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Infected people are isolated to minimize the spread of pandemic diseases. Therefore, the factors related to self-isolation (SI) should not be neglected, and it is important to investigate the factors leading the infected (or possibly infected) people to choose to self-isolate. In this paper, we tried to show that the theory of planned behavior provides a useful conceptual framework for SI when facing a pandemic risk, and a regression method with Chinese provincial (Guangdong Province) data was applied to investigate how attitude (ATT), subjective norms (SN), and perceived behavioral control (PBC) influence SI when facing a pandemic emergency. The results and the robustness tests confirm that ATT, SN, and PBC have a significant positive influence on SI when facing a pandemic emergency. ATT plays the most important role, followed by SN and then PBC. Based on the factors of SI, we found, through theoretical and empirical analyses, at least three important aspects that local governments need to consider to encourage citizens to self-isolate when facing a pandemic.

Single-carrier frequency division multiple access (SC-FDMA) has become a popular alternative to orthogonal frequency division multiple access (OFDM) in multi-user communication on LTE uplink systems. This is primarily due to the low peak-to-average power ratio (PAPR) of SC-FDMA compared to that of OFDM. Long-term evolution (LTE) uses SC-FDMA on the uplink to exploit this PAPR advantage to reduce transmit power amplifier (PA) back-off in mobile user terminals. However, the latest generation of communication systems requires high power efficiency and a large quantity of capacity in transmitting mobile data, bringing out some other critical problems: 1) the nonlinearity of Radio Frequency (RF) power amplifiers inevitably affects the power efficiency. Working beyond the saturation point is the main reason for power amplifiers having nonlinear properties; 2) In order to obtain adequate capacity, wide bandwidth is applied to the latest communication systems. Since in previous systems the pre-distorter would focus on memory-less distortion with relatively narrow bandwidth, this change makes memory distortion become a serious issue, degrading the transmission quality in the wireless communication systems. The intent of this thesis is to present nonlinear analysis of the SC-FDMA Spectrum with the RF power amplifier. Relevant mathematical models were considered and applied to the RF power amplifier in terms of intermodulation products and the third-order intercept point. The equivalent mathematical model is applied for the first time to SC-FDMA signals and with the previous formulation of the PA model, the derivation of the expressions for spectrum regrowth of amplified SC-FDMA signals was first established and finally simulated with Matlab software. The digital pre-distortion (DPD) technology was also applied to SC-FDMA signals for the first time in this thesis. An inverse intermodulation and Autoregressive Moving-Average (IM-ARMA) model was introduced to linearize the PA distortion with memory in the LTE uplink system. The DPD was finally implemented by Matlab R2010b. Conclusions are drawn that amplified power emission levels can be expressed by the form of third-order intercept point (IP3). The expressions for spectrum regrowth of amplified SC-FDMA signals have been verified for the first time with the comparison of simulation, measurement and calculation results. The effects of third order intermodulation have a greater impact than higher order components with respect to out-of-band emission power levels. Furthermore, the DPD algorithm reduced the spectrum regrowth of SC-FDMA signals by 12 dB. The proposed pre-distorter can effectively solve the distortion problem caused by the memory effect in RF power amplifier.

iThis book focuses on the importance for China to correct the present imbalance in the relationship between the financial sector and the real economy. The book looks at China's current financial system in terms of \"extractive\" and \"inclusive\". It asserts that the financial sector is producing huge \"siphonic effects\" that distort the overall development of the Chinese economy. Like a giant magnet, the financial sector attracts too many innovation factors, such as talents, capital and entrepreneurship away from the real economy and inhibits the development of the latter. Hence, the book argues that China's financial system must now be thoroughly reformed to become an inclusive financial system, where finance and the rest of the economy can co-exist and develop in support of each other.ii iii

To explore the effect of environment conditions on immune activity of fish, eight immune-associated genes responsible for innate immunity were selected from the Gen Bank, i.e. Pgrn-a, Ifit2, P-hepcidin, Lect2, β2m, Irf1, Il25 and Hsp96, and the m RNA expressions of them in the kidney of cultured large yellow croaker Larimichthys crocea in different sea areas in the East China Sea were examined with q PCR techniques. In the contrasts of immune-associated gene expression between areas and populations, significant differences were found, expression levels of these immune-associated genes were lower in the clear water area than in the poor water quantity area, and lower in May than in October. MY was more sensitive to environmental factors than DQ, which was coincident with the water quality in the culturing areas. Differential analyses of the expression levels of these immune-associated genes showed that significant up-regulation could be triggered by poor environmental factors. The expression patterns indicated that the expression levels of these genes were sensitive to ecological changes, thereby the immune-associated genes, especially Pgrn-a, Ifit2, β2m, Il25 and Hsp96, might serve as immediate and sensitive indicators of population immunologic vigor and ecosystem health. But the expression of immunity-associated genes at the level of gene transcription is highly influenced by multiple factors, and the exact causes or influencing factors of the up-regulation or down-regulation of these genes still need further thorough investigation.

Predicting crystal properties is essential for understanding structure-property relationships and accelerating material discovery. However, conventional approaches like experimental measurements or density functional theory calculations are resource-intensive, limiting their scalability. While machine learning offers a promising alternative by learning complex structure-property relationships from data, existing models often rely on labeled data, adopt representations insufficiently capturing essential structural characteristics, and lack integration of physics, limiting their generalizability and interpretability. Here, we introduce CLOUD (Crystal Language mOdel for Unified and Differentiable materials modeling), a transformer-based framework trained on a Symmetry-Consistent Ordered Parameter Encoding (SCOPE) that encodes crystal symmetry, Wyckoff positions, and composition in a compact, coordinate-free string representation. Pre-trained on over six million crystals, CLOUD is fine-tuned on downstream tasks and achieves competitive performance across diverse material properties, demonstrating strong scaling with respect to data and model size. Furthermore, as a proof-of-concept of differentiable materials modeling, CLOUD is applied to predict the phonon-related properties by integrating with the Debye model. This approach enforces thermodynamic consistency and enables temperature-dependent property prediction without requiring additional data. These results demonstrate CLOUD’s potential as a scalable and physics-informed foundation model for crystalline materials, unifying symmetry-consistent representations with physics-grounded learning for property prediction and materials discovery. CLOUD is a scalable foundation model trained on symmetry-consistent encodings. Pre-trained on millions of crystals, it integrates accurate property prediction with differentiable physics for thermodynamically consistent materials modeling.

Arrestins recognize different receptor phosphorylation patterns and convert this information to selective arrestin functions to expand the functional diversity of the G protein-coupled receptor (GPCR) superfamilies. However, the principles governing arrestin-phospho-receptor interactions, as well as the contribution of each single phospho-interaction to selective arrestin structural and functional states, are undefined. Here, we determined the crystal structures of arrestin2 in complex with four different phosphopeptides derived from the vasopressin receptor-2 (V2R) C-tail. A comparison of these four crystal structures with previously solved Arrestin2 structures demonstrated that a single phospho-interaction change results in measurable conformational changes at remote sites in the complex. This conformational bias introduced by specific phosphorylation patterns was further inspected by FRET and 1 H NMR spectrum analysis facilitated via genetic code expansion. Moreover, an interdependent phospho-binding mechanism of phospho-receptor-arrestin interactions between different phospho-interaction sites was unexpectedly revealed. Taken together, our results provide evidence showing that phospho-interaction changes at different arrestin sites can elicit changes in affinity and structural states at remote sites, which correlate with selective arrestin functions. The interaction between a GPCR, such as the vasopressin receptor-2 (V2R), and arrestin depends on the receptors’ phosphorylation pattern. Here authors use FRET and NMR to analyze the phosphorylation patterns of the V2R-arrestin complex and show that phospho-interactions are the key determinants of selective arrestin conformational states and correlated functions.

Background Human selection has a long history of transforming crop genomes. Peach ( Prunus persica ) has undergone more than 5000 years of domestication that led to remarkable changes in a series of agronomically important traits, but genetic bases underlying these changes and the effects of artificial selection on genomic diversity are not well understood. Results Here, we report a comprehensive analysis of peach evolution based on genome sequences of 480 wild and cultivated accessions. By focusing on a set of quantitative trait loci (QTLs), we provide evidence supporting that distinct phases of domestication and improvement have led to an increase in fruit size and taste and extended its geographic distribution. Fruit size was predominantly selected during domestication, and selection for large fruits has led to the loss of genetic diversity in several fruit weight QTLs. In contrast, fruit taste-related QTLs were successively selected for by domestication and improvement, with more QTLs selected for during improvement. Genome-wide association studies of 11 agronomic traits suggest a set of candidate genes controlling these traits and potential markers for molecular breeding. Candidate loci for genes that contributed to the adaption to low-chill regions were identified. Furthermore, the genomic bases of divergent selection for fruit texture and local breeding for different flavors between Asian and European/North American cultivars were also determined. Conclusions Our results elucidate the genetic basis of peach evolution and provide new resources for future genomics-guided peach breeding.

Background Genome structural variations (SVs) have been associated with key traits in a wide range of agronomically important species; however, SV profiles of peach and their functional impacts remain largely unexplored. Results Here, we present an integrated map of 202,273 SVs from 336 peach genomes. A substantial number of SVs have been selected during peach domestication and improvement, which together affect 2268 genes. Genome-wide association studies of 26 agronomic traits using these SVs identify a number of candidate causal variants. A 9-bp insertion in Prupe.4G186800 , which encodes a NAC transcription factor, is shown to be associated with early fruit maturity, and a 487-bp deletion in the promoter of PpMYB10.1 is associated with flesh color around the stone. In addition, a 1.67 Mb inversion is highly associated with fruit shape, and a gene adjacent to the inversion breakpoint, PpOFP1 , regulates flat shape formation. Conclusions The integrated peach SV map and the identified candidate genes and variants represent valuable resources for future genomic research and breeding in peach.

Background Plant metabolites reshaped by nature and human beings are crucial for both their lives and human health. However, which metabolites respond most strongly to selection pressure at different evolutionary stages and what roles they undertake on perennial fruit crops such as peach remain unclear. Results Here, we report 18,052 significant locus-trait associations, 12,691 expression-metabolite correlations, and 294,676 expression quantitative trait loci (eQTLs) for peach. Our results indicate that amino acids accumulated in landraces may be involved in the environmental adaptation of peaches by responding to low temperature and drought. Moreover, the contents of flavonoids, the major nutrients in fruits, have kept decreasing accompanied by the reduced bitter flavor during both domestication and improvement stages. However, citric acid, under the selection of breeders’ and consumers’ preference for flavor, shows significantly different levels between eastern and western varieties. This correlates with differences in activity against cancer cells in vitro in fruit from these two regions. Based on the identified key genes regulating flavonoid and acid contents, we propose that more precise and targeted breeding technologies should be designed to improve peach varieties with rich functional contents because of the linkage of genes related to bitterness and acid taste, antioxidant and potential anti-cancer activity that are all located at the top of chromosome 5. Conclusions This study provides powerful data for future improvement of peach flavor, nutrition, and resistance in future and expands our understanding of the effects of natural and artificial selection on metabolites.

Quantitative real-time PCR (qRT-PCR) has been emerged as an effective method to explore the gene function and regulatory mechanisms. However, selecting appropriate reference gene (s) is a prerequisite for obtaining accurate qRT-PCR results. Peach is one of important fruit in Rosaceae and is widely cultivated worldwide. In this study, to explore reliable reference gene (s) in peach with different types during fruit ripening and softening (S1–S4), nine candidate reference genes ( EF-1α , GAPDH , TBP , UBC , eIF-4α , TUB-A , TUB-B , ACTIN , and HIS ) were selected from the whole-genome data. Then, the expression levels of the nine selected genes were detected using qRT-PCR in three peach types, including ‘Hakuho’ (melting type), ‘Xiacui’ (stony hard type), ‘Fantasia’ and ‘NJC108’ (non-melting type) cultivars were detected using qRT-PCR. Four software (geNorm, NormFinder, BestKeeper and RefFinder) were applied to evaluate the expression stability of these candidate reference genes. Gene expression was characterized in different peach types during fruit ripening and softening stages. The overall performance of each candidate in all samples was evaluated. The Actin gene ( ACTIN ) was a suitable reference gene and displayed excellent stability in ‘Total’ set, ‘Hakuho’ samples, S3 and S4 fruit developmental stages. Ubiquitin C gene ( UBC ) showed the best stability in most independent samples, including ‘Fantasia’, ‘NJC108’, S2 sets. Elongation factor-1 α gene ( EF-1α ) was the most unstable gene across the set of all samples, ‘NJC108’ and S2 sets, while showed the highest stability in ‘Xiacui’ samples. The stability of candidate reference genes was further verified by analyzing the relative expression level of ethylene synthase gene of Prunus persica ( PpACS1 ) in fruit ripening and softening periods of ‘Hakuho’. Taken together, the results from this study provide a basis for future research on the mining of important functional genes, expression patterns and regulatory mechanisms in peach.