Interspecific Variation and Phylogenic Architecture of Pinus densata and the Hybrid of Pinus tabuliformis×Pinus Yunnanensis in the Pinus densata Habitat: an Electrical Impedance Spectra Perspective

We evaluated a novel and non-destructive method of the electrical impedance spectroscopy (EIS) to elucidate the genetic and evolutionary relationship of homoploid hybrid conifer of Pinus densata (P.d) and its parental species Pinus tabuliformis (P.t) and Pinus yunnanensis (P.y), as well as the artificial hybrids of the P.t and P.y. Field common garden tests of96 trees sampled from 760 seedlings and 480 EIS records of 1,440 needles assessed the interspecific variation of the P.d, P.t, P.y and the artificial hybrids. We found that (1) EIS at different frequencies diverged significantly among germplasms; P.y was the highest, P.t was the lowest, and their artificial hybrids were within the range of P.t and P.y; (2) maternal species effect of EIS magnitudes in the hybrids and P.d was stronger than the paternal species characteristics; (3)EIS of the artificial hybrid confirmed the mid-parent and partial maternal species characteristics;(4) unified exponential model of EIS for the interspecific and hybrids can be constructed as |Z|=Af -B; (5) cluster analysis for species and hybrid combinations in total corroborated with the previous hybrid model of Pinus densata. Our non-destructive EIS method complemented the previous finding that Pinus densata was originated from P.t and P.y. We conclude that the impedance would be a viable indicator to investigate the interspecific genetic variations of conifers

Capacitance Characteristics of Pinus densata, Pinus tabuliformis, Pinus yunnanensis and the hybrids Pinus tabuliformis × Pinus Yunnanensis

We employed capacitance to evaluate the kinship and interspecific variation of homoploid hybrid conifer Pinus densata, P. tabuliformis, P. yunnanensis and artificial hybrids of P. tabuliformis (maternal parent) and P. yunnanensis (paternal parent) which were cultivated and selected in the common garden experiment. By measuring capacitance spectra under different voltage frequencies, we could differentiate different germplasms based on the electrical response. We aims to demonstrate that P. densata as the hybrid of P. tabuliformis and P. yunnanensis based on the capacitance values of the species, and to provide new evidence to the previously known biological evidence, as well as and the parental effect on the hybrids. Our results revealed that capacitance values between the species are significantly different in the spectra where P. yunnanensis positioned at the lowest and P. densata was much higher than all other species, indicating that P. densata had possessed a great capacity to store electrical energy. The capacitance spectra of P. densata and the artificial hybrid are not similar, which rejected our hypothesis. Both of the capacitance values of P. densata and the hybrids were closer to P. tabuliformis than to P. yunnanensis, which shows that the maternal influence was stronger than the paternal influence. Correlation analysis on the relationship between capacitance and fitnessrelated characteristics showed that capacitance is negatively correlated to mortality rate, and positively correlated with second-year survival rate. High capacitance values of P. densata and some of the hybrids reveal their superior adaptability to harsh environment in the Tibet Plateau. We concluded that capacitance as a new indicator for plant fitness and evolution evidence of homoploid hybrid conifers

Use of Structural Equation Modelling and Neural Network to Analyse Shared Parking Choice Behaviour

The shared parking mode represents a feasible solution to the persistent problem of parking scarcity in urban areas. This paper aims to examine the shared parking choice behaviours using a combination of structural equation modelling (SEM) and neural network, taking into account both the parking location characteristics and the travellers’ characteristics. Data were collected from a commercial district in Nanjing, China, through an online questionnaire survey covering 11 factors affecting shared parking choice. The method involved two steps: firstly, SEM was applied to examine the influence of these factors on shared parking choice. Following this, the seven factors with the strongest correlation to shared parking choice were used to train a neural network model for shared parking prediction. This SEM-informed model was found to outperform a neural network model trained on all eleven factors across precision, recall, accuracy, F1 and AUC metrics. The research concluded that the selected factors significantly influence shared parking choice, reinforcing the hypothesis regarding the importance of parking location and traveller characteristics. These findings provide valuable insights to support the effective implementation and promotion of shared parking

Oxidation Degradation of Rhodamine B in Aqueous by UV

The UV photolysis of persulfate (S2O8 2−) is a novel advanced oxidation technologies (AOTs), which leads to the formation of strong oxidizing radicals, sulfate radicals (SO4 •−). The effect of oxidant S2O8 2− concentration, initial dye concentration, initial pH of solution, and various inorganic anions (Cl−, H2PO4 −, and HCO3 −) were investigated using Rhodamine B (RhB), a kind of xanthene dye, as a model pollutant. With the increase of oxidant S2O8 2−, more SO4 •− produced to attack RhB molecules and result in the increase of RhB degradation. While the improvement was not sustained above a critical value, beyond which degradation rate does not increase. Initial pH of solution had great effect on the RhB degradation rate during UV/S2O8 2− system. SO4 •− is rather stable in acidic solutions, while increasing system pH results in the transformation of SO4 •− to •OH. The effects of three inorganic anions (Cl−, H2PO4 −, and HCO3 −) all had some negative effect on the degradation of RhB. Based on the RhB solution changes of the UV-vis absorption intensity during the UV/S2O8 2− treatment, decolorization of RhB accompanied the destruction of aromatic ring structures of RhB molecules

Effect of Yuanbao Maple Tea Powder with High Chlorogenic Acid Content on Bread Quality

Using Yuanbao maple leaves as raw materials, the extraction process of chlorogenic acid in leaves was optimized, and single-factor and orthogonal experiments were carried out on ultrasonic temperature, time, and solid-liquid ratio through ultrasonic extraction. The results showed that the optimal level of the experiment was when the ratio of solid to liquid was 16:1, the concentration of ethanol was 60%, and the ultrasonic time was 15 min, and the extraction amount was 6.86% (mass fraction). Under the optimal extraction process conditions, the dynamic content of chlorogenic acid in the growth cycle of Yuanbaofeng in 2020 was analyzed. The results showed that the content of chlorogenic acid in the leaves of Yuanbaofeng in June was the highest, and the content in September was the least. In order to further explore the effect of Yuanbao maple tea powder on bread quality, different proportions of Yuanbao maple tea powder were added to bread to study its sensory effects on bread. The effects of scores, moisture content, texture, polyphenol content, antioxidant activity and other qualities. The results show that the water holding capacity, elasticity and anti-oxidation of bread are the best when the addition amount of GTB is 0.5%. Less elastic, more difficult to chew, and gradually unstable antioxidant properties

Learning to Generalize Provably in Learning to Optimize

Learning to optimize (L2O) has gained increasing popularity, which automates the design of optimizers by data-driven approaches. However, current L2O methods often suffer from poor generalization performance in at least two folds: (i) applying the L2O-learned optimizer to unseen optimizees, in terms of lowering their loss function values (optimizer generalization, or ``generalizable learning of optimizers"); and (ii) the test performance of an optimizee (itself as a machine learning model), trained by the optimizer, in terms of the accuracy over unseen data (optimizee generalization, or ``learning to generalize"). While the optimizer generalization has been recently studied, the optimizee generalization (or learning to generalize) has not been rigorously studied in the L2O context, which is the aim of this paper. We first theoretically establish an implicit connection between the local entropy and the Hessian, and hence unify their roles in the handcrafted design of generalizable optimizers as equivalent metrics of the landscape flatness of loss functions. We then propose to incorporate these two metrics as flatness-aware regularizers into the L2O framework in order to meta-train optimizers to learn to generalize, and theoretically show that such generalization ability can be learned during the L2O meta-training process and then transformed to the optimizee loss function. Extensive experiments consistently validate the effectiveness of our proposals with substantially improved generalization on multiple sophisticated L2O models and diverse optimizees. Our code is available at: https://github.com/VITA-Group/Open-L2O/tree/main/Model_Free_L2O/L2O-Entropy.Comment: This paper is accepted in AISTATS 202

Shorter telomere length in children with autism spectrum disorder is associated with oxidative stress

ObjectiveAutism spectrum disorder (ASD) is a highly heterogeneous neurodevelopmental disorder caused by a complex interaction between genetic and environmental risk factors. The balance between antioxidant capacity and oxidative stress (OS) induced free radicals may be crucial during the pathophysiological development of ASD.MethodsIn this study, 96 children with ASD who met the diagnostic and statistical manual of mental disorders were collected, and the number of children in the typical development (TD) group was matched by 1:1. Digital PCR (dPCR) for telomere length (TL) expression in ASD in peripheral blood leukocytes. Urine levels of 8-hydroxy-2-deoxyguanosine (8-OHdG) content were measured by tandem triple quadrupole mass spectrometry and corrected by urinary creatinine levels. The levels of superoxide dismutase (SOD), catalase (CAT), and capacity (AOC) were detected by kits.ResultsThe TL of the ASD group was shorter than the TD group (p < 0.01) and had some accurate predictive significance for the identification of ASD (AUC = 0.632, 95% CI: 0.533–0.710, p = 0.002). Both 8-OHdG content and SOD activity in the ASD group were significantly higher than those in the TD group (p < 0.05). Shortened TL (Monofactor: 2.20 (1.22, 3.96), p = 0.009; Multifactor: 2.22 (1.22, 4.00), p = 0.008) and reduced CAT activity (Monofactor: 2.31 (1.28, 4.17), p = 0.006; Multifactor: 2.31 (1.28, 4.18), p = 0.006) are risk factors for the development of ASD, while reduced 8-OHdG content (Monofactor: 0.29 (0.14, 0.60), p = 0.001; Multifactor: 0.27 (0.13, 0.57), p = 0.001) and reduced SOD activity (Monofactor: 0.55 (0.31, 0.98), p = 0.042; Multifactor: 0.54 (0.30, 0.98), p = 0.042) are protective factors for the development of ASD.ConclusionIn this study, TL and OS were significantly different between the ASD group and the TD group. As guanine-rich telomere sequences were likely damaged by oxygen free radicals, creating OS, which is a factor in the incidence and progression of ASDs. In conclusion, oxidative damage occurs in the bodies of children with ASD, which may lead to sustained disease progression and severe clinical manifestations. We assume that timely supplementation of antioxidants is very likely to be a potential treatment for early intervention in children with ASD. Identification and detection of OS-related biomarkers may contribute to early diagnosis and timely interventions in young patients with ASD

Improving Heterogeneous Model Reuse by Density Estimation

This paper studies multiparty learning, aiming to learn a model using the private data of different participants. Model reuse is a promising solution for multiparty learning, assuming that a local model has been trained for each party. Considering the potential sample selection bias among different parties, some heterogeneous model reuse approaches have been developed. However, although pre-trained local classifiers are utilized in these approaches, the characteristics of the local data are not well exploited. This motivates us to estimate the density of local data and design an auxiliary model together with the local classifiers for reuse. To address the scenarios where some local models are not well pre-trained, we further design a multiparty cross-entropy loss for calibration. Upon existing works, we address a challenging problem of heterogeneous model reuse from a decision theory perspective and take advantage of recent advances in density estimation. Experimental results on both synthetic and benchmark data demonstrate the superiority of the proposed method.Comment: 9 pages, 5 figues. Accepted by IJCAI 202