A Rapid Evaluation Method for Unsaturation of Camellia Oil Based on Raman Spectroscopy Technology

To evaluate the degree of unsaturation of different varieties of Camellia oil, it was necessary to establish a rapid evaluation method with a narrow iodine value range (iodine value difference less than 10). In this study, a rapid quantitative prediction model for iodine value of oil in high-resolution Raman spectroscopy based on linear regression and gradient descent method was established. The Raman signals (785 nm) about 39 group of Camellia oil samples and 10 group of commercially oils were firstly collected. Then, the intensity ratio of peaks of 1656 cm−1 and 1440 cm−1 (I1656/1440) were selected through smoothing algorithm least squares smoothing filter (Savitzky-Golay), polynomial fitting and deconvolution algorithm Lorentzian. A credible model was obtained through correlation analysis with the iodine value of corresponding oil samples. The coefficient of determination (R2) of the test set of the constructed quantitative model was >0.82, the mean square error (MSE) was <0.73 and the root mean square error (RMSE) was <0.85. This quantitative model of edible oil iodine value can accurately and efficiently predict the unsaturation degree of Camellia oil, etc

Neuronal lack of PDE7a disrupted working memory, spatial learning, and memory but facilitated cued fear memory in mice

12 p.-5 fig.Background: PDEs regulate cAMP levels which is critical for PKA activity-dependent activation of CREB-mediated transcription in learning and memory. Inhibitors of PDEs like PDE4 and Pde7 improve learning and memory in rodents. However, the role of PDE7 in cognition or learning and memory has not been reported yet.Methods:Therefore, we aimed to explore the cognitive effects of a PDE7 subtype, PDE7a, using combined pharmacological and genetic approaches.Results: PDE7a-nko mice showed deficient working memory, impaired novel object recognition, deficient spatial learning & memory, and contextual fear memory, contrary to enhanced cued fear memory, highlighting the potential opposite role of PDE7a in the hippocampal neurons. Further, pharmacological inhibition of PDE7 by AGF2.20 selectively strengthens cued fear memory in C57BL/6 J mice, decreasing its extinction but did not affect cognitive processes assessed in other behavioral tests. The further biochemical analysis detected deficient cAMP in neural cell culture with genetic excision of the PDE7a gene, as well as in the hippocampus of PDE7a-nko mice in vivo. Importantly, we found overexpression of PKA-R and the reduced level of pPKA-C in the hippocampus of PDE7a-nko mice, suggesting a novel mechanism of the cAMP regulation by PDE7a. Consequently, the decreased phosphorylation of CREB, CAMKII, eif2a, ERK, and AMPK, and reduced total level of NR2A have been found in the brain of PDE7a-nko animals. Notably, genetic excision of PDE7a in neurons was not able to change the expression of NR2B, BDNF, synapsin1, synaptophysin, or snap25.Conclusion: Altogether, our current findings demonstrated, for the first time, the role of PDE7a in cognitive processes. Future studies will untangle PDE7a-dependent neurobiological and molecular-cellular mechanisms related to cAMP-associated disorders.This work was supported by the Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions No: 2022SHIBS0004: Basic research and free exploration project of Shenzhen Science and technology innovation Commission (JCYJ20190808113007570).Peer reviewe

Efficacy of apatinib 250 mg combined with chemotherapy in patients with pretreated advanced breast cancer in a real-world setting

ObjectivesThis study evaluated the efficacy and safety of apatinib (an oral small-molecule tyrosine kinase inhibitor targeting VEGFR-2) 250 mg combined with chemotherapy in patients with pretreated metastatic breast cancer in a real-world setting.Patients and methodsA database of patients with advanced breast cancer who received apatinib between December 2016 and December 2019 in our institution was reviewed, and patients who received apatinib combined with chemotherapy were included. Progression-free survival (PFS), overall survival (OS), the objective response rate (ORR), the disease control rate (DCR), and treatment-related toxicity were analyzed.ResultsIn total, 52 evaluated patients with metastatic breast cancer previously exposed to anthracyclines or taxanes who received apatinib 250 mg combined with chemotherapy were enrolled in this study. Median PFS and OS were 4.8 (95% confidence interval [CI] = 3.2–6.4) and 15.4 months (95% CI = 9.2–21.6), respectively. The ORR and DCR were 25% and 86.5%, respectively. Median PFS for the previous line of treatment was 2.1 months (95% CI = 0.65–3.6), which was significantly shorter than that for the apatinib–chemotherapy combination (p &lt; 0.001). No significant difference was identified in the ORR and PFS among the subgroups(subtypes, target lesion, combined regimens and treatment lines). The common toxicities related to apatinib were hypertension, hand-foot syndrome, proteinuria, and fatigue events.ConclusionApatinib 250 mg combined with chemotherapy provided favorable efficacy in patients with pretreated metastatic breast cancer regardless of molecular types and treatment lines. The toxicities of the regimen were well tolerated and manageable. This regimen could be a potential treatment option in patients with refractory pretreated metastatic breast cancers

The rising death burden of atrial fibrillation and flutter in low-income regions and younger populations

ObjectiveThe aim of the study was to depict the global death burden of atrial fibrillation and/or flutter (AFF) between 1990 and 2019 and predict this burden in the next decade.MethodsWe retrieved annual death data on cases and rates of AFF between 1990 and 2019 from the Global Burden of Disease (GBD) Study 2019 and projected the trends for 2020–2029 by developing the Bayesian age-period-cohort model.ResultsThe global number of deaths from AFF increased from 117,038.00 in 1990 to 315,336.80 in 2019. This number is projected to reach 404,593.40 by 2029. The age-standardized mortality rates (ASMRs) of AFF have increased significantly in low- to middle-sociodemographic index (SDI) regions, which will surpass that in high SDI regions and reach above 4.60 per 100,000 by 2029. Globally, women have a higher ASMR than men, which is largely attributed to disproportionately higher mortality in women than men in lower SDI regions. Notably, AFF-related premature mortality continues to worsen worldwide. A pandemic of high systolic blood pressure and high body mass index (BMI) largely contributes to AFF-associated death. In particular, low- to middle-SDI regions and younger populations are increasingly affected by the rapidly growing current and future risk of high BMI.ConclusionThe global death burden of AFF in low-income countries and younger generations have not been sufficiently controlled in the past and will continue growing in the future, which is largely attributed to metabolic risks, particularly for high BMI. There is an urgent need to implement effective measures to control AFF-related mortality

User Characteristic Aware Participant Selection for Mobile Crowdsensing

Mobile crowdsensing (MCS) is a promising sensing paradigm that leverages diverse embedded sensors in massive mobile devices. One of its main challenges is to effectively select participants to perform multiple sensing tasks, so that sufficient and reliable data is collected to implement various MCS services. Participant selection should consider the limited budget, the different tasks locations, and deadlines. This selection becomes even more challenging when the MCS tries to efficiently accomplish tasks under different heat regions and collect high-credibility data. In this paper, we propose a user characteristics aware participant selection (UCPS) mechanism to improve the credibility of task data in the sparse user region acquired by the platform and to reduce the task failure rate. First, we estimate the regional heat according to the number of active users, average residence time of users and history of regional sensing tasks, and then we divide urban space into high-heat and low-heat regions. Second, the user state information and sensing task records are combined to calculate the willingness, reputation and activity of users. Finally, the above four factors are comprehensively considered to reasonably select the task participants for different heat regions. We also propose task queuing strategies and community assistance strategies to ensure task allocation rates and task completion rates. The evaluation results show that our mechanism can significantly improve the overall data quality and complete sensing tasks of low-heat regions in a timely and reliable manner

Service Benefit Aware Multi-Task Assignment Strategy for Mobile Crowd Sensing

Mobile crowd sensing (MCS) systems usually attract numerous participants with widely varying sensing costs and interest preferences to perform tasks, where accurate task assignment plays an indispensable role and also faces many challenges (e.g., how to simplify the complicated task assignment process and improve matching accuracy between tasks and participants, while guaranteeing submitted data credibility). To overcome these challenges, we propose a service benefit aware multi-task assignment (SBAMA) strategy in this paper. Firstly, service benefits of participants are modeled based on their task difficulty, task history, sensing capacity, and sensing positivity to meet differentiated requirements of various task types. Subsequently, users are then clustered by enhanced fuzzy clustering method. Finally, a gradient descent algorithm is designed to match task types to participants achieving the maximum service benefit. Simulation results verify that the proposed task assignment strategy not only effectively reduces matching complexity but also improves task completion rate

Towards Optimized DFA Attacks on AES under Multibyte Random Fault Model

Differential Fault Analysis (DFA) is one of the most practical methods to recover the secret keys from real cryptographic devices. In particular, DFA on Advanced Encryption Standard (AES) has been massively researched for many years for both single-byte and multibyte fault model. For AES, the first proposed DFA attack requires 6 pairs of ciphertexts to identify the secret key under multibyte fault model. Until now, the most efficient DFA under multibyte fault model proposed in 2017 can complete most of the attacks within 3 pairs of ciphertexts. However, we note that the attack is not fully optimized since no clear optimization goal was set. In this work, we introduce two optimization goals as the fewest ciphertext pairs and the least computational complexity. For these goals, we manage to figure out the corresponding optimized key recovery strategies, which further increase the efficiency of DFA attacks on AES. A more accurate security assessment of AES can be completed based on our study of DFA attacks on AES. Considering the variations of fault distribution, the improvement to the attack has been analyzed and verified

Stochastic Latency Guarantee in Wireless Powered Virtualized Sensor Networks

How to guarantee the data rate and latency requirement for an application with limited energy is an open issue in wireless virtualized sensor networks. In this paper, we integrate the wireless energy transfer technology into the wireless virtualized sensor network and focus on the stochastic performance guarantee. Firstly, a joint task and resource allocation optimization problem are formulated. In order to characterize the stochastic latency of data transmission, effective capacity theory is resorted to study the relationship between network latency violation probability and the transmission capability of each node. The performance under the FDMA mode and that under the TDMA mode are first proved to be identical. We then propose a bisection search approach to ascertain the optimal task allocation with the objective to minimize the application latency violation probability. Furthermore, a one-dimensional searching scheme is proposed to find out the optimal energy harvesting time in each time block. The effectiveness of the proposed scheme is finally validated by extensive numerical simulations. Particularly, the proposed scheme is able to lower the latency violation probability by 11.6 times and 4600 times while comparing with the proportional task allocation scheme and the equal task allocation scheme, respectively