Accuracy Evaluation on the Respiration Rate Estimation using Off-the-shelf Pulse-Doppler Radar

This student paper presents preliminary results of using a pulse Doppler radar to detect the respiration rate of human subjects, examining the accuracy of the approach and evaluating the parameters to obtain the most precise result. In the study, the respiration data is recorded by repeatedly detecting people seated in front of the radar at different ranges as well as different aspect angles each time. Then Movement Target Indication, short-time Fourier transform and the analysis of the choice of doppler bins and window size of STFT are performed to evaluate the respiration rate and its precision. The results indicate that the respiration rate can be successfully detected at various ranges and angles and the relationship between Doppler bins and window size in processing is also observed to help us find the most accurate respiration rate

A lectin gene is involved in the defense of Pleurotus ostreatus against the mite predator Tyrophagus putrescentiae

The storage mite, Tyrophagus putrescentiae, found worldwide in many habitats, is an important pest of edible mushrooms. Excessive chemical spraying for pest control has been linked to environmental pollution, health risks, insecticide resistance development, and food safety. Host resistance can be sustainable and cost-effective and provide effective and economical pest control. Previous studies have reported that the oyster mushroom Pleurotus ostreatus has evolved effective defense mechanisms against T. putrescentiae attack, but the underlying mechanism remains unclear. Here we report that a lectin gene from P. ostreatus mycelia, Polec2, induced fungal resistance to mite grazing. Polec2 belongs to a galectin-like lectin classification, encoding a protein with β-sandwith-fold domain. Overexpression of Polec2 in P. ostreatus led to activation of the reactive oxygen species (ROS)/mitogen-activated protein kinases (MAPKs) signaling pathway, salicylic acid (SA), and jasmonate (JA) biosynthesis. The activation resulted in bursts of antioxidant activities of catalases (CAT), peroxidases (POD), superoxide dismutases (SOD), and increased production of SA, JA, jasmonic acid-isoleucine (JA-Ile) and jasmonic acid methyl ester (MeJA), accompanied by reduced T. putrescentiae feeding and suppressed its population. We also provide an overview of the phylogenetic distribution of lectins across 22 fungal genomes. Our findings shed light on the molecular mechanisms of P. ostreatus’ defense against the mite predator and will be useful in investigating the molecular basis of fungi-fungivory interactions and gene mining for pest-resistance genes

Chlorogenic Acid Ameliorates Damage Induced by Fluorene-9-Bisphenol in Porcine Sertoli Cells

4,4′-(9-Fluorenylidene) diphenol (BPFL, also known as BHPF and fluorene-9-bisphenol) is a novel bisphenol A substitute that is used in the plastics industry as an organic synthesis intermediate and is a potential endocrine disruptor. However, the deleterious effects of BPFL on porcine Sertoli cells (SCs) and the possible underlying mechanisms are still unclear. Chlorogenic acid (CA) is a free radical scavenger in the cellular antioxidant system that prevents oxidative damage and apoptosis. In the present research, we found that BPFL induced impairments in porcine SCs in a dose-dependent manner and that CA protected porcine SCs against BPFL exposure-induced impairments. Cell viability, proliferation and apoptosis assay results revealed that BPFL exposure could inhibit porcine SC proliferation and induce apoptosis, while CA supplementation ameliorated the effects of BPFL. Further analysis revealed that BPFL exposure induced oxidative stress, mitochondrial membrane potential dysfunction and DNA damage accumulation. Transcriptome analysis and further real-time quantitative PCR and Western blot results showed that BPFL exposure induced endoplasmic reticulum stress and apoptosis. Supplementation with CA dramatically ameliorated these phenotypes in BPFL-exposed porcine SCs. Overall, the present research reveals the possible underlying mechanisms by which BPFL exposure induced impairments and CA supplementation protected against these impairments in porcine SCs

ULK1/2 Constitute a Bifurcate Node Controlling Glucose Metabolic Fluxes in Addition to Autophagy

揭示了在外界能量供应缺乏时，细胞通过激活ULK1来介导葡萄糖分解代谢重编程以维持胞内的能量与氧化还原稳态的详细机制，并创新地发现了ULK1独立于自噬的关键功能。基于自噬和糖代谢与人类健康的重要相关性，该研究将很可能为我们预防和治疗各类代谢疾病提供新的思路和药物靶点。Metabolic reprogramming is fundamental to biological homeostasis, enabling cells to adjust metabolic routes after sensing altered availability of fuels and growth factors. ULK1 and ULK2 represent key integrators that relay metabolic stress signals to the autophagy machinery. Here, we demonstrate that, during deprivation of amino acid and growth factors, ULK1/2 directly phosphorylate key glycolytic enzymes including hexokinase (HK), phosphofructokinase 1 (PFK1), enolase 1 (ENO1), and the gluconeogenic enzyme fructose-1,6-bisphosphatase (FBP1). Phosphorylation of these enzymes leads to enhanced HK activity to sustain glucose uptake but reduced activity of FBP1 to block the gluconeogenic route and reduced activity of PFK1 and ENO1 to moderate drop of glucose-6-phosphate and to repartition more carbon flux to pentose phosphate pathway (PPP), maintaining cellular energy and redox homeostasis at cellular and organismal levels. These results identify ULK1/2 as a bifurcate-signaling node that sustains glucose metabolic fluxes besides initiation of autophagy in response to nutritional deprivation.State Key Program of National Natural Science of China， the 973 Program；National Natural Science Foundation of China for Fostering Talents in Basic Research ；the Foundation for Innovative Research Groups of the National Natural Science Foundation of China； and the 111 Project of Education of China

Quantifying Multi-modal Public Transit Accessibility for Large Metropolitan Areas: A Time-dependent Reliability Modeling Approach

The temporal dimensions of public transit accessibility have recently garnered an increasing amount of interest. However, the existing literature on transit accessibility is heavily based on oversimplified assumptions that transit services operate at deterministic speeds using predetermined timetables. These measurements may overestimate transit accessibility, especially for large metropolitan areas where inter- and intra-modal transfers are frequent. To handle travel time uncertainty, a multi-modal transit accessibility modeling approach is proposed to account for realistic variations in travel time and service reliability. The proposed approach is applied to the mapping of transit accessibility in Shenzhen (China), where transit services exhibit significant travel time variations over space and time. Compared to traditional transit accessibility measures, our method has been demonstrated to better capture intrinsic spatial and temporal accessibility variations with complex multi-modal transit networks. Normal distribution of inter-stop travel times and constant travel speed between GPS sampling points are assumed to simply the computation, which we consider to adjust in future studies to better quantify the dynamics of transit accessibility across space and time

ConvLSTM Coupled Economics Indicators Quantitative Trading Decision Model

Time series prediction methods based on deep learning have been widely used in quantitative trading. However, the price of virtual currency represented by Bitcoin has random fluctuation characteristics, which is extremely misleading for time series prediction. In this paper, a virtual currency quantitative trading model is established, which uses a convolution long short term memory (ConvLSTM) deep learning method to predict the transaction price, and uses the evaluation model composed of Chandler momentum oscillator (CMO), percentage price oscillator (PPO), stop and reverse(SAR) and other economic indicators to make further decisions. The model quantitatively classifies the random wandering characteristics by fusing economic indicators and extracts the symmetric economic laws among them, making full use of deep learning methods to extract spatial and temporal features within the data. The 2016–2021 Bitcoin value dataset published on Kaggle was used for simulated investment. The results show that compared with other existing decision models, it shows better performance and robustness, and shows good stability in dealing with the interdependence of long-term and short-term data. Our work provides a new idea for short-term prediction of long time series data affected by multiple complex factors: coupling deep learning methods with prior knowledge to complete prediction and decision making

Comparison of hybrid machine learning models to predict short-term meteorological drought in Guanzhong region, China

Reliable drought prediction plays a significant role in drought management. Applying machine learning models in drought prediction is getting popular in recent years, but applying the stand-alone models to capture the feature information is not sufficient enough, even though the general performance is acceptable. Therefore, the scholars tried the signal decomposition algorithm as a data pre-processing tool, and coupled it with the stand-alone model to build ‘decomposition-prediction’ model to improve the performance. Considering the limitations of using the single decomposition algorithm, an ‘integration-prediction’ model construction method is proposed in this study, which deeply combines the results of multiple decomposition algorithms. The model tested three meteorological stations in Guanzhong, Shaanxi Province, China, where the short-term meteorological drought is predicted from 1960 to 2019. The meteorological drought index selects the Standardized Precipitation Index on a 12-month time scale (SPI-12). Compared with stand-alone models and ‘decomposition-prediction’ models, the ‘integration-prediction’ models present higher prediction accuracy, smaller prediction error and better stability in the results. This new ‘integration-prediction’ model provides attractive value for drought risk management in arid regions. HIGHLIGHTS Machine learning model has great value in short-term meteorological drought prediction.; Signal decomposition algorithm as a data pre-processing tool can significantly improve the prediction performance of machine learning model.; Deeply combining the results of multiple decomposition algorithms could achieve higher prediction accuracy.; The ‘integration-prediction’ model provides a new way for drought prediction in arid regions.

Evaluation of the Teaching Quality and Ability of Hospital Administrators by Overseas Distinguished Teachers

In recent years, China has been actively promoting the Belt and Road Initiative. In the context of the gradual opening of the medical market, it is urgent to improve the health management knowledge and scientific research capabilities of the hospital administrators, further enhance the hospital management level, so that the hospitals can obtain a greater edge in the international medical competition. Using overseas intelligence to promote the internationalization of education is one of the important means. The Affiliated Cancer Hospital and Institute of Guangzhou Medical University has invited Portuguese management professors to establish an academic and educational cooperation relationship, and invited them to offer training for hospital administrators. This article uses questionnaires to survey the trainees participating in this training and evaluates the teaching quality of overseas teachers. The results showthat the interviewed students generally present high affirmation to the training effects of the training courses and the teaching quality of overseas distinguished teachers

Identification and characterization of Prunus persica miRNAs in response to UVB radiation in greenhouse through high-throughput sequencing

Abstract Background MicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression of target mRNAs involved in plant growth, development, and abiotic stress. As one of the most important model plants, peach (Prunus persica) has high agricultural significance and nutritional values. It is well adapted to be cultivated in greenhouse in which some auxiliary conditions like temperature, humidity, and UVB etc. are needed to ensure the fruit quality. However, little is known about the genomic information of P. persica under UVB supplement. Transcriptome and expression profiling data for this species are therefore important resources to better understand the biological mechanism of seed development, formation and plant adaptation to environmental change. Using a high-throughput miRNA sequencing, followed by qRT-PCR tests and physiological properties determination, we identified the responsive-miRNAs under low-dose UVB treatment and described the expression pattern and putative function of related miRNAs and target genes in chlorophyll and carbohydrate metabolism. Results A total of 164 known peach miRNAs belonging to 59 miRNA families and 109 putative novel miRNAs were identified. Some of these miRNAs were highly conserved in at least four other plant species. In total, 1794 and 1983 target genes for known and novel miRNAs were predicted, respectively. The differential expression profiles of miRNAs between the control and UVB-supplement group showed that UVB-responsive miRNAs were mainly involved in carbohydrate metabolism and signal transduction. UVB supplement stimulated peach to synthesize more chlorophyll and sugars, which was verified by qRT-PCR tests of related target genes and metabolites’ content measurement. Conclusion The high-throughput sequencing data provided the most comprehensive miRNAs resource available for peach study. Our results identified a series of differentially expressed miRNAs/target genes that were predicted to be low-dose UVB-responsive. The correlation between transcriptional profiles and metabolites contents in UVB supplement groups gave novel clues for the regulatory mechanism of miRNAs in Prunus. Low-dose UVB supplement could increase the chlorophyll and sugar (sorbitol) contents via miRNA-target genes and therefore improve the fruit quality in protected cultivation of peaches