Excessive palmitic acid disturbs macrophage α-ketoglutarate/succinate metabolism and causes adipose tissue insulin resistance associated with gestational diabetes mellitus.

Abnormal polarization of adipose tissue macrophages (ATMs) results in low-grade systemic inflammation and insulin resistance (IR), potentially contributing to the development of diabetes. However, the underlying mechanisms that regulate the polarization of ATMs associated with gestational diabetes mellitus (GDM) remain unclear. Thus, we aimed to determine the effects of abnormal fatty acids on macrophage polarization and development of insulin resistance in GDM. Levels of fatty acids and inflammation were assessed in the serum samples and adipose tissues of patients with GDM. An in vitro cell model treated with palmitic acid was established, and the mechanisms of palmitic acid in regulating macrophage polarization was clarified. The effects of excessive palmitic acid on the regulation of histone methylations and IR were also explored in the high-fat diet induced GDM mice model. We found that pregnancies with GDM were associated with increased levels of serum fatty acids, and inflammation and IR in adipose tissues. Increased palmitic acid could induce mitochondrial dysfunction and excessive ROS levels in macrophages, leading to abnormal cytoplasmic and nuclear metabolism of succinate and α-ketoglutarate (αKG). Specifically, a decreased nuclear αKG/succinate ratio could attenuate the enrichment of H3K27me3 at the promoters of pro-inflammatory cytokines, such as IL-1ß, IL-6, and TNF-α, leading to cytokine secretion. Importantly, GDM mice treated with GSK-J4, an inhibitor of histone lysine demethylase, were protected from abnormal pro-inflammatory macrophage polarization and excessive production of pro-inflammatory cytokines. Our findings highlight the importance of the metabolism of αKG and succinate as transcriptional modulators in regulating the polarization of ATMs and the insulin sensitivity of adipose tissue, ensuring a normal pregnancy. This novel insight sheds new light on gestational fatty acid metabolism and epigenetic alterations associated with GDM.

Associations of air pollution exposures in preconception and pregnancy with birth outcomes and infant neurocognitive development: analysis of the Complex Lipids in Mothers and Babies (CLIMB) prospective cohort in Chongqing, China.

OBJECTIVES: To investigate the associations of traffic-related air pollution exposures in early pregnancy with birth outcomes and infant neurocognitive development. DESIGN: Cohort study. SETTING: Eligible women attended six visits in the maternity clinics of two centres, the First Affiliated Hospital of Chongqing Medical University and Chongqing Health Centre for Women and Children. PARTICIPANTS: Women who were between 20 and 40 years of age and were at 11-14 weeks gestation with a singleton pregnancy were eligible for participation. Women were excluded if they had a history of premature delivery before 32 weeks of gestation, maternal milk allergy or aversion or severe lactose intolerance. 1273 pregnant women enrolled in 2015-2016 and 1174 live births were included in this analysis. EXPOSURES: Air pollution concentrations at their home addresses, including particulate matter with diameter ≤2.5 µm (PM2.5) and nitrogen dioxide (NO2), during pre-conception and each trimester period were estimated using land-use regression models. OUTCOME MEASURES: Birth outcomes (ie, birth weight, birth length, preterm birth, low birth weight, large for gestational age and small for gestational age (SGA) status) and neurodevelopment outcomes measured by the Chinese version of Bayley Scales of Infant Development. RESULTS: An association between SGA and per-IQR increases in NO2 was found in the first trimester (OR: 1.57, 95% CI: 1.06 to 2.32) and during the whole pregnancy (OR: 1.33, 99% CI: 1.01 to 1.75). Both PM2.5 and NO2 exposure in the 90 days prior to conception were associated with lower Psychomotor Development Index scores (ß: -6.15, 95% CI: -8.84 to -3.46; ß: -2.83, 95% CI: -4.27 to -1.39, respectively). Increased NO2 exposure was associated with an increased risk of psychomotor development delay during different trimesters of pregnancy. CONCLUSIONS: Increased exposures to NO2 during pregnancy were associated with increased risks of SGA and psychomotor development delay, while increased exposures to both PM2.5 and NO2 pre-conception were associated with adverse psychomotor development outcomes at 12 months of age. TRIAL REGISTRATION NUMBER: ChiCTR-IOR-16007700.

Gut dysbiosis contributes to SCFAs reduction-associated adipose tissue macrophage polarization in gestational diabetes mellitus.

AIMS: The prevalence of gestational diabetes mellitus (GDM) has spurred investigations into various interconnected factors, among which gut dysbiosis is notably prominent. Although gut dysbiosis is strongly associated with GDM, the specific role of the gut microbiome in the pathogenesis of GDM remains unknown. This study aims to explore the pathogenesis of GDM from gut microbiota. MATERIALS AND METHODS: In our study, we constructed two GDM mice models: one induced by a high-fat diet (HFD) and the other through fecal microbiota transplantation (FMT) from GDM patients. In vitro, we used a co-culture system of RAW264.7 and 3T3-L1 adipocytes. KEY FINDINGS: We induced a GDM-like state in pregnant mice by FMT from GDM patients, which was consistent with the HFD model. A potential mechanism identified involves the diminished abundance of SCFA-producing microbiota, which reduces SCFAs, particularly propionic acid and butyric acid. In vitro, butyric and propionic acids were observed to alleviate LPS-induced TLR4-NF-κB activation, thereby reducing inflammation levels and inhibiting adipose insulin resistance via the PI3K/AKT signaling pathway. This reduction appears to trigger the polarization of adipose tissue macrophages toward M1 and promote insulin resistance in adipose tissue. SIGNIFICANCE: Our study fills this knowledge gap by finding that alterations in gut microbiota have an independent impact on hyperglycemia and insulin resistance in the GDM state. In vivo and in vitro, gut dysbiosis is linked to adipose tissue inflammation and insulin resistance via the bacterial product SCFAs in the GDM state, providing new insights into the pathogenesis of GDM.

Application of a feedforward control-based intervention for preventing hypothermia in trauma patients in a pre-hospital emergency setting.

OBJECTIVE: To investigate the efficacy of a feedforward control-based intervention strategy for preventing hypothermia among trauma patients during pre-hospital emergency care. METHODS: We conducted a retrospective analysis comparing trauma patients treated before and after implementing the intervention, with 40 cases in each group. All patients received emergency care from the Fuzhou Emergency Center on the scene. Multivariate analysis was used to explore the risk factors for hypothermia. The effective rate, incidence of adverse reactions, quality of body temperature management, medical staff's knowledge, attitudes, and behaviors regarding mild hypothermia prevention, coagulation function, treatment time at various stages, prognosis score, and treatment situation were compared between the two groups. RESULTS: The adverse reactions, intervention methods, and degree of cognitive improvement were influencing factors for hypothermia. The effective rate (92.50%) in the feedforward control group was higher than that in the non-feedforward control group (65.00%), with a lower incidence of adverse reactions (2.50%). The temperature management quality score of the feedforward control group (6.23±0.62) was higher. The feedforward control group achieved a higher quality score for temperature management (6.23±0.62) and exhibited a greater understanding of hypothermia prevention among trauma patients (P<0.05). Compared to the non-feedforward control group, the feedforward control group showed improved coagulation function, better performance in treatment time at each node, and higher prognosis scores. CONCLUSION: The intervention model based on feedforward control can effectively improve the standard of pre-hospital emergency care and prevent the incidence of hypothermia in trauma patients.

Unraveling implicit human behavioral effects on dynamic characteristics of Covid-19 daily infection rates in Taiwan.

We investigate the dynamic characteristics of Covid-19 daily infection rates in Taiwan during its initial surge period, focusing on 79 districts within the seven largest cities. By employing computational techniques, we extract 18 features from each district-specific curve, transforming unstructured data into structured data. Our analysis reveals distinct patterns of asymmetric growth and decline among the curves. Utilizing theoretical information measurements such as conditional entropy and mutual information, we identify major factors of order-1 and order-2 that influence the peak value and curvature at the peak of the curves, crucial features characterizing the infection rates. Additionally, we examine the impact of geographic and socioeconomic factors on the curves by encoding each of the 79 districts with two binary characteristics: North-vs-South and Urban-vs-Suburban. Furthermore, leveraging this data-driven understanding at the district level, we explore the fine-scale behavioral effects on disease spread by examining the similarity among 96 age-group-specific curves within urban districts of Taipei and suburban districts of New Taipei City, which collectively represent a substantial portion of the nation's population. Our findings highlight the implicit influence of human behaviors related to living, traveling, and working on the dynamics of Covid-19 transmission in Taiwan.

The metabolic role of the CD73/adenosine signaling pathway in HTR-8/SVneo cells: A Double-Edged Sword?

The ecto-5'-nucleotidase (CD73)/adenosine signaling pathway has been reported to regulate tumor epithelial-mesenchymal transition (EMT), migration and proliferation. However, little is known about the metabolic mechanisms underlying its role in trophoblast proliferation and migration. In this study, we aimed to investigate the metabolic role of the CD73/adenosine signaling pathway on the proliferation and migration of trophoblast. We found that CD73 levels were upregulated in preeclamptic placentas compared with the placentas of normotensive pregnant women. EMT and migration of HTR-8/SVneo cells were enhanced when treated with a CD73 inhibitor (100 µM) in vitro. Conversely, excessive adenosine (25 or 50 µM) suppressed trophoblast cell EMT, migration and proliferation. RNA-seq, metabolomics and seahorse findings showed that adenosine treatment resulted in increased expression of PDK1, suppression of aerobic respiration, glycolysis and amino acids synthesis, as well as increased utilization of short-chain fatty acids (SCFAs). Furthermore, the 13C-adenosine isotope tracking experiment demonstrated that adenosine served as a carbon source for the tricarboxylic acid (TCA) cycle. Our results reveal the role of adenosine in regulating trophoblast energy metabolism is like a double-edged sword - either inhibiting aerobic respiration or supplementing carbon sources into metabolic flux. CD73/adenosine signaling regulated trophoblast EMT, migration, and proliferation by modulating energy metabolism. This study indicates that CD73/adenosine signaling potentially plays a role in the occurrence of placenta-derived diseases, including preeclampsia.

The Influence of Sidedness in Unilateral Cleft Lip and Palate on Mid Facial Growth at Five Years of Age.

OBJECTIVE: To determine whether facial growth at five years is different for children with a left versus right sided cleft lip and palate. DESIGN: Retrospective cohort study. SETTING: Seven UK regional cleft centres. PATIENTS: Patients born between 2000-2014 with a complete unilateral cleft lip and palate (UCLP). MAIN OUTCOMES MEASURE: 5-Year-Old's Index scores. RESULTS: 378 children were included. 256 (68%) had a left sided UCLP and 122 (32%) had a right sided UCLP. 5-Year-Old's index scores ranged from 1 (good) to 5 (poor). There was a higher proportion of patients getting good scores (1 and 2) in left UCLP (43%) compared to right UCLP (37%) but there was weak evidence for a difference (Adjusted summary odds ratio 1.27, 95% CI 0.87 to 1.87; P = .22). CONCLUSIONS: Whilst maxillary growth may be different for left versus right sided UCLP, definitive analysis requires older growth indices and arch forms.

Manchester Intermittent Diet in Gestational Diabetes Acceptability Study (MIDDAS-GDM): a two-arm randomised feasibility protocol trial of an intermittent low-energy diet (ILED) in women with gestational diabetes and obesity in Greater Manchester.

INTRODUCTION: The prevalence of gestational diabetes mellitus (GDM) is rising in the UK and is associated with maternal and neonatal complications. National Institute for Health and Care Excellence guidance advises first-line management with healthy eating and physical activity which is only moderately effective for achieving glycaemic targets. Approximately 30% of women require medication with metformin and/or insulin. There is currently no strong evidence base for any particular dietary regimen to improve outcomes in GDM. Intermittent low-energy diets (ILEDs) are associated with improved glycaemic control and reduced insulin resistance in type 2 diabetes and could be a viable option in the management of GDM. This study aims to test the safety, feasibility and acceptability of an ILED intervention among women with GDM compared with best National Health Service (NHS) care. METHOD AND ANALYSIS: We aim to recruit 48 women with GDM diagnosed between 24 and 30 weeks gestation from antenatal clinics at Wythenshawe and St Mary's hospitals, Manchester Foundation Trust, over 13 months starting in November 2022. Participants will be randomised (1:1) to ILED (2 low-energy diet days/week of 1000 kcal and 5 days/week of the best NHS care healthy diet and physical activity advice) or best NHS care 7 days/week until delivery of their baby. Primary outcomes include uptake and retention of participants to the trial and adherence to both dietary interventions. Safety outcomes will include birth weight, gestational age at delivery, neonatal hypoglycaemic episodes requiring intervention, neonatal hyperbilirubinaemia, admission to special care baby unit or neonatal intensive care unit, stillbirths, the percentage of women with hypoglycaemic episodes requiring third-party assistance, and significant maternal ketonaemia (defined as ≥1.0 mmol/L). Secondary outcomes will assess the fidelity of delivery of the interventions, and qualitative analysis of participant and healthcare professionals' experiences of the diet. Exploratory outcomes include the number of women requiring metformin and/or insulin. ETHICS AND DISSEMINATION: Ethical approval has been granted by the Cambridge East Research Ethics Committee (22/EE/0119). Findings will be disseminated via publication in peer-reviewed journals, conference presentations and shared with diabetes charitable bodies and organisations in the UK, such as Diabetes UK and the Association of British Clinical Diabetologists. TRIAL REGISTRATION NUMBER: NCT05344066.

C-Reactive Protein-Albumin Ratio (CAR): A More Promising Inflammation-Based Prognostic Marker for Patients Undergoing Curative Hepatectomy for Hepatocellular Carcinoma.

Background: Systemic inflammatory response is a hallmark of cancer and plays a significant role in the development and progression of various malignant tumors. This research aimed to estimate the prognostic function of the C-reactive protein-albumin ratio (CAR) in patients undergoing hepatectomy for hepatocellular carcinoma (HCC) and compare it with other inflammation-based prognostic scores, including the neutrophil-lymphocyte ratio, platelet-lymphocyte ratio, monocyte-lymphocyte ratio, systemic immune inflammation index, prognostic index, Glasgow prognostic score, and modified Glasgow prognostic score. Methods: Retrospective analysis was conducted on data from 1039 HCC cases who underwent curative liver resection. The prognostic performance of CAR was compared with other scores using the area under the time-dependent receiver operating characteristic (t-ROC) curve. Multivariable Cox regression analyses were performed to confirm independent predictors for disease-free survival (DFS) and overall survival (OS). Results: The area under the t-ROC curve for CAR in the evaluation of DFS and OS was significantly greater than that of other scores and alpha-fetoprotein (AFP). Patients were stratified based on the optimal cut-off value of CAR, and the data revealed that both DFS and OS were remarkably worse in the high-CAR set compared to the low-CAR set. Multivariable Cox analysis demonstrated that CAR was an independent prognostic parameters for assessing DFS and OS. Regardless of AFP levels, all patients were subsequently divided into significantly different subgroups of DFS and OS based on CAR risk stratification. Similar results were observed when applying CAR risk stratification to other scoring systems. CAR also showed good clinical applicability in patients with different clinical features. Conclusion: CAR is a more effective inflammation-based prognostic marker than other scores and AFP in predicting DFS as well as OS among patients with HCC after curative hepatectomy.

Few-shot transfer learning for personalized atrial fibrillation detection using patient-based siamese network with single-lead ECG records.

The proliferation of wearable devices has allowed the collection of electrocardiogram (ECG) recordings daily to monitor heart rhythm and rate. For example, 24-hour Holter monitors, cardiac patches, and smartwatches are widely used for ECG gathering and application. An automatic atrial fibrillation (AF) detector is required for timely ECG interpretation. Deep learning models can accurately identify AFs if large amounts of annotated data are available for model training. However, it is impractical to request sufficient labels for ECG recordings for an individual patient to train a personalized model. We propose a Siamese-network-based approach for transfer learning to address this issue. A pre-trained Siamese convolutional neural network is created by comparing two labeled ECG segments from the same patient. We sampled 30-second ECG segments with a 50% overlapping window from the ECG recordings of patients in the MIT-BIH Atrial Fibrillation Database. Subsequently, we independently detected the occurrence of AF in each patient in the Long-Term AF Database. By fine-tuning the model with the 1, 3, 5, 7, 9, or 11 ECG segments ranging from 30 to 180 s, our method achieved macro-F1 scores of 96.84%, 96.91%, 96.97%, 97.02%, 97.05%, and 97.07%, respectively.

Parameter-Free State Estimation Based on Kalman Filter with Attention Learning for GPS Tracking in Autonomous Driving System.

GPS-based maneuvering target localization and tracking is a crucial aspect of autonomous driving and is widely used in navigation, transportation, autonomous vehicles, and other fields.The classical tracking approach employs a Kalman filter with precise system parameters to estimate the state. However, it is difficult to model their uncertainty because of the complex motion of maneuvering targets and the unknown sensor characteristics. Furthermore, GPS data often involve unknown color noise, making it challenging to obtain accurate system parameters, which can degrade the performance of the classical methods. To address these issues, we present a state estimation method based on the Kalman filter that does not require predefined parameters but instead uses attention learning. We use a transformer encoder with a long short-term memory (LSTM) network to extract dynamic characteristics, and estimate the system model parameters online using the expectation maximization (EM) algorithm, based on the output of the attention learning module. Finally, the Kalman filter computes the dynamic state estimates using the parameters of the learned system, dynamics, and measurement characteristics. Based on GPS simulation data and the Geolife Beijing vehicle GPS trajectory dataset, the experimental results demonstrated that our method outperformed classical and pure model-free network estimation approaches in estimation accuracy, providing an effective solution for practical maneuvering-target tracking applications.

Hair and cord blood element levels and their relationship with air pollution, dietary intake, gestational diabetes mellitus, and infant neurodevelopment.

BACKGROUND & AIMS: Exposure to a range of elements, air pollution, and specific dietary components in pregnancy has variously been associated with gestational diabetes mellitus (GDM) risk or infant neurodevelopmental problems. We measured a range of pregnancy exposures in maternal hair and/or infant cord serum and tested their relationship to GDM and infant neurodevelopment. METHODS: A total of 843 pregnant women (GDM = 224, Non-GDM = 619) were selected from the Complex Lipids in Mothers and Babies cohort study. Forty-eight elements in hair and cord serum were quantified using inductively coupled plasma-mass spectrometry analysis. Binary logistic regression was used to estimate the associations between hair element concentrations and GDM risk, while multiple linear regression was performed to analyze the relationship between hair/cord serum elements and air pollutants, diet exposures, and Bayley Scales of infant neurodevelopment at 12 months of age. RESULTS: After adjusting for maternal age, BMI, and primiparity, we observed that fourteen elements in maternal hair were associated with a significantly increased risk of GDM, particularly Ta (OR = 9.49, 95% CI: 6.71, 13.42), Re (OR = 5.21, 95% CI: 3.84, 7.07), and Se (OR = 5.37, 95% CI: 3.48, 8.28). In the adjusted linear regression model, three elements (Rb, Er, and Tm) in maternal hair and infant cord serum were negatively associated with Mental Development Index scores. For dietary exposures, elements were positively associated with noodles (Nb), sweetened beverages (Rb), poultry (Cs), oils and condiments (Ca), and other seafood (Gd). In addition, air pollutants PM2.5 (LUR) and PM10 were negatively associated with Ta and Re in maternal hair. CONCLUSIONS: Our findings highlight the potential influence of maternal element exposure on GDM risk and infant neurodevelopment. We identified links between levels of these elements in both maternal hair and infant cord serum related to air pollutants and dietary factors.

Probiotic Escherichia coli Nissle 1917-derived outer membrane vesicles modulate the intestinal microbiome and host gut-liver metabolome in obese and diabetic mice.

Introduction: Obesity and diabetes are common chronic metabolic disorders which can cause an imbalance of the intestinal flora and gut-liver metabolism. Several studies have shown that probiotics, including Escherichia coli Nissle 1917 (EcN), promote microbial balance and metabolic health. However, there are no studies on how EcN outer membrane vesicles (EcN-OMVs) influence the intestinal microflora and affect the metabolic disorders of obesity and diabetes. Methods: In this study, we evaluated the effects of EcN-OMVs on high-fat diet (HFD)-induced obesity and HFD + streptozotocin (STZ)-induced diabetes. Results: EcN-OMVs could reduce body weight, decrease blood glucose, and increase plasma insulin in obese mice. Similarly, EcN-OMVs treatment could modify the ratio of Firmicutes/Bacteroidetes in the gut, elevate intestinal short-chain fatty acid (SCFA)-producing flora, and influence the SCFA content of the intestine. Furthermore, the intestinal metabolites ornithine and fumaric acid, hepatic ω-6 unsaturated fatty acids, and SCFAs were significantly increased after administering EcN-OMVs. Discussion: Overall, this study showed that EcN-OMVs might act as post-biotic agents that could modulate gut-liver metabolism and ameliorate the pathophysiology of obesity and diabetes.

Metabolomics analysis of serum metabolites during endometrial transformation: association with recurrent implantation failure in hormonal replacement therapy-frozen embryo transfers cycles.

PURPOSE: The purpose of this study was to investigate alterations in serum metabolites during endometrial transformation and possible associations with recurrent implantation failure (RIF) in hormonal replacement therapy (HRT)-frozen embryo transfer (FET) cycles. METHODS: We performed a prospective study involving 100 patients scheduled for HRT-FET cycles during January 2022 to April 2022. Blood serum samples were collected on the day of progesterone administration (dPA) and on the third day of progesterone administration (d3PA). Gas chromatography-mass spectrometry (GC-MS) analysis was performed to identify and quantify serum metabolites. A nested case-control study including 19 RIF patients and 19 matching controls was conducted to explore the predictive value of serum metabolites for RIF. Partial least squares discriminant analysis (PLS-DA) and receiver operating characteristic (ROC) curve analysis were performed to establish prediction models. MAIN RESULTS: We identified 105 serum metabolites, with 76 of them exhibiting significant alterations during the initial 3 days of endometrial transformation. Metabolites involved in amino acid metabolism and tricarboxylic acid (TCA) cycle showed lower levels during endometrial transformation. In the nested case-control study, the prediction model based on the ratio of serum metabolites between d3PA and dPA showed the highest area under the ROC curve (AUC), accuracy, and R2 and Q2 values. Eight metabolites, including indol-3-propionic acid, beta-alanine, myristoleic acid, malic acid, indole, DL-isocitric acid, proline, and itaconic acid, exhibited high predictive values for RIF. CONCLUSION: This study demonstrates alterations in serum metabolites during endometrial transformation, particularly in amino acid metabolism and TCA cycle. The identified metabolites, especially indol-3-propionic acid and malic acid, show potential as predictive markers for RIF. These findings contribute to a better understanding of the metabolic changes associated with endometrial receptivity and provide insights for the development of personalized approaches to improve implantation outcomes in FET cycles.

Chorionicity-associated variation in metabolic phenotype of cord blood in twin.

BACKGROUND: Monochorionic (MC) twins present a higher incidence of unfavorable clinical perinatal outcomes than dichorionic (DC) twins, often in association with placental vascular anastomosis. In this study, we profiled the umbilical cord plasma metabolomes of uncomplicated MC and DC twin pregnancies and related these to several offspring outcomes, previously associated with birthweight. METHODS: Umbilical vein blood samples were collected at birth from 25 pairs of uncomplicated MC twins and 24 pairs of uncomplicated DC twins. The samples were subjected to gas chromatography-mass spectrometry-based metabolomics. 152 metabolites were identified from the cord plasma samples of MC and DC twins. Partial least squares discriminant analysis and pathway analysis were performed to compare within DC/MC twin pairs and between DC and MC twins. A generalized estimating equation (GEE) model was utilized to explore the correlation between metabolic differences and birthweight discordance within and between twin pairs. RESULTS: Our study revealed clear differences between the metabolite profiles of umbilical cord plasma of MC and DC twins. Metabolite profiles in MC within twin pairs and DC within twin pairs were characterized by the differences in 2 - hydroxyglutaramic acid levels and nicotinamide levels, respectively. The metabolic pathways of GSH, tryptophan, and fatty acid metabolism, were significantly downregulated in MC twins compared to DC twins. In addition, the concentration of caffeine and decamethyl-cyclopentasiloxane (D5) was positively correlated with birthweight in MC and DC twins. CONCLUSION: This study demonstrated that the altered metabolites in umbilical plasma made contributions to the different chorionicities between uncomplicated MC twins and DC twins. The chorionicity of twins seems to affect the metabolic cross-talk between co-twin pairs and be related to birthweight discordance of twins.

Location estimation based on feature mode matching with deep network models.

Introduction: Global navigation satellite system (GNSS) signals can be lost in viaducts, urban canyons, and tunnel environments. It has been a significant challenge to achieve the accurate location of pedestrians during Global Positioning System (GPS) signal outages. This paper proposes a location estimation only with inertial measurements. Methods: A method is designed based on deep network models with feature mode matching. First, a framework is designed to extract the features of inertial measurements and match them with deep networks. Second, feature extraction and classification methods are investigated to achieve mode partitioning and to lay the foundation for checking different deep networks. Third, typical deep network models are analyzed to match various features. The selected models can be trained for different modes of inertial measurements to obtain localization information. The experiments are performed with the inertial mileage dataset from Oxford University. Results and discussion: The results demonstrate that the appropriate networks based on different feature modes have more accurate position estimation, which can improve the localization accuracy of pedestrians in GPS signal outages.

Deep Spatio-Temporal Graph Network with Self-Optimization for Air Quality Prediction.

The environment and development are major issues of general concern. After much suffering from the harm of environmental pollution, human beings began to pay attention to environmental protection and started to carry out pollutant prediction research. A large number of air pollutant predictions have tried to predict pollutants by revealing their evolution patterns, emphasizing the fitting analysis of time series but ignoring the spatial transmission effect of adjacent areas, leading to low prediction accuracy. To solve this problem, we propose a time series prediction network with the self-optimization ability of a spatio-temporal graph neural network (BGGRU) to mine the changing pattern of the time series and the spatial propagation effect. The proposed network includes spatial and temporal modules. The spatial module uses a graph sampling and aggregation network (GraphSAGE) in order to extract the spatial information of the data. The temporal module uses a Bayesian graph gated recurrent unit (BGraphGRU), which applies a graph network to the gated recurrent unit (GRU) so as to fit the data's temporal information. In addition, this study used Bayesian optimization to solve the problem of the model's inaccuracy caused by inappropriate hyperparameters of the model. The high accuracy of the proposed method was verified by the actual PM2.5 data of Beijing, China, which provided an effective method for predicting the PM2.5 concentration.

The effect of growth hormone on the metabolome of follicular fluid in patients with diminished ovarian reserve.

BACKGROUND: Increasing evidence supports that the co-treatment with growth hormone (GH) enhances ovarian response and oocyte quality during controlled ovarian stimulation (COS) in patients with diminished ovarian reserve (DOR). The composition of follicular fluid (FF) plays an essential role in oocyte development and mirrors the communication occurring between the oocyte and follicular microenvironment. However, the effect of GH on the FF metabolome remains unclear. METHODS: This prospective observational study recruited DOR patients undergoing in vitro fertilization (IVF) cycles with minimal stimulation protocol for COS. Each patient receiving GH co-treatment was matched to a patient without GH co-treatment by propensity score matching. The FF was collected after isolating oocytes and assayed by gas chromatograph-mass spectrometry (GC-MS) metabolomics. The Pearson correlation was performed to evaluate the relationship between the number of oocytes retrieved and the levels of differential metabolites. The KEGG database was used to map differential metabolites onto various metabolic pathways. RESULTS: One hundred thirty-four FF metabolites were identified by GC-MS metabolomics. Twenty-four metabolites, including glutathione, itaconic acid and S-adenosylmethionin (SAM) showed significant differences between the GH and control groups (p-value < 0.05 and q-value < 0.1). In addition, the number of oocytes retrieved was significantly higher in the GH group compared to the control group (3 vs 2, p = 0.04) and correlated with the levels of five differential metabolites. Among them, the levels of antioxidant metabolite itaconic acid were upregulated by GH administration, while SAM levels were downregulated. CONCLUSIONS: The co-treatment with GH during COS may improve oocyte development by altering FF metabolite profiles in DOR patients. However, given the downregulation of SAM, a regulator of genomic imprinting, the potential risk of imprinting disturbances should not be neglected.