Short-Term Speed Prediction Using Remote Microwave Sensor Data: Machine Learning versus Statistical Model

Recently, a number of short-term speed prediction approaches have been developed, in which most algorithms are based on machine learning and statistical theory. This paper examined the multistep ahead prediction performance of eight different models using the 2-minute travel speed data collected from three Remote Traffic Microwave Sensors located on a southbound segment of 4th ring road in Beijing City. Specifically, we consider five machine learning methods: Back Propagation Neural Network (BPNN), nonlinear autoregressive model with exogenous inputs neural network (NARXNN), support vector machine with radial basis function as kernel function (SVM-RBF), Support Vector Machine with Linear Function (SVM-LIN), and Multilinear Regression (MLR) as candidate. Three statistical models are also selected: Autoregressive Integrated Moving Average (ARIMA), Vector Autoregression (VAR), and Space-Time (ST) model. From the prediction results, we find the following meaningful results: (1) the prediction accuracy of speed deteriorates as the prediction time steps increase for all models; (2) the BPNN, NARXNN, and SVM-RBF can clearly outperform two traditional statistical models: ARIMA and VAR; (3) the prediction performance of ANN is superior to that of SVM and MLR; (4) as time step increases, the ST model can consistently provide the lowest MAE comparing with ARIMA and VAR

Printability and Applicability of 3D Printing System Loaded with Chlorogenic Acid Hydrogel

Three-dimensional food printing (3DFP) is an efficient way of food processing in line with the future lifestyle. As a delivery system, hydrogel has become a research hotspot because of its remarkable characteristics such as directed delivery. The purpose of this study was to explore the effects of 3DFP on the structure, physical properties and functions of hydrogels containing methylcellulose (MC), chlorogenic acid (CA) and hyaluronic acid (HA) for the purpose of revealing the printability and applicability of hydrogels in 3DFP processing. Texture properties, rheological properties, microstructure, embedding rate and digestive properties of the 3D printed products were measured. The results showed that the best CA-loaded hydrogel system for 3DFP processing consisted of MC, HA and CA at a mass ratio of 8:0.5:0.5. Its printed product showed the smallest width deviation (13.40%), the highest hardness, the maximum elasticity, and the minimum adhesiveness, had compact structure and uniform porosity, was not easy to collapse, and had good supportability and the best printing moldability. 3DFP well optimized the physical structure of hydrogel without changing its chemical properties. The embedding rate of CA was 22.09 percentage points higher than that before 3D printing. In simulated gastrointestinal digestion test, the release rate of CA from the printed product was significantly higher than that of the unprinted samples, showing a good sustained release effect, and the in vitro release of CA was fitted to the Ritger-Peppas model. These results showed that the hydrogel system had good printability and applicability, and 3DFP could significantly improve the targeted release of CA loaded in hydrogel

Proteomics reveals the significance of vacuole Pi transporter in the adaptability of Brassica napus to Pi deprivation

Vacuolar Pi transporters (VPTs) have recently been identified as important regulators of cellular Pi status in Arabidopsis thaliana and Oryza sativa. In the oil crop Brassica napus, BnA09PHT5;1a and BnC09PHT5;1a are two homologs of AtPHT5;1, the vacuolar Pi influx transporter in Arabidopsis. Here, we show that Pi deficiency induces the transcription of both homologs of PHT5;1a genes in B. napus leaves. Brassica PHT5;1a double mutants (DM) had smaller shoots and higher cellular Pi concentrations than wild-type (WT, Westar 10), suggesting the potential role of BnPHT5;1a in modulating cellular Pi status in B. napus. A proteomic analysis was performed to estimate the role of BnPHT5;1a in Pi fluctuation. Results show that Pi deprivation disturbs the abundance of proteins in the physiological processes involved in carbohydrate metabolism, response to stimulus and stress in B. napus, while disruption of BnPHT5;1a genes may exacerbate these processes. Besides, the processes of cell redox homeostasis, lipid metabolic and proton transmembrane transport are supposed to be unbalanced in BnPHT5;1a DM under the -Pi condition. Noteworthy, disruption of BnPHT5;1a genes severely alters the abundance of proteins related to ATP biosynthesis, and proton/inorganic cation transmembrane under normal Pi condition, which might contribute to B. napus growth limitations. Additionally, seven new protein markers of Pi homeostasis are identified in B. napus. Taken together, this study characterizes the important regulatory role of BnPHT5;1a genes as vacuolar Pi influx transporters in Pi homeostasis in B. napus

Dual inhibition of glycolysis and glutaminolysis as a therapeutic strategy in the treatment of ovarian cancer

Cancer cell metabolism is required to support the biosynthetic demands of cell growth and cell division, and to maintain reduction oxidaton (redox) homeostasis. This study was designed to test the effects of glucose and glutamine on ovarian cancer cell growth and explore the inter-relationship between glycolysis and glutaminolysis. The SKOV3, IGROV-1 and Hey ovarian cancer cell lines were assayed for glucose, pyruvate and glutamine dependence by analyzing cytotoxicity, cell cycle progression, apoptosis and ATP production. As determined by MTT assay, glucose stimulated cell growth while the combination of glucose, glutamine and pyruvate resulted in the greatest stimulation of cell proliferation. Furthermore, 2-deoxy-glucose (2-DG) and 3-bromopyruvate (3-BP) induced apoptosis, caused G1 phase cell cycle arrest and reduced glycolytic activity. Moreover, 2-DG in combination with a low dose of aminooxyacetate (AOA) synergistically increased the sensitivity to 2-DG in the inhibition of cell growth in the ovarian cancer cell lines. These studies suggest that dual inhibition of glycolysis and glutaminolysis may be a promising therapeutic strategy for the treatment of ovarian cancer

Single-cell analysis reveals the COL11A1+ fibroblasts are cancer-specific fibroblasts that promote tumor progression

Background: Cancer-associated fibroblasts (CAFs) promote tumor progression through extracellular matrix (ECM) remodeling and extensive communication with other cells in tumor microenvironment. However, most CAF-targeting strategies failed in clinical trials due to the heterogeneity of CAFs. Hence, we aimed to identify the cluster of tumor-promoting CAFs, elucidate their function and determine their specific membrane markers to ensure precise targeting.Methods: We integrated multiple single-cell RNA sequencing (scRNA-seq) datasets across different tumors and adjacent normal tissues to identify the tumor-promoting CAF cluster. We analyzed the origin of these CAFs by pseudotime analysis, and tried to elucidate the function of these CAFs by gene regulatory network analysis and cell-cell communication analysis. We also performed cell-type deconvolution analysis to examine the association between the proportion of these CAFs and patients’ prognosis in TCGA cancer cohorts, and validated that through IHC staining in clinical tumor tissues. In addition, we analyzed the membrane molecules in different fibroblast clusters, trying to identify the membrane molecules that were specifically expressed on these CAFs.Results: We found that COL11A1+ fibroblasts specifically exist in tumor tissues but not in normal tissues and named them cancer-specific fibroblasts (CSFs). We revealed that these CSFs were transformed from normal fibroblasts. CSFs represented a more activated CAF cluster and may promote tumor progression through the regulation on ECM remodeling and antitumor immune responses. High CSF proportion was associated with poor prognosis in bladder cancer (BCa) and lung adenocarcinoma (LUAD), and IHC staining of COL11A1 confirmed their specific expression in tumor stroma in clinical BCa samples. We also identified that CSFs specifically express the membrane molecules LRRC15, ITGA11, SPHK1 and FAP, which could distinguish CSFs from other fibroblasts.Conclusion: We identified that CSFs is a tumor specific cluster of fibroblasts, which are in active state, may promote tumor progression through the regulation on ECM remodeling and antitumor immune responses. Membrane molecules LRRC15, ITGA11, SPHK1 and FAP could be used as therapeutic targets for CSF-targeting cancer treatment

NT1014, a novel biguanide, inhibits ovarian cancer growth in vitro and in vivo

Abstract Background NT1014 is a novel biguanide and AMPK activator with a high affinity for the organic cation-specific transporters, OCT1 and OCT3. We sought to determine the anti-tumorigenic effects of NT1014 in human ovarian cancer cell lines as well as in a genetically engineered mouse model of high-grade serous ovarian cancer. Methods The effects of NT1014 and metformin on cell proliferation were assessed by MTT assay using the human ovarian cancer cell lines, SKOV3 and IGROV1, as well as in primary cultures. In addition, the impact of NT1014 on cell cycle progression, apoptosis, cellular stress, adhesion, invasion, glycolysis, and AMPK activation/mTOR pathway inhibition was also explored. The effects of NT1014 treatment in vivo was evaluated using the K18 − gT121+/−; p53fl/fl; Brca1fl/fl (KpB) mouse model of high-grade serous ovarian cancer. Results NT1014 significantly inhibited cell proliferation in both ovarian cancer cell lines as well as in primary cultures. In addition, NT1014 activated AMPK, inhibited downstream targets of the mTOR pathway, induced G1 cell cycle arrest/apoptosis/cellular stress, altered glycolysis, and reduced invasion/adhesion. Similar to its anti-tumorigenic effects in vitro, NT1014 decreased ovarian cancer growth in the KpB mouse model of ovarian cancer. NT1014 appeared to be more potent than metformin in both our in vitro and in vivo studies. Conclusions NT1014 inhibited ovarian cancer cell growth in vitro and in vivo, with greater efficacy than the traditional biguanide, metformin. These results support further development of NT1014 as a useful therapeutic approach for the treatment of ovarian cancer

Optimal Blood Pressure Control Target for Older Patients with Hypertension: A Systematic Review and Meta-Analysis

Objective: This study evaluated the optimal systolic blood pressure (SBP) target for older patients with hypertension.Method: A Bayesian network meta-analysis was conducted. The risk of bias of the included studies was assessed by using a modified version of the Cochrane risk of bias. The trial outcomes comprised the following clinical events: major adverse cardiovascular events (MACE), cardiovascular mortality, all-cause mortality, myocardial infarction, heart failure and stroke.Results: A total of six trials were included. We reclassified all treatment therapies into three conditions according to the final achieved SBP after intervention (<130 mmHg, 130–139 mmHg and ≥140 mmHg). Our results demonstrated that anti-hypertensive treatment with an SBP target <130 mmHg, compared with treatment with an SBP target ≥140 mmHg, significantly decreased the incidence of MACE (OR 0.43, 95%CI 0.19–0.76), but no statistical difference was found in other comparisons. Although the results showed a trend toward more intensive anti-hypertension therapy having better effects on preventing cardiovascular mortality, all-cause mortality, myocardial infarction, heart failure, and stroke, no significant differences were found among groups.Conclusions: Our meta-analysis suggested that SBP <130 mmHg might be the optimal BP control target for patients ≥60 years of age; however, further evidence is required to support our findings