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In materials genetic engineering, data-driven machine learning techniques have garnered significant attention as a powerful new tool in the field of magnesium alloys. Traditional empirical trial-and-error methods and those based on density functional theory have struggled to keep pace with the continuous advancements in material science needs owing to high time costs and low efficiency. By relying on statistical methods instead of solving physical equations, machine learning can quickly predict material properties at a low cost, provided the connection between descriptors and target properties is identified. This capability can streamline the experimental process. Magnesium and its alloys show tremendous potential in aerospace, automotive, and other fields owing to their low density and high specific strength. However, their industrialization has been limited by several challenges, including the varied effects of different alloying elements, preparation and processing defects, deformation difficulties, and the common trade-off between strength and ductility. Machine learning can accelerate the discovery of novel magnesium alloys or processing parameters, and explore the relationships between their physicochemical characteristics and target properties. This paper comprehensively and systematically reviews the research progress of machine learning applications in magnesium alloys. It introduces the basic processes and various methods of machine learning, including data set collection, data preprocessing, model building, and performance evaluation. The classification of machine learning algorithms is summarized briefly. The paper then focuses on the research achievements of machine learning applied in many aspects, such as machining processes, microstructure, mechanical properties, corrosion resistance, hydrogen storage properties, intrinsic properties (reinforcement mechanism, anisotropy, etc.) and inverse design. Factors such as alloy compositions, test temperature and time, second phase, and Schmid factor can be considered as features and input into machine learning models for training. These models not only accelerate the design of novel high-performance magnesium alloys but also enhance the understanding of magnesium alloy mechanisms. Additionally, the paper analyzes some urgent issues in the research and application of machine learning in magnesium alloys. These include insufficient prediction of the chemical and physical properties of magnesium alloys, the nascent stage of predicting the design and service performance of magnesium alloy components, and the lack of high-quality data sets. Finally, the paper proposes future research directions and development trends in the application of machine learning in magnesium alloys.

Virtual fitting applications have brought new vitality and immersive experiences to the fashion industry. As users adapt to the new fashion consumption experience enabled by virtual fitting, their demands are becoming increasingly diverse and complex. Therefore, it’s essential to deeply understand real user demands and use design resources effectively to meet these demands. This study assesses virtual fitting products on the market using smart data analysis techniques. Through panel group interviews, we’ve identified and preliminarily assessed user demands. We’ve categorized and prioritized these needs using the Kano model and transformed them into concrete design requirements using the Quality Function Deployment (QFD) method, establishing a system of design requirements. These requirements fall into three categories: visual design, interactive design, and emotional design. Each category includes core, secondary core, and peripheral core requirements. Eight core design requirements, such as personal image customization, extensive accumulation of apparel model libraries, and brand fan engagement were identified as top priorities for implementation. Eight secondary core design requirements, including realistic digital fabric rendering effects, real-time updates of the latest apparel models, and sharing of virtual fitting images should be integrated into design practices as resources allow. Nine peripheral core design requirements, like apparel aligning with mainstream aesthetic preferences, customizable avatar body dimensions, and minimalist user interface design can serve as references for inclusion in design practices. This research provides essential theoretical support for resource allocation in the design and iteration of virtual fitting applications, offering design solutions to meet the multifaceted real user demands in the rapidly changing field of digital fashion.

Abstract Modic changes are radiographic features associated with microfracture, low-virulence organism infection and chronic inflammation with inflammatory cell infiltration in the vertebral endplate region. Mast cells, as innate immune cells similar to macrophages, are present in painful degenerated intervertebral discs. However, the involvement and mechanisms of mast cells in the development of Modic changes remain unclear. Herein, we found increased mast cell infiltration in samples from patients with Modic changes and in mouse models of Modic changes. To clarify the role of mast cells in the progression of Modic changes, we used mast cell-deficient (KITW-SH/W-SH) mice to construct a model of Modic changes and found that the severity of Modic changes in KITW-SH/W-SH mice was significantly lower than that in WT mice. These findings were further supported by the use of a mast cell-specific activator (compound 48/80) and a stabilizer (cromolyn). Furthermore, we found that mast cells were not activated via the classic IgE pathway in the Modic change models and that Mrgprb2 is the specific receptor for mast cell activation reported in recent studies. Then, we utilized Mrgprb2 knockout mice to demonstrate that Mrgprb2 knockout inhibited mast cell activation and thus reduced the degree of Modic changes. Transcriptomic sequencing revealed aberrant PI3K-AKT and MAPK pathway activation in the Mrgprb2-deficient mast cells. Additionally, Mrgpbrb2-activated mast cells regulate immune niches by recruiting macrophages, promoting M1 polarization and reducing M2 polarization, thereby promoting the progression of Modic changes. These findings suggest that mast cells may serve as a novel therapeutic target for addressing Modic changes.

Abstract Merging structural variations (SVs) at the population level presents a significant challenge, yet it is essential for conducting comprehensive genotypic analyses, especially in the era of pangenomics. Here, we introduce PanPop, a tool that utilizes an advanced sequence-aware SV merging algorithm to efficiently merge SVs of various types. We demonstrate that PanPop can merge and optimize the majority of multiallelic SVs into informative biallelic variants. We show its superior precision and lower rates of missing data compared to alternative software solutions. Our approach not only enables the filtering of SVs by leveraging multiple SV callers for enhanced accuracy but also facilitates the accurate merging of large-scale population SVs. These capabilities of PanPop will help to accelerate future SV-related studies.

Abstract Background Osteosarcoma represents a serious clinical challenge due to its widespread genomic alterations, tendency for drug resistance and distant metastasis. New treatment methods are urgently needed to address those treatment difficulties in osteosarcoma to improve patient prognoses. In recent years, small-molecule based anion transporter have emerged as innovative and promising therapeutic compound with various biomedical applications. However, due to a lack of efficient delivery methods, using ion transporters as therapeutic drugs in vivo remains a major challenge. Result Herein, we developed self-assembled supramolecular drugs based on small-molecule anion transporters, which exhibited potent therapeutic effect towards osteosarcoma both in vitro and in vivo. The anion transporters can disrupt intracellular ion homeostasis, inhibit proliferation, migration, epithelial-mesenchymal transition process, and lead to osteosarcoma cell death. RNA sequencing, western blot and flow cytometry indicated reprogramming of HOS cells and induced cell death through multiple pathways. These pathways included activation of endoplasmic reticulum stress, autophagy, apoptosis and cell cycle arrest, which avoided the development of drug resistance in osteosarcoma cells. Functionalized with osteosarcoma targeting peptide, the assembled supramolecular drug showed excellent targeted anticancer therapy against subcutaneous xenograft tumor and lung metastasis models. Besides good tumor targeting capability and anti-drug resistance, the efficacy of the assembly was also attributed to its ability to regulate the tumor immune microenvironment in vivo. Conclusions In summary, we have demonstrated for the first time that small-molecule anion transporters are capable of killing osteosarcoma cells through multiple pathways. The assemblies, OTP-BP-L, show excellent targeting and therapeutic effect towards osteosarcoma tumors. Furthermore, the supramolecular drug shows a strong ability to regulate the tumor immune microenvironment in vivo. This work not only demonstrated the biomedical value of small-molecule anion transporters in vivo, but also provided an innovative approach for the treatment of osteosarcoma.

ObjectiveResearch data suggests that ultrasound-assisted wound debridement (UAWD) can effectively promote the healing of diabetic foot ulcers (DFU). However, existing research is not consistent with this viewpoint. Therefore, we conducted this study to investigate the effect of UAWD on the healing of diabetic foot ulcers.MethodsFrom the establishment of the database to January 2024, we searched 8 databases to study the effectiveness and safety of UAWD in the treatment of DFU. Two authors independently screened the qualifications of the articles, while two authors extracted relevant data. Statistical analysis was conducted using Review Manager 5.4 and STATA 18.0 software.ResultsA total of 11 randomized controlled studies were included, with 6 countries and 696 participants participating. Our findings showed that UAWD was associated with a significant benefit in healing rate (OR = 2.60, 95% CI: [1.67, 4.03], P < 0.0001, I2 = 25%), wound healing time (MD = -11.94, 95% CI: [-23.65, -0.23], P = 0.05, I2 = 99%), percentage reduction in wound size (MD = 14.2, 95% CI: [10.8, 17.6], P = 0.47, I2 = 32%), effectiveness of treatment (OR = 10.3, 95% CI: [4.68, 22.66], P < 0.00001, I2 = 0%). Moreover, UAWD did not cause any significant adverse reactions. However, there was no obvious difference in wound blood perfusion (MD = 0.25, 95% CI: [-0.01, 0.52], P = 0.06, I2 = 90%), transcutaneous oxygen partial pressure (MD = 14.34, 95% CI: [-10.03, 38.71], P = 0.25, I2 = 98%).ConclusionUAWD can significantly improve wound healing rate, shorten wound healing time, accelerate wound area reduction, and improve clinical treatment effectiveness without significant adverse reactions. Although there is no significant difference in transcutaneous oxygen pressure and wound blood flow perfusion between UAWD and SWC. So we look forward to more scientifically blinded, placebo-controlled, high-quality studies in the future, to enable researchers to obtain more complete and accurate analytical data, in order to improve the scientific and credibility of the evidence.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/, identifier CRD42024501198.

IntroductionThis study examines the impact of environmental accidents on Generation Z’s purchase behavior towards green apparel, integrating the Theory of Planned Behavior and the Norm Activation Theory to conceptualize how different dimensions of environmental accidents influence consumer behavior.MethodsEmploying focus groups and questionnaires, this research captures data on Generation Z’s perceptions and behaviors towards green apparel in the context of environmental accidents. The collected data were analyzed using SPSS software, with structural equation modeling employed to test the research hypotheses.ResultsThe findings indicate that the scale (H12 = 0.545), the degree of suddenness (H18 = 0.357), nature of the effect (H14 = 0.295), and duration (H17 = 0.289) of environmental accidents significantly influence Generation Z’s awareness of consequences, behavioral attitudes, subjective norms, and perceived behavioral control regarding the purchase of green clothing. Notably, the scale of environmental accidents has a significant impact on perceived behavioral control, which in turn significantly affects Generation Z’s intention to purchase green apparel (H3 = 0.5).DiscussionThis study elucidates the impact of environmental accidents on Generation Z’s green apparel purchase intentions. The findings highlight Generation Z’s environmental awareness and social responsibility, influencing their purchasing decisions. This research offers practical insights for brands to enhance green marketing strategies, focusing on product quality, transparency, and consumer education to align with Generation Z’s values and expectations. Future research should explore additional factors affecting purchase behavior and strategies to bridge the intention-behavior gap.

Abstract Yak has been subject to natural selection, human domestication and interspecific introgression during its evolution. However, genetic variants favored by each of these processes have not been distinguished previously. We constructed a graph-genome for 47 genomes of 7 cross-fertile bovine species. This allowed detection of 57,432 high-resolution structural variants (SVs) within and across the species, which were genotyped in 386 individuals. We distinguished the evolutionary origins of diverse SVs in domestic yaks by phylogenetic analyses. We further identified 334 genes overlapping with SVs in domestic yaks that bore potential signals of selection from wild yaks, plus an additional 686 genes introgressed from cattle. Nearly 90% of the domestic yaks were introgressed by cattle. Introgression of an SV spanning the KIT gene triggered the breeding of white domestic yaks. We validated a significant association of the selected stratified SVs with gene expression, which contributes to phenotypic variations. Our results highlight that SVs of different origins contribute to the phenotypic diversity of domestic yaks.

Abstract Subnival glasshouse plants provide a text-book example of high-altitude adaptation with reproductive organs enclosed in specialized semi-translucent bracts, monocarpic reproduction and continuous survival under stress. Here, we present genomic, transcriptomic and metabolomic analyses for one such plant, the Noble rhubarb (Rheum nobile). Comparative genomic analyses show that an expanded number of genes and retained genes from two recent whole-genome duplication events are both relevant to subnival adaptation of this species. Most photosynthesis genes are downregulated within bracts compared to within leaves, and indeed bracts exhibit a sharp reduction in photosynthetic pigments, indicating that the bracts no longer perform photosynthesis. Contrastingly, genes related to flavonol synthesis are upregulated, providing enhanced defense against UV irradiation damage. Additionally, anatomically abnormal mesophyll combined with the downregulation of genes related to mesophyll differentiation in bracts illustrates the innovation and specification of the glass-like bracts. We further detect substantial accumulation of antifreeze proteins (e.g. AFPs, LEAs) and various metabolites (e.g. Proline, Protective sugars, procyanidins) in over-wintering roots. These findings provide new insights into subnival adaptation and the evolution of glasshouse alpine plants.

During the backward extrusion process, the impact of the long-period stacking ordered (LPSO) phase on dynamic recrystallization (DRX) in the Mg-10Y-2Zn-0.5Zr alloy was studied. The results show that DRX at the α-Mg/18R-LPSO interface belongs to the discontinuous dynamic recrystallization (DDRX) mechanism, and the DRX at the α-Mg/14H-LPSO interface belongs to the continuous dynamic recrystallization (CDRX) mechanism. Under the action of strain force, the 18R-LPSO interface migrates locally to form a “convex” structure. It was easy to produce high-density dislocations around the structure, which promoted DDRX at the α-Mg/18R-LPSO interface. The lamellar 14H-LPSO would undergo severe kink and form a large number of kink bands, providing favorable nucleation sites for CDRX grains. With the increase of the extrusion strain force, the lamellar 14H-LPSO kink angle gradually increases, which promotes the nucleation of CDRX grains at the kink band boundary. In addition, the production of 14H-LPSO kink activates prismatic slip, pyramidal slip and pyramidal slip in the α-Mg matrix.

Abstract Rhubarb is the collective name for various perennial plants from the genus Rheum L. and the Polygonaceae family. They are one of the most ancient, commonly used, and important herbs in traditional Chinese medicine. Rhubarb is a major source of anthraquinones, but how they are synthesized remains largely unknown. Here, we generate a genome sequence assembly of one important medicinal rhubarb R. tanguticum at the chromosome level, with 2.76 Gb assembled into 11 chromosomes. The genome is shaped by two recent whole-genome duplication events and recent bursts of retrotransposons. Metabolic analyses show that the major anthraquinones are mainly synthesized in its roots. Transcriptomic analysis reveals a co-expression module with a high correlation to anthraquinone biosynthesis that includes key chalcone synthase genes. One CHS, four CYP450 and two BGL genes involved in secondary metabolism show significantly upregulated expression levels in roots compared with other tissues and clustered in the co-expression module, which implies that they may also act as candidate genes for anthraquinone biosynthesis. This study provides valuable insights into the genetic bases of anthraquinone biosynthesis that will facilitate improved breeding practices and agronomic properties for rhubarb in the future.

The transverse leakage of leaky surface acoustic waves (LSAWs) occurs on 42°YX-lithium tantalate substrates (42LT), which increases the insertion loss, narrows the bandwidth and flattens the roll-off of band 40/41 SAW filters and duplexers. In this work, LSAW characteristics with different metal materials and thicknesses are calculated by the finite element method (FEM), which determines the IDT material and thickness used for band 40/41 SAW filter design. To deeply understand transverse leakage and suppress it, the effects of different gap and dummy lengths on transverse leakage are simulated and discussed. Then, a new technique of using a wider dummy without any additional lithographic or depositing processes is proposed to suppress the leakage. Its effectiveness is validated by both simulations and experiments. Then, the technique is extended to applications of band 40 and 41 SAW filters. The experimental results show that with the wider dummy structure, the band 40 and 41 SAW filters achieve a more than 0.2 dB improvement in the insertion loss, a wider bandwidth and a steeper roll-off characteristic. This technique may also be extended to the other band SAW filter applications.

Abstract Exosomes are a new way of the communication between the tumor cell and macrophage in the micro-environment. The macrophage can be induced to different phenotypes according to the different tumors. In the present study, long-chain noncoding RNA HOTAIR (lncRNA HOTAIR) was highly expressed in LSCC and exosomes. The pathway of exosomal lncRNA HOTAIR inducing macrophage to M2 polarization in the LSCC was investigated. The carcinoma tissues and adjacent tissues were collected from 104 LSCC cases, and the positive relationship between CD163-/CD206-M2 macrophage infiltration and clinical phase, lymph node spreading and pathological phase in LSCC was observed. To examine the role of exosomal lncRNA HOTAIR, macrophages were co-cultured with LSCC-exosomes of high lncRNA HOTAIR expression or transferred with HOTAIR mimics. It was suggested that exosomal lncRNA HOTAIR can induce macrophages to M2 polarization by PI3K/p-AKT/AKT signaling pathway. Furthermore, exo-treated M2 macrophages facilitate the migration, proliferation, and EMT of LSCC.

Abstract For many clonally propagated species, the accumulation of somatic mutations is the principal driver of declines in yield and quality. However, somatic mutations may also promote genetic diversification. Thus, elucidating somatic mutation rates and patterns is important to understand the genetic basis undergirding the emergence of commercially valuable traits and developmental processes. In this study, we studied the effect of short‐time clonal domestication of Populus alba var. pyramidalis, a species that has been propagated by cutting for the last 67 years. We found that: (1) the somatic mutation rate for P. alba var. pyramidalis is 9.24 × 10−9, which is higher than rates observed in related species; (2) there were more mutations near heterozygous regions, and a larger proportion of CpG and CHG sites were associated with somatic mutations, which may be related to the blocking of DNA repair by methylation; and (3) deleterious mutations were not shared by multiple individuals, and all occurred in heterozygous states, demonstrating the strong selective pressures that act against deleterious mutations. Taken together, the results of our study provide a global view of somatic mutation that will aid efforts to understand the genetic basis of commercially valuable traits and to improve clonally breeding species.

Abstract Graphene oxide (GO) is playing an increasing role in many technologies. However, it remains unanswered how to strategically distribute the functional groups to further enhance performance. We utilize deep reinforcement learning (RL) to design mechanically tough GOs. The design task is formulated as a sequential decision process, and policy-gradient RL models are employed to maximize the toughness of GO. Results show that our approach can stably generate functional group distributions with a toughness value over two standard deviations above the mean of random GOs. In addition, our RL approach reaches optimized functional group distributions within only 5000 rollouts, while the simplest design task has 2 × 1011 possibilities. Finally, we show that our approach is scalable in terms of the functional group density and the GO size. The present research showcases the impact of functional group distribution on GO properties, and illustrates the effectiveness and data efficiency of the deep RL approach.

Abstract The northern fringe area of the East Asian summer monsoon (EASM) between arid and semiarid regions is a fragile eco-environment zone and ecological transition zone, and it is highly sensitive to climate change. Predicting the future migration of the northern boundary of the EASM is important for understanding future East Asian climate change and formulating of decisions on ecological protection and economic development in arid and semiarid regions. The reanalysis dataset and simulations of 23 models from the Coupled Models Intercomparison Project Phase 6 (CMIP6) were used to investigate the response of the boundary of the ESAM to the global warming. The multi-model ensemble showed a northwestward migration of the EASM northern boundary during the near-term (2020–2060) and late-term (2061–2099) of the twenty-first century under various Shared Socioeconomic Pathways (SSPs). The northern boundary migrated northwestward by 23–28 and 74–76 km in the near-term and late-term respectively, under SSP1-2.6, 2-4.5 and 3-7.0 and by ~ 44 km and ~ 107 km respectively during the near-term and late-term under SSP5-8.5. During the twenty-first century, under various SSPs, the surface of the East Asian subcontinent warmed more than the ocean, thereby increasing the contrast of near-surface temperature and sea level pressure in summer between the East Asian subcontinent and the surrounding oceans. In turn, the intensified land–sea thermal contrast reinforced the EASM meridional circulation and thus transported more moisture from the Indian Ocean into northern China. Additionally, a poleward migration and weakening of the East Asian subtropical westerly jet would also favor an increase in precipitation, eventually caused a northwestward migration of the EASM northern boundary. The results suggest that the arid and semiarid ecotone will become wetter, which could dramatically improve the eco-environment in the future.

Abstract Sigma‐1 receptor (Sigmar1) is a specific chaperone located in the mitochondria‐associated endoplasmic reticulum membrane (MAM) and plays a role in several physiological processes. However, the role of Sigmar1 in bone homeostasis remains unknown. Here, we show that mice lacking Sigmar1 exhibited severe osteoporosis in an ovariectomized model. In contrast, overexpression of Sigmar1 locally alleviated the osteoporosis phenotype. Treatment with Sigmar1 agonists impaired both human and mice osteoclast formation in vitro. Mechanistically, SERCA2 was identified to interact with Sigmar1 based on the immunoprecipitation‐mass spectrum (IP‐MS) and co‐immunoprecipitation (co‐IP) assays, and Q615 of SERCA2 was confirmed to be the critical residue for their binding. Furthermore, Sigmar1 promoted SERCA2 degradation through Hrd1/Sel1L‐dependent ER‐associated degradation (ERAD). Ubiquitination of SERCA2 at K460 and K541 was responsible for its proteasomal degradation. Consequently, inhibition of SERCA2 impeded Sigmar1 deficiency enhanced osteoclastogenesis. Moreover, we found that dimemorfan, an FDA‐approved Sigmar1 agonist, effectively rescued bone mass in various established bone‐loss models. In conclusion, Sigmar1 is a negative regulator of osteoclastogenesis, and activation of Sigmar1 by dimemorfan may be a potential treatment for osteoporosis in clinical practice.

SAW devices with a multi-layered piezoelectric substrate have excellent performance due to advantages such as a high quality factor, Q, low loss insertion, large bandwidth, etc. Prior to manufacturing, a comprehensive analysis and proper design are essential to evaluating the device’s key performance indicators, including the Bode Q value, bandwidth, and transverse mode suppression. This study explored the performance of SAW resonators employing a 42°Y-X LiTaO3 (LT) thin-plate-based multi-layered piezoelectric substrate. The thicknesses for each layer of the 42°Y-X LT/SiO2/poly-Si/Si substrate were optimized according to the index of phase velocity, Bode Q value, and bandwidth. The effect of the device structure parameters on the dispersion curve and slowness curve was studied, and a flat slowness curve was found to be favorable for transverse mode suppression. In addition, the design of the dummy configuration was also optimized for the suppression of spurious waves. Based on the optimized design, a one-port resonator on the 42°Y-X LT/SiO2/poly-Si/Si substrate was fabricated. The simulation results and measurements are presented and compared, which provides guidelines for the design of new types of SAW devices configured with complex structures.

Abstract Aims/Introduction Body aging is a universal biological process. With aging, cells undergo a series of physiological changes. The main feature is cell proliferation decline, although the cells still have normal functions. Pancreatic β‐cells are no exception. However, the physiological senescence of β‐cells, and the resulting function and transcriptome changes have rarely attracted attention. The specific senescence phenotype of β‐cells remains unknown. Materials and Methods Pancreatic samples from three female C57BL/6 mice with aged 2.5 months (young) mice and 20 months (old) were digested to a single‐cell suspension and analyzed, with 10× Genomics single‐cell ribonucleic acid sequencing, β‐cells were determined by biosynthesis analysis, and differences between old and young mice were identified. Results A total of 47 differential genes with significant and statistical significance were screened in β‐cells (fold change >1.5, P

AimTriiodothyronine (T3) administration significantly eliminates hepatic steatosis and also has a therapeutic effect on non-alcoholic steatohepatitis (NASH). However, the potential mechanism by which T3-mediated exercise improves NASH is unknow. This study aimed to explore the effect of aerobic exercise on liver injury in NASH.MethodsAerobic exercise was conducted to explore the effects of exercise on liver injury in NASH model induced by Atherosclerotic (Ath) diet. Biochemical evaluations, histological staining and real-time PCR were first applied to confirm the amelioration effects of exercise on NASH. RNA-sequencing (RNA-seq) analysis for livers of each group were further used to identify the underlying mechanisms of aerobic exercise. Bioinformatics methods were used to explore the key functional pathways involved in the improvement of liver tissue in NASH mice by aerobic exercise.ResultsAerobic exercise improved hepatic steatosis, lobular inflammation and fibrosis in NASH mice. multiple inflammation-related pathways were significantly enriched in the liver of NASH group and improved by aerobic exercise. The results of gene set variation analysis (GSVA) showed a higher enrichment score of T3 response signature in NASH mice with exercise. Increased Dio1 expression in the liver of NASH with exercise mice and increased circulating FT3 and FT4 levels upon aerobic exercise were confirmed.ConclusionsWe found that aerobic exercise could significantly reduce hepatic lipid accumulation, inflammatory infiltration and fibrosis progression in the liver of NASH mice. Hepatic thyroid hormone signaling activation and circulating thyroid hormones is potentially involved in the amelioration effect of aerobatic exercise on NASH progression.

Abstract In this study we presented a composite standard chronology, spanning 1635–2018 to reconstruct May–July moisture variation in north central China. Our reconstruction revealed four severe dry epochs and five pronounced wet epochs. Additionally, spatial correlation analysis of our reconstruction with the actual self-calibrating Palmer drought severity index showed that our reconstruction was representative of large-scale May–July moisture changes. Both the severe dry and pronounced wet epochs showed one-to-one correspondence with other reconstructions nearby during their common periods, which demonstrated the reliability of our reconstruction backwards in time. Spectral analysis showed that significant spectral peaks were found at 2.1–3.8 years, which fell within the overall bandwidth of the El Niño-Southern Oscillations (ENSO). The spatial correlation patterns between our reconstruction and sea surface temperature (SST) in the equatorial eastern Pacific further confirmed the link between regional moisture and ENSO, with warm-phase ENSO resulting in low moisture and vice-versa. However, this link was time-dependent during the past four centuries, and was modulated by different phases of SST in the tropical Indian Ocean. Additionally, significant peaks at 24.9–46.5 years and spatial correlation patterns indicated that the Pacific Decadal Oscillation and the North Atlantic Oscillation may be the possible forcing factors of regional moisture at lower frequencies.

Abstract The low‐energy layer edge states (LESs) from quasi 2D hybrid perovskite single crystals have shown great potential because of their nontrivial photoelectrical properties. However, the underlying formation mechanism of the LESs still remains controversial. Also, the presence or creation of the LESs is of high randomness due to the lack of proper techniques to manually generate these LESs. Herein, using a single crystals platform of quasi‐2D (BA)2(MA)n−1PbnI3n+1 (n > 1) perovskites, the femtosecond laser ablation approach to design and write the LESs with a high spatial resolution is reported. Fundamentally, these LESs are of smaller bandgap 3D MAPbI3 nanocrystals which are formed by the laser‐induced BA escaping from the lattice and thus the lattice shrinkage from quasi‐2D to 3D structures. Furthermore, by covering the crystal with tape, an additional high‐energy emission state corresponding to the reformation of (BA)2PbI4 (n = 1) within the irradiation region is generated. This work presents a simple and efficient protocol to manually write LESs on single crystals and thus lays the foundation for utilizing these LESs to further enhance the performance of future photoelectronic devices.

Abstract Genome size varies greatly across the flowering plants and has played an important role in shaping their evolution. It has been reported that many factors correlate with the variation in genome size, but few studies have systematically explored this at the genomic level. Here, we scan genomic information for 74 species from 74 families in 38 orders covering the major groups of angiosperms (the taxonomic information was acquired from the latest Angiosperm Phylogeny Group (APG IV) system) to evaluate the correlation between genome size variation and different genome characteristics: polyploidization, different types of repeat sequence content, and the dynamics of long terminal repeat retrotransposons (LTRs). Surprisingly, we found that polyploidization shows no significant correlation with genome size, while LTR content demonstrates a significantly positive correlation. This may be due to genome instability after polyploidization, and since LTRs occupy most of the genome content, it may directly result in most of the genome variation. We found that the LTR insertion time is significantly negatively correlated with genome size, which may reflect the competition between insertion and deletion of LTRs in each genome, and that the old insertions are usually easy to recognize and eliminate. We also noticed that most of the LTR burst occurred within the last 3 million years, a timeframe consistent with the violent climate fluctuations in the Pleistocene. Our findings enhance our understanding of genome size evolution within angiosperms, and our methods offer immediate implications for corresponding research in other datasets.

AbstractAlthough many temperature time-series reconstructions, constructed on the basis of tree-ring widths, with lengths of centuries to millennia have been published for many parts of the southeastern Tibetan Plateau (SETP), field reconstruction has been unaddressed to date. Here, we present a tree-ring network comprising 53 chronologies (one δ13C chronology, one regional maximum latewood density chronology, and 51 width chronologies). Using the modified point-by-point reconstruction method, a temperature field consisting of data from 28 meteorological stations in the SETP over the past 200–500 y (the common period was from 1730 to 1998; the longest single period was from 1480 to 2002) was reconstructed. Principal component analysis and rotated principal component analysis results indicated that the reconstructed temperature field may be representative of spatial distribution characteristics. The temperature changes in the SETP were similar. The correlation coefficients between the reconstructed regional average temperature and each observed series were 0.44–0.83 (86% were greater than 0.6, and all the significance tests reported a level of 0.01). Temperature changes were mainly synchronous with those in the middle and low latitudes of the Northern Hemisphere. We then explored the influence of possible climatic forcing and found that the Indian Ocean Basin mode may be a principal factor controlling the interannual variations in the STEP summer temperatures.

Abstract Background To investigate associations between long-term shoulder loading and sagittal spino-pelvic morphology in Chinese farmers from radiology evidences. Methods We retrospectively analyzed 463 back pain patients who attended outpatient and inpatient departments of two hospitals from January 2016 to December 2018, and who had long, standing lateral X-rays according to inclusion and exclusion criteria. One hundred eighty-four of them were farmers with a long history of heavy shoulder loading for over 20 years in their young age, while others were office workers with no reported long-term shoulder loading history. The following parameters were measured by three researchers independently and then analyzed statistically: thoracic kyphosis (TK), lumbar lordosis (LL), thoracolumbar kyphosis (TLK), T9 sagittal offset (T9SO), T1 sagittal offset (T1SO), sacral slope (SS), pelvic incidence (PI), pelvic tilt (PT), C7 tilt (C7T), spino-pelvic angle (SSA), and sagittal vertical axis (SVA). Results The “Loading group” included 86 males and 98 females with average age 73.3 (SD 8.3) years, whereas the “Non-loading group” included 126 males and 153 females with average age 63.7 (SD 14.1) years. Age was significantly higher in the loading group (p < 0.001), but gender, height, weight, BMI, and BMD were not significantly different (p > 0.05). The following spino-pelvic parameters were significantly greater (p < 0.05) in the loading group: TK (mean 39.1° vs 32.8°), TLK (25.8° vs 10.1°), and T9SO (12.2° vs 10.1°). Other values were not significantly different between the two groups (p > 0.05). Conclusion Long-term shoulder loading in youth is a risk factor for pathological thoracic kyphosis especially in the lower thoracic spinal segments when farmers getting older.

Ocean temperature, salinity, and electric conductivity are essential ocean properties. Their structure and changes directly impact physical, chemical and biological processes in oceans. Since the 1970s, numerous researches have focused on the morphological analysis of vertical profiles in oceanography. However, due to the complexity of an ocean environment, most of them are conducted at local scales or only focus on single elements, e.g., ocean temperature or salinity. This work aims to achieve the joint classification of the vertical structure of ocean properties at a global scale and present two-dimension regional characteristics. Based on 150 seawater profiles from the National Oceanographic Data Center, this work explores such characteristics of ocean temperature, salinity and electric conductivity in the deep sea and achieves global-scale joint classification. We demonstrate that their vertical features have clear regional characteristics and can be classified into four types, i.e., bidirectional gradient, homo-dromous contravariant, homo-dromous gradient, and homodromous gradient (salinity) ones. In addition, our results prove that there exist the power-law distributions of these three factors in intermediate water, which may be explained through the self-organization theory. Moreover, the `up-tail' phenomenon is widely discovered in the vertical structure of electric conductivity, and it may be considered as a combined effect caused by temperature, pressure, and salinity.

There are mainly three limitations of the traditional facial attribute editing techniques: 1) incapability of generating an arbitrary facial image with high-resolution; 2) being unable to generate and edit new facial images synthesized by the computer and 3) limited diversity of edited images. This paper presents a method for generating and editing images simultaneously. It incorporates a high-resolution facial image generator, a multi-label classifier, and a Generalized Linear Model (GLM). Experimental results show that our method can generate arbitrary high-resolution facial images, edit computer-synthesized images, perform multi-attribute editing, and effectively control the intensity and style of the generated images. Besides, the approach has high efficiency and flexibility, allowing rapid migration of attribute information from the data set. We design a graphical interface program, which can be integrated as a mobile application.

The artificial potential field approach is an efficient path planning method. However, to deal with the local-stable-point problem in complex environments, it needs to modify the potential field and increases the complexity of the algorithm. This study combines improved black-hole potential field and reinforcement learning to solve the problems which are scenarios of local-stable-points. The black-hole potential field is used as the environment in a reinforcement learning algorithm. Agents automatically adapt to the environment and learn how to utilize basic environmental information to find targets. Moreover, trained agents adopt variable environments with the curriculum learning method. Meanwhile, the visualization of the avoidance process demonstrates how agents avoid obstacles and reach the target. Our method is evaluated under static and dynamic experiments. The results show that agents automatically learn how to jump out of local stability points without prior knowledge.

Machine learning methods for health care delivery optimization have the potential to improve retention in HIV care, a critical target of global efforts to end the epidemic. However, these methods have not been widely applied to medical record data in low- and middle-income countries. We used an ensemble decision tree approach to predict risk of disengagement from HIV care (missing an appointment by ≥28 days) in Tanzania. Our approach used routine electronic medical records (EMR) from the time of antiretroviral therapy (ART) initiation through 24 months of follow-up for 178 adults (63% female). We compared prediction accuracy when using EMR-based predictors alone and in combination with sociodemographic survey data collected by a research study. Models that included only EMR-based indicators and incorporated changes across past clinical visits achieved a mean accuracy of 75.2% for predicting risk of disengagement in the next 6 months, with a mean sensitivity of 54.7% for targeting the 30% highest-risk individuals. Additionally including survey-based predictors only modestly improved model performance. The most important variables for prediction were time-varying EMR indicators including changes in treatment status, body weight, and WHO clinical stage. Machine learning methods applied to existing EMR data in resource-constrained settings can predict individuals' future risk of disengagement from HIV care, potentially enabling better targeting and efficiency of interventions to promote retention in care.

Abstract Background As an inflammatory factor and oncogenic driver protein, the pleiotropic cytokine macrophage migration inhibitory factor (MIF) plays a crucial role in the osteosarcoma microenvironment. Although 4‐iodo‐6‐phenylpyrimidine (4‐IPP) can inactivate MIF biological functions, its anti‐osteosarcoma effect and molecular mechanisms have not been investigated. In this study, we identified the MIF inhibitor 4‐IPP as a specific double‐effector drug for osteosarcoma with both anti‐tumour and anti‐osteoclastogenic functions. Methods The anti‐cancer effects of 4‐IPP were evaluated by wound healing assay, cell cycle analysis, colony formation assay, CCK‐8 assay, apoptosis analysis, and Transwell migration/invasion assays. Through the application of a luciferase reporter, chromatin immunoprecipitation assays, and immunofluorescence and coimmunoprecipitation analyses, the transcriptional regulation of the NF‐κB/P‐TEFb complex on c‐Myb‐ and STUB1‐mediated proteasome‐dependent MIF protein degradation was confirmed. The effect of 4‐IPP on tumour growth and metastasis was assessed using an HOS‐derived tail vein metastasis model and subcutaneous and orthotopic xenograft tumour models. Results In vitro, 4‐IPP significantly reduced the proliferation and metastasis of osteosarcoma cells by suppressing the NF‐κB pathway. 4‐IPP hindered the binding between MIF and CD74 as well as p65. Moreover, 4‐IPP inhibited MIF to interrupt the formation of downstream NF‐κB/P‐TEFb complexes, leading to the down‐regulation of c‐Myb transcription. Interestingly, the implementation of 4‐IPP can mediate small molecule‐induced MIF protein proteasomal degradation via the STUB1 E3 ligand. However, 4‐IPP still interrupted MIF‐mediated communication between osteosarcoma cells and osteoclasts, thus promoting osteoclastogenesis. Remarkably, 4‐IPP strongly reduced HOS‐derived xenograft osteosarcoma tumourigenesis and metastasis in an in vivo mouse model. Conclusions Our findings demonstrate that the small molecule 4‐IPP targeting the MIF protein exerts an anti‐osteosarcoma effect by simultaneously inactivating the biological functions of MIF and promoting its proteasomal degradation. Direct destabilization of the MIF protein with 4‐IPP may be a promising therapeutic strategy for treating osteosarcoma.

Studying the variation in the boundary position of the Asian summer monsoon in the Tibetan Plateau (TP) region and its potential drivers is important for understanding the climate in this region. Three sets of mean monthly precipitation data from 1980 to 2019 were sourced from the Global Precipitation Climatology Centre, the Climate Research Unit, and China Meteorological Information Service Centre. Several indicators that represent the Asian summer monsoon boundary (ASMB) were selected to compare their applicability to the TP region and elucidate the changes in the location of the ASMB in the TP over the last four decades. The results showed that the ASMB in the TP region extends in a southwest-northeast direction, with a clear north-south variation. It reaches as far north as the Kunlun Mountains and as far south as the Himalayas. The largest amplitude in spatial fluctuation occurs in the middle of the TP, and the smallest amplitude occurs at both ends of the region. A “small-large-small” fluctuation pattern was observed from west to east. The water vapor mainly originates from the South Asian region. The South Asian summer monsoon can move the ASMB position northward, whereas the westerly wind moves the ASMB position southward. Variation in the ASMB in the TP region is closely associated with the South Asian monsoon and westerly wind.

In this paper, we propose a novel intelligent model, called AttentiveHerb, for simulating the doctor's inquiry and prescription that is composed by a series of herbs. It can automatically simulate some principles and learns the interaction between symptoms and herbs from clinical records of traditional herbal medicine. This model consists of two different attention mechanisms for distinguishing the main symptoms and paying different attention to different symptoms. By experiments, in terms of the predicted prescriptions, 51% of the total cases are in full accordance with the labels; in 1.09% of cases, all herbs of a label can be found in the predicted prescription and the predicted prescription have other additional herbs; in 15.4% of cases, all herbs of a predicted prescription can be found in their corresponding label; in 22.41% of cases, several herbs in each predicted prescription overlap with its label; and 10.1% of cases are completely different from the label. In summary, 67.49% of the predicted prescriptions are close to the labels, and 89.9% contain the same herbs with the labels, which indicates that the prescriptions generated by our model are close to those by doctors. Besides, our model can recommend herbs that do not appear in the label prescriptions but are useful for relieving symptoms. It shows that our model can learn some interactions between herbs and symptoms. With enough normalized traditional herbal medical records, this model works more accurately. This study also provides a benchmark for the upcoming researches in intelligent inquiry and prescription generation of traditional herbal medicine.

Speciation is the key evolutionary process for generating biological diversity and has a central place in evolutionary and ecological research. How species diverge and adapt to different habitats is one of the most exciting areas in speciation studies. Here, we sequenced 55 individuals from three closely related species in the genus Carpinus: Carpinus tibetana, Carpinus monbeigiana, and Carpinus mollicoma to understand the strength and direction of gene flow and selection during the speciation process. We found low genetic diversity in C. tibetana, which reflects its extremely small effective population size. The speciation analysis between C. monbeigiana and C. mollicoma revealed that both species diverged ∼1.2 Mya with bidirectional gene flow. A total of 291 highly diverged genes, 223 copy number variants genes, and 269 positive selected genes were recovered from the two species. Genes associated with the diverged and positively selected regions were mainly involved in thermoregulation, plant development, and response to stress, which included adaptations to their habitats. We also found a great population decline and a low genetic divergence of C. tibetana, which suggests that this species is extremely vulnerable. We believe that the current diversification and adaption study and the important genomic resource sequenced herein will facilitate the speciation studies and serve as an important methodological reference for future research.

Poplars are worldwidely cultivated with ecologically and economically important value. Populus alba var. pyramidalis (= P. bolleana) is a main tree of the farmland shelter-belt system in the arid region of Northwest China due to its rapid growth, erect stems, and high biomass production. However, the full-length messenger RNA (mRNA) sequences and complete structure of P. alba var. pyramidalis remain unclear. In this study, using single-molecular real-time (SMRT) and next-generation high-throughput sequencing (NGS) platform, we sequenced transcripts from leaf, root, xylem, and phloem of P. alba var. pyramidalis, to obtain the full-length mRNA transcripts and annotate the complete structure. In total, 86,327 mapped full-length non-chimeric (FLNC) reads were identified, with 705 previously unannotated loci and 3,410 long noncoding RNAs (lncRNAs) and 174 fusion genes found. Alternative spicing (AS) events were detected in 7,536 genes, of which 4,652 genes had multiple AS events. A total of 10,213 alternative polyadenylation (APA) sites were identified, with two or more APA sites observed in 2,212 genes. Our transcriptome data provided the full-length sequences and gene isoforms of transcripts for P. alba var. pyramidalis, which will be helpful in improving our understanding for the genome annotation and gene structures of P. alba var. pyramidalis.

The quantitative understanding of human behavior is a key issue in modern science. Recently, inhomogeneous human activities have been described by bursts (consecutive activities separated by long periods of inactivity) and characterized by fat-tailed inter-event time (interval between two activities) distributions. However, the dynamics between number of activities and activity duration are still unclear. In this study, we analyzed 133 million toll-free call records from China to study the dynamics between call frequency and call duration. We confirmed that both call frequency and call duration exhibit circadian cycles and weekly cycles. By analyzing intraday patterns of these two metrics, we found the opposite volatility and clustered distributions. Results of clustering analysis showed that calling activity to toll-free numbers can be clustered into four clusters. In the "Work" cluster, the distribution of call duration was significantly different from that in the other clusters. The corresponding time of "Work" cluster was much shorter than estimates based on common sense. Intraday patterns and clustering results showed that both call frequency and call duration are primarily related to circadian cycles, the nature of human beings, and that work is a secondary factor that affects these variables. Moreover, we found a strong positive correlation between call frequency and call duration, as well as polarization of joint probability. The polarization indicates two extremes in inhomogeneous calling activity to toll-free numbers, i.e., either people are very busy or very idle. The empirical probability of the extreme was approximately four times that of random probability. Our findings may have great usage for studying the dynamics of inhomogeneous human behavior.

Notably known for its extraordinary thermal and mechanical properties, graphene is a favorable building block in various cutting-edge technologies such as flexible electronics and supercapacitors. However, the almost inevitable existence of defects severely compromises the properties of graphene, and defect prediction is a difficult, yet important, task. Emerging machine learning approaches offer opportunities to predict target properties such as defect distribution by exploiting readily available data, without incurring much experimental cost. Most previous machine learning techniques require the size of training data and predicted material systems of interest to be identical. This limits their broader application, because in practice a newly encountered material system may have a different size compared with the previously observed ones. In this paper, we develop a transferable learning approach for graphene defect prediction, which can be used on graphene with various sizes or shapes not seen in the training data. The proposed approach employs logistic regression and utilizes data on local vibrational energy distributions of small graphene from molecular dynamics simulations, in the hopes that vibrational energy distributions can reflect local structural anomalies. The results show that our machine learning model, trained only with data on smaller graphene, can achieve up to 80% prediction accuracy of defects in larger graphene under different practical metrics. The present research sheds light on scalable graphene defect prediction and opens doors for data-driven defect detection for a broad range of two-dimensional materials.

Due to the lack of long-term climate records, our understanding of paleoclimatic variability in the Tibetan Plateau (TP) is still limited. In this study, we developed a tree-ring width (TRW) chronology based on tree-ring cores collected from our study site, southeastern TP. This chronology responded well to the mean maximum temperatures of May–June and was thus used to reconstruct early summer (May–June) maximum temperature during the period 1541–2019. The reconstruction explained 33.6% of the climatic variance during the calibration period 1962–2019. There were 34 extremely warm years (7.2% of total years) and 36 extremely cold years (7.5% of total years) during the reconstruction period. The spatial correlation analysis and the comparison with other local temperature reconstructions confirmed the reliability and representativeness of our reconstruction. The results of the ensemble empirical mode decomposition (EEMD) analysis indicated quasi-oscillations of 2.9–4.2 years, 4.5–8.3 years, 11.1–15.4 years, 20–33.3 years, 50.4 years, 159.7 years, and 250 years in this temperature reconstruction which may be associated with ENSO cycles, solar activity, and PDO.

A macroscopic three-dimensional physical simulating model of multi-well fracture-cavity units was designed and constructed based on similarity theory. The characteristics and the water breakthrough pattern of fracture-cavity reservoirs developed in bottom water depletion and water injection modes were investigated by the model. The results show that, in bottom water drive, under the effect of bottom water depletion and water breakthrough, the wells had high productivity in early stage and fast decline. After energy supplement by injecting water, the productivity rebounded in a short time and then began a slow decline. The bottom water tended to coning to the wells at the place of bottom water entry. The water breakthrough pattern is spot pattern and the water breakthrough time is controlled by the well's connectivity to the bottom water; the water injection can inhibit coning and intrusion of bottom water, turning the spot pattern water breakthrough in bottom water drive period into planar line form, and the water breakthrough time in water injection period was mainly influenced by the well depth. The water cut of wells in water flooding multi-well fracture-cavity units changes in three patterns: slow rise, staircase rise and abrupt watered-out, which is influenced by the reservoir type and the coordination number. When the well encounters cavity, the water cut increasing rate slows down with the increase of the coordination number; when the well drilled fractures, the water cut changes in staircase pattern with the increase of coordination number. Key words: fracture-cavity reservoir, macroscopic three-dimensional physical simulation model, similarity theory, bottom water coning, inhibit coning by water injection, varying pattern of water cut

Vegetation phenology regulates many ecosystem processes and is an indicator of the biological responses to climate change. It is important to model the timing of leaf senescence accurately, since the canopy duration and carbon assimilation are strongly determined by the timings of leaf senescence. However, the existing phenology models are unlikely to accurately predict the end of the growing season (EGS) on large scales, resulting in the misrepresentation of the seasonality and interannual variability of biosphere-atmosphere feedbacks and interactions in coupled global climate models. In this paper, we presented a novel large-scale temperature dominated model integrated with the physiological adaptation of plants to the local temperature to assess the spatial pattern and interannual variability of the EGS. Our model was validated in all temperate vegetation types over the Northern Hemisphere. The results indicated that our model showed better performance in representing the spatial and interannual variability of leaf senescence, compared with the original phenology model in the Integrated Biosphere Simulator (IBIS). Our model explained approximately 63% of the EGS variations, whereas the original model explained much lower variations (coefficient of determination R2 = 0.01-0.18). In addition, the differences between the EGS reproduced by our model and the MODIS EGS at 71.3% of the pixels were within 10 days. For the original model, it is only 26.1%. We also found that the temperature threshold (TcritTm) of grassland was lower than that of woody species in the same latitudinal zone.

To evaluate rice plant type precisely at the seedling stage, we established a quantitative evaluation method using an image analysis. P-type Fourier descriptors, which could apply an “open-curve”, were used for the plant type, and the coefficients were summarized as the scores of principal components (PCs) by principal component analysis. At the same time, “conventional plant type traits”, leaf blade length, leaf blade angle and inter-leaf-blade length, were measured by traditional plant type measurement methods. Based on the PC scores, the plant type for each PC was reconstructed by inverse Fourier transformation and the morphological characteristics were evaluated. To examine whether our method could appropriately identify the characteristics of the varieties, we discriminated the varieties using the PC scores by support vector machines. The varieties were also discriminated using the conventional traits of plant type. The results indicated that both traits had equal discrimination efficiency. In addition, the combination of conventional traits and scores had the highest discrimination efficiency. The relationship between the PC scores and conventional traits was examined by multiple regression analysis. The PC scores were not correlated with size-related traits. From these results, our research clarified that a plant type evaluation method using P-type Fourier descriptors could evaluate rice plant type precisely combined with conventional methods.

Measuring volatility in financial markets is a primary challenge in the theory and practice of risk management and is essential when developing investment strategies. Although the vast literature on the topic describes many different models, two nonparametric measurements have emerged and received wide use over the past decade: realized volatility and absolute return volatility. The former is strongly favored in the financial sector and the latter by econophysicists. We examine the memory and clustering features of these two methods and find that both enable strong predictions. We compare the two in detail and find that although realized volatility has a better short-term effect that allows predictions of near-future market behavior, absolute return volatility is easier to calculate and, as a risk indicator, has approximately the same sensitivity as realized volatility. Our detailed empirical analysis yields valuable guidelines for both researchers and market participants because it provides a significantly clearer comparison of the strengths and weaknesses of the two methods.