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Abstract Background Anti-N-methyl-D-aspartic receptor encephalitis (Anti-NMDAR encephalitis) is the most prevalent form of autoimmune encephalitis in pediatric patients. Autonomic dysfunction is a frequent symptom of Anti-NMDAR encephalitis, yet it often goes unnoticed by pediatricians. Studies have indicated that pediatric patients with autonomic dysfunction exhibit a poorer prognosis compared to those without. To date, research on autonomic dysfunction in encephalitis has predominantly focused on adults, with no studies conducted on pediatric populations. This analysis examines the clinical features of pediatric patients with Anti-NMDAR encephalitis complicated by autonomic dysfunction. Methods We performed a retrospective analysis of patients diagnosed with Anti-NMDAR encephalitis at the Department of Neurology, Children’s Hospital affiliated to the Capital Institute of Pediatrics, from June 2017 to June 2023. Patients were categorized based on the presence or absence of autonomic dysfunction during their illness. We summarized and compared the clinical features of children with autonomic dysfunction and analyzed the risk factors for its development in pediatric Anti-NMDAR encephalitis patients. Results A total of 56 children were included in this study. Twenty-two (39.3%) exhibited autonomic nervous dysfunction. The most prevalent symptom of autonomic dysfunction was cardiovascular autonomic dysfunction(21/22, 95%),with the specific manifestations being sinus tachycardia (8 cases), ventricular premature beats (2 cases), atrioventricular block (2 cases), atrial premature beats (3 cases), and sinus bradycardia (4 cases),hypertension(1 case) and cardiac arrest(1 case). Other symptoms included gland secretion dysfunction (19/22, 86%),ventilate dysfunction(3/22,14%), thermoregulatory dysfunction (3/22,14%), bladder dysfunction(2/22,9%). Compared to the group without autonomic dysfunction, the group with dysfunction showed significantly higher rates of prodrome infection, tumor complications (all ovarian teratoma), consciousness disturbance, elevated cerebrospinal fluid protein, initiation of second-line and long-term immunotherapy, length of hospital stay, and hospitalization costs (P

Abstract Peripheral nerve injury (PNI) occurs due to damage of peripheral nerves, with healthcare professionals playing significant roles in PNI rehabilitation. This study aimed to explore the knowledge, attitudes, and practices (KAP) towards PNI rehabilitation among healthcare professionals. This cross-sectional study was conducted on June 2023 in China and healthcare professionals were enrolled. A total of 611 valid questionnaires were collected, with 62.52% female respondents. Mean scores for KAP were 14.26 ± 2.044 (possible range: 0–19), 29.77 ± 3.622 (possible range: 7–35), and 41.55 ± 9.523 (possible range: 11–55), respectively. Multivariate logistic regression revealed positive associations of professional titles (OR = 1.743, 95% CI: 1.083–2.804), occupation (OR = 1.833, 95% CI: 1.151–2.919), and involvement in treatment or care of PNI patients (OR = 1.462, 95% CI: 1.024–2.088) with knowledge. Knowledge (OR = 1.155, 95% CI: 1.042–1.280), gender (OR = 2.140, 95% CI: 1.255–3.646), education (OR = 2.258, 95% CI: 1.131–4.507), and involvement in treatment or care of PNI patients (OR = 2.463, 95% CI: 1.460–4.155) were positively associated with attitude. Attitude (OR = 1.214, 95% CI: 1.148–1.283), bachelor's degree education (OR = 0.548, 95% CI: 0.326–0.919), master's degree or higher (OR = 0.545, 95% CI: 0.308–0.964), having rehabilitation training for PNI (OR = 2.485, 95% CI: 1.633–3.781), and involvement in treatment or care of PNI patients (OR = 2.093, 95% CI: 1.395–3.138) were independently associated with practice. Healthcare professionals exhibited moderate knowledge, positive attitudes, and moderate practices towards the PNI rehabilitation. Those involved in the treatment or care of PNI have significantly higher KAP. Targeted interventions were needed to enhance understanding and promote proactive engagement in clinical practice.

ObjectiveThis study explores the associations between four macro-level factors—Economic Development (ED), Economic Inequality (EI), Governmental Willingness and capacities to invest in Public Health (GWPH) and Public Health-Related Infrastructures (PHRI)—and three mental health indicators: depressive symptoms, cognitive function and life satisfaction, among middle-aged and older adults in China.Materials and methodsWe obtained individual-level data from the Harmonised China Health and Retirement Longitudinal Survey (H-CHARLS) 2018 and acquired our provincial-level data from the Chinese Statistical Yearbook. Two-level linear mixed models are used to examine the associations. Supplementary analyses are carried out to test the robustness of the study.ResultsThere are provincial variations in macro-level factors. Depressive symptoms and cognitive functions also vary across provinces, whereas life satisfaction does not. We find that ED contributes to better depressive status. EI contributes to worse cognitive functions and life dissatisfaction. GWPH and PHRI are not associated with mental health.ConclusionThe study suggests that macro-level ED contributes to better depressive status. EI and potential systematic inequality lead to worsened cognitive functions and life dissatisfaction. It is too soon to generalise whether institutional factors like GWPH and PHRI are good or bad for mental health, but the current public health system in China does not adequately support mental and cognitive health.

AbstractBackground A growing number of studies show that people with similar blood glucose levels have different levels of glycosylated haemoglobin (HbA1c), and relying only on HbA1c may lead to clinical decision-making errors. The haemoglobin glycation index (HGI) quantifies the difference in HbA1c among individuals and is strongly linked to the risk of cardiovascular disease. However, the connection between this phenomenon and the poor outcomes of patients with acute decompensated heart failure (ADHF) is currently unknown.Patients and Methods This retrospective, single-centre-based cohort study included 1531 hospitalized patients with ADHF from September 2010 to January 2020. The HGI is calculated from the difference between the observed and predicted HbA1c values [predicted HbA1c = 0.024 × fasting plasma glucose (FPG) (mg/dL)+3.1]. The endpoints examined in the study included all-cause death, cardiovascular (CV) death, and major adverse cardiac events (MACE). We fitted multivariable-adjusted Cox proportional hazard models to investigate the association between the HGI and clinical outcomes.Results During the five-year follow-up, 427 (27.9%) patients died from all causes, 232 (15.6%) from CV death, and 848 (55.4%) from MACE. The restricted cubic spline analysis also showed that the cumulative risk of all-cause and CV deaths decreased linearly with increasing HGI. According to multivariate Cox proportional hazard models, the highest tertile of the HGI was associated with a lower incidence of all-cause and cardiovascular deaths [all-cause death, adjusted hazard ratio (HR): 0.720, 95% confidence interval (CI): 0.563–0.921, p = 0.009; CV death, adjusted HR: 0.619, 95% CI: 0.445–0.861, p = 0.004]. A 1% increase in the HGI was associated with a 12.5% reduction in the risk of all-cause death and a 20.8% reduction in the risk of CV death.Conclusions A high HGI was directly associated with a reduction in all-cause and CV deaths but was not associated with MACE. These findings may be helpful in the management of patients with ADHF.

The ladybird beetle Micraspis discolor plays an important role as a predator of various arthropods within Asia’s rice ecosystems. While pollen could serve as an alternative diet for this beetle, facilitating mass-rearing, its pollinivory might inadvertently result in attacks on crop pollen. This study aims to explore the role of gut bacteria on pollinivory of M. discolor. We found that antibiotic treatment significantly reduced the performance of M. discolor when fed Brassica campestris pollen. However, the treatment did not significantly affect their performance when fed an alternative diet of Ephestia kuehniella eggs. Further, we found that antibiotics can eliminate a strain of Serratia marcescens, SmMd, which is a specific gut bacterium in M. discolor. Moreover, the performance of M. discolor showed some degree of recovery when SmMd was reintroduced into its diet. Therefore, we propose that gut bacteria, particularly SmMd, play a significant role in pollen use by this ladybird beetle. This insight enhances our understanding of the important role of gut bacteria in insect adaptation to diverse diets and can potentially optimize the utilization of M. discolor in biological control strategies.

ObjectiveTo study the characteristics of event related potential P300 in preschool children with attention deficit hyperactivity disorder (ADHD) aged 4–6 years (4≤ age

Microwave heating has the characteristics of simple control and high efficiency, in which electromagnetic force plays an important role. Using the UDF function and energy equation in FLUENT software, the electromagnetic force model in the tube is established. The mixing process of cerium chloride solution under different heating modes, the mixing effect of Lorentz force and the preparation process of cerium oxide under different inlet material velocities were numerically simulated. CFD-Post and MATLAB software are used to post-process the data of the whole fluid area. Through the above analysis, the best process conditions of numerical simulation and experiment are finally determined. The results show that microwave heating has better mixing effect and lower material loss compared with conventional heating, in which electromagnetic force plays a promoting role; Under the condition of microwave heating, the product conversion rate is high, and the concentration of cerium oxide is high and distributed evenly. The more appropriate mixing method of cerium chloride and preparation method of cerium oxide are microwave heating, and the more appropriate inlet velocity of materials is 0.15 m/s.

ABSTRACT Polymerase chain reaction-denaturing gradient gel electrophoresis (PCR-DGGE) is a traditional non-culture technique that can provide a fingerprint of the microbial community. In the field of gut microbiota analysis, PCR-DGGE still holds potential for development. In the present study, we utilized an improved nested PCR-DGGE approach targeting the V3 region of 16S ribosomal DNA to investigate the impact of whole grain highland hull-less barley (WHLB), a cereal known for its significant hypocholesterolemic effect, on the gut microbiota profiles of high-fat diet rats. Seventy-two male Sprague–Dawley rats were divided into four groups and fed a normal control diet, a high-fat diet, or a high-fat diet supplemented with a low or high dose of WHLB for 4 or 8 weeks. The results revealed that the dominant bands varied among different dose groups and further changed with different treatment times. The compositions of bacterial communities in feces and cecal content were similar, but the dominant bacterial bands differed. After performing double DGGE, extracting the bands, sequencing the DNA, and aligning the sequences, a total of 19 bands were classified under the Firmicutes and Bacteroidetes phyla, while two bands were identified as unclassified uncultured bacteria. The relative abundance of Lactobacillus gasseri, Uncultured Prevotella sp., and Clostridium sp. increased following the administration of WHLB. Illumina-based sequencing was employed to assess the reliability of DGGE, demonstrating its reliability in analyzing the dominant taxonomic composition, although it may have limitations in accurately detecting the alpha diversity of bacterial species.IMPORTANCEWhile next-generation sequencing has overshadowed polymerase chain reaction-denaturing gradient gel electrophoresis (PCR-DGGE), the latter still holds promise for advancing gut microbiota analysis due to its unique advantages. In this study, we used optimized nested PCR-DGGE to investigate the gut microbiota profile of high-fat diet rats after administering whole grain highland hull-less barley. High-throughput sequencing was employed to validate the DGGE results. Our results proved the reliability of PCR-DGGE for analyzing the dominant taxonomic composition while also providing visual evidence of a notable relationship between the composition of cecal and fecal microbial communities, highlighting substantial differences in both richness and abundance.

Abstract LMNA gene encodes lamin A/C protein which participates in the construction of nuclear lamina, the mutations of LMNA result in a wide variety of diseases known as laminopathies. LMNA-related dilated cardiomyopathy(LMNA-DCM) is one of the more common laminopathy which characterized by progressive heart failure and arrhythmia. However, the mutation features of LMNA-DCM are yet to be elucidated. Herein we described a dilated cardiomyopathy family carrying novel variant c.467G > C(p.Arg156Pro) of LMNA as heterozygous pathogenic variant identified by whole-exome sequencing. With the help of Alphafold2, we predicted mutant protein structure and found an interrupted α-helix region in lamin A/C. In the analysis of 49 confirmed pathogenic missense of laminopathies, Chi-square test showed the DCM phenotype was related to the α-helix region mutation (p

Abstract Background Fibrosis-5 (FIB-5) index is a marker of liver fibrosis and has been shown to have a good prognostic value for patients with acute heart failure (AHF), and C-reactive protein (CRP) has inflammatory properties and predicts adverse prognosis in patients with HF. However, the long-term prognostic value of FIB-5 index combined with CRP in patients with acute decompensated HF (ADHF) is yet unclear. Methods This retrospective study included 1153 patients with ADHF hospitalized from January 2018 to May 2022.The FIB-5 index was calculated as (albumin [g/L]×0.3 + PLT count [109/L]×0.05)−(ALP [U/L]×0.014 + AST to ALT ratio×6 + 14). Patients were stratified into the following four groups according to the median value of FIB-5 index (=-2.11) and CRP (= 4.5): Group 1 had a high FIB-5 index (FIB-5 index >-2.11) and a low CRP (CRP ≤ 4.5); Group 2 had both low FIB-5 index and low CRP; Group 3 had both high FIB-5 index and high CRP; Group 4 had a low FIB-5 index (FIB-5 index ≤-2.11) and a high CRP (CRP > 4.5). The endpoint was major adverse cardiac and cerebral events (MACCEs). Multivariate Cox analysis was used to evaluate the association of the combination with the development of MACCEs. Net reclassification improvement (NRI) and integrated discrimination improvement (IDI) analysis were used to compare the accuracy of the combination with a single prognostic factor for predicting the risk of MACCEs. Results During the mean follow-up period of 584 ± 12 days, 488 (42.3%) patients had MACCEs. Kaplan–Meier analysis revealed that the incidence of MACCEs was different in the four groups (P

PurposeProstate-specific membrane antigen (PSMA)-targeted imaging has gained increasing interest in its application in prostate cancer lesion detection. Compared with 68Galium (68Ga), 18Fluoride (18F)-labeled imaging agent has easier syntheses, lower price, and a longer half-time. 2-(3-{1-Carboxy-5-[(6-[18F]fluoro-pyridine-3-carbonyl)-amino]-pentyl}-ureido)-pentanedioic acid positron emission tomography (18F-DCFPyL PET) has been recently approved by the U.S. Food and Drug Administration. Several studies have proven its superiority to conventional imaging techniques in detecting prostate cancer lesions. However, the impact of 18F-DCFPyL PET on the management of patients with prostate cancer is not well established. Thus, we performed a systematic review and meta-analysis of available data to evaluate the impact of 18F-DCFPyL PET on the management of patients with prostate cancer.MethodsThe PubMed, Embase, Scopus, and Cochrane databases were searched up to April 2024. Studies that reported the proportion of changes in management after 18F-DCFPyL PET was performed in patients with prostate cancer were included. The Grading of Recommendations Assessment, Development, and Evaluation system was used for the quality evaluation of the included studies. The proportion of changes in management was pooled using a random effects model. Meta-regression analyses were performed to assess the potential correlation between the PET positivity and management changes.ResultsFourteen studies (3,078 patients with prostate cancer) were included in our review and analysis. The pooled percentage of management changes was 43.5% (95% confidence interval [CI]: 33–54%). In patients with biochemical recurrent and for primary staging, the pooled percentage was 50% (95% CI: 39–60%) and 22% (95% CI: 15–29%), respectively. In the meta-regression analyses, PET positivity was detected as a significant predictor of management change (p = 0.0023).Conclusion18F-DCFPyL PET significantly affects the management of patients with prostate cancer. Higher PET positivity rate significantly correlated with a higher proportion of management changes in patients with prostate cancer. However, more studies are still needed to confirm the important role of 18F-DCFPyL PET in the management of prostate cancer.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/#myprospero, CRD42022339178.

In predicting building energy (affected by seasons), there are issues like inefficient hyperparameter optimization and inaccurate predictions, it is unclear whether spatial and temporal attention improves performance. This study proposes a method based on Bayesian Optimization (BO), Spatial-Temporal Attention (STA), and Long Short-Term Memory (LSTM). Seven improved LSTM models (BO-LSTM, SA-LSTM, TA-LSTM, STA-LSTM, BO-SA-LSTM, BO-TA-LSTM, and BO-STA-LSTM) are compared with LSTM and the impacts of seasonal variations on BO-STA-LSTM are analysed using different sample types and time domain analysis. To further demonstrate the efficiency of the proposed method, comparisons with convolutional neural network (CNN) and temporal convolutional network (TCN) are performed, and followed by validation with new datasets. The findings indicate that adding STA and BO to LSTM enhances the average R2 of prediction performance by 0.0885. BO alone contributes 0.0717, while adding attention improves 0.0560. BO-STA-LSTM achieves higher prediction accuracy for similar test and training samples or a test sample size of 14,016, effectively capturing seasonal, trend, and peak energy patterns. Additionally, BO-STA-LSTM outperforms CNN and TCN, demonstrating superior prediction accuracy.

Abstract Objective The study aimed to evaluate the prognostic value of relative wall thickness (RWT) in the patients with ST-segment elevation myocardial infarction (STEMI). Methods A total of 866 patients with STEMI admitted in Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School from November 2010 to December 2018 were enrolled in the current study retrospectively. Three methods were used to calculate RWT: RWTPW, RWTIVS+PW and RWTIVS. The included patients were divided according to the median values of RWTPW, RWTIVS+PW, and RWTIVS, respectively. Survival analysis were performed with Kaplan–Meier plot and multivariate Cox proportional hazard model was established to evaluate the adjusted hazard ratio of the three kinds of RWT for all cause death, cardiac death and MACE (major adverse cardiac death). Results There was no significance for the survival analysis between the low and high groups of RWTPW, RWTIVS+PW and RWTIVS at 30 days and 12 months. Nonetheless, the cumulative incidence of all cause death and cardiac death in the low group of RWTPW and RWTIVS+PW was higher than those in the high group at 60 months. The cumulative incidence of MACE in the low group of RWTPW was higher than the high group at 60 months. Multivariate Cox regression model showed that RWTPW were inversely associated with long-term cardiac death and MACE in STEMI patients. In the subgroup analysis, three calculations of RWT had no predictive value for the patients with anterior myocardial infarction. By contrast, RWTPW was the most stable independent predictor for the long-term outcomes of the patients with non-anterior myocardial infarction. Conclusion RWTPW, RWTIVS+PW and RWTIVS had no predictive value for the long-term clinical outcomes of patients with anterior myocardial infarction, whereas RWTPW was a reliable predictor for all cause death, cardiac death and MACE in patients with non-anterior myocardial infarction.

The eggs of the Mediterranean flour moth, Ephestia kuehniella, are frequently utilized as alternative diets and have demonstrated promising outcomes when consumed by various insects. Nonetheless, the specific reasons for their effectiveness remain unclear. In our study, we assessed the developmental performance of the ladybird Propylea japonica when fed E. kuehniella eggs, alongside 12 factitious prey or artificial diets. Our findings revealed that ladybirds fed E. kuehniella eggs displayed a performance comparable to those fed the natural prey Megoura crassicauda. Transcriptome profiling of larvae raised on E. kuehniella eggs and M. crassicauda revealed that genes upregulated in the former group were enriched in metabolic pathways associated with carbohydrates, lipids, and other essential nutrients. This suggests that E. kuehniella eggs may have a higher nutrient content compared to natural prey. Furthermore, a notable downregulation in the expression of immune effector genes, such as Attacin and Coleoptericin, was observed, which might be attributed to the lower microbial content in E. kuehniella eggs compared to M. crassicauda. We suggest that the difference between E. kuehniella eggs and M. crassicauda as food sources for P. japonica lies in their nutrient and microbial contents. These findings provide valuable insights for the advancement of innovative artificial breeding systems for natural enemies.

Currently, the incidence of prostate cancer is increasing, and it has become a great threat to men’s health. The detection, staging, and follow-up of prostate cancer patients are inseparable from morphology or magnetic resonance imaging (MRI). However, these do not fully meet the needs of diagnosis and patient management. In particular, owing to the late diagnosis, metastatic castration-resistant prostate cancer (mCRPC) patients usually have poor survival and few options for further effective treatment. Prostate-specific membrane antigen (PSMA), because of its overexpression on prostate cancer cells, has gained interest due to its application in the imaging and theranostics field. Several PSMA radioligands have been developed for imaging and treating prostate cancer. Many clinical trials have assessed the efficacy and safety profiles of these radionuclide agents and show promise in patients who have exhausted other standard treatment options. To date, several small compounds for targeting PSMA have been developed, and 68Ga-PSMA-11 and 18F-DCFPyL have been approved by the United States (US) Food and Drug Administration (FDA) for imaging of prostate cancer. 111In- or 99mTc-labeled PSMA-ligand can guide surgeons searching for radioactive metastatic lymph nodes, and 177Lu- or 225Ac-labeled PSMA-ligand can be used for internal radiotherapy. Moreover, some molecules for therapeutic application are undergoing different stages of clinical trials. In this review, we present current perspectives on the use of PSMA-targeted imaging and theranostics in prostate cancer. As PSMA-targeted imaging and therapeutics are becoming the standard of care for prostate cancer patients, we emphasize the importance of integrating nuclear medicine physicians into multidisciplinary oncology teams.

Straightness measurements of fully mechanized coal faces are a core requirement for intelligent, comprehensive coal mining. In coal mining, using an inertial measurement unit (IMU) assisted by an odometer for straightness measurement is vital for automating longwall mining. IMUs are less vulnerable to dim and harsh underground mining conditions. However, IMU divergence cannot be controlled solely by the forward velocity obtained from the odometer. To address this problem without external sensors, this paper proposes a straightness measurement method based on a fiber optic gyroscope-IMU/odometer and shearer motion constraints. The motion of the shearer is constrained to correct the pitch and roll attitude angle errors in strapdown inertial navigation solution (SINS) attitude solutions. An extended Kalman filter was established to combine the SINS and dead reckoning using the output from the odometer. Finally, the position estimation obtained by Kalman filter fusion was converted into straightness. An experiment based on a custom-built test rail demonstrated that the proposed algorithm effectively improved the accuracy of straightness measurements in multiple cutting cycles. The error of the results in the horizontal plane was reduced by 45% compared to the traditional IMU/odometer method. This study is important in promoting the unmanned and intelligent development of China’s coal industry.

Abstract Background Anti-mitochondrial antibody (AMA)-positive inflammatory myopathy, a rare type of idiopathic inflammatory myopathy which was frequently difficult to diagnose, can affect muscles and the structure and electrical conduction of the heart. Early identification and treatment of this myopathy can prevent serious cardiovascular adverse events and improve cardiac function. Case presentation We report a patient who experienced repeated syncope, ventricular tachycardia (VT) and heart failure accompanied by weakness and muscle atrophy. He was initially diagnosed with dilated cardiomyopathy and received implantable cardioverter-defibrillator therapy. He was subsequently misdiagnosed as muscular dystrophy due to progressive muscular atrophy. However, the patient developed repeated and refractory VT storms that were not alleviated by conventional therapy. Finally, he was diagnosed with AMA-positive inflammatory myopathy with cardiac injuries. The patient was markedly recovered by being treated with immunosuppressive and immunomodulatory therapy. Conclusion AMA could be screened when discovering myopathies accompanied by unexplained cardiac symptoms. Our findings provide insights into the diagnosis and therapy of this rare and severe disease.

Artificial intelligence of things (AIoT)-enabled intelligent automatic train operation (iATO) is an urgently needed technology to expand the capability of ATO in addressing the real-time responsiveness and dynamic online challenges to energy-efficient train trajectory optimization (TTO) and its associated ride-comfort, punctuality, and safety issues in modern urban rail transit networks. This paper proposes a three-step supervised reinforcement learning-based intelligent energy-efficient train trajectory optimization (SRL-IETTO) approach for iATO by hybrid-integrating deep reinforcement learning (DRL) and supervised learning. First, multiple objectives are formulated based on real-time train operation and systematically integrated into the RL algorithm by a binary function-based goal-directed reward design method. Second, an IETTO model is established to handle uncertain disturbances in real-time train operation and generate optimal energy-efficient train trajectories online by optimizing energy efficiency and receiving supervisory information from trajectories of pre-trained TTO models. Finally, numerical simulations are implemented to validate the effectiveness of the SRL-IETTO using in-service subway line data. The results demonstrate the superiority and improved energy saving of the proposed approach and confirm its adaptability to online trip time adjustments within the practical running time range under uncertain disturbances with less trip time error compared to other intelligent TTO algorithms.

Background & Aims: Phosphoglycerate dehydrogenase (PHGDH), the rate-limiting enzyme of the de novo serine synthesis pathway (SSP), has been implicated in the carcinogenesis and metastasis of hepatocellular carcinoma (HCC) because of its excessive expression and promotion of SSP. In previous experiments we found that SSP flux was diminished by knockdown of zinc finger E-box binding homeobox 1 (ZEB1), a stimulator of HCC metastasis, but the underlying mechanism remains largely unknown. Here, we aimed to determine how SSP flux is regulated by ZEB1 and the contribution of such regulation to carcinogenesis and progression of HCC. Methods: We used genetic mice with Zeb1 knockout in liver specifically to determine whether Zeb1 deficiency impacts HCC induced by the carcinogen diethylnitrosamine plus CCl4. We explored the regulatory mechanism of ZEB1 in SSP flux using uniformly-labeled [13C]-glucose tracing analyses, liquid chromatography–mass spectrometry, real-time quantitative polymerase chain reaction, luciferase report assay, and chromatin immunoprecipitation assay. We determined the contribution of the ZEB1–PHGDH regulatory axis to carcinogenesis and metastasis of HCC by cell counting assay, methyl thiazolyl tetrazolium (MTT) assay, scratch wound assay, Transwell assay, and soft agar assay in vitro, orthotopic xenograft, bioluminescence, and H&E assays in vivo. We investigated the clinical relevance of ZEB1 and PHGDH by analyzing publicly available data sets and 48 pairs of HCC clinical specimens. Results: We identified that ZEB1 activates PHGDH transcription by binding to a nonclassic binding site within its promoter region. Up-regulated PHGDH augments SSP flux to enable HCC cells to be more invasive, proliferative, and resistant to reactive oxygen species and sorafenib. Orthotopic xenograft and bioluminescence assays have shown that ZEB1 deficiency significantly impairs the tumorigenesis and metastasis of HCC, and such impairments can be rescued to a large extent by exogenous expression of PHGDH. These results were confirmed by the observation that conditional knockout of ZEB1 in mouse liver dramatically impedes carcinogenesis and progression of HCC induced by diethylnitrosamine/CCl4, as well as PHGDH expression. In addition, analysis of The Cancer Genome Atlas database and clinical HCC samples showed that the ZEB1–PHGDH regulatory axis predicts poor prognosis of HCC. Conclusions: ZEB1 plays a crucial role in stimulating carcinogenesis and progression of HCC by activating PHGDH transcription and subsequent SSP flux, deepening our knowledge of ZEB1 as a transcriptional factor in fostering the development of HCC via reprogramming the metabolic pathway.

In harsh marine environment, corrosion is a serious problem, among which microbiologically influenced corrosion is particularly common and complex. This work studied corrosion of X65 steel by planktonic and sessile iron oxidizing bacteria (IOB), i.e. Pseudomonas sp. isolated from coastal water of Qingdao, and inhibition effect of cathodic polarization. Results demonstrate that both the planktonic and sessile IOB caused the corrosion of X65 steel by promoting the oxidation of ferrous ions, but the contribution of sessile cells was far greater than that of planktonic cells. Cathodic polarization kills sessile cells on the metal surface and prevent attachment of IOB. With a negative shift of the cathodic potential, the inhibition effect was enhanced. Cathodic polarization altered interface properties of X65 steel, affecting adhesion and corrosion process of IOB cells. However, when X65 steel was sufficiently polarized to −1050 mV vs. SCE, IOB produced more extracellular polymeric substances to resist stimulation by an external electric field. Therefore, cathodic polarization could not completely remove sessile IOB cells, and pitting corrosion existed on the metal surface.

Based on the limitation of the characteristics of materials, in the transmission and distribution network of power system, the relationship between cable current carrying capacity and temperature is inseparable. Improving the ampacity of power cables within the allowable range of temperature is important to the power grid. Using COMSOL multi physical field simulation software, the model of cable to calculate the current carrying capacity is established, and the relationship between cable capacity and temperature field is analyzed. On this premise, the effects of cable placement, convective thermal conductivity and different environmental conditions on the temperature field distribution are further analyzed. The simulation results show that increasing the cable spacing, improving the convective thermal conductivity and maintaining a low ambient temperature the current carrying capacity of the cable can be improved.

Film cooling technology is of great significance to enhance the performance of aero-engines and extend service life. With the increasing requirements for film cooling efficiency, researchers and engineers have carried out a lot of work on the precision and digital measurement of cooling holes. Based on the above, this paper outlines the importance and principles of film cooling technology and reviews the evolution of cooling holes. Also, this paper details the traditional measurement methods of the cooling hole used in current engineering scenarios with their limitations and categorizes digital measurement methods into five main types, including probing measurement technology, optical measurement technology, infrared imaging technology, computer tomography (CT) scanning technology, and composite measurement technology. The five types of methods and integrated automated measurement platforms are also analyzed. Finally, through a generalize and analysis of cooling hole measurement methods, this paper points out technical challenges and future trends, providing a reference and guidance for forward researches.

Weather data errors affect energy management by influencing the accuracy of building energy predictions. This study presents a long short-term memory (LSTM) prediction model based on the “Energy Detective” dataset (Shanghai, China) and neighboring weather station data. The study analyzes the errors of different weather data sources (Detective and A) at the same latitude and longitude. Subsequently, it discusses the effects of weather errors from neighboring weather stations (Detective, A, B, C, and D) on energy forecasts for the next hour and day including the selection process for neighboring weather stations. Furthermore, it compares the forecast results for summer and autumn. The findings indicate a correlation between weather errors from neighboring weather stations and energy consumption. The median R-Square for predicting the next hour reached 0.95. The model’s predictions for the next day exhibit a higher Prediction Interval Mean Width (139.0 in summer and 146.1 in autumn), indicating a greater uncertainty.

A lack of available information on heating, ventilation, and air-conditioning (HVAC) systems can affect the performance of data-driven fault-tolerant control (FTC) models. This study proposed an in situ selective incremental calibration (ISIC) strategy. Faults were introduced into the indoor air (Ttz1) thermostat and supply air temperature (Tsa) and chilled water supply air temperature (Tchws) sensors of a central air-conditioning system. The changes in the system performance after FTC were evaluated. Then, we considered the effects of the data quality, data volume, and variable number on the FTC results. For the Ttz1 thermostat and Tsa sensor, the system energy consumption was reduced by 2.98% and 3.72% with ISIC, respectively, and the predicted percentage dissatisfaction was reduced by 0.67% and 0.63%, respectively. Better FTC results were obtained using ISIC when the Ttz1 thermostat had low noise, a 7-day data volume, or sufficient variables and when the Tsa and Tchws sensors had low noise, a 14-day data volume, or limited variables.

BackgroundMyocardial infarction (MI) is a common cardiac condition with a high incidence of morbidity and mortality. Despite extensive medical treatment for MI, the development and outcomes of post-MI heart failure (HF) continue to be major factors contributing to poor post-MI prognosis. Currently, there are few predictors of post-MI heart failure.MethodsIn this study, we re-examined single-cell RNA sequencing and bulk RNA sequencing datasets derived from the peripheral blood samples of patients with myocardial infarction, including patients who developed heart failure and those who did not develop heart failure after myocardial infarction. Using marker genes of the relevant cell subtypes, a signature was generated and validated using relevant bulk datasets and human blood samples.ResultsWe identified a subtype of immune-activated B cells that distinguished post-MI HF patients from non-HF patients. Polymerase chain reaction was used to confirm these findings in independent cohorts. By combining the specific marker genes of B cell subtypes, we developed a prediction model of 13 markers that can predict the risk of HF in patients after myocardial infarction, providing new ideas and tools for clinical diagnosis and treatment.ConclusionSub-cluster B cells may play a significant role in post-MI HF. We found that the STING1, HSPB1, CCL5, ACTN1, and ITGB2 genes in patients with post-MI HF showed the same trend of increase as those without post-MI HF.

With the advancement of autonomous driving technologies, road network data have attracted a lot of attention as a virtual source of information. Traditional node–arc road networks are no longer able to match the demands of high-precision location awareness. Thus, lane-level road networks with more information have become a research hotspot. Furthermore, street-view pictures are a popular data source for building a lane-level road network because they provide a significant quantity of road information. The current method of constructing lane-level road networks based on street-view images performs feature extraction in image space and then projects it into geographic space. Hence, due to perspective and other rules, there are conflicts and overlaps in the exact locations after projecting the results of the street-view pictures from different viewpoints into geographic space. Because, to the best of the author's knowledge, there is no process for optimizing the overall geographic space results, the current study does not meet the demand for the accurate and comprehensive acquisition of lane-level road network in complete areas. However, this study proposed a lane-level road network construction method based on street-view image data, focusing on aggregating and optimizing the picture space extraction results in geographic space to improve the accuracy while aligning the results more consistent with the vector data requirements of geographic information systems. The experimental results show that by using street-view picture data, this technology can establish a submeter lane-level road network, which can be used for low-cost road data collection and updating.

Understanding the oxidation reaction mechanism of asphalt is critical to improving its aging resistance. Here, quantum chemistry (QC) was used to study the oxidation reaction mechanism of 12 typical asphalt molecules. On this basis, the molecular models of aged asphalt were established, and the aging resistance optimization strategies of asphalt were proposed. The results show that the oxidation sites for oxygen-containing or heteroatoms-free molecules are on aromatic carbons (I molecules). Reducing hydrogen and hydroxyl radicals can improve its aging resistance. The oxidation sites for sulfur- or nitrogen-containing molecules are on heteroatoms (II molecules). Reducing oxygen free radicals content can improve its aging resistance. The molecules containing sulfur and cata-condensed polycyclic aromatic hydrocarbons (PAHs) (such as R5) have the strongest oxidation reactivity. Reducing its content can improve asphalt aging resistance. Combining the QC and experiment results, LH90 contains more I molecules, while UP70 and MM70 contain more II molecules (especially As3 and R5). The aging resistance of UP70 and MM70 is poor than LH90. Reducing the content of As3 or R5 molecules or the content of oxygen radicals can improve the aging resistance of UP70 and MM70. These findings lay a foundation for designing and preparing asphalt with excellent aging resistance.

The inhomogeneity of microstructures and mechanical properties in an as-quenched thin specification NM450TP wear-resistant steel plate were quantitatively investigated. The results show that the microstructures exhibit inhomogeneous distribution through the thickness and the area percentage of martensite and ferrite grains varies regularly through the thickness, and the content of ferrite on the top surface of the plate is found to be two times that of ferrite at the core location and more than that of ferrite at the bottom surface. In addition, the steel plate exhibits the obvious anisotropy of tensile properties, the tensile strength paralleling to the rolling direction is lower than that along the transverse direction while the elongation paralleling to the rolling direction is better than that along the transverse direction. The result indicates that the deformation degree of prior austenite grains during hot rolling and the content of martensite after quenching dominate the mechanical properties while the ferrite content is not the main factor affecting the plasticity. The findings provide experimental evidences and lay a theoretical foundation for analyzing the subsequent processing.

Due to energy constraints and people’s increasing requirements for indoor thermal comfort, improving energy efficiency while ensuring thermal comfort has become the focus of research in the design and operation of HVAC systems. This study took office rooms with few people occupying them in Wuhan as the research object. The EnergyPlus-Fluent co-simulation method was used to study the impact of 12 forms of air distribution on the thermal environment and air-conditioner energy consumption. The results indicate that 3 m/s supply air velocity and 45° supply air angle are more suitable for the case model in this study. The EnergyPlus-Fluent co-simulation method used in this paper provides a reference for the study of indoor environments in offices with few people occupying them.

In order to explore the application of passive solar house in primary school buildings, the temperature fluctuation characteristics of three kinds of passive solar houses—the direct beneficiary window type, the heat storage wall type and the additional sunlight type—in a primary school building were obtained by continuously testing in the coldest month of winter. Based on the investigation of winter rest pattern and indoor temperature demand of local primary schools, the time division heating demand of primary schools in northwest rural areas was determined. According to the results of the above two aspects of research, the optimal matching and correspondence between the three types of solar houses and the heating requirements of each functional building was obtained, which can provide a basis for the selection of the appropriate passive solar house heating technology for primary schools in northwest rural areas.

Faults are inevitable in building energy systems, such as heating, ventilation, air conditioning and refrigeration systems. With the increasing utilization of these systems, their fault detection and diagnosis has become more significant for maintaining their operation and performance. The rapid development of data analytics has led to fault detection and diagnosis using data-based algorithms considering building intelligence. Feature engineering, which focuses on generating near minimum and optimal model inputs from the original dataset, is a fundamental technique to balance efficiency and accuracy of fault detection and diagnosis models. The generated inputs are fault features which ensures a better description of fault operations owing to their stronger fault indicative capability than original data. However, since most feature engineering research has been implemented alongside the development of data-based algorithms, most studies fail to effectively distinguish and understand their roles in fault detection and diagnosis models. To fill this research gap, this study presented the problems and mechanisms associated with feature engineering, comprehensively reviewed about 200 papers on the topic of feature engineering for heating, ventilation, air conditioning and refrigeration system fault detection and diagnosis tasks over the past 15 years. Firstly, feature engineering studies are divided according to their feature-generation technique, into manual and automated techniques. Next, feature engineering studies are analysed in terms of data type, system type, fault type, operation statue, methods and fault features. The desirable characteristics of feature engineering works are then discussed in terms of their capability of rapidly generating indicative, decoupled, interpretable and new fault features to enhance fault detection and diagnosis accuracy; the efficiency, reliability and robustness of feature engineering process; the mutual adaptability between feature engineering and fault detection and diagnosis; and their widespread applicability for more fault types and system operations. The challenges affecting feature engineering studies are discussed in terms of data volume, data diversity and quality, along with the performance evaluation of feature engineering with respect to feature engineering algorithms, fault detection and diagnosis models, fault features and online application constraints. Future work should aim to design a hybrid, heuristic, robust and automated feature engineering strategy under the guidance of expert knowledge and practical application constraints that consider the relationships between fault-related features and real-time fault impacts with energy consumption, occupants' thermal comfort, etc.

It is of great significance to develop a scientific and reasonable evaluation system for improving the teaching quality in colleges or universities. Traditional teaching evaluation systems are always limited to subjectivity and injustice for single dimensional evaluation index, subjective weight setting, and inefficient integration. In this paper, neural networks are introduced to evaluate teaching quality in an objective way. Firstly, three sub-networks S-NET, P-NET and L-NET are trained on the evaluation sets of students, peers, and leaders. To boost the performance of a single network, we offer an integrating way to synthesize the results of three sub-networks. The integrated network I-NET is trained on the multi-dimensional data sets to acquaint comprehensive evaluations. The experimental results demonstrate the feasibility of three sub-networks with the results that the average error rate of S-NET is 1.457%, P-NET is 1.003% and L-NET is 1.528%. To be further, the prominent evaluation ability of I-NET is also verified with the accuracy of 98.59% on the verification data sets. Finally, online teaching evaluating system based on the SSM framework is designed to provide a convenient and friendly interface.

For heating, ventilation and air-conditioning systems (HVACs), their operating performance depends largely on sensors and their accurate measurements. Various types of faults are inevitable to sensors amounted in HVACs during the system lifespan, which eventually makes system deviated from the normal operation. To explore sensor fault impacts on different HVACs, the thermostat offset fault was taken as an example. For the same office building in Wuhan, this study compared its influences on operational performance of three HVACs including the ground source heat pump (GSHP) system, the variable refrigerant flow (VRF) system and the chiller + boiler (CB) system. The three HVACs were simulated simultaneously by EnergyPlus for the same target building with same outdoor meteorological environment to make a relatively fair comparison. The thermostat offset fault was introduced into the three HVACs with bias amplitudes ranging from −5–5 °C at the interval of 1 °C. Further, the fault impacts were quantitatively analyzed from three aspects in terms of system energy consumption, operating performance and indoor thermal comfort. Results indicated that the thermostat offset fault with different bias amplitudes and offset directions have different impacts on the three systems. Compared with the other two systems, GSHP is less affected by the introduced thermostat offset fault.

Summary: Background: Ultrasound is a critical non-invasive test for preoperative diagnosis of ovarian cancer. Deep learning is making advances in image-recognition tasks; therefore, we aimed to develop a deep convolutional neural network (DCNN) model that automates evaluation of ultrasound images and to facilitate a more accurate diagnosis of ovarian cancer than existing methods. Methods: In this retrospective, multicentre, diagnostic study, we collected pelvic ultrasound images from ten hospitals across China between September 2003, and May 2019. We included consecutive adult patients (aged ≥18 years) with adnexal lesions in ultrasonography and healthy controls and excluded duplicated cases and patients without adnexa or pathological diagnosis. For DCNN model development, patients were assigned to the training dataset (34 488 images of 3755 patients with ovarian cancer, 541 442 images of 101 777 controls). For model validation, patients were assigned to the internal validation dataset (3031 images of 266 patients with ovarian cancer, 5385 images of 602 with benign adnexal lesions), external validation datasets 1 (486 images of 67 with ovarian cancer, 933 images of 268 with benign adnexal lesions), and 2 (1253 images of 166 with ovarian cancer, 5257 images of 723 benign adnexal lesions). Using these datasets, we assessed the diagnostic value of DCNN, compared DCNN with 35 radiologists, and explored whether DCNN could augment the diagnostic accuracy of six radiologists. Pathological diagnosis was the reference standard. Findings: For DCNN to detect ovarian cancer, AUC was 0·911 (95% CI 0·886–0·936) in the internal dataset, 0·870 (95% CI 0·822–0·918) in external validation dataset 1, and 0·831 (95% CI 0·793–0·869) in external validation dataset 2. The DCNN model was more accurate than radiologists at detecting ovarian cancer in the internal dataset (88·8% vs 85·7%) and external validation dataset 1 (86·9% vs 81·1%). Accuracy and sensitivity of diagnosis increased more after DCNN-assisted diagnosis than assessment by radiologists alone (87·6% [85·0–90·2] vs 78·3% [72·1–84·5], p

The p53 gene has the highest mutation frequency in tumors, and its inactivation can lead to malignant transformation, such as cell cycle arrest and apoptotic inhibition. Persistent high-risk human papillomavirus (HR-HPV) infection is the leading cause of cervical cancer. P53 was inactivated by HPV oncoprotein E6, promoting abnormal cell proliferation and carcinogenesis. To study the treatment of cervical intraepithelial neoplasia (CIN) and cervical cancer by restoring p53 expression and inactivating HPV oncoprotein, and to verify the effectiveness of nano drugs based on nucleic acid delivery in cancer treatment, we developed poly (beta-amino ester)537, to form biocompatible and degradable nanoparticles with plasmids (expressing p53 and targeting E7). In vitro and in vivo experiments show that nanoparticles have low toxicity and high transfection efficiency. Nanoparticles inhibited the growth of xenograft tumors and successfully reversed HPV transgenic mice’s cervical intraepithelial neoplasia. Our work suggests that the restoration of p53 expression and the inactivation of HPV16 E7 are essential for blocking the development of cervical cancer. This study provides new insights into the precise treatment of HPV-related cervical lesions.

Wireless underwater sensor networks have various applications—such as ocean exploration and deep-sea disaster monitoring—making them a hot topic in the research field. To cover a larger area and gather more-precise information, building large-scale underwater sensor networks has become a trend. In such networks, acoustic signals are used to transmit messages in an underwater environment. Their features of low speed and narrow bandwidth make media access control (MAC) protocols unsuitable for radio communications. Furthermore, a network consists of a large number of randomly deployed nodes, making it impossible to pre-define an optimized routing table or assign a central controller to coordinate the message propagation process. Thus, optimized routing should emerge via interaction among individual nodes in the network. To address these challenges, in this paper we propose a communication coordinator under the time division multiple access (TDMA) framework. Each node in the network is equipped with such a coordinator so that messages in the network can be sent following the shortest path in a self-organized way. The coordinator consists of a slot distributor and a forwarding guide. With the slot distributor, nodes in the sensor network occupy proper communication slots and the network finally converges to the state without communication collision. This is achieved with a set of ecological niche- and pheromone-inspired laws, which encourage nodes to occupy slots that can decrease the waiting time for a node to send a message packet while weakening the enthusiasm for a node to occupy the slots that it fails to occupy several times. With the forwarding guide, a node can send the message packet to the best successor node so that the message packet can be sent to the base station along the shortest path. It has been proven that the laws in the forwarding guide are equivalent to the Dijkstra Algorithm. Simulation experiment results indicate that with our coordinator, the network can converge to the state without collision using fewer coordination messages. In addition, the time needed to send a message to the destination is shorter than that of the classical Aloha protocol.

Recurrent neural networks (RNNs) are widely used to process sequence-related tasks such as natural language processing. Edge cloud computing systems are in an asymmetric structure, where task managers allocate tasks to the asymmetric edge and cloud computing systems based on computation requirements. In such a computing system, cloud servers have no energy limitations, since they have unlimited energy resources. Edge computing systems, however, are resource-constrained, and the energy consumption is thus expensive, which requires an energy-efficient method for RNN job processing. In this paper, we propose a low-overhead, energy-aware runtime manager to process tasks in edge cloud computing. The RNN task latency is defined as the quality of service (QoS) requirement. Based on the QoS requirements, the runtime manager dynamically assigns RNN inference tasks to edge and cloud computing systems and performs energy optimization on edge systems using dynamic voltage and frequency scaling (DVFS) techniques. Experimental results on a real edge cloud system indicate that in edge systems, our method can reduce the energy up to 45% compared with the state-of-the-art approach.

Biodegradable implants are of great interest in orthopaedic applications but have been limited by low mechanical strength. Here, the authors examine systematically in detail the strengthening of biodegradable zinc by alloying with beneficial elements using mechanical, biodegradability and biocompatibility testing.

Crustal movement and land subsidence will cause tower foundation settlement, which seriously affects the safety of transmission line operation. This paper proposed a short-term failure warning method for transmission tower under land subsidence condition based on finite element method, strain monitoring and time series prediction. By simulating the stress distribution of the tower in actual foundation settlement, the weak components of the settlement tower were founded. Combined with the finite element simulation results in various conditions, the reliability of simulation results is verified, and the strain variation range of the corresponding measuring point is obtained. By installing the real-time strain monitoring system and establishing the autoregressive integrated moving average (ARIMA) time series forecast model, warning of the failure of the tower can be sounded in short time according to the strain monitoring and forecasting results. The field results show that the tower is in good condition and with no risk in short term. The method proposed by this paper is economical and easy to operate, which can be used to conduct the emergency treatment of the dangerous tower under land subsidence condition.

This is the first report on creep properties of biodegradable zinc alloys at human body temperature. The extruded Zn-0.1Li (wt.%) alloy exhibits appreciable creep deformation at 37°C under stresses ranging 80–230 MPa within 500 h. The creep deformation at low stresses (80–155 MPa, about 0.4–0.8 yield strength) is characterized by a stress exponent of about 4 and an activation energy close to that for self-diffusion of zinc, which are typical of dislocation creep. The implications of creep at human body temperature for durability of biodegradable zinc alloys as stents are discussed.

The effect of alignment error on the coupling efficiency and beam quality of a Gaussian beam coupled into a large-core multimode fiber is studied in this paper. The equations for evaluating the effect of alignment error on the coupling efficiency are derived separately, and verified with the method of simulation. The calculation and simulation results obtained are highly consistent. In the same way, the effects of alignment error on the beam power distribution are discussed. The results show that the lateral error will change the path of partial light to a large extent, and have a greater influence on the power distribution of the Gaussian beam than the longitudinal error and angular error do. Keywords: Alignment error, Coupling efficiency, Beam quality, Multimode fiber

High-aspect-ratio structures have become increasingly important in MEMS devices. In situ, real-time critical dimension and depth measurement for high-aspect-ratio structures is critical for optimizing the deep etching process. Through-focus scanning optical microscopy (TSOM) is a high-throughput and inexpensive optical measurement method for critical dimension and depth measurement. Thus far, TSOM has only been used to measure targets with dimension of 1 μm or less, which is far from sufficient for MEMS. Deep learning is a powerful tool that improves the TSOM performance by taking advantage of additional intensity information. In this work, we propose a convolutional neural network model-based TSOM method for measuring individual high-aspect-ratio trenches on silicon with width up to 30 μm and depth up to 440 μm. Experimental demonstrations are conducted and the results show that the proposed method is suitable for measuring the width and depth of high-aspect-ratio trenches with a standard deviation and error of approximately a hundred nanometers or less. The proposed method can be applied to the semiconductor field.

This study developed a thermo-metallurgical-mechanical simulation method to calculate the temperature field and residual stress distribution in the NM450TP wear-resistant steel welded joints. During the simulation, the solid-state phase transformation and softening effect of NM450TP wear-resistant steel was considered. The simulation results were compared with the experimental results, which verified the feasibility of this method. The influences of solid-state phase transformation and softening effect on the welding residual stress distribution were discussed. The numerical simulation results showed that the solid-state phase transformation had a more significant effect on the magnitude and distribution of the longitudinal residual stress than that of the transverse residual stress. The softening effect had a significant influence on the peak value of the longitudinal residual stress and had little influence on the transverse residual stress. Comparing the numerical simulation results with the experimental results, it could be seen that the calculation results of the welding residual stress were in the best agreement with the experimental measurement results when the solid-state transformation and softening effects were considered at the same time.

Sensor drift fault calibration is essential to maintain the operation of heating, ventilation and air conditioning systems (HVAC) in buildings. Bayesian inference (BI) is becoming more and more popular as a commonly used sensor fault calibration method. However, this method focused mainly on sensor bias fault, and it could be difficult to calibrate drift fault that changes with time. Therefore, a dynamic calibration method for sensor drift fault of HVAC systems based on BI is developed. Taking the drift fault calibration of the chilled water supply temperature sensor of the chiller as an example, the performance of the proposed dynamic calibration method is evaluated. Results show that the combination of the Exponentially Weighted Moving-Average (EWMA) method with high detection accuracy and the proposed BI dynamic calibration method can effectively improve the calibration accuracy of drift fault, and the Mean Absolute Percentage Error (MAPE) value between the calibrated and normal data is less than 5%.

Insects are generally associated with gut bacterial communities that benefit the hosts with respect to diet digestion, limiting resource supplementation, pathogen defense, and ecological niche expansion. Heteroptera (true bugs) represent one of the largest and most diverse insect lineages and comprise species consuming different diets and inhabiting various ecological niches, even including underwater. However, the bacterial symbiotic associations have been characterized for those basically restricted to herbivorous stink bugs of the infraorder Pentatomomorpha. The gut microbiota associated with the megadiverse heteropteran lineages and the implications of ecological and diet variance remain largely unknown. Here, we conducted a bacterial 16S rRNA amplicon sequencing of the gut microbiota across 30 species of true bugs representative of different ecological niches and diets. It was revealed that Proteobacteria and Firmicute were the predominant bacterial phyla. Environmental habitats and diets synergistically contributed to the diversity of the gut bacterial community of true bugs. True bugs living in aquatic environments harbored multiple bacterial taxa that were not present in their terrestrial counterparts. Carnivorous true bugs possessed distinct gut microbiota compared to phytophagous species. Particularly, assassin bugs of the family Reduviidae possessed a characterized gut microbiota predominantly composed of one Enterococcus with different Proteobacteria, implying a specific association between the gut bacteria and host. Overall, our findings highlight the importance of the comprehensive surveillance of gut microbiota association with true bugs for understanding the molecular mechanisms underpinning insect–bacteria symbiosis.

The identification of oil species and their characteristics in oil spills is particularly important for their efficient disposal. Since aromatic hydrocarbons (AHs) with various characteristics are the major fluorescent components of oils in seawater, they can be used to detect different oil species in seawater. Here, we developed a composition method using the fluorescence spectra of eight AH categories analyzed by gas chromatography-mass spectrometry (GC-MS) to evaluate the contribution of AHs to the total fluorescence of six different oil species. Qualitative and quantitative difference analysis of the experimental and composite fluorescence spectra was performed based on the redshift of the main peak position, the fluorescence intensity distance, and the generalized included angle cosine, while correlation analysis was used to establish the relationship between the different fluorescence spectral parameters and the American Petroleum Institute gravity and viscosity of the oil species. The fluorescence spectra recorded for heavy oil samples indicated a reduction in the fluorescence signal of fluorene series and an increase in the contribution of acenaphthene and pyrene series, indicating that the currently developed composition method by fluorescence spectroscopy combined with GC-MS can be used to distinguish and identify oil species in seawater samples.

The fully insulated busbar has been extensively used in power and shipboard applications due to its favorable economic efficiency and excellent performance. Because of contact resistance and larger insulation thermal resistance, the joint, in certain circumstances, form a hotspot in the circuit. For hotspot temperature monitoring, therefore, this paper proposed an indirect approach which consists of radial direction temperature calculation (RDTC) in the busbar and axial direction temperature calculation (ADTC) in the conductor. The RDTC is to calculate the conductor temperatures from the surface temperatures of busbar in two different spots near the joint through the transient thermal network. The ADTC is to estimate the hotspot temperature from the conductor temperatures obtained in the RDTC using a fitted functional expression. The influence of solar radiation on the temperature distribution is greatly reduced by averaging the surface temperature in the RDTC and adjusting the functional form in the ADTC. A temperature-rise test on a practical insulated busbar taped joint was performed outdoors in hot summer to validate this approach. The calculated hotspot temperature agrees well with the measured result with a maximum error of only 3.8 K, indicating the high model precision and strong robustness to the solar radiation impact. Some factors in the applications of temperature monitoring are also discussed. This method is expected to be applied to the engineering application, thus improving the condition monitoring of the fully insulated busbar.

The discovery of thermodynamically stable thermoelectric materials for power generation has relied on empirical methods that were not effective. Here, the authors apply the inverse design approach to identify and experimentally realize TaFeSb-based half Heuslers with high thermoelectric performance.

In order to realize the management of various street objects in smart cities and smart transportation, it is very important to determine their geolocation. Current positioning methods of street-view images based on mobile mapping systems (MMSs) mainly rely on depth data or image feature matching. However, auxiliary data increase the cost of data acquisition, and image features are difficult to apply to MMS data with low overlap. A positioning method based on threshold-constrained line of bearing (LOB) overcomes the above problems, but threshold selection depends on specific data and scenes and is not universal. In this paper, we propose the idea of divide–conquer based on the positioning method of LOB. The area to be calculated is adaptively divided by the driving trajectory of the MMS, which constrains the effective range of LOB and reduces the unnecessary calculation cost. This method achieves reasonable screening of the positioning results within range without introducing other auxiliary data, which improves the computing efficiency and the geographic positioning accuracy. Yincun town, Changzhou City, China, was used as the experimental area, and pole-like objects were used as research objects to test the proposed method. The results show that the 6104 pole-like objects obtained through object detection realized by deep learning are mapped as LOBs, and high-precision geographic positioning of pole-like objects is realized through region division and self-adaptive constraints (recall rate, 93%; accuracy rate, 96%). Compared with the existing positioning methods based on LOB, the positioning accuracy of the proposed method is higher, and the threshold value is self-adaptive to various road scenes.