Meanwhile, the MCAG module is designed to correct the low-level features in both spatial and channel dimensions and assist the high-level features to recover the up-sampling and pinpoint non-regular breast tumor features. In addition, this paper used the Patch Merging operation instead of the pooling method to prevent the loss of breast ultrasound image information. Experiments were conducted on two breast ultrasound datasets, Dataset B and BUSI, and the results show that the method in this paper has superior segmentation performance and better generalization.

Anomaly detection of liquid levels in molds is an important task in continuous casting. Data that consists of a series of liquid levels in mold during a continuous casting process can be viewed as a time series, on which Time Series Anomaly Detection (TSAD) methods can be applied. However, the abnormal and normal data in the liquid data in the mold sequence share similar features. And due to manual control limitations, the anomaly sequence in liquid level in mold data lasts longer. Therefore, using existing TSAD methods based on AutoEncoders (AEs) often results in high false positive rates. In this paper, a novel framework is proposed for anomaly detection of liquid level in mold by using unsupervised deep-learning-based TSAD. The framework decomposes a time series into normal and error sequences. A forecasting network reconstructs the normal sequence to solve the first issue, which allows the proposed method to consider the context. An error extraction network generates errors from the original sequence to solve the second issue. It removes anomalies from the original sequence during training to prevent anomaly pollution and allows the forecasting network’s training to be free from anomaly pollution. A new dynamic threshold method is proposed to identify anomalies. The proposed method is evaluated on the actual casting dataset by comparing it with baseline methods. The experiment results indicate that the proposed framework outperforms some of the best anomaly detection methods in terms of accuracy, precision, and F1 score.

Continuous casting production is an important stage in smelting high-quality steel, and automatic casting control based on artificial intelligence is a key technology to improve the continuous casting process and the product quality. By controlling the opening degree of the stopper rod reasonably, the mold can be filled with liquid steel stably in the specified time window, and automatic casting can be realized. In this paper, an automatic casting control method of continuous casting based on an improved Soft Actor–Critic (SAC) algorithm is proposed. Firstly, a relational model of the stopper rod opening degree and the liquid steel outflow velocity is established according to historical casting data. Then the Markov Decision Process (MDP) model of the automatic casting problem and the reinforcement learning framework based on the SAC algorithm are established. Finally, a Heterogeneous Experience Pool (HEP) is introduced to improve the SAC algorithm. According to the simulation results, the proposed algorithm can predict the stopper rod opening degree sequence under the constraint of the target liquid level curve. Under different billet specifications and interference conditions, an accuracy of 80% of liquid level in the mold and a stopper rod opening degree stability rate of 75% can be achieved, which is 4.29% and 3.17% higher than those for the baseline algorithms, respectively.

A endo-1,4-β-mannanase (CcMan5C) gene was cloned from Coprinopsis cinerea and heterologously expressed in Pichia pastoris, and the recombinant enzyme was purified by Ni-affinity chromatography and identified by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF/TOF-MS). CcMan5C hydrolyzed only locust bean gum galactomannan (LBG) but not α-mannan from S. cerevisiae or Avicel cellulose, oat spelt xylan, or laminarin from Laminaria digitata. CcMan5C exhibited distinctive catalytic features that were different from previously reported β-mannanases. (1) CcMan5C is the first reported fungal β-mannase with an optimal alkalic pH of 8.0–9.0 for hydrolytic activity under assay conditions. (2) CcMan5C is the first reported alkalic fungal β-mannase with an optimal temperature of 70 °C for hydrolytic activity under assay conditions. (3) The organic solvents methanol, ethanol, isopropanol, and acetone at concentrations of 10% or 20% did not inhibit CcMan5C activity, while 10% or 20% isopropanol and acetone even enhanced CcMan5C activity by 9.20–34.98%. Furthermore, CcMan5C tolerated detergents such as Tween 20 and Triton X-100, and its activity was even enhanced to 26.2–45.6% by 1% or 10% Tween 20 and Triton X-100. (4) CcMan5C solution or lyophilized CcMan5C exhibited unchanged activity and even increasing activity after being stored at −20 °C or −80 °C for 12 months and retained above 50% activity after being stored at 4 °C for 12 months. These features make CcMan5C a suitable candidate for the detergent industry and paper and pulp industry.

Duck plague (DP) is a highly contagious viral disease in ducks caused by the duck plague virus (DPV). The DPV, a member of Herpesviridae, poses a severe threat to the waterfowl farming industry worldwide. In this study, we reported a recent outbreak of DPV in domestic laying ducks at 310 days of age from southern China in December 2021. The gross lesion, histopathologic examination, molecular detection, and genetic characterization studies of DPV are described here. As a result, gross lesions such as an enlarged congestive spleen and liver were observed. Liver with vacuolar degeneration and small vacuoles and spleen with hemosiderosis were remarkable microscopic findings. Our results suggested that the liver had the highest viral load, followed by the trachea, pancreas, kidney, brain, spleen, and heart. In addition, DPV was successfully isolated in chicken embryo fibroblast cell culture and designated as DP-GD-305-21. The UL2, UL12, UL41, UL47, and LORF11 genes of DP-GD-305-21 shared a high nucleotide homology with the Chinese virulent (CHv) strain and the Chinese variant (CV) strain. In conclusion, this study reports the isolation and molecular characterization of DPV from a recent outbreak in southern China. Our results contributed to the understanding of the pathological and molecular characterization of currently circulating DPV in China.

In this work, we propose a novel dual optical frequency comb (DOFC) generation scheme based on dual cascaded difference frequency generation (DCDFG). Feasible designs are introduced that enable the two sets of cascaded optical waves, initially generated by DCDFG in an aperiodically periodically poled lithium niobate (APPLN) crystal with a pump wave and two signal waves, then transferred to high-order Stokes waves by oscillations of cascaded Stokes waves and the optimization of phase mismatching of each-order DCDFG; finally, a DOFC was constructed. We demonstrate a high-performance DOFC with characteristics of high repetition frequency difference, tunable repetition frequency difference, high flatness, and a tunable spectral distribution range by providing a theoretical framework. We argue that the scheme proposed in this work is promising for achieving a high-quality DOFC.

Dietary inflammatory potential has been proven to be correlated with the incidence of diabetes and cardiovascular diseases. However, the evidence regarding the impact of dietary inflammatory patterns on long-term mortality is scarce. This cohort study aims to investigate the dietary inflammatory pattern of the general US individuals by baseline glycemic status and to estimate its association with long-term mortality. A total of 20,762 general American adults with different glycemic statuses from the National Health and Nutrition Examination Survey were included. We extracted 24-h dietary information, and the dietary inflammatory index (DII) was calculated. The outcomes were defined as 5-year all-cause and cardiovascular mortality. Compared with the normoglycemia group, individuals with prediabetes and type 2 diabetes had higher DII scores (overall weighted p < 0.001). Compared with low DII scores, participants with high DII scores were at a higher risk of long-term all-cause mortality (HR: 1.597, 95% CI: 1.370, 1.861; p < 0.001) and cardiovascular mortality (HR: 2.036, 95% CI: 1.458, 2.844; p < 0.001). The results were stable after adjusting for potential confounders. Moreover, the prognostic value of DII for long-term all-cause mortality existed only in diabetic individuals but not in the normoglycemia or prediabetes group (p for interaction = 0.006). In conclusion, compared to the normoglycemia or prediabetes groups, participants with diabetes had a higher DII score, which indicates a greater pro-inflammatory potential. Diabetic individuals with higher DII scores were at a higher risk of long-term all-cause and cardiovascular mortality.

Mitogen-activated protein kinase (MAPK) pathways, such as the high-osmolarity glycerol mitogen-activated protein kinase (HOG) pathway, are evolutionarily conserved signaling modules responsible for transmitting environmental stress signals in eukaryotic organisms. Here, we identified the MAPK homologue in the HOG pathway of Coprinopsis cinerea, which was named CcSakA. Furthermore, during the development of the fruiting body, CcSakA was phosphorylated in the fast elongating apical part of the stipe, which meant that CcSakA was activated in the apical elongating stipe region of the fruiting body. The knockdown of CcSakA resulted in a shorter stipe of the fruiting body compared to the control strain, and the expression of phosphomimicking mutant CcSakA led to a longer stipe of the fruiting body compared to the control strain. The chitinase CcChiE1, which plays a key role during stipe elongation, was downregulated in the CcSakA knockdown strains and upregulated in the CcSakA phosphomimicking mutant strains. The results indicated that CcSakA participated in the elongation of stipes in the fruiting body development of C. cinerea by regulating the expression of CcChiE1. Analysis of the H2O2 concentration in different parts of the stipe showed that the oxidative stress in the elongating part of the stipe was higher than those in the non-elongating part. The results indicated that CcSakA of the HOG pathway may be activated by oxidative stress. Our results demonstrated that the HOG pathway transmits stress signals and regulates the expression of CcChiE1 during fruiting body development in C. cinerea.

Aiming at the task allocation problem of heterogeneous unmanned underwater vehicle (UUV) swarms, this paper proposes a dynamic extended consensus-based bundle algorithm (DECBBA) based on consistency algorithm. Our algorithm considers the multi-UUV task allocation problem that each UUV can individually complete multiple tasks, constructs a “UUV-task” matching matrix and designs new marginal utility, reward and cost functions for the influence of time, path and UUV voyage. Furthermore, in view of the unfavorable factors that restrict the underwater acoustic communication range between UUVs in the real environment, our algorithm complete dynamic task allocation of UUV swarms with optimization in load balance indicator by the update of the UUV individual and the task completion status in the discrete time stage. The performance indicators (including global utility and task completion rate) of the dynamic task allocation algorithm in the scenario with communication constraints can be well close to the static algorithm in the ideal scenario without communication constraints. The simulation experiment results show that the algorithm proposed in this paper can quickly and efficiently obtain the dynamic and conflict-free task allocation assignment of UUV swarms with great performance.

Salpingitis is manifested as hemorrhagic follicular inflammation exudations and peritonitis, leading to reduced egg production and high culling of breeder flocks. From 2018 to 2021, increasing salpingitis during egg peak is threatening the poultry industry post-artificial insemination, both in breeder layers and breeder ducks across China. In our study, Escherichia coli (E. coli), Enterococcus faecalis(E. faecalis) and Chlamydia psittaci (C. psittaci) were isolated and identified from the diseased oviducts using biochemical tests and PCR. To identify and isolate pathogenicity, we inoculated the isolates into laying hens via an intravaginal route. Later, laying hens developed typical salpingitis after receiving the combination of the aforementioned three isolates (1 × 105 IFU/mL of C. psittaci and 1 × 106 CFU/mL of E. faecalis and E. coli, respectively), while less oviduct inflammation was observed in the layers inoculated with the above isolate alone. Furthermore, 56 breeder ducks were divided into seven groups, eight ducks per group. The birds received the combination of three isolates, synergic infection of E. coli and E. faecalis, and C. psittaci alone via vaginal tract, while the remaining ducks were inoculated with physiological saline as the control group. Egg production was monitored daily and lesions of oviducts and follicles were determined post-infection on day 6. Interestingly, typical salpingitis, degenerated follicles and yolk peritonitis were obviously found in the synergic infection of three isolates and the birds inoculated with C. psittaci alone developed hemorrhagic follicles and white exudates in oviducts, while birds with E. faecalis or E. coli alone did not develop typical salpingitis. Finally, higher E. coli loads were determined in the oviducts as compared to E. faecalis and C. psittaci infection. Taken together, the combination of E. coli and E. faecalis, and C. psittaci could induce typical salpingitis and yolk peritonitis both in laying hens and breeder ducks. Secondary infection of E. coli and E. faecalis via artificial insemination is urgently needed for investigation against salpingitis.

Nanofibers of the poorly water-soluble antibiotic ciprofloxacin (CIP) were fabricated in the form of an amorphous solid dispersion by using poly(vinyl pyrrolidone) as a polymer matrix, by the low-cost electrospinning method. The solubility of the nanofibers as well as their in vitro diffusion were remarkably higher than those of the CIP powder or the physical mixture of the two components. The fiber size and morphology were optimized, and it was found that the addition of the CIP to the electrospinning solution decreased the nanofiber diameter, leading to an increased specific surface area. Structural characterization confirmed the interactions between the drug and the polymer and the amorphous state of CIP inside the nanofibers. Since the solubility of CIP is pH-dependent, the in vitro solubility and dissolution studies were executed at different pH levels. The nanofiber sample with the finest morphology demonstrated a significant increase in solubility both in water and pH 7.4 buffer. Single medium and two-stage biorelevant dissolution studies were performed, and the release mechanism was described by mathematical models. Besides, in vitro diffusion from pH 6.8 to pH 7.4 notably increased when compared with the pure drug and physical mixture. Ciprofloxacin-loaded poly(vinyl pyrrolidone) (PVP) nanofibers can be considered as fast-dissolving formulations with improved physicochemical properties.

In this study, we overcame the limitations of single-enzyme system catalysis by codisplaying Candida rugosa lipase 1 (CRL1) and Rhizopus oryzae lipase (ROL) on the cell surfaces of the whole-cell catalyst Pichia pastoris to produce biodiesel from tallow seed oil. We screened double antibiotic-resistant strains on tributyrin plates, performed second electroporation based on single-displayed ROL on GS115/KpRS recombinants and single-displayed CRL1 on GS115/ZCS recombinants and obtained an ROL/CRL1 codisplay on P. pastoris GS115 surfaces. The maximum activity of the codisplaying GS115/pRCS recombinant was 470.59 U/g dried cells, which was 3.9-fold and 1.3-fold higher than that of single-displayed ROL and CRL1, respectively. When self-immobilized lipases were used as whole-cell catalysts, the rate of methyl ester production from GS115/pRCS harboring ROL and CRL1 was 1.4-fold higher than that obtained with single-displayed ROL. Therefore, biodiesel catalysis by synergetic codisplayed enzymes is an alternative biodiesel production strategy.

Traffic scene construction and simulation has been a hot topic in the community of intelligent transportation systems. In this paper, we propose a novel framework for the analysis and synthesis of traffic elements from road image sequences. The proposed framework is composed of three stages: traffic elements detection, road scene inpainting, and road scene reconstruction. First, a new bidirectional single shot multi-box detector (BiSSD) method is designed with a global context attention mechanism for traffic elements detection. After the detection of traffic elements, an unsupervised CycleGAN is applied to inpaint the occlusion regions with optical flow. The high-quality inpainting images are then obtained by the proposed image inpainting algorithm. Finally, a traffic scene simulation method is developed by integrating the foreground and background elements of traffic scenes. The extensive experiments and comparisons demonstrate the effectiveness of the proposed framework.

Candida tropicalis is a pathogenic fungus that can cause opportunistic infections in humans. The ability of Candida species to transition between yeast and filamentous growth forms is essential to their ability to undergo environmental adaptation and to maintain virulence. In other fungal species, such as Candida albicans, N-acetylglucosamine (GlcNAc) can induce filamentous growth, whereas it suppresses such growth in C. tropicalis. In the present study, we found that knocking out the GlcNA-specific transporter gene NGT1 was sufficient to enhance C. tropicalis filamentous growth on Lee’s plus GlcNAc medium. This suggests that GlcNAc uptake into C. tropicalis cells is essential to the disruption of mycelial growth. As such, we further studied how GlcNAc catabolism-related genes were able to influence C. tropicalis filamentation. We found that HXK1 overexpression drove filamentous growth on Lee’s media containing glucose and GlcNAc, whereas the deletion of the same gene disrupted this filamentous growth. Interestingly, the deletion of the DAC1 or NAG1 genes impaired C. tropicalis growth on Lee’s plus GlcNAc plates. Overall, these results indicate that HXK1 can serve as a positive regulator of filamentous growth, with excess GlcNAc-6-PO4 accumulation being toxic to C. tropicalis. These findings may highlight novel therapeutic targets worthy of future investigation.

We present a theoretical investigation on simultaneous generation of two pairs of Stokes and terahertz (THz) waves from coupled optical parametric oscillations (OPOs) with a quasi-phase-matching (QPM) scheme. The two pairs of Stokes and THz waves are generated by stimulated polariton scattering (SPS) from periodically-inverted GaP. By analyzing the QPM conditions of coupled OPOs we find that the two THz waves with any frequency below the transverse optical (TO) mode frequency of GaP can be simultaneously generated with a suitable pump wavelength. We calculate the photon flux densities of the two THz waves by solving the coupled wave equations. The calculation results indicate that the two THz waves can be efficiently generated with high pump intensities, particularly in lower THz frequency band.

To the editor, We would like to thank Dr. Hurtado-Torres for his valuable comments on our article entitled “Chocolate Consumption and Risk of Coronary Heart Disease, Stroke, and Diabetes: A Meta-Analysis of Prospective Studies”, which was published in Nutrients in July 2017 [1]. [...]

Although epidemiological studies have examined the role of chocolate in preventing cardiometabolic disease, the results remain inconsistent. Herein, we conducted a meta-analysis of prospective studies to determine the association between chocolate intake and risk of coronary heart disease (CHD), stroke, and diabetes. A systematical search in PubMed and Embase through March 2017, together with reference scrutiny of relevant literatures, was performed to identify eligible studies. Relative risks (RRs) and 95% confidence intervals (CIs) were pooled using random effect models. Fourteen prospective studies of primary prevention with 508,705 participants were finally included, with follow-up durations ranging from 5 to 16 years. The summary RRs for the highest versus lowest chocolate consumption were 0.90 (95% CI: 0.82–0.97; n = 6) for CHD, 0.84 (95% CI: 0.78–0.90; n = 7) for stroke, and 0.82 (95% CI: 0.70–0.96; n = 5) for diabetes. Dose–response meta-analysis suggested a nonlinear association of chocolate consumption with all outcomes. For both CHD and stroke, there was little additional risk reduction when consuming chocolate ≥3 servings/week (one serving was defined as 30 g of chocolate). For diabetes, the peak protective effect of chocolate emerged at 2 servings/week (RR: 0.75, 95% CI: 0.63–0.89), with no benefit observed when increasing consumption above 6 servings/week. In conclusion, chocolate intake is associated with decreased risks of CHD, stroke, and diabetes. Consuming chocolate in moderation (≤6 servings/week) may be optimal for preventing these disorders.

Hexagonal boron nitride (h-BN) is a well-known material whose use is almost restricted to lubricating applications in domains ranging from metallurgy to cosmetics. Howover, h-BN displays many other interesting properties, opening new perspectives for other engineering applications, such as as a solid lubricant in aeronautics, as the perfect substrate to graphene for electronic devices, etc. However, all these promising developments require tailored h-BN shapes displaying a high level of crystallization, ensuring its properties for the long term. Here, we developed three strategies, all associated with the Polymer Derived Ceramics (PDCs) route, to prepare highly crystallized supported thick coatings and self-standing nanosheets. The first strategy concerns the innovative implementation of a Rapid Thermal Annealing to prepare micrometric h-BN coatings on thermal sensitive substrates. Compared to conventional treatment the crystallization of h-BN has successfully lowered to about 300 °C. The second strategy consists of an additivation of the used polymer precursor. Effect of lithium nitride as a crystallization promoter was investigated lowering the onset crystallization temperature from 1400 °C (traditionally) to 1000 °C. This novel synthetic route allows preparing self-standing highly crystallized h-BN nanolayers. Finally, the third strategy is based on a unique combination of the PDCs route with Spark Plasma Sintering to profit of both approaches. This original method leads to large and well-crystallized flakes available for a subsequent exfoliation.

The growing trend of using smartphones as personal computing platforms to access and store private information has stressed the demand for secure and usable authentication mechanisms. This paper investigates the feasibility and applicability of using motion-sensor behavior data for user authentication on smartphones. For each sample of the passcode, sensory data from motion sensors are analyzed to extract descriptive and intensive features for accurate and fine-grained characterization of users’ passcode-input actions. One-class learning methods are applied to the feature space for performing user authentication. Analyses are conducted using data from 48 participants with 129,621 passcode samples across various operational scenarios and different types of smartphones. Extensive experiments are included to examine the efficacy of the proposed approach, which achieves a false-rejection rate of 6.85% and a false-acceptance rate of 5.01%. Additional experiments on usability with respect to passcode length, sensitivity with respect to training sample size, scalability with respect to number of users, and flexibility with respect to screen size were provided to further explore the effectiveness and practicability. The results suggest that sensory data could provide useful authentication information, and this level of performance approaches sufficiency for two-factor authentication on smartphones. Our dataset is publicly available to facilitate future research.