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Abstract Addressing the issue that the Transformer decoder struggles to capture local features for monotonic alignment in speech recognition, and simultaneously incorporating language model cold fusion training into the decoder, an enhanced decoder-based speech recognition model is investigated. The enhanced decoder separates and combines the two attention mechanisms in the Transformer decoder into cross-attention layers and a self-attention language model module. The cross-attention layers are utilized to capture local features more efficiently from the encoder output, and the self-attention language model module is used to pre-train with additional domain-related text, followed by cold fusion training. Experimental results on the Mandarin Aishell-1 dataset demonstrate that when the encoder is a Conformer, the enhanced decoder achieves a 16.1% reduction in character error rate compared to the Transformer decoder. Furthermore, when the language model is pre-trained with suitable text data, the performance of the cold fusion-trained model is further enhanced.

Abstract Background and aims Diabetic peripheral neuropathy (DPN) and diabetic nephropathy (DN) both have microcirculation dysfunction. Urinary albumin-to-creatinine ratio (UACR) is a biomarker for DN. We aimed to explore the links between DPN and UACR in patients with type 2 diabetes mellitus (T2DM). Methods A total of 195 T2DM patients were defined as Control or DPN group. Clinical parameters were compared, and the association between HbA1c (or UACR) and DPN was analyzed. Risk factors for DPN were observed, and the diagnostic values of HbA1c and UACR were assessed. Results Compared with 104 participants without DPN, 91 individuals with DPN exhibited higher HbA1c and UACR levels. In all patients, increased HbA1c and UACR were identified as risk factors for DPN in individuals with T2DM. Moreover, increased HbA1c was a risk factor for DPN in volunteers without DN, whereas elevated UACR was determined as a risk factor for DPN in participants with DN. The cut-off point for HbA1c (7.65%) in patients without DN had a sensitivity of 86.0% and specificity of 44.6%, while the cut-off point for UACR (196.081 mg/g) in patients with DN had a sensitivity of 52.9% and specificity of 76.2%. Conclusion Elevated HbA1c and UACR levels are risk factors for DPN and may serve as potential biomarkers for DPN in T2DM patients.

AimThis study aims to investigate the role of activated partial thrombin time (APTT) as a potential mediator in the relationship between suboptimal glycemic control and diabetic peripheral neuropathy (DPN) in individuals with type 2 diabetes mellitus (T2DM).MethodsA total of 183 T2DM patients were enrolled in this study. Comprehensive clinical data, including coagulation parameters and nerve conduction velocity, were collected and compared between patients with and without DPN. Subsequent correlation and regression analyses were conducted to explore the associations among APTT, HbA1c levels, and nerve conduction velocities. Moreover, mediation analyses were performed to evaluate the total, direct, and indirect effects of HbA1c on specific nerve conduction velocities, with APTT serving as a mediator.ResultsIn comparison to 101 patients without DPN, 82 patients with DPN exhibited significantly elevated levels of HbA1c and decreased levels of APTT. Notably, levels of APTT and HbA1c were correlated with conduction velocities of Tibial nerve motor fibers, as well as sensory fibers of the Ulnar nerve, Median nerve, and Sural nerve. Furthermore, both elevated HbA1c and decreased APTT were identified as risk factors for DPN in T2DM individuals. Mediation analysis showed that APTT mediated the indirect effect of HbA1c on the conduction velocities of sensory fibers in both the ulnar nerve and sural nerve (95% CI: -0.3448, -0.0135; -0.3523, -0.0180). APTT mediated the relationship between HbA1c and the conduction velocities of sensory fibers in the ulnar nerve or sural nerve by 34.66% or 22.03%, respectively.ConclusionsIn patients with T2DM, uncontrolled HbA1c and shorter APTT emerges as risk factors for DPN. Additionally, the effect of increased HbA1c upon DPN, especially for influenced conduction velocities of sensory fibers in both the ulnar nerve and sural nerve may partly medicated by decreased APTT.

Abstract Objective Type 2 diabetes mellitus (T2DM) has beenis known as an important risk factor for cognitive impairment. Meanwhile, the liver plays a central role in the development of T2DM and insulin resistance. The present study attempted to identify and validate marker genes for mild cognitive impairment (MCI) in patients with T2DM. Methods In this study, insulin resistance-related differentially expressed genes were identified from the liver tissues of individuals with T2DM and those with normal glucose tolerance using the Gene Expression Omnibus database and MCI-associated genes were identified using the GeneCards database. Next, enrichment analysis was performed with overlapping T2DM and MCI genes, followed by the identification of specific genes using the LASSO logistic regression and SVM-RFE algorithms. An important experiment involved the implementation of clinical and in vitro validation using real-time quantitative polymerase chain reaction (RT-qPCR). Finally, multiple linear regression, binary logistic regression, and receiver operating characteristic curve analyses were performed to investigate the relationship between the key gene and cognitive function in these patients. Result The present study identified 40 overlapping genes between MCI and T2DM, with subsequent enrichment analysis revealing their significant association with the roles of neuronal and glial projections. The marker gene complement receptor 1(CR1) was identified for both diseases using two regression algorithms. Based on RT-qPCR validation in 65 T2DM patients with MCI (MCI group) and 65 T2DM patients without MCI (NC group), a significant upregulation of CR1 mRNA in peripheral blood mononuclear cells was observed in the MCI group (P

Abstract Soft robots spark a revolution in human–machine interaction. However, developing high‐performance soft actuators remains challenging due to trade‐offs among output force, driving distance, control precision, safety, and compliance. Here, addressing the lack of long‐distance, high‐precision flexible linear actuators, an innovative pneumatic flexible linear actuator (PFLA) is introduced, inspired by the smooth and controlled process observed in snakes ingesting sizable food, such as eggs. This PFLA combines a soft tube, emulating the snake's body cavity, with a pneumatically driven piston. Through the joint modulation of moving resistance and driving force by pneumatic pressure, the PFLA exhibits exceptional motion control capabilities, including self‐holding without pressure supply, smooth low‐speed motion (down to 0.004 m s–1), high‐speed motion (up to 5.6 m s–1) with low air pressure demand, and a self‐protection mechanism. Highlighting its adaptability and versatility, the PFLA finds applications in various settings, including a wearable assistive devices, a manipulator capable of precise path tracking and positioning, and rapid transportation in diverse environments for pipeline inspection and firefighting. This PFLA combines biomimetic principles with sophisticated fluidic actuation to achieve long‐distance, flexible, precise, and safe actuation, offering a more adaptive solution for force/motion transmission, particularly in challenging environments.

ObjectiveThis study aimed to investigate the role of galectin-3 (Gal-3; coded by LGALS3 gene), as a biomarker for MCI in T2DM patients and to develop and validate a predictive nomogram integrating galectin-3 with clinical risk factors for MCI prediction. Additionally, microRNA regulation of LGALS3 was explored.MethodsThe study employed a cross-sectional design. A total of 329 hospitalized T2DM patients were recruited and randomly allocated into a training cohort (n = 231) and a validation cohort (n = 98) using 7:3 ratio. Demographic data and neuropsychological assessments were recorded for all participants. Plasma levels of galectin-3 were measured using ELISA assay. We employed Spearman’s correlation and multivariable linear regression to analyze the relationship between galectin-3 levels and cognitive performance. Furthermore, univariate and multivariate logistic regression analyses were conducted to identify independent risk factors for MCI in T2DM patients. Based on these analyses, a predictive nomogram incorporating galectin-3 and clinical predictors was developed. The model’s performance was evaluated in terms of discrimination, calibration, and clinical utility. Regulatory miRNAs were identified using bioinformatics and their interactions with LGALS3 were confirmed through qRT-PCR and luciferase reporter assays.ResultsGalectin-3 was identified as an independent risk factor for MCI, with significant correlations to cognitive decline in T2DM patients. The developed nomogram, incorporating Gal-3, age, and education levels, demonstrated excellent predictive performance with an AUC of 0.813 in the training cohort and 0.775 in the validation cohort. The model outperformed the baseline galectin-3 model and showed a higher net benefit in clinical decision-making. Hsa-miR-128-3p was significantly downregulated in MCI patients, correlating with increased Gal-3 levels, while Luciferase assays confirmed miR-128-3p’s specific binding and influence on LGALS3.ConclusionOur findings emphasize the utility of Gal-3 as a viable biomarker for early detection of MCI in T2DM patients. The validated nomogram offers a practical tool for clinical decision-making, facilitating early interventions to potentially delay the progression of cognitive impairment. Additionally, further research on miRNA128’s regulation of Gal-3 levels is essential to substantiate our results.

As the Internet of Things (IoT) continues to evolve, its inherent diversity and rapid iteration pose challenges for the development and deployment of malicious traffic detection systems. This study aims to develop a scalable detection architecture that can accurately identify malicious traffic in IoT environments, while protecting user privacy. We propose a deep-learning-based model that is capable of training and updating within an IoT setting. The payload of the data packets is encoded to enhance the feature extraction capability of the model. The model is then trained using federated learning/edge computing to ensure data privacy. The final model parameters are stored in an IPFS file storage system with the corresponding hash value stored in the blockchain, ensuring the correctness of the model parameters during expansion. Experiments were conducted in a Docker environment using the CIC IoT Dataset 2022 and CIC IoT Dataset 2023. The results demonstrate that the proposed architecture achieves a pre-training accuracy of 98.6% for known abnormal traffic and 79% for unknown malicious traffic. After a few rounds of training, the accuracies improved to 99.5% and 89.4%. The model also exhibited robust performance during expansion.

Abstract Aims Insulin resistance (IR) is a pivotal factor in the pathogenesis of type 2 diabetes mellitus (T2DM) and non-alcoholic fatty liver disease (NAFLD). Nevertheless, the impact of IR on cognitive dysfunction in T2DM patients with NAFLD remains inadequately understood. We aim to investigate the effect of IR on mild cognitive impairment (MCI) in T2DM individuals with NAFLD. Materials and methods 143 T2DM individuals were categorized into Non-MCI and MCI groups, as well as Non-NAFLD and NAFLD groups. Clinical parameters and cognitive preference test outcomes were compared. Correlation and regression analyses were executed to explore the interconnections between IR and cognitive details across all T2DM patients, as well as within the subgroup of individuals with NAFLD. Results In comparison to the Non-MCI group, the MCI group displayed elevated HOMA-IR levels. Similarly, the NAFLD group exhibited higher HOMA-IR levels compared to the Non-NAFLD group. Additionally, a higher prevalence of MCI was observed in the NAFLD group as opposed to the Non-NAFLD group. Notably, HOMA-IR levels were correlated with Verbal Fluency Test (VFT) and Trail Making Test-B (TMTB) scores, both related to executive functions. Elevated HOMA-IR emerged as a risk factor for MCI in the all patients. Intriguingly, increased HOMA-IR not only correlated with TMTB scores but also demonstrated an influence on TMTA scores, reflecting information processing speed function in patients with NAFLD. Conclusion IR emerges as a contributory factor to cognitive dysfunction in T2DM patients. Furthermore, it appears to underlie impaired executive function and information processing speed function in T2DM individuals with NAFLD.

Abstract Background Interleukin (IL)‐10 plays a notable role in the inflammatory‐associated mild cognitive impairment (MCI). We aimed to investigate whether IL‐10 and its upstream factors exert an impact on MCI in type 2 diabetes mellitus (T2DM) patients. Methods A total of 117 T2DM patients were recruited and divided into Control group and MCI group based on the presence or absence of MCI. Clinical parameters were collected. The Montreal Cognitive Assessment (MoCA) was conducted for global cognitive function. Digit Span Test (DST), Verbal Fluency Test (VFT), and Trail Making Test‐B (TMTB) were used to evaluate the executive functions of the diabetic patients. Trail Making Test‐A (TMTA) was performed to examine the information processing speed function. Patients' scene memory was examined by Logical Memory Test (LMT). After the baseline data were compared, correlation and regression analyses were performed to explore the relationship among IL‐10, miR‐let‐7c‐5p and cognitive function. Results Compared to 80 patients in the control group, 37 patients in the MCI group exhibited lower IL‐10 in plasma and higher miR‐let‐7c‐5p levels in exosomes from plasma. The IL‐10 level was negatively associated with MoCA. Likewise, miR‐let‐7c‐5p levels were negatively correlated with IL‐10 levels and MoCA. Elevated miR‐let‐7c‐5p levels and decreased IL‐10 levels are risk factors for MCI in T2DM patients. Increased miR‐let‐7c‐5p and downregulated IL‐10 may influence VFT and TMTB, respectively, associated with executive function. Conclusions We demonstrated that IL‐10 is correlated to the executive function of T2DM patients. Decreased IL‐10 may result from the regulation of miR‐let‐7c‐5p in exosomes.

Abstract Social insects are prone to pathogen infection because of high exposure rates from social interactions. However, it remains unclear whether queens have enhanced pathogen resistance, because reproduction is largely confined to queens. Here, we used a natural host–pathogen system, the subterranean termite Reticulitermes chinensis and the entomopathogenic fungus Metarhizium anisopliae, to investigate the differences in allogrooming, locomotion, and immune gene expression between queens and workers against pathogen infection. We found that fungal infection significantly reduced survival in both queens and workers. Infected queens received significantly more grooming time from sanitary nestmates than infected workers, but they returned much less grooming time to sanitary nestmates than infected workers. Infection resulted in a reduction in the average locomotion speed and distance of queens but had no effect on worker locomotion. Infection resulted in upregulated expression of two immune genes (termicin and transferrin), two antioxidant genes (CAT and SOD), and phosphate genes CYP450 in queens but not in workers. Our results indicated that eusocial termites evolved strategies that prioritize the reproductive castes' welfare in defending against the pathogen infection to ensure continued reproduction and colony persistence.

Abstract Background Increased levels of low-density lipoprotein cholesterol (LDL-C) have been identified as one potential risk factor for diabetic peripheral neuropathy (DPN) in patients. The current study seeks to clarify the link between LDL-C, hyperglycemia, and DPN in patients with type 2 diabetes mellitus (T2DM). Methods Here, a total of 120 T2DM individuals were recruited. These volunteers with T2DM were divided into 2 groups, based on the presence or absence of peripheral neuropathy. Additionally, their baseline characteristics were compared. Association among LDL-C and glycosylated hemoglobin (HbA1c) levels and DPN, particularly with respect to specific nerve conduction velocity were analyzed. To identify factors influencing DPN, regression was performed. Furthermore, mediation analysis was employed to evaluate the indirect, direct and total effects of LDL-C on specific nerve conduction velocity, with HbA1c serving as a mediator. Results Compared to 55 patients without DPN, 65 patients with DPN demonstrated elevated levels of LDL-C and HbA1c. Both LDL-C and HbA1c have been found to be associated with reduced the motor fiber conduction velocities of Ulnar (or the Common peroneal) nerve in diabetic patients. HbA1c is one of the known risk factors for DPN in individuals with T2DM. Further mediation analysis revealed that the effect of LDL-C on the Ulnar (or the Common peroneal) nerve motor fiber conduction velocities are fully mediated by HbA1c in patients with T2DM. Conclusions The impact of elevated LDL-C levels upon the Ulnar (or the Common peroneal) nerve motor fiber conduction velocities in patients with T2DM was found to be entirely mediated by increased HbA1c levels.

Abstract Background Male factors-caused decline in total fertility has raised significant concern worldwide. LncRNAs have been identified to play various roles in biological systems, including spermatogenesis. This study aimed to explore the role of lncRNA5251 in mouse spermatogenesis. Methods The expression of lncRNA5251 was modulated in mouse testes in vivo or spermatogonial stem cells (C18-4 cells) in vitro by shRNA. Results The sperm motility in two generations mice after modulation of lncRNA5251 (muF0 and muF1) was decreased significantly after overexpression of lncRNA5251. GO enrichment analysis found that knockdown lncRNA5251 increased the expression of genes related to cell junctions, and genes important for spermatogenesis in mouse testes. Meanwhile, overexpressing lncRNA5251 decreased the gene and/or protein expression of important genes for spermatogenesis and immune pathways in mouse testes. In vitro, knockdown lncRNA5251 increased the expression of genes for cell junction, and the protein levels of some cell junction proteins such as CX37, OCLN, JAM1, VCAM1 and CADM2 in C18-4 cells. LncRNA5251 is involved in spermatogenesis by modulation of cell junctions. Conclusion This will provide a theoretical basis for improving male reproductive ability via lncRNA.

Abstract The surface properties of biomaterials are the key factors for the success of artificial implants in the body. The creation of patterns on titanium alloys by laser surface texturing techniques can modify the surface to make it multifunctional. The evolution of the surface morphology of Ti6Al4V alloy textured by a nanosecond laser with 1064 nm wavelength in the air is studied. Laser surface texturing treatment is performed on the titanium alloy through different pulse numbers, power, pulse width and scan times to obtain different morphologies. The 2D cross‐section profile shows that the morphology can be divided into three types in the evolution process of various pulse numbers, powers and pulse widths: bump‐shaped, hump‐shaped and crater‐shaped. It is found that the effect of pulse width on morphology mainly depends on power. The effects of laser parameters on the height of bumps and the evolution of morphology are the main research points to analyse the topography evolution. The causes of bumps are also analysed. Energy dispersive spectrometer measures the area irradiated by the laser, and it is found that the oxygen content of the bump is up to 43.1%, which can speculate that the bump is the result of the oxidation reaction.

High-strength and lightweight 7XXX Al alloys are widely applied in aerospace industries. Stress corrosion cracking (SCC) in these alloys has been extensively discussed, and electrochemical corrosion should be brought to the forefront when these materials are used in marine atmospheric environments. This work obtained the corrosion potentials (Ecorr) and corrosion rates of 40 as-cast 7XXX Al alloys by potentiodynamic polarization tests and immersion tests, respectively; then, chemical compositions and physical features were used to build a machine learning model to predict these parameters. RFR was used for the prediction model of Ecorr with the features Cu, Ti, Al, and Zn, and GPR for that of the corrosion rate with the features of specific heat, latent heat of fusion, and proportion of p electrons. The physical meaning and reasonability were discussed based on the analysis of corrosion morphology and precipitated composition. This work provides a reference for the design of corrosion-resistant 7XXX Al alloys and shows a method of conducting corrosion mechanism evaluation by using machine learning.

To verify the effect of sleep ambient music intervention (SAMI) on sleep quality and mental status of college students, and to further explore the minimum effective duration of SAMI, this study was designed as a pre-and post-intervention self-controlled exploratory study. Participants were subjected to a one-week no-intervention test, then 4 weeks of music intervention followed. Subjective sleep quality data were collected using the Pittsburgh Sleep Quality Index (PSQI); objective sleep quality data were collected with Actigraphy; and mental status data were collected using the State–Trait Anxiety Inventory (STAI) and the Beck Depression Inventory-II (BDI-II). Data were analyzed and processed using mixed-effects models and repeated measures. The results showed that compared with the no-intervention week, college students’ subjective sleep quality, objective sleep onset latency (SOL), trait anxiety, and depression symptom were reduced at week 1; week 2; week 3; week 4 under SAMI; state anxiety of college students at week 3 and week 4 under SAMI were also reduced. And there were differences in sleep quality among college students of different genders too. Compared with females, males had worse sleep efficiency (SE), shorter total sleep time (TST), and more awaking times (AT). In addition, 3 days was the minimum effective length for SAMI to shorten objective SOL, and 2 days was the minimum effective length to shorten the subjective SOL of college students. The findings of this study suggest that SAMI can improve subjective sleep quality, shorten objective SOL, and reduce anxiety and depression in college students. Interventions for more than 3 days had a significant effect on shortening SOL and long-term effects seemed to emerge after 3 weeks.

Studies have identified that mating induces a series of physiological changes in animals. In this period, males tending to invest more energy, immune peptides, and other substances to reduce the cost of living for females. This results in lower survival rates in later life than females. Meanwhile, both males and females shorten lifespans due to reproduction. However, the reasons why termites' queens and kings are both extremely long-lived and highly fecund are unclear. Therefore, this study aimed to examine the effects of mating on the expression of immune and DNA repair genes for lifespan extension in termite queens and kings. Here, we reported that mated queens show relatively higher expression of immune genes (phenoloxidase, denfensin, termicin, transferrin), antioxidant genes (CAT, SOD), detoxification genes (GST, CYP450) than virgin queens in the Reticulitermes chinensis. In addition, mated kings also highly expressed these genes, except for termicin, transferrin, GST, and CYP450. After mating, both queens and kings significantly upregulated the expression of DNA repair genes (MLH1, BRCA1, XRCC3, RAD54-like). Mismatch repair genes (MMR) MSH2, MSH4, MSH6 were considerably increased in mated queens, while MSH4, MSH5, MSH6 were upregulated in mated kings. Our results suggest that mating increases the expression of immune and DNA repair genes in the termite queens and kings, and thus possibly improving their survival during reproductive span due to the omnipresent pathogens.

To solve the problem that existing contrastive prediction based self-supervised speech representation learning methods need to construct a large number of negative samples, and their performance depends on large training batches, requiring a lot of computing resources, a new speech representation learning method based on contrastive learning using only positive samples was proposed.Combined with reconstruction loss, the proposed method could obtain better representation with lower training cost.The proposed method was inspired by the idea of the SimSiam method in image self-supervised representation learning.Using the siamese network architecture, two random augmentations of the input speech signals were processed by the same encoder network, then a feed-forward network was applied on one side, and a stop-gradient operation was applied on the other side.The model was trained to maximize the similarity between two sides.During training processing, negative samples were not required, so small batch size could be used and training efficiency was improved.Experimental results show that the representation model obtained by the new method achieves or exceeds the performance of existing mainstream speech representation learning models in multiple downstream tasks.

Abstract Laser surface texturing (LST) is a non‐contact manufacturing process for fabricating functional surfaces in a manner that improves the corresponding wettability, and is widely used in biomedicine and industry. Laser surface texturing is a facile approach that is compatible with various materials, can result in a hierarchical texture, and enables a high degree of surface wetting (i.e., extreme wetting). In addition to surface structures, surface chemical modification is a primary factor in producing extreme wetting surfaces. This review discusses the effects of various surface textures and surface chemistries on wettability. Optimal laser parameters for the desired surface texture are based on the fundamental wettability and laser mechanism. In particular, bumps in the morphology are conducive to obtaining extreme wetting. Diverse surface chemical strategies result in extreme wetting by different mechanisms. This paper makes a rigorous evaluation of the laser parameters and optimal surface chemical modifications by elucidating the relationships between the surface structure, surface chemical modification, and wettability, and in so doing, determines the final wettability. The unresolved problems of LST are presented in the conclusion. This review provides guidance, development directions, and an integrated framework for LST, which will be useful for fabricating extreme wetting surfaces on various metals.

BackgroundCephalosporins are used as first-line antimicrobial prophylaxis for orthopedics surgeries. However, alternative antibiotics are usually used in the presence of penicillin allergy (PA), which might increase the risk of surgical site infection (SSI). This study aimed to analyze the relationship between SSI after orthopedic surgeries and PA among surgical candidates and related alternative antibiotic use.MethodsIn this single-center retrospective cohort study, we compared inpatients with and without PA from January 2015 to December 2021. The primary outcome was SSI, and the secondary outcomes were SSI sites and perioperative antibiotic use. Moreover, pathogen characteristics of all SSIs were also compared between the two cohorts.ResultsAmong the 20,022 inpatient records, 1704 (8.51%) were identified with PA, and a total of 111 (0.55%) SSI incidents were reported. Compared to patients without PA, patients with PA had higher postoperative SSI risk (1.06%, 18/1704 vs. 0.51%, 93/18318), shown both in multivariable regression analysis (odds ratio [OR] 2.11; 95% confidence interval [CI], 1.26-3.50; p= 0.004) and propensity score matching (OR 1.84; 95% CI, 1.05-3.23; p= 0.034). PA was related to elevated deep SSI risk (OR 2.79; 95% CI, 1.47-5.30; p= 0.002) and had no significant impact on superficial SSI (OR 1.39; 95% CI, 0.59-3.29; p= 0.449). The PA group used significantly more alternative antibiotics. Complete mediation effect of alternative antibiotics on SSI among these patients was found in mediation analysis. Pathogen analysis revealed gram-positive cocci as the most common pathogen for SSI in our study cohort, while patients with PA had higher infection rate from gram-positive rods and gram-negative rods than non-PA group.ConclusionCompared to patients without PA, patients with PA developed more SSI after orthopedic surgeries, especially deep SSI. The elevated infection rate could be secondary to the use of alternative prophylactic antibiotics

Abstract Background Low-density lipoprotein cholesterol (LDL-C) metabolic disorder is common in individuals with diabetes. The role of LDL-C in mild cognitive impairment (MCI) remains to be explored. We aim to investigate the associations between LDL-C at different levels and details of cognition decline in patients with type 2 diabetes mellitus (T2DM). Methods Patients with T2DM (n = 497) were recruited. Clinical parameters and neuropsychological tests were compared between patients with MCI and controls. Goodness of fit was assessed to determine the linear or U-shaped relationship between LDL-C and cognitive function. The cut-off point of LDL-C was calculated. Correlation and regression were carried out to explore the relationship between cognitive dysfunction and LDL-C levels above and below the cut-off point. Results Although no significant difference in LDL-C levels was detected in 235 patients with MCI, compared with 262 patients without MCI, inverted-U-shaped association was determined between LDL-C and Montreal Cognitive Assessment (MoCA). The cut-off point of LDL-C is 2.686 mmol/l. LDL-C (>2.686 mmol/l) is positively related to Trail Making Test B (TMTB) indicating executive function. LDL-C (

Abstract Background UEV1A encodes a ubiquitin-E2 variant closely associated with tumorigenesis and metastasis, but its underlying mechanism in promoting metastasis remains to be investigated. Methods In this study, we experimentally manipulated UEV1A and CT45A gene expression and monitored their effects on cancer-related gene expression, cell migration and the signal transduction cascade. Results It was found that UEV1A overexpression induces CT45A family gene expression in breast cancer cells. Indeed, ectopic expression of UEV1A was sufficient to induce CT45A and its downstream genes involved in tumorigenesis, epithelial-mesenchymal transition (EMT), stemness and metastasis, and to promote cell migration and EMT signaling. Consistently, depletion of CT45A abolished the above effects, indicating that CT45A is a critical downstream effector of Uev1A. The Uev1A-induced cell migration and EMT signaling was dependent on AKT but independent of NF-κB, indicating that CT45A acts downstream of the AKT pathway. Conclusions Based on previous reports and observations in this study, we propose that the Ubc13-Uev1A complex activates AKT through K63-linked polyubiquitination, which leads to enhanced CT45A expression, stimulated cell migration and EMT signaling in breast cells. Since similar effects were also observed in a colorectal cancer cell line, the Ubc13/Uev1A-AKT-CT45A axis may also promote tumorigenesis and metastasis in other tissues.

Accurate perception of the performance degradation of fuel cell is very important to detect its health state. However, inconsistent operating conditions of fuel cell vehicles in the test result in errors in the data. In order to obtain a more credible degradation rate, this study proposes a novel method to classify the experimental data collected under different working conditions into similar operating conditions by using dimensionality reduction and clustering algorithms. Firstly, the experimental data collected from fuel cell vehicles belong to high-dimensional data. Then projecting high-dimensional data into three-dimensional feature vector space via principal component analysis (PCA). The dimension-reduced three-dimensional feature vectors are input into the clustering algorithm, such as K-means and density-based noise application spatial clustering(DBSCAN). According to the clustering results, the fuel cell voltage data with similar operating conditions can be classified. Finally, the selected voltage data can be used to precisely represent the true performance degradation of an on-board fuel cell stack. The results show that the voltage using the K-means algorithm declines the fastest, followed by the DBSCAN algorithm, finally the original data, which indicates that the performance of the fuel cell actually declines faste. Early intervention can prolong its life to the greatest extent.

Abstract Background It is imperative to preoperatively distinguish dural ossification (DO) and thus anticipate the risks and outcome of the surgery for patients with ossification of ligamentum flavum (OLF). However, studies have disagreed as to the efficacy of the radiographic signs or factors to predict DO and surgical outcome. In additon, the association between the cerebrospinal fluid cross-section area ratio (CCAR) and DO or clinical outcome had not been reported. The purpose of this study was to analyse CCAR and its role in prediction of DO and neurological function recovery rate in patients with OLF. Methods Fifty-two consecutive patients with OLF, who underwent posterior thoracic decompression and fusion between September 2012 and March 2019 at a single institution, were retrospectively reviewed. Demographic data, radiographic signs of DO, CCAR, pre- and postoperative modified Japanese Orthopedic Association (mJOA) score were recorded. Results There were 27 patients in the DO group and 25 patients in the non-DO group, with a mean age at surgery of 57.4 years and 53.9 years, respectively. No significant differences were found in sex, age, segment of maximum compression and preoperative mJOA score between the two groups. The receiver operating characteristic curve showed that the value of CCAR had a relatively high value for diagnosis of DO and prediction of neurological function recovery rate (P = .000). According to the value of CCAR, three zones were defined as DO zone (≤14.3%), non-DO zone (≥44.5%), and gray zone (14.3 to 44.5%). When the value of CCAR≤14.3%, the recovery rate was poor or fair, while it had good or excellent recovery when CCAR≥45.2%. Conclusion The value of CCAR had a high diagnostic value for prediction of DO and neurological function recovery rate in patients with OLF.

To verify the fragility of the Grad-CAM, a Grad-CAM attack method based on adversarial patch was proposed.By adding a constraint to the Grad-CAM in the classification loss function, an adversarial patch could be optimized and the adversarial image could be synthesized.The adversarial image guided the Grad-CAM interpretation result towards the patch area while the classification result remains unchanged, so as to attack the interpretations.Meanwhile, through batch-training on the dataset and increasing perturbation norm constraint, the generalization and the multi-scene usability of the adversarial patch were improved.Experimental results on the ILSVRC2012 dataset show that compared with the existing methods, the proposed method can attack the interpretation results of the Grad-CAM more simply and effectively while maintaining the classification accuracy.

Macroeconomic predicting is a research hotspot in the field of predicting. The accuracy of predicting often directly affects the rationality of decision-making, especially for defense expenditure predicting. This paper studies the residual correction method of prediction model based on time series. Firstly, based on the grey nonlinear Verhulst prediction model, Fourier series is introduced in this paper to correct the residual sequence once and establish a residual correction model. On this basis, this paper also introduces Markov related concepts, creatively introduces the two-dimensional residual data into Markov state transition matrix, classifies it by K-means clustering analysis, and calculates its parameters by PSO algorithm to realize the secondary accurate correction of residual. Finally, a PSO-Markov residual correction method based on Verhulst-Fourier model is proposed. Tested by examples, this method effectively improves the prediction accuracy of the model, and the prediction is more reliable and accurate.

Advances in wind power system modeling have produced widespread socioeconomic benefits for alleviating global environmental problems. However, previous studies mainly payed attention to point forecasts of wind power system, with the absence of its uncertainty quantification analysis and outlier detection, which cannot facilitate further development in this field. In this paper, a novel monitoring-forecasting system, including the analysis module, the outlier detection module, the probabilistic forecasting module, and the evaluation module, is proposed for uncertainty modeling of wind power. In the analysis module, recurrence analysis techniques are developed, aiming at characterizing complicated patterns of wind power. Furthermore, the interval partitioning-based isolation forest algorithm, which can effectively address the effects of swamping and masking, is first developed in the outlier detection module for wind power. Superior to the traditional point forecasting method that cannot perform quantitative characterization of the intrinsic uncertainties in wind power forecasting, an advanced probabilistic forecasting method based on Gaussian process regression (GPR) with an optimal kernel function scenario, cooperating with a feature selection method, is first presented in the probabilistic forecasting module, indicating that the forecast skill of GPR is significantly enhanced. Finally, the proposed system is validated using real wind power data with high resolution from Spain in the evaluation module, solidly demonstrating its high reliability and flexibility compared to benchmarks considered in this study.

Abstract Breast cancer is the most common cancer among women and accounts for 30% of all female malignancies worldwide. Breast cancer stem cells (BCSCs) are a small population of breast cancer cells that exhibit multiple characteristics including differentiation capacity, self-renewal and therapeutic resistance. Recently, BCSCs have attracted attention due to their modulation of breast tumor behaviors and drug resistance. miRNAs are small noncoding mRNAs involved in virtually all biological processes, including stem cell development, maintenance and differentiation. In breast cancer, miRNAs appear to be multi-faceted since they can act as either suppressors or oncogenes to regulate breast cancer progression. This review summarizes the critical roles of miRNAs in regulating multiple signaling pathways such as Wnt/β-catenin, Notch, PI3K/AKT/mTOR, BMI-1 and STAT3 that are important for the BCSC maintenance.

Objective: To compare the biomechanical characteristics of two transforaminal thoracic interbody fusion cages based on the Chinese population thoracic anatomy. Method: Computed tomography scans of the thoracic spine of 150 patients from our institution were collected and analysed. Two cages were designed based on the anatomical parameters of these patients. Further, we used 3D finite element analysis models to compare the stability of two cages by using Mimics 17.0 and ANSYS 15.0 software. Result: Two kinds of thoracic cages (box and kidney-shaped) were designed. Under the displacement working condition, the two new fusion cages could achieve immediate postoperative stability, but the kidney-shaped cage was better than the box-shaped cage. Under the stress working condition, no highly focused stress area was found in either cages, but the kidney-shaped cage experienced less stress than the box-shaped cage. Conclusion: The kidney-shaped cage is more stable and experiences lesser stress than the box-shaped cage after thoracic intervertebral fusion, and it is more suitable for Chinese transforaminal thoracic interbody fusion. The translational potential of this article: This article is about thoracic fusion cage design and finite element analysis (FEA) analysis based on the thoracic anatomy parameters. For there is currently no suitable thoracic fusion cage for transforaminal thoracic interbody fusion, the results in this article may have the potential of transferring the two designed cages into clinical use. Keywords: Anatomy parameters, Finite element analysis, Thoracic interbody cage

In this paper, based on the gneiss outcrop of Bozhong 196 gas field in China, uniaxial compression and Brazil splitting tests were conducted by using cores of different orientations. The following compression properties were studied: the elastic compression modulus, Poisson’s ratio and compressive strength of the gneiss outcrop. The following tensile properties were studied: the tensile modulus, the tensile strength and peak energy rate of gneiss outcrop. The results demonstrate the following: (1) The elastic compression modulus, compressive strength, tensile strength and peak energy rate of gneiss specimens with horizontal core-taking are greater than those with vertical core-taking. (2) The elastic compression modulus, Poisson’s ratio and compressive strength of horizontally cored gneiss specimens are 29.688–45.760 GPa, 0.186–0.386, and 174.94–147.80 MPa, respectively; the elastic compression modulus, Poisson’s ratio and compressive strength of the vertical gneiss specimens are 26.541–32.602 GPa, 0.429–0.476 and 169.37–134.46 MPa. (3) The tensile modulus of the horizontal gneiss specimens is 4.93–5.98 GPa. The tensile modulus of the vertical gneiss specimens is 0.96–2.11 GPa. The tensile modulus of the horizontal gneiss specimens is five times that of the vertical gneiss specimens. The elastic compression modulus of gneiss is 5–20 times that of the tensile modulus. (4) The tensile strength and peak energy rate of horizontally cored gneiss specimens are 14.33–17.55 MPa and 2598.67–4049.53 J/m2, respectively. The tensile strength of the vertical gneiss specimens is 6.12–9.65 MPa, and the peak energy rate is 715.74–1515.30 J/m2. (5) There is a good linear relationship between the peak energy rate and tensile strength of gneiss. The research results can provide a scientific and reasonable reference for in situ fracturing of Bozhong 196 gas field.

Real-time dynamic monitoring of orchard grape leaf diseases can greatly improve the efficiency of disease control and is of great significance to the healthy and stable development of the grape industry. Traditional manual disease-monitoring methods are inefficient, labor-intensive, and ineffective. Therefore, an efficient method is urgently needed for real-time dynamic monitoring of orchard grape diseases. The classical deep learning network can achieve high accuracy in recognizing grape leaf diseases; however, the large amount of model parameters requires huge computing resources, and it is difficult to deploy to actual application scenarios. To solve the above problems, a cross-channel interactive attention mechanism-based lightweight model (ECA-SNet) is proposed. First, based on 6,867 collected images of five common leaf diseases of measles, black rot, downy mildew, leaf blight, powdery mildew, and healthy leaves, image augmentation techniques are used to construct the training, validation, and test set. Then, with ShuffleNet-v2 as the backbone, an efficient channel attention strategy is introduced to strengthen the ability of the model for extracting fine-grained lesion features. Ultimately, the efficient lightweight model ECA-SNet is obtained by further simplifying the network layer structure. The model parameters amount of ECA-SNet 0.5× is only 24.6% of ShuffleNet-v2 1.0×, but the recognition accuracy is increased by 3.66 percentage points to 98.86%, and FLOPs are only 37.4 M, which means the performance is significantly better than other commonly used lightweight methods. Although the similarity of fine-grained features of different diseases image is relatively high, the average F1-score of the proposed lightweight model can still reach 0.988, which means the model has strong stability and anti-interference ability. The results show that the lightweight attention mechanism model proposed in this paper can efficiently use image fine-grained information to diagnose orchard grape leaf diseases at a low computing cost.

The accurate identification of apple leaf diseases is of great significance for controlling the spread of diseases and ensuring the healthy and stable development of the apple industry. In order to improve detection accuracy and efficiency, a deep learning model, which is called the Coordination Attention EfficientNet (CA-ENet), is proposed to identify different apple diseases. First, a coordinate attention block is integrated into the EfficientNet-B4 network, which embedded the spatial location information of the feature by channel attention to ensure that the model can learn both the channel and spatial location information of important features. Then, a depth-wise separable convolution is applied to the convolution module to reduce the number of parameters, and the h-swish activation function is introduced to achieve the fast and easy to quantify the process. Afterward, 5,170 images are collected in the field environment at the apple planting base of the Northwest A&F University, while 3,000 images are acquired from the PlantVillage public data set. Also, image augmentation techniques are used to generate an Apple Leaf Disease Identification Data set (ALDID), which contains 81,700 images. The experimental results show that the accuracy of the CA-ENet is 98.92% on the ALDID, and the average F1-score reaches .988, which is better than those of common models such as the ResNet-152, DenseNet-264, and ResNeXt-101. The generated test dataset is used to test the anti-interference ability of the model. The results show that the proposed method can achieve competitive performance on the apple disease identification task.

The cost prediction is an important part of macroeconomic prediction. The fitting degree of the prediction model not only directly influences the prediction accuracy but also determines the effectiveness of the information provided to the decision maker, especially in the defense economy area. This paper uses the model improving method of reverse prediction and makes the best of the advantage of the Verhulst model of reverse prediction which can solve the problem of ‘small sample, uncertainty’ for the prediction of the defense expenditure. Based on the establishment of a reverse prediction Verhulst model which corrects the initial value, the BP neural network and the combined prediction model based on the BP neural network and improved Verhulst model is further established from the residual sequence dimension with the introduction of the BP neural network, and the analysis validation is conducted through the comparison to the Verhulst-BP model established based on the residual sequence of the traditional Verhulst model. China defense expenditure data collected is tested by practice, showing that this combined prediction model can improve the prediction accuracy greatly.

Abstract Background Ubiquitin-conjugating enzyme variant UEV1A is required for Ubc13-catalyzed K63-linked poly-ubiquitination that regulates several signaling pathways including NF-κB, MAPK and PI3K/AKT. Previous reports implicate UEV1A as a potential proto-oncogene and have shown that UEV1A promotes breast cancer metastasis through constitutive NF-кB activation. Ubc13-Uev1A along with TARF6 can also ubiquitinate AKT but its downstream events are unclear. Methods In this study, we experimentally manipulated UEV1 expression in two typical breast cancer cell lines MDA-MB-231 and MCF7 under serum starvation conditions and monitored AKT activation and its downstream protein levels, as well as cellular sensitivity to chemotherapeutic agents. Results We found that overexpression of UEV1A is sufficient to activate the AKT signaling pathway that in turn inhibits FOXO1 and BIM expression to promote cell survival under serum starvation conditions and enhances cellular resistance to chemotherapy. Consistently, experimental depletion of Uev1 in breast cancer cells inhibits AKT signaling and promotes FOXO1 and BIM expression to reduce cell survival under serum starvation stress and enhance chemosensitivity. Conclusions Uev1A promotes cell survival under serum starvation stress through the AKT-FOXO1-BIM axis in breast cancer cells, which unveals a potential therapeutic target in the treatment of breast cancers.

The diagenetic evolution of the moldic-pore-containing gypsum dolomites of the Middle Ordovician Majiagou Formation in the eastern Ordos Basin, central China, was studied by means of petrological, mineralogical, and geochemical analyses, to improve our understanding of heterogeneity in high-quality reservoirs. The aim of the study was to elucidate the processes that resulted in reservoir tightness and the formation of sweet spots, to guide future exploration. Results show that the moldic-pore-containing gypsum dolomites are the most favorable reservoir in the study area, with a mean porosity of 4.96% and a mean permeability of 0.748 mD. The development and preservation of gypsum moldic pores were the main factors causing reservoir heterogeneity. Specifically, moldic pore development was controlled by the sedimentary microfacies, whereas pore preservation was related to dissolution and filling during telogenetic diagenesis. There were three main dissolution-filling stages that took place in three settings: penecontemporaneous, epigenetic, and burial. These processes controlled the formation of reservoir sweet spots. Early-consolidated dolomite deposits were frequently exposed to the atmosphere in the penecontemporaneous environment under the influence of high-frequency sedimentary cycles, which led to the dissolution of evaporite minerals and consequent formation of gypsum moldic pores, accompanied by infilling by freshwater calcite. During epigenesis, the porosity initially increased due to karstification, then significantly decreased because of calcite infilling resulting from long subaerial exposure (120 Myr), which contributed to reservoir heterogeneity. Finally, during burial the high-temperature and high-pressure conditions led to chemical compaction and continuous tightening of the reservoir, although some burial dissolution also took place. In conclusion, the variable paleo-topography resulted in differences in the intensity of pore filling among the blocks in the study area, resulting in reservoir heterogeneity. Keywords: Dolomite, Tight carbonate rock, Gypsum moldic pores, Dissolution, Middle Ordovician Majiagou Formation, Ordos Basin

Abstract Karstification in carbonate successions has an important influence on hydrocarbon accumulation. Taking the Ordos Basin, currently the largest petroliferous basin in China, as an example, this study examines the large‐scale, long‐term (~120 Myr) paleokarst at the top of the Ordovician. The objectives of the study are to characterize the karst paleogeomorphology of this area, to explain the inconsistency between existing understandings of karst paleogeomorphology and exploration in the eastern Ordos Basin, and to reveal the control of paleokarst on natural gas accumulation and its paleogeographic significance. A total of 860 exploration wells were used for detailed stratigraphic correlation and analysis, along with core observations, well‐logging analyses, physical property characterization, and isotope analyses. Results of residual thickness and moldic thickness reconstruction reveal variation in karst paleogeomorphology between north and south in the eastern Ordos Basin, differing from the traditionally recognized E‐W variation. Two geomorphic units are classified as follows: the karst highland and the karst slope from north to south, with the karst slope being subdivided into northern and southern slope areas. The karst highland area has negligible reservoir capacity and hydrocarbon accumulation owing to the enhanced denudation that occurred there. In contrast, the northern karst slope shows favorable reservoir properties and has abundant gas wells according to well‐logging interpretations, whereas the southern karst slope is of poor reservoir quality and hosts mainly water wells. Differences in dissolution‐filling effects controlled by the surface paleodrainage system are suggested to be the main contributor to differential reservoir space preservation, which, together with the variable width and depth of source rocks in the grooves (thereby variably exposing source rock), further promoted differential gas accumulation. The Ordos Basin and its periphery in the southwestern North China Craton (NCC) show inheritance of sedimentary‐tectonic patterns from the Middle Ordovician to the Late Carboniferous. These results should provide a reference for hydrocarbon exploration in the Ordovician of other basins in the NCC in which karst occurs and karst basins worldwide, and deepens understanding of the paleogeographic framework in the context of regional uplift of the North China Platform.

Articular cartilage defects caused by various reasons are relatively common in clinical practice, but the lack of efficient therapeutic methods remains a substantial challenge due to limitations in the chondrocytes’ repair abilities. In the search for scientific cartilage repair methods, gene therapy appears to be more effective and promising, especially with acellular biomaterial-assisted procedures. Biomaterial-mediated gene therapy has mainly been divided into non-viral vector and viral vector strategies, where the controlled delivery of gene vectors is contained using biocompatible materials. This review will introduce the common clinical methods of cartilage repair used, the strategies of gene therapy for cartilage injuries, and the latest progress.

Objective: This study aimed to design a weighted co-expression network and a breast cancer (BC) prognosis evaluation system using a specific whole-genome expression profile combined with epithelial-mesenchymal transition (EMT)-related genes; thus, providing the basis and reference for assessing the prognosis risk of spreading of metastatic breast cancer (MBC) to the bone. Methods: Four gene expression datasets of a large number of samples from GEO were downloaded and combined with the dbEMT database to screen out EMT differentially expressed genes (DEGs). Using the GSE20685 dataset as a training set, we designed a weighted co-expression network for EMT DEGs, and the hub genes most relevant to metastasis were selected. We chose eight hub genes to build prognostic assessment models to estimate the 3-, 5-, and 10-year survival rates. We evaluated the models’ independent predictive abilities using univariable and multivariable Cox regression analyses. Two GEO datasets related to bone metastases from BC were downloaded and used to perform differential genetic analysis. We used CIBERSORT to distinguish 22 immune cell types based on tumor transcripts. Results: Differential expression analysis showed a total of 304 DEGs, which were mainly related to proteoglycans in cancer, and the PI3K/Akt and the TGF-β signaling pathways, as well as mesenchyme development, focal adhesion, and cytokine binding functionally. The 50 hub genes were selected, and a survival-related linear risk assessment model consisting of eight genes (FERMT2, ITGA5, ITGB1, MCAM, CEMIP, HGF, TGFBR1, F2RL2) was constructed. The survival rate of patients in the high-risk group (HRG) was substantially lower than that of the low-risk group (LRG), and the 3-, 5-, and 10-year AUCs were 0.68, 0.687, and 0.672, respectively. In addition, we explored the DEGs of BC bone metastasis, and BMP2, BMPR2, and GREM1 were differentially expressed in both data sets. In GSE20685, memory B cells, resting memory T cell CD4 cells, T regulatory cells (Tregs), γδ T cells, monocytes, M0 macrophages, M2 macrophages, resting dendritic cells (DCs), resting mast cells, and neutrophils exhibited substantially different distribution between HRG and LRG. In GSE45255, there was a considerable difference in abundance of activated NK cells, monocytes, M0 macrophages, M2 macrophages, resting DCs, and neutrophils in HRG and LRG. Conclusions: Based on the weighted co-expression network for breast-cancer-metastasis-related DEGs, we screened hub genes to explore a prognostic model and the immune infiltration patterns of MBC. The results of this study provided a factual basis to bioinformatically explore the molecular mechanisms of the spread of MBC to the bone and the possibility of predicting the survival of patients.

Background/Aims: Currently, scientists attempt to improve outcome of spinal cord injury (SCI) via reducing secondary injury during SCI. Oxidative stress is critical for pathophysiology of secondary damage, thus we mainly focused on the anti-oxidant effects of Lycium barbarum polysaccharides (LBPs) on PC-12 and SH-SY5Y cells as well as the underlying mechanisms. Methods: Oxidative stress was induced by H2O2 stimulation. Effects of LBPs on cell viability, apoptosis, and expression of proteins associated with apoptosis and autophagy in H2O2-induced cells were assessed by CCK-8 assay, flow cytometry assay and Western blot analysis, respectively. Then, expression of miR-194 was determined by qRT-PCR. Expression of miR-194 was dysregulated, and whether LBPs affected H2O2-treated cells through modulating miR-194 was verified. The expression of key kinases in the PI3K/AKT pathway and the intracellular levels of ROS and NO were testified by Western blot analysis and flow cytometry with fluorescent probes. Results: H2O2-induced decrease of cell viability and increases of apoptosis and autophagy in PC-12 cells were mitigated by LBPs treatment. Next, we found that miR-194 expression was both down-regulated by LBPs treatment in PC-12 and SH-SY5Y cells. More experiments consolidated that influence of LBPs on H2O2-treated cells was reversed by miR-194 overexpression while was augmented by miR-194 inhibition. LBPs elevated the phosphorylated levels of PI3K and AKT and reduced levels of ROS and NO through miR-194. Conclusion: LBPs alleviated H2O2-induced decrease of cell viability, and increase of apoptosis and autophagy through down-regulating miR-194. Moreover, LBPs activated the PI3K/AKT pathway and reduced oxidative stress through miR-194.

The information of tomato young fruits acquisition has an important impact on monitoring fruit growth, early control of pests and diseases and yield estimation. It is of great significance for timely removing young fruits with abnormal growth status, improving the fruits quality, and maintaining high and stable yields. Tomato young fruits are similar in color to the stems and leaves, and there are interference factors, such as fruits overlap, stems and leaves occlusion, and light influence. In order to improve the detection accuracy and efficiency of tomato young fruits, this paper proposes a method for detecting tomato young fruits with near color background based on improved Faster R-CNN with an attention mechanism. First, ResNet50 is used as the feature extraction backbone, and the feature map extracted is optimized through Convolutional Block Attention Module (CBAM). Then, Feature Pyramid Network (FPN) is used to integrate high-level semantic features into low-level detailed features to enhance the model sensitivity of scale. Finally, Soft Non-Maximum Suppression (Soft-NMS) is used to reduce the missed detection rate of overlapping fruits. The results show that the mean Average Precision (mAP) of the proposed method reaches 98.46%, and the average detection time per image is only 0.084 s, which can achieve the real-time and accurate detection of tomato young fruits. The research shows that the method in this paper can efficiently identify tomato young fruits, and provides a better solution for the detection of fruits with near color background.

A wideband microwave photonic phase shifter implemented based on a special dual-parallel Mach-Zehnder modulator (DP-MZM) consisting of two sub-MZMs and a polarization rotator is proposed and demonstrated. A microwave signal to be phase shifted is applied to the two sub-MZMs, via a 90° hybrid coupler, to generate two orthogonally polarized intensity-modulated optical signals, which are combined at a polarization beam combiner. A phase-shifted microwave signal is obtained by detecting the combined signal at a photodetector. The tuning of the phase shift is realized by tuning the DC bias voltages applied to the sub-MZMs. Since the phase shift tuning has been done electrically, high speed phase tuning can be implemented. The proposed phase shifter is experimentally demonstrated. A continuous phase tuning from 0° to 360° with small magnitude variations of less than ±1 dB and phase ripple standard deviation of less than 2.7° in a decade bandwidth from 2.5 to 25 GHz is realized. The system insertion loss is measured to be 10.8 dB. Investigation on the cause of magnitude and phase deviations is also performed by simulations, which are confirmed by experimental measurements.

The discovery of IDO1 and HDAC1 dual inhibitors may provide a novel strategy for cancer treatment by taking advantages of both immunotherapeutic and epigenetic drugs. In this paper, saprorthoquinone (1) and 13 of its analogues from Salvia prionitis Hance were investigated for their SAR against IDO1, the results demonstrated the ortho-quinone was a key pharmacophore. Then a series of IDO1 and HDAC dual inhibitors connected by appropriate linkers were designed, synthesized, and evaluated from the hit compound saprorthoquinone (1). Among them, compound 33d showed balanced activity against both IDO1 (IC50 = 0.73 μM) and HDAC1 (IC50 = 0.46 μM). Importantly, the structure of 33d suggested that an ortho-quinone pharmacophore and a N-(2-aminophenyl) amide pharmacophore were necessary for the IDO inhibition and HDAC inhibition respectively. Meanwhile, these two pharmacophore groups should be combined by a pentane linker. Moreover, the binding modes of 33d to the enzyme active site showed that the hydrogen bond with Leu234 of IDO1 appeared to confer increased potency to this class of inhibitors, which may explain the higher activity of 33d. This study provides a new strategy for future IDO1/HDAC dual inhibitors with synergistic antitumor activity started from lead compound 33d.

CCN is communication architecture that takes content as a primitive,and retrieves content by name,not by host IP again.It builds on publish/subscribe and network-embedded caching with bandwidth-storage tradeoff.Since it is a clean-slate design,there are many issues to be explored.The concepts,characteristics,network model and recent advances of CCN were introduced.The challenges and some key issues CCN faced were also emphasized.In order to demonstrate the feasibility and implementations,two CCN system samples were also presented.