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BackgroundThe relationship between atherogenic index of plasma (AIP) and triglyceride glucose-body mass index (TyG-BMI) and sarcopenia has not been studied in the United States (US) population.MethodsThis research included 4,835 people from the National Health and Nutrition Examination Survey (NHANES) conducted between 2011 and 2018. The relationship between sarcopenia and TyG-BMI, as well as the AIP index, was examined through the utilization of restricted cubic spline (RCS) analysis, subgroup analysis, and multivariate logistic regression analysis. Diagnostic value of AIP and TyG-BMI for sarcopenia was compared by receiver operating characteristic (ROC) curves.ResultsIn this research, 428 people with sarcopenia were identified among the 4,835 subjects that were included in the experiment. AIP and sarcopenia were positively associated with an odds ratio (OR) of 1.58 and a 95% confidence interval (CI) of (1.07, 2.34) on fully adjusted multivariate logistic regression analysis. Similarly, TyG-BMI and sarcopenia were positively associated with an OR of 8.83 and a 95% CI of (5.46, 14.26). AIP and sarcopenia had a non-linear positive connection (P-value

Abstract Background Focusing on key indicators of drought resistance is highly important for quickly mining candidate genes related to drought resistance in cotton. Results In the present study, drought resistance was identified in drought resistance-related RIL populations during the flowering and boll stages, and multiple traits were evaluated; these traits included three key indicators: plant height (PH), single boll weight (SBW) and transpiration rate (Tr). Based on these three key indicators, three groups of extreme mixing pools were constructed for BSA-seq. Based on the mapping interval of each trait, a total of 6.27 Mb QTL intervals were selected on chromosomes A13 (3.2 Mb), A10 (2.45 Mb) and A07 (0.62 Mb) as the focus of this study. Based on the annotation information and qRT‒PCR analysis, three key genes that may be involved in the drought stress response of cotton were screened: GhF6'H1, Gh3AT1 and GhPER55. qRT‒PCR analysis of parental and extreme germplasm materials revealed that the expression of these genes changed significantly under drought stress. Cotton VIGS experiments verified the important impact of key genes on cotton drought resistance. Conclusions This study focused on the key indicators of drought resistance, laying the foundation for the rapid mining of drought-resistant candidate genes in cotton and providing genetic resources for directed molecular breeding of drought resistance in cotton.

The high energy coordination compounds Cu(TZCA)2(ClO4)2 (ECCs-1) was prepared by 1H-tetrazole-5-carbohydrazide (TZCA) with a high energy skeleton and a strong coordination ability group. At the same time, the reaction activity of the ligand was explored, and the single crystal structure of it and intermediate were obtained. The structures of all substances were characterized by IR and EA. And the structure and composition of ECCs-1 are confirmed by ESP, AC, SEM and ICP-OES. Physical and chemical properties tests show that ECCs-1 has an acceptable thermal stability (Td = 177°C) and extremely sensitive mechanical stimulation (IS = 1 J, FS = 5 N). The comprehensive performance test results show that ECCs-1 has excellent initiation ability. In addition, the decomposition mechanism of ECCs-1 is explored from two aspects of experiment and theoretical calculation.

The application of autonomous navigation technology of electric crawler tractors is an important link in the development of intelligent greenhouses. Aiming at the characteristics of enclosed and narrow space and uneven ground potholes in greenhouse planting, to improve the intelligence level of greenhouse electric crawler tractors, this paper develops a navigation system of electric crawler tractors for the greenhouse planting environment based on LiDAR technology. The navigation hardware system consists of five modules: the information perception module, the control module, the communication module, the motion module, and the power module. The software system is composed of three layers: the application layer, the data processing layer, and the execution layer. The developed navigation system uses LiDAR, Inertial Measurement Unit (IMU) and wheel speed sensor to sense the greenhouse environment and the crawler tractor’s information, employs the Gmapping algorithm to build the greenhouse environment map, and utilizes the adaptive Monte Carlo positioning algorithm for positioning. The simulation test of different global path planning algorithms in Matlab shows that the A* algorithm obtains the optimal overall global path. In the scene of map 5, the path planned by the A* algorithm is the most significant, and the number of inflection points is reduced by 40.00% and 87.50%, respectively; meanwhile, the path length is the same as that of the Dijkstra algorithm, but the runtime is reduced by 68.87% and 81.49%, respectively; compared with the RRT algorithm, the path length is reduced by 7.27%. Therefore, the A* algorithm and the Dynamic Window Approach (DWA) method are used for tractor navigation and obstacle avoidance, which ensures global path optimality while also achieving effective local path planning for obstacle avoidance. The test results suggest that the maximum lateral deviation of the built map is 6 cm, and the maximum longitudinal deviation is 16 cm, which meets the requirement of map accuracy. Additionally, the results of the navigation accuracy test indicate that the maximum lateral deviation of navigation is less than 13 cm, the average lateral deviation is less than 7 cm, and the standard lateral deviation is less than 8 cm. The maximum heading deviation is less than 14°, the average heading deviation is less than 7°, and the standard deviation is less than 8°. These results show that the developed navigation system meets the navigation accuracy requirements of electric crawler tractors in the greenhouse environment.

Drought stress significantly affects the growth, development, and yield of cotton, triggering the response of multiple genes. Among them, ascorbate peroxidase (APX) is one of the important antioxidant enzymes in the metabolism of reactive oxygen species in plants, and APX enhances the ability of plants to resist oxidation, thus increasing plant stress tolerance. Therefore, enhancing the activity of APX in cells is crucial to improving plant stress resistance. Previous studies have isolated differentially expressed proteins under drought stress (GhAPX7) in drought-resistant (KK1543) and drought-sensitive (XLZ26) plants. Thus, this study analyzed the expression patterns of GhAPX7 in different cotton tissues to verify the drought resistance function of GhAPX7 and explore its regulatory pathways. GhAPX7 had the highest expression in cotton leaves, which significantly increased under drought stress, suggesting that GhAPX7 is essential for improving antioxidant capacity and enzyme activities in cotton. GhAPX7 silencing indirectly affects pronounced leaf yellowing and wilting in drought-resistant and drought-sensitive plants under drought stress. Malondialdehyde (MDA) content was significantly increased and chlorophyll and proline content and APX enzyme activity were generally decreased in silenced plants compared to the control. This result indicates that GhAPX7 may improve drought resistance by influencing the contents of MDA, chlorophyll, proline, and APX enzyme activity through increased expression levels. Transcriptome analysis revealed that the drought-related differentially expressed genes between the control and treated groups enriched plant hormone signal transduction, MAPK signaling, and plant–pathogen interaction pathways. Therefore, the decreased expression of GhAPX7 significantly affects the expression levels of genes in these three pathways, reducing drought resistance in plants. This study provides insights into the molecular mechanisms of GhAPX7 and its role in drought resistance and lays a foundation for further research on the molecular mechanisms of response to drought stress in cotton.

PurposeThe predictors of tracheostomy decannulation in patients with disorders of consciousness (DOC) are not comprehensively understood, making prognosis difficult. The primary objective of this study was to identify predictors of tracheostomy decannulation in patients with disorders of consciousness (DOC). The secondary aim was to evaluate the feasibility and safety of the modified Evans blue dye test (MEBDT) in tracheostomized DOC patients.MethodsThis retrospective study included all patients with disorders of consciousness (DOC) who underwent tracheostomy and were admitted between January 2016 and September 2022. Age, sex, etiology, initial Glasgow coma scale (GCS), initial Coma Recovery Scale-Revised (CRS-R), diagnosis of unresponsive wakefulness syndrome (UWS) or minimal consciousness state (MCS), MEBDT, initial modified Rankin scale (mRS), and initial Functional Oral Intake Scale (FOIS) were collected upon study enrollment. The relationship between clinical characteristics and cannulation status was investigated through a Cox regression model.ResultsA total of 141 patients were included in the study. The average age of these patients was 52.5 ± 16.7 years, with 42 (29.8%) being women. During the study period, 86 subjects (61%) underwent successful decannulation. Univariate analysis revealed that decannulated patients exhibited a significantly better conscious state compared to those without decannulation (CRS-R: p

In the context of intensive network construction, how to realize automatic, intelligent, and experienceguaranteed base station energy consumption management has become a pain point for current operators in operation management.A 3G/4G/5G smart base station power saving solution with full intelligent detection, full-scenario modeling, and full-process self-service was innovatively proposed.The coverage scene was distinguished by a dynamic time warping algorithm, and the SARIMA model was used to predict the time frame to build the model dynamically.Monitoring indicators ensured quality of experience, automatically issue power-saving strategies, and promptly raise SMS alerts.In the case that the user had no perception, the energy consumption of the base station was saved to the greatest extent at the cell granularity level.This method had been implemented on a pilot study in a province.The average annual power saving efficiency of a single station in the pilot area can reach 9.24% per day per station, which can be put in actual production.

Abstract Face detection is playing a pivotal role for crowd counting and abnormal events detection. However, it is vulnerable to face presentation attacks by printed photos, videos, and 3D masks of real human faces. Although numerous detection techniques based on deep learning have been employed to address the problem of face presentation attacks, there are still several weaknesses in these approaches, such as high algorithm complexity and a lack of detection ability. To overcome these weaknesses, a method based on a multilevel fusion network with Laplacian embedding (MFNet‐LE) for the detection of face presentation attacks is proposed. First, a shallow network that contains just three layers was developed, which makes the model faster. Then, an optimised multilevel fusion strategy was developed to combine the input with the output of all previous layers to improve the detection ability of the method. Finally, a Laplacian embedding algorithm is introduced to maintain the inter‐class discrimination and penalise the intra‐class distance. Under the joint supervision of Laplacian loss and softmax loss, the proposed approach can obtain more discriminative features, which enhance the accuracy of attack detection. Experiments were conducted with three public databases for face presentation attacks: CASIA FASD, Idiap Replay Attack database and MSU USSA. The results demonstrate that the MFNet‐LE model can outperform the state‐of‐the‐art methods.

BackgroundExploring the brain reorganization patterns associated with language recovery would promote the treatment of global aphasia. While functional near-infrared spectroscopy (fNIRS) has been widely used in the study of speech and language impairment, its application in the field of global aphasia is still limited.AimsWe aimed to identify cortical activation patterns of patients with global aphasia during naming and repetition tasks.Methods and proceduresWe recruited patients with post-stroke aphasia from the Department of Rehabilitation Medicine at Huashan Hospital. These individuals were diagnosed with global aphasia without cognitive impairments, as assessed by speech-language pathology evaluations. Age- and sex-matched healthy controls were recruited from the greater Shanghai area. During fNIRS measurement, patients and healthy controls completed the picture-naming and phrase repetition task. Cortical activation patterns on each of these language tasks were then compared between groups.Outcomes and resultsA total of nine patients with global aphasia and 14 healthy controls were included in this study. Compared with the healthy subjects, patients with global aphasia showed increased activation in the left Broca's area, middle temporal gyrus (MTG), superior temporal gyrus (STG), and pre-motor and supplementary motor cortex (SMA) (p < 0.05) in the picture-naming task. Furthermore, the latency of the oxyhemoglobin (HbO) concentration in the left supramarginal gyrus (SMG) region had a strong negative correlation with their score of the naming task (p < 0.01). In the phrase repetition task, decreased activation was detected in the left SMA and SMG (p < 0.05) of patients relative to controls.ConclusionThe left SMG plays a critical role in the language function of patients with global aphasia, especially in their abilities to name and repeat. fNIRS is a promising approach to revealing the changes in brain activities in patients with aphasia, and we believe it will contribute to a deeper understanding of the neurological mechanisms and the establishment of a novel treatment approach for global aphasia.

BackgroundFindings on the associations between psychological symptoms and driving behaviors in private car drivers are inadequate.MethodThe study consisted of 3,115 private car drivers in Yulin, China. The measurements included socio-demographic data, traffic violations, accidents, and Symptom Checklist-90 (SCL-90). In addition, an ordered logistic regression model was employed to examine the association between each psychological symptom and risky driving behaviors.ResultsThe overall prevalence rate of any self-reported psychological symptom was 10.24%, with 9.22% for males and 11.49% for females. Among them, obsessive-compulsive, interpersonal sensitivity, additional items, hostility, and depression were the five most common psychological symptoms, with prevalence rates of 7.90, 6.29, 6.00, 5.91, and 5.62%, respectively. Any psychological symptom factor was associated with a higher risk of traffic violations and accidents. However, the intensity of the correlations varied, with obsessive-compulsive symptoms the strongest in general traffic violations and anxiety symptoms in traffic accidents. All psychological symptoms except phobic anxiety and paranoid ideation contributed to a higher risk of failing the driver's license test.ConclusionsThe prevalence rate of psychological symptoms was high in private car drivers. This study calls for an urgent need to establish a pilot tertiary prevention strategy to reduce risky driving behaviors through psychological symptom screening and interventions among private car drivers.

Abstract Background Recurrent tuberculosis (TB) contributes to the burden of TB. The study was designed to explore the time of diagnostic delay and risk of delay in patients with recurrent TB in China. Methods A total of 13,334 patients with new and recurrent TB registered in Yulin a city in China were included. The Kaplan-Meier survival curve was employed to estimate the median delay time. The mixed-effects survival model was used to identify the correlates associated with diagnostic delay. The outcome of interest in the model was"being diagnosed". Results We found that 6.5% of cases with TB were attributed to recurrence. The median delay time of recurrent TB cases (73 days) was more than twice as long as that of new TB (35 days). Individuals with recurrent TB had a higher risk of diagnostic delay than new TB (HR, 0.5, 95%CI, 0.5–0.6). Factors associated with diagnostic delay differed between new TB and recurrent TB cases. Immigrants (HR, 0.5, 95%CI, 0.3–0.9), cases notified by way of recommendation (HR, 0.6, 95%CI, 0.4–0.9) and diagnosed at TB dispensary (HR, 0.4, 95%CI, 0.3–0.6) were associated with a higher risk of a longer delay for recurrent TB cases. Conclusions The proportion of TB cases attributed to recurrence was high. Patients with recurrent TB had a longer delay time and a higher risk of diagnostic delay. Further interventions to improve diagnostic delay should focus on screening for TB in immigrants, improving public health services at the lowest healthcare level and update of TB diagnosis and management model.

Zearalenone (ZEN), one of the most contaminated Fusarium toxins worldwide, is very common in contaminating wheat, corn oil and other foods. People are more vulnerable to ZEN exposure with more daily caloric intake, yet little is known about the combined effect of different dietary patterns with mycotoxins. This study aimed to compare the effects of long-term ZEN exposure on the overall biochemical landscape of the “gut–blood–liver axis” under normal diet and high-fat diet (HFD) using a combined multi-omics approach. The results indicated that ZEN exposure, possibly via the phenylalanine metabolic pathway, led to dysbiosis of mouse flora, suppression of short-chain fatty acids (SCFAS) metabolism, systemic inflammatory responses, and disturbances in serum and liver metabolism, which were exacerbated in synergy with HFD and ultimately led to a more severe state of lipid metabolism in the liver. We further found that ZEN exposure attenuated the indole-3-propionic acid (IPA) metabolic pathway, enhanced 2-hydroxybutyric acid metabolism in serum, and attenuated β-alanine metabolism in liver which was positively correlated with the abundance of Prevotellaceae UCG-004, Prevotellaceae UCG-001, and Prevotellaceae NK3B31 groups. The results highlighted the damaging effects of ZEN on the gut-blood-liver axis under different dietary patterns, which might serve as a reference for future studies exploring the combined effects of fungal toxins and multiple dietary patterns.

Although three monovalent EV-A71 vaccines have been launched in mainland China since 2016, hand, foot, and mouth disease (HFMD) still causes a considerable disease burden in China. Vaccines' use may change the epidemiological characters of HFMD. Spatial autocorrelation analysis and space-time scan statistics analysis were used to explore the spatiotemporal distribution pattern of this disease at the provincial level in mainland China. The effects of meteorological factors, socio-economic factors, and health resources on HFMD incidence were analyzed using Geodetector. Interrupted time series (ITS) was used to analyze the impact of the EV-A71 vaccine on the incidence of HFMD. This study found that the median annual incidence of HFMD was 153.78 per 100,000 (ranging from 120.79 to 205.06) in mainland China from 2011 to 2018. Two peaks of infections were observed per year. Children 5 years and under were the main morbid population. The spatial distribution of HFMD was presented a significant clustering pattern in each year (P

As a critical preprocessing technique, low-illumination image enhancement has a wide range of practical applications. It aims to improve the visual perception of a given image captured without sufficient illumination. Conventional low-illumination image enhancement methods are typically implemented by improving image brightness, enhancing image contrast, and suppressing image noise simultaneously. Nevertheless, recent advances in this area are dominated by deep-learning-based solutions, and consequently, various deep neural networks have been proposed and applied to this field. Therefore, this paper briefly reviews the latest low-illumination image enhancement, ranging from its related algorithms to its unsolved open issues. Specifically, current low-illumination image enhancement methods based on deep learning are first sorted out and divided into four categories: supervised learning methods, unsupervised learning methods, semi-supervised learning methods, and zero-shot learning methods. Then, existing low-light image datasets are summarized and analyzed. In addition, various quality assessment indices for low-light image enhancement are introduced in detail. We also compare 14 representative algorithms in terms of both objective evaluation and subjective evaluation. Finally, the future development trend of low-illumination image enhancement and its open issues are summarized and prospected.

Background Diabetes mellitus (DM) is common among patients with schizophrenia. However, information on patients comorbid DM and schizophrenia is limited in China. The present study investigated the prevalence of DM and its clinical characteristics in Chinese inpatients with chronic schizophrenia. Methods A cross-sectional study was performed in Chinese inpatients with chronic schizophrenia. Diagnosis of Diabetes was established using World Health Organization diagnostic criteria for diabetes mellitus (persistent fasting glucose levels ≥ 126 mg/dl or 2-h plasma glucose ≥ 200 mg/dL after a 75-g Oral Glucose Tolerance Test). Patients were also measured height, weight, waist circumference, hip circumference, triglyceride level, and cholesterol level. Patients’ psychiatric symptoms were measured by the Positive and Negative Syndrome Scale (PANSS). Binary logistic regression analysis was performed to examine the associated demographic and clinical variables in chronic schizophrenia. Results A total of 988 inpatients (64.6% male, average age of 47.19 ± 12.55) was recruited. The prevalence of DM in Chinese patients with chronic schizophrenia was 13.8% (95% CI [11.6–15.9]%). Logistic regression analysis showed that overweight (OR = 1.90, 95% CI [1.20–3.03], p = 0.006), obesity (OR = 1.85, 95% CI [1.07–3.21], p = 0.028), comorbid hypertension (OR = 2.14, 95% CI [1.34–3.42], p = 0.002), and course of schizophrenia (OR = 1.03, 95% CI [1.01–1.06], p = 0.040) were significantly associated with the DM risk in patients with schizophrenia. Conclusion The findings indicated that diabetes mellitus was non-negligible in patients with chronic schizophrenia. Patients with schizophrenia should be regularly monitored for DM. Overweight/obesity, long duration of schizophrenia, and comorbid hypertension possibly were risk factors for diabetes.

Coronavirus disease 2019 (COVID-19) has become a pandemic, and many Chinese college students both in China and abroad were house-quarantined. This study aimed to investigate the prevalence and symptoms of delayed-onset post-traumatic stress disorder (PTSD) and coping strategies among Chinese overseas and domestic college students during this pandemic. A questionnaire was opportunistically distributed to Chinese college students studying both domestically and abroad six months after the COVID-19 outbreak. The questionnaire consisted of IES-R, SCSQ, and SSRS. The average score of delayed-onset PTSD in our population was 21.411 (full mark, 88 points), which reflected a total high level of delayed-onset PTSD symptoms. Statistical differences were shown between students who have been back to universities during the pandemic or not in the hyperarousal dimension (p = 0.016). Three coping strategies were recognized to influence the respondent’s delayed-onset PTSD symptoms, and there was a significant correlation between social support and the coping strategies students chose. A moderate to high level of delayed-onset PTSD was observed among both Chinese overseas and domestic college students 6 months after the COVID-19 outbreak. The useful coping strategies and powerful social supports are significantly important to help them stay mentally healthy and alleviate delayed-onset PTSD during the COVID-19 pandemic.

Dysphagia is one of the most common functional disorders after stroke, with an incidence of up to 30%-50%, resulting in malnutrition and aspiration pneumonia, which could be life-threatening. The pathogenic mechanism of dysphagia after stroke involves multiple brain functional areas and neural networks. It is generally believed that dysphagia after stroke is related to the lesions of cerebral cortex, subcortical structures and brainstem swallowing center. There are significant differences in the mechanism and clinical manifestations of dysphagia caused by different lesion sites. Understanding the mechanism is conducive to the development of more targeted and individualized rehabilitation therapy in clinical practice, which has an important impact on the prognosis of patients. Except for the traditional swallowing treatment programs commonly used, such as swallowing motion-sensory training, peripheral neuromuscular electrical stimulation, acupuncture, postural training, and dietary adjustment, people have become more and more interested in central nervous regulation in the treatment of swallowing dysfunction. Transcranial Magnetic Stimulation(TMS), as a non-invasive neuroregulation technique, achieves the purpose of neuroregulation by regulating the excitability of the cerebral cortex. With the development of research, different pulse stimulation patterns have been developed and experimentally applied in the treatment of post-stroke dysphagia. Repetitive transcranial magnetic stimulation(rTMS)is a repeated, continuous and constant intensity stimulation of selected brain regions. Theta burst stimulation(TBS)is based on rTMS with the addition of cluster rhythmic stimulation, which has lower stimulus intensity and shorter stimulus duration. Paired associative stimulation(PAS)combines central TMS with peripheral electrical stimulation at certain intervals to activate brain networks in pairs. Each mode of treatment has its own characteristics. This article reviews the application of different stimulation modes in healthy people and patients with dysphagia after stroke, analyzes the current research progress and limitations in this field, and provides a new idea for the clinical research and treatment of deglutition disorder after stroke from three points.

In this paper, we address the problem of weakly supervised object localization using region weighting. For a weakly labelled image/video, the inside regions have different relevance to its semantic label. We first over-segment an image/video to get super-pixel/voxel regions, and assign each region with a latent weight to represent its support to the semantic label, then regress the weights to right values by optimizing the classification according to the weak labels. We adopt logistic regression as our base model due to its good performance in multiple-instance setting. The latent region weights are incorporated into the objective function as an interpretation of region combination at feature-level. The weights and the model parameters are optimized in an alternate manner. With the updates of the weights, the model is trained on the semantic regions independently of the background, therefore the learned model is capable of distinguishing object and non-object regions, and generating irregular-shape object localization. The method overcomes the limitations of applying multiple-instance learning to visual object localization. Experimental results on three datasets validates the effectiveness of the proposed method.

Abstract Background With great changes over the past 10 years in China, especially the rapid economic development, population mobility, urbanization and aging, dynamic change on risk of delay, to our knowledge, has not been well studied in China. The study was to explore risk of delay in diagnosis of new pulmonary tuberculosis (PTB) and dynamic changes in risk of delay in Northwest China. Methods From January 1, 2008 to December 31, 2017, a total of 13,603 people with new PTB registered in Yulin city of Shaanxi province were included. The median delay time was estimated by Kaplan-Meier survival curve. Time delay curves of year-, gender-year-, age-year- and smear-year specific were examined using log-rank test. Two-level mixed-effects survival model was used to calculate the hazard ratio (HR) and 95% confidence interval (95%CI) for factors associated with diagnostic delay. Time delay was defined as time interval between the onset of PTB symptoms and being diagnosed. The outcome variable of interest was defined as “being diagnosed” in survival analysis. Results The 10-year delay time was 33 days (Interquartile Range, 16–65). Annual median delay time gradually decreased from 60 days to 33 days during the past 10 years. The probability that individuals were diagnosed since onset of PTB symptoms increased by 1.29 times in 2017 when compared to 2008. Female (Hazard Ratio (HR), 95%CI, 0.95(0.91–0.99)), age>45 years (HR, 95%CI, 0.87(0.82–0.93)) and smear positive (HR, 95%CI, 0.86(0.78–0.95)) were associated with increased risk of diagnostic delay over 10-year timespan. However, Age>45 years and smear positive showed trend to be protective factors in the past 5 years. Conclusions Time and risk of delay in diagnosis of new PTB had declined over the past 10 years. However, more attentions should be paid to the fact that female still suffered from higher risk of diagnostic delay. We noted a potential reversal in traditional risk factors such as age>45 and smear positive. Those dynamic changes deserved further attention.

Abstract Bisphenol A (BPA), a member of the environmental endocrine disruptors (EDCs), has recently received increased attention because of its effects on brain insulin resistance. Available data have indicated that brain insulin resistance may contribute to neurodegenerative diseases. However, the associated mechanisms that underlie BPA-induced brain-related outcomes remain largely unknown. In the present study, we identified significant insulin signaling disturbances in the SH-SY5Y cell line that were mediated by BPA, including the inhibition of physiological p-IR Tyr1355 tyrosine, p-IRS1 tyrosine 896, p-AKT serine 473 and p-GSK3α/β serine 21/9 phosphorylation, as well as the enhancement of IRS1 Ser307 phosphorylation; these effects were clearly attenuated by insulin and rosiglitazone. Intriguingly, Alzheimer’s disease (AD)-associated pathological proteins, such as BACE-1, APP, β-CTF, α-CTF, Aβ 1–42 and phosphorylated tau proteins (S199, S396, T205, S214 and S404), were substantially increased after BPA exposure, and these effects were abrogated by insulin and rosiglitazone treatment; these findings underscore the specific roles of insulin signaling in BPA-mediated AD-like neurotoxicity. Thus, an understanding of the regulation of insulin signaling may provide novel insights into potential therapeutic targets for BPA-mediated AD-like neurotoxicity.

The anti-slip (AS) layer of the cement concrete pavement (CCP) can probability decrease traffic accidents and extend the service life of the road. Epoxy resin has excellent adhesion and mechanical strength, making it possible in this field. Herein, a flexible waterborne epoxy resin (WER) without a small molecule emulsifier was synthesized by bisphenol A epoxy resin, poly (propylene glycol) diglycidyl ether (PDE), diethylenetriamine (DETA). The mechanism of the group reaction was characterized by fourier transform infrared (FTIR), and dynamic scanning calorimetry (DSC) determined the heat of curing of WER. Then, the physical properties of WER were enhanced by the silane coupling agent modified nano-silica (SCA-NS) and the composite’s water absorption rate and mechanical propriety were evaluated. Finally, the pavement performance of the AS layer was prepared and tested as well as compared with the commercial waterborne epoxy resin (CWER) and emulsified asphalt (EA). The result shows EA has low durability and adhesion strength than epoxy resin. The small molecule emulsifier and low-flexible resistance of CWER decrease water stability and anti-sliding durability of the AS layer. Due to applied nanomaterial reinforced and without small molecule emulsifiers, the WER base AS layer shows extremely high adhesion strength, water stability, and skid-resistance durability at addition 3 wt% of SCA-NS, which have a great potential for CCP.

A cost-efficient and practical strategy was developed for preparing high thermal conductive epoxy packaging composites. The effective conductive network was constructed by the bridging effect between boron nitride (BN) and spherical silica (SiO _2 ). Compared to the epoxy (EP) composites with randomly dispersed BN and SiO _2 , the EP/SiO _2 @BN showed a great enhancement in thermal conduction. The thermal conductivity of EP/SiO _2 @BN reached to 0.86 W m ^−1 K ^−1 with 60 wt% content of hybrid filler, which was 91% higher than that of EP/SiO _2 samples and was around 12% higher than that of epoxy composites with unmodified BN and SiO _2 . In addition, the EP/SiO _2 @BN exhibited lower thermal interface resistance in comparison with EP/SiO _2 &BN composites according to the effective medium theory (EMT). The encapsulation of BN on the surface of SiO _2 greatly enhanced the thermal transfer efficiency of the epoxy matrix and showed great potential in the epoxy packaging practical application.

Lipopolysaccharide (LPS) is the main virulence factor of Gram-negative bacteria, which can incite inflammation in tissues by inducing cells to secrete a variety of proinflammatory mediators, including cytokines, chemokines, interleukins, and prostaglandins. Herein, we chose LPS as an inducer to establish an inflammatory model of HeLa cells, and explored the effects of LPS on energy metabolism. We treated HeLa cells with different concentrations (0, 0.4, 1.0, 2.0, 4.0, and 6.0 μg/mL) of LPS for 24 h, and explored its effects on intercellular adenosine triphosphate (ATP) levels, intercellular nitrous oxide (NO) content, mitochondrial functions, and enzyme activities related to energy metabolism. Furthermore, we used metabonomics to study the metabolites that participated in energy metabolism. We found a positive correlation between LPS concentrations and intracellular ATP levels. In addition, LPS increased intracellular NO production, altered mitochondrial functions, strengthened glycolytic enzyme activities, and changed metabolites related to energy metabolism. Hence, in this study, we showed that LPS can strengthen energy metabolism by enhancing glycolysis, which could be used as an early diagnostic biomarker or a novel therapeutic target for inflammation-associated cancers.

Micro/nanomotors are self-propelled machines that can convert various energy sources into autonomous movement. With the great advances of nanotechnology, Micro/Nanomotors of various geometries have been designed and fabricated over the past few decades. Among them, the tubular Micro/Nanomotors have a unique morphology of hollow structures, which enable them to possess a strong driving force and easy surface functionalization. They are promising for environmental and biomedical applications, ranging from water remediation, sensing to active drug delivery and precise surgery. This article gives a comprehensive and clear review of tubular Micro/Nanomotors, including propulsion mechanisms, fabrication techniques and applications. In the end, we also put forward some realistic problems and speculate about corresponding methods to improve existing tubular Micro/Nanomotors.

Motivated by heat dissipation, the rigid-compliant hybrid cellular expansion mechanisms with motion amplification and superposition are proposed in this paper. Compared with existing studies, the expansion mechanism is not only easy to realize the plane tessellation via cellular design due to its regular polygon structure but also has the ability of motion amplification and superposition due to its compliant displacement amplifier and rigid scissors. First, the scheme of expansion mechanisms, especially the working principle of motion amplification and superposition, is introduced. The configuration design of a family of expansion mechanisms is presented, including varying number of edges, concave/convex property, and inner/outer layout. Second, the constraint condition and analytical modeling of relations between output performances of expansion mechanisms and dimensional parameters are carried out. Third, the displacement amplification ratio of expansion mechanisms and output performances of several typical expansion mechanisms when they act as cells to tessellate a plane with a constrained area are analyzed. Finally, the output performances of expansion mechanisms are verified via the finite element analysis. The results show that proposed cellular expansion mechanisms are beneficial for realizing plane tessellation and offer motion amplification and superposition, which provide prospects in the field of mechanism design such as metamaterials. [ABSTRACT FROM AUTHOR]