Is exponential gravity a viable description for the whole cosmological history?

Here we analysed a particular type of $F(R)$ gravity, the so-called exponential gravity which includes an exponential function of the Ricci scalar in the action. Such term represents a correction to the usual Hilbert-Einstein action. By using Supernovae Ia, Barionic Acoustic Oscillations, Cosmic Microwave Background and $H(z)$ data, the free parameters of the model are well constrained. The results show that such corrections to General Relativity become important at cosmological scales and at late-times, providing an alternative to the dark energy problem. In addition, the fits do not determine any significant difference statistically with respect to the $\Lambda$CDM model. Finally, such model is extended to include the inflationary epoch in the same gravitational Lagrangian. As shown in the paper, the additional terms can reproduce the inflationary epoch and satisfy the constraints from Planck data.Comment: 20 pages, 6 figures, analysis extended, version published in EPJ

Vehicle Communication using Secrecy Capacity

We address secure vehicle communication using secrecy capacity. In particular, we research the relationship between secrecy capacity and various types of parameters that determine secrecy capacity in the vehicular wireless network. For example, we examine the relationship between vehicle speed and secrecy capacity, the relationship between the response time and secrecy capacity of an autonomous vehicle, and the relationship between transmission power and secrecy capacity. In particular, the autonomous vehicle has set the system modeling on the assumption that the speed of the vehicle is related to the safety distance. We propose new vehicle communication to maintain a certain level of secrecy capacity according to various parameters. As a result, we can expect safer communication security of autonomous vehicles in 5G communications.Comment: 17 Pages, 12 Figure

Advances in imaging findings of preeclampsia-related reversible posterior leukoencephalopathy syndrome

Preeclampsia (PE)-related reversible posterior leukoencephalopathy syndrome (RPLS) is a common complication of hypertensive disorders of pregnancy. The syndrome usually occurs after 20 weeks of gestation and can lead to brain injury. Severe headache, seizures, disturbance of consciousness, and other neurological symptoms may occur in severe cases. PE-RPLS has high morbidity and mortality rates and seriously damages maternal and fetal health. In recent years, the continuous advancement of medical imaging technology has provided an important imaging basis for the early diagnosis and prognostic evaluation of RPLS. This article mainly details the research status of the etiology and pathogenesis of PE-RPLS and describes its characteristic imaging findings, especially MRI findings, to provide new insights into its early diagnosis, early treatment, and improvement of prognosis

YOLOv8-ACU: improved YOLOv8-pose for facial acupoint detection

IntroductionAcupoint localization is integral to Traditional Chinese Medicine (TCM) acupuncture diagnosis and treatment. Employing intelligent detection models for recognizing facial acupoints can substantially enhance localization accuracy.MethodsThis study introduces an advancement in the YOLOv8-pose keypoint detection algorithm, tailored for facial acupoints, and named YOLOv8-ACU. This model enhances acupoint feature extraction by integrating ECA attention, replaces the original neck module with a lighter Slim-neck module, and improves the loss function for GIoU.ResultsThe YOLOv8-ACU model achieves impressive accuracy, with an [email protected] of 97.5% and an [email protected]–0.95 of 76.9% on our self-constructed datasets. It also marks a reduction in model parameters by 0.44M, model size by 0.82 MB, and GFLOPs by 9.3%.DiscussionWith its enhanced recognition accuracy and efficiency, along with good generalization ability, YOLOv8-ACU provides significant reference value for facial acupoint localization and detection. This is particularly beneficial for Chinese medicine practitioners engaged in facial acupoint research and intelligent detection

Transcriptional Profiling of the Dose Response: A More Powerful Approach for Characterizing Drug Activities

The dose response curve is the gold standard for measuring the effect of a drug treatment, but is rarely used in genomic scale transcriptional profiling due to perceived obstacles of cost and analysis. One barrier to examining transcriptional dose responses is that existing methods for microarray data analysis can identify patterns, but provide no quantitative pharmacological information. We developed analytical methods that identify transcripts responsive to dose, calculate classical pharmacological parameters such as the EC50, and enable an in-depth analysis of coordinated dose-dependent treatment effects. The approach was applied to a transcriptional profiling study that evaluated four kinase inhibitors (imatinib, nilotinib, dasatinib and PD0325901) across a six-logarithm dose range, using 12 arrays per compound. The transcript responses proved a powerful means to characterize and compare the compounds: the distribution of EC50 values for the transcriptome was linked to specific targets, dose-dependent effects on cellular processes were identified using automated pathway analysis, and a connection was seen between EC50s in standard cellular assays and transcriptional EC50s. Our approach greatly enriches the information that can be obtained from standard transcriptional profiling technology. Moreover, these methods are automated, robust to non-optimized assays, and could be applied to other sources of quantitative data

Beyond Gap Junction Channel Function: the Expression of Cx43 Contributes to Aldosterone-Induced Mesangial Cell Proliferation via the ERK1/2 and PKC Pathways

Aims: This study aimed to explore the precise mechanism and signaling pathways of mesangial cell (MC) proliferation from a new point of view considering Connexin 43 (Cx43). Methods: MC proliferation was measured by the incorporation of 3H-thymidine (3H-TdR). Cx43 was over-expressed in MC cells using lipofectamine 2000, and the expression level was tested with reverse transcription-polymerase chain reaction (RT-PCR) and Western blot analyses. The gap junction channel function was explored by Lucifer Yellow scrape loading and dye transfer (SLDT), and the intracellular calcium concentrations ([Ca2+]i) were characterized by confocal microscopy on cells loaded with Fura-3/AM. Results: There was an inverse correlation between Cx43 expression and MC proliferation (P0.05). Our data also showed that the mineralcorticoid receptor (MR) antagonist spironolactone, ERK1/2 inhibitor PD98059 and PKC inhibitor GF109203X could attenuate the down-regulation of Cx43 expression in Aldo-induced MC proliferation; however, the PI3K inhibitor LY294002 could block MC proliferation without affecting Cx43 expression at either the mRNA or protein level. In addition, Aldo promoted MC proliferation in parallel with increasing [Ca2+]i (PConclusions: Our study provides preliminary evidence that Cx43 is an important regulator of Aldo-promoted MC proliferation. Furthermore, reduced Cx43 expression promoted MC proliferation independent of the gap junction channel function, and this process might be mediated through the ERK1/2- and PKC-dependent pathways

The Effects of a Macromolecular Charring Agent with Gas Phase and Condense Phase Synergistic Flame Retardant Capability on the Properties of PP/IFR Composites

In order to improve the efficiency of intumescent flame retardants (IFRs), a novel macromolecular charring agent named poly(ethanediamine-1,3,5-triazine-p-4-amino-2,2,6,6-tetramethylpiperidine) (PETAT) with gas phase and condense phase synergistic flame-retardant capability was synthesized and subsequently dispersed into polypropylene (PP) in combination with ammonium polyphosphate (APP) via a melt blending method. The chemical structure of PETAT was investigated by Fourier transform infrared spectroscopy (FTIR), and 1H nuclear magnetic resonance (NMR) spectroscopy. Thermal properties of the PETAT and IFR systems were tested by thermogravimetric-derivative thermogravimetric analysis (TGA-DTG) and thermogravimetry–Fourier transform infrared spectroscopy (TG-FTIR). The mechanical properties, thermal stability, flame-retardant properties, water resistance, and structures of char residue in flame-retardant composites were characterized using tensile and flexural strength property tests, TGA, limiting oxygen index (LOI) values before and after soaking, underwritten laboratory-94 (UL-94) vertical burning test, cone calorimetric test (CCT), scanning electron microscopy with energy dispersive X-ray spectrometry (SEM-EDXS), and FTIR. The results indicated that PETAT was successfully synthesized, and when the ratio of APP to PETAT was 2:1 with 25 wt % loading, the novel IFR system could reduce the deterioration of tensile strength and enhance the flexural strength of composites. Meanwhile, the flame-retardant composite was able to pass the UL-94 V-0 rating with an LOI value of 30.3%, and the peak of heat release rate (PHRR), total heat release (THR), and material fire hazard values were considerably decreased compared with others. In addition, composites also exhibited excellent water resistance properties compared with traditional IFR composites. SEM-EDXS and FTIR analyses of the char residues, as well as TG-FTIR analyses of IFR were used to investigate the flame-retardant mechanism of the APP/PETAT IFR system. The results indicated that the efficient flame retardancy of PP/IFR composites could be attributed to the synergism of the free radical-quenching and char layer-protecting mechanisms in the gas phase and condense phase, respectively

Machine learning-based analysis and prediction of meteorological factors and urban heatstroke diseases

IntroductionHeatstroke is a serious clinical condition caused by exposure to high temperature and high humidity environment, which leads to a rapid increase of the core temperature of the body to more than 40°C, accompanied by skin burning, consciousness disorders and other organ system damage. This study aims to analyze the effect of meteorological factors on the incidence of heatstroke using machine learning, and to construct a heatstroke forecasting model to provide reference for heatstroke prevention.MethodsThe data of heatstroke incidence and meteorological factors in a city in South China from May to September 2014–2019 were analyzed in this study. The lagged effect of meteorological factors on heatstroke incidence was analyzed based on the distributed lag non-linear model, and the prediction model was constructed by using regression decision tree, random forest, gradient boosting trees, linear SVRs, LSTMs, and ARIMA algorithm.ResultsThe cumulative lagged effect found that heat index, dew-point temperature, daily maximum temperature and relative humidity had the greatest influence on heatstroke. When the heat index, dew-point temperature, and daily maximum temperature exceeded certain thresholds, the risk of heatstroke was significantly increased on the same day and within the following 5 days. The lagged effect of relative humidity on the occurrence of heatstroke was different with the change of relative humidity, and both excessively high and low environmental humidity levels exhibited a longer lagged effect on the occurrence of heatstroke. With regard to the prediction model, random forest model had the best performance of 5.28 on RMSE and dropped to 3.77 after being adjusted.DiscussionThe incidence of heatstroke in this city is significantly correlated with heat index, heatwave, dew-point temperature, air temperature and zhongfu, among which the heat index and dew-point temperature have a significant lagged effect on heatstroke incidence. Relevant departments need to closely monitor the data of the correlated factors, and adopt heat prevention measures before the temperature peaks, calling on citizens to reduce outdoor activities

A study of TCM master Yan Zhenghua's medication rule in prescriptions for digestive system diseases based on Apriori and complex system entropy cluster

Objective: To explore Yan Zhenghua's drug selection rule for treating digestive system diseases using data mining. Methods: The 609 medical records of digestive system diseases treated by Yan Zhenghua were collected and the herbs in these recipes were examined using a data mining technique. The correlativity between herb pairs and association rules was studied using an Apriori algorithm and the correlativity among multi-herbs was studied using a complex system entropy cluster technique. Results: Yan Zhenghua's treatment of digestive system diseases featured 15 herbs prescribed at least 159 times each, 22 herb pairs prescribed at least 155 times each, and eight frequently used herb core combinations. A confidence greater than 0.91 and a support level greater than 20% were achieved using the modified mutual information method. Conclusion: The data mining results conformed to findings from clinical practice. The data mining method is a valuable technique with which to study the experience of famous, elderly traditional Chinese medicine physicians