Browse Articles

Effects of the alloying Mn and Co on the martensitic transformation behaviors and damping properties of Cu-Al-Fe high-temperature shape memory alloys

High-resolution ptychographic nanoimaging under high pressure with X-ray beam scanning

We present an approach to nanoscale-resolution high-sensitivity imaging of internal material structure under in situ/operando conditions for virtually any sample environment. When bulky or heavy sample environment is required state-of-the-art X-ray imaging techniques, such as scanning and full-field microscopy or holography fail to deliver high-resolution imaging capabilities due to either i) extremely small optics’ working distance for magnification-based methods or ii) the inability to precisely control heavy sample position in the case of lens-less methods. In this work, we address those challenges for a scanning lens-less imaging method called ptychography. Instead of precisely controlling the sample position during raster scan in a focused, confined X-ray beam, we are scanning that beam across the sample. This overcomes the constraints on scanning procedure imposed by sample size/weight and delivers unmatched scanning speed while maintaining high precision of beam position during the scan. We directly applied our approach, showcasing phase contrast nanoimaging with diamond anvil cells, and visualized intricate details of the melting and oxidation of laser-irradiated iron under pressure of 50 GPa.

Bayesian-optimized machine learning boosts actual evapotranspiration prediction in water-stressed agricultural regions of China

Abstract The accurate estimation of actual evapotranspiration (AET) is crucial for sustainable water resource management, especially in water-scarce and agriculturally intensive regions like Beijing and Tianjin, China. Traditional methods for AET estimation, whether empirical or physically based, often face limitations due to high data requirements, limited scalability, and sensitivity to input uncertainties. This creates a critical research gap in providing reliable AET predictions under data-limited conditions. To address this, we evaluated the efficacy of integrating four advanced machine learning (ML) models: Support Vector Machine (SVM), Gaussian Process Regression (GPR), Ensemble Tree, and Neural Network, with Bayesian hyperparameter optimization for AET modeling using the high-resolution TerraClimate dataset spanning 1958–2022. Key meteorological variables, including maximum and minimum temperature (T max and T min ), solar radiation (SR), wind speed (WS), vapor pressure deficit (VPD), and precipitation (PPT), were selected through rigorous correlation and multicollinearity analyses. Model performance was assessed using the coefficient of determination (R 2 ), mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE) on a 75:25 train-test split. Results demonstrate that the optimizable GPR model achieved the highest predictive accuracy (RMSE = 5.54, R 2  = 0.98 on test data), outperforming other ML approaches and traditional empirical models. PPT, T min , and T max emerged as the most influential predictors for AET. Our findings reveal that ML models, particularly when optimized via Bayesian techniques, yield a robust, scalable, and data-efficient alternative for AET estimation in regions with limited meteorological records. This study establishes a new benchmark for AET modeling, with significant implications for irrigation scheduling, drought monitoring, and integrated water management in the North China Plain and comparable agro-ecological regions.

Universal exact solutions for multiphysical inhomogeneities and inclusions in Fourier space

Physical inhomogeneities—spanning atomic-scale crystal defects and quantum dots to macroscopic features like bone cavities and geological faults—are fundamental to both engineered and natural systems. The study of how these inclusions influence material fields has driven critical advances across multiple disciplines, from composite materials and solid-state physics to biomechanics and geophysics. Eshelby revolutionized this field with the equivalent inclusion method (EIM), transforming inhomogeneous problems into homogeneous ones by introducing an equivalent eigen-strain. However, determining Eshelby’s equivalent eigen-strain for arbitrary inclusions has remained a long-standing challenge. Here, we resolve this challenge and applied the EIM to multiphysics problems, deriving three-dimensional universal exact solutions in Fourier space for the generalized equivalent eigenfield, encompassing eigen-strain, eigen-electric, and eigen-magnetic fields. These solutions apply to both single and multiple inclusions of arbitrary shape, inhomogeneity, and anisotropy. Our results yield exact expressions for the effective properties of magnetoelectroelastic composites. We demonstrate the framework’s versatility through complex multiphase applications, including a general expression for stress intensity factors of arbitrarily shaped cracks, a criterion for designing auxetic materials via engineered elastic inhomogeneities and an optimized pathway to enhance magnetoelectric coupling. This work establishes a universal approach to multiphysics inhomogeneous inclusion problems, rooted in multiple disciplines and spanning multiple length scales. It offers both profound fundamental insights and practical utility, paving the way for advancements in diverse fields.

From hypothesis to robust evidence in a nationwide multicenter case–control study on 1490 women: milk stasis and idiopathic granulomatous mastitis

Single-cell metabolome and RNA-seq multiplexing on single plant cells

Plants produce valuable natural products used for a wide variety of industrial applications. Thus, there is enormous interest in elucidating the biosynthetic pathways that are responsible for the production of these compounds. Identification of the genes that comprise these biosynthetic pathways has been enabled by gene-to-metabolite networks that are generated from transcriptomic and metabolomic datasets. Recent advances in both single-cell RNA-seq (scRNA-seq) and single-cell mass spectrometry metabolomics (scMS) have enabled the measurement of either gene expression or metabolite levels in individual cells. However, these datasets can only be used to indirectly correlate gene expression levels with metabolite concentrations at the single-cell level. In this proof-of-concept study, performed on cells derived from the leaves of the medicinal plant Catharanthus roseus , we demonstrate that both scRNA-seq and scMS can be applied to the same plant cell, thereby enabling direct comparisons between gene expression and metabolite levels. Protoplasts are sorted into 96-well plates using a microfluidics-based robot and then lysed under conditions that are suitable for both scMS and SMART-seq single-cell protocols. This multiplexing approach reveals both qualitative and quantitative correlations between metabolite levels and biosynthetic gene expression in individual cells. This integrated approach sheds light on the underlying processes driving complex plant biosynthesis.

A streamline-based production optimization method for waterflooding reservoirs

The genetic lottery goes to school: Better schools compensate for the effects of students’ genetic differences

In this paper, we investigate whether better schools can compensate for the effects of children’s genetic differences. To this end, we combine data from the Norwegian Mother, Father, and Child Cohort Study (MoBa) with Norwegian register data to estimate the interaction between measures of children’s predisposition to education and school quality. We use MoBa’s genetic data to compute polygenic indices for educational attainment ( PGI EA ). Importantly, MoBa includes genetic data on mother-father-child trios, allowing us to identify causal genetic effects using within-family variation. We calculate school value-added measures from Norwegian register data, allowing us to causally estimate school quality effects. Leveraging the advantages of both data sources, we provide a causally identified study of gene–environment interactions in the school context. We find evidence for substitutability of PGI EA and school quality in reading but not numeracy: A 1 SD increase of school quality decreases the impact of a 1 SD increase of PGI EA on reading test scores by 6%. The substitutability arises through gains of students at the lower end of the PGI EA distribution. This suggests that investments in school quality may help reduce educational inequalities arising from genetic differences between students.

Cuckoo optimization algorithm via Grey Wolf Optimizer for usage in engineering optimization and optimal power flow with renewable energy sources

The dominant sink of oceanic calcium carbonate occurs in undersaturated seawater

Calcium carbonate (CaCO 3 ) particles formed by marine organisms dissolve in seawater and on the ocean floor, constituting a key component of the global ocean carbon cycle. However, neither the magnitude nor the distribution of CaCO 3 dissolution in the modern oceans is well constrained. Here we diagnose CaCO 3 dissolution rates in major oceanic regions and reconstruct CaCO 3 settling flux using a data-constrained ocean circulation model and observational climatologies of global seawater alkalinity and dissolved oxygen. Our approach distinguishes the effects of ocean circulation and CaCO 3 dissolution on seawater alkalinity and dissolved oxygen, and involves only one free parameter, a restoring timescale, which is constrained by compiling carbonate export rates estimated in the literature. We find that CaCO 3 dissolution in shallow waters (above the saturation horizon of aragonite) is much smaller than some recent estimates. Excluding the upper 114 m of the euphotic zone, the fraction of CaCO 3 dissolution within aragonite saturated waters is only about 16.5 ± 5.5%. About 39.8 ± 4.5% of CaCO 3 dissolution occurs below the aragonite saturation horizon but above the saturation horizon of calcite. The remaining 43.7 ± 3.8% dissolves in the deep ocean in undersaturated seawater. These results suggest that the seawater CaCO 3 saturation state is the primary control on CaCO 3 dissolution in the ocean.

A modified near-field target localization method based on vector diagonal loading

A steady-state pool of calcium-dependent actin is maintained by Homer and controls epithelial mechanosensation

Epithelial cells are inherently contractile and in homeostasis, tissue integrity is maintained by balancing the uneven contractile forces in neighboring cells at the cell–cell interface. By contrast, epithelial cells can utilize an imbalance in contractile force to communicate various information to induce tissue-wide response as in wound healing. Contractility is generated and processed at the apical junctional complex (AJC) by the dynamic behavior of the actin cytoskeleton. Calcium signaling can pattern cellular responses based on its reach and amplitude and the actin cytoskeleton is supported by its wide ranging effects on actin regulators. Calcium transients regulate various cell behaviors associated with actin remodeling, such as in damage response and developmental morphogenesis. Here, we report that calcium maintains an adaptive pool of AJC-associated actin that is sensitive to tension and encoded by calcium dynamics. For this, the recently identified epithelial polarity module Homer-MUPP1/PatJ is required. Homer regulates calcium signaling in various tissue contexts through interaction with numerous components of the endoplasmic reticulum (ER) and plasma membrane (PM) calcium signal toolkit. Knockout of either Homer or MUPP1/PatJ attenuated tension-induced calcium response and severely disrupted wound healing migration, which is dependent on guidance input through AJC tension. We also show that Homer is integral to early embryonic neurodevelopment as its suppression causes failure of neural tube closure. Our findings highlight the critical role of localized calcium dynamics on AJC actin remodeling and cellular behavior, elucidating the means of tissue coordination through intercellular tension.

Exploring the correlation between eosinophils with clinical risk of severe pneumonia based on MIMIC-IV data

Exceptional diversity of allorecognition receptors in a nonvertebrate chordate reveals principles of innate allelic discrimination

Highly polymorphic allorecognition systems have been characterized in numerous invertebrate species, and exhibit discriminatory capabilities reminiscent of vertebrate adaptive immunity. As these systems utilize germline encoded receptors, the mechanisms underlying allelic discrimination are unknown. The invertebrate chordate, Botryllus schlosseri, undergoes a natural transplantation reaction controlled by a highly polymorphic, polygenic locus (called the fuhc) with over 1,000 allelic haplotypes found worldwide. Two individuals are compatible if they share one or both fuhc alleles, and we had found that polymorphic discrimination is due to the integration of signals from two allorecognition receptors encoded within the fuhc locus, called fester and uncle fester. Here we show that these two receptors are members of an extended family consisting of >35 genes, now called the Fester family ( FF ), and coexpressed with members of another diverse gene family, the fester coreceptors ( FcoR ). Both FF and FcoR are Immunoglobulin superfamily members and each FcoR encodes conserved tyrosine signal transduction motifs, including ITIMs or hemITAMs. FF and FcoR are expressed and encoded as cognate pairs in two polymorphic haplotypes: one within the fuhc locus, and another on a separate chromosome, and remarkably, copy number variation between haplotypes is of gene pairs. Furthermore, two FcoR genes can swap ITIMs and hemITAMs by alternative splicing, suggesting that dynamic tuning of activating and inhibitory signaling is required for allelic discrimination. These results indicate that conserved signal processing mechanisms are the foundation of both allelic discrimination in Botryllus , and recurring convergent evolution of allorecognition receptors observed from invertebrates to mammals.

Pharmacovigilance study of adverse events of vedolizumab based on the FAERS database

Aberrant X chromosome dosage compensation causes hybrid male inviability in <i>Caenorhabditis</i>

Zygotic reproductive isolation frequently initiates with hybrid incompatibility in the heterogametic sex, such as males in XX/XY systems. The genetic basis of hybrid male incompatibility has long remained elusive. Here, we show that crosses of Caenorhabditis nigoni males with C. briggsae females result in insufficient expression of Cbr-xol-1 , an X-linked master switch responsible for intimately linked sex determination and dosage compensation pathways, consequently triggering aberrant X-chromosome repression in males, and ultimately leading to embryonic inviability. In contrast, male embryos from the reciprocal cross maintain normal expression level of C. nigoni xol-1 genes, consistent with their viability. We further demonstrate that the cis-regulatory regions of Cbr-xol-1 and Cni-xol-1 have functionally diverged. Finally, X transcription is also aberrantly repressed in lethal hybrid male embryos from crosses between gonochoristic species C. latens and C. remanei . Our results suggest an evolutionary scenario in which incompatibility of the dosage compensation system leads to reproductive isolation.

Deep learning-assisted CBCT segmentation provides reliable volumetric assessment of mandibular defects compared with micro-CT for 3D printing and surgical planning

Genetic regulation of the estrogen receptor and inherited predisposition to breast cancer

For many families severely affected with breast cancer, no inherited causal allele has been detected in any tumor suppressor gene. In an effort to understand the genetics underlying breast cancer in these families, we evaluated 136 such families for coinheritance of breast cancer with each of 79 common variants reported as high-confidence “risk alleles” for breast cancer by meta-analyses of genome-wide association studies. Simulations based on allele frequencies and family structures revealed one (and only one) of these 79 variants to cosegregate with breast cancer in the families significantly more frequently than expected by chance. This variant (rs2046210) is located 180 kb proximal to ESR1 , encoding the estrogen receptor alpha. Reporter assays in MCF7 cells revealed enhancement by the genomic segment at this site of activity of ESR1 promoters, but no difference in effect among alternative haplotypes. In contrast, the 600 kb genomic region including ESR1 and rs2046210 harbored 11 rare variants, each of which cosegregated with breast cancer in one or a few families. For 9 of these 11 variants, reporter assays indicated significant allele-specific effects on ESR1 promoters, with the breast-cancer-linked allele of each variant yielding higher promoter activity. At the site with the most striking effect, the breast-cancer-linked allele was associated with increased binding by transcription factor AP2-gamma TFAP2C in both MCF7 and T47D cells. These results demonstrate coinheritance with breast cancer of rare alleles that increase activity of ESR1 promoters, and suggest that rare ESR1 regulatory alleles may contribute to inherited predisposition to breast cancer.

Impact of environmental training, awareness, and green values on sustainable food waste management

Lamin A/C loss promotes R-loop-mediated genomic instability and poor survival in small-cell lung cancer

Lamin A/C ( LMNA ), a key component of the nuclear envelope, is essential for maintaining nuclear integrity and genome organization [W. Xie et al. , Curr. Biol. 26 , 2651–2658 (2016)]. While LMNA dysregulation has been implicated in genomic instability across cancer and aging, the underlying mechanisms remain poorly understood [S. Graziano et al. , Nucleus 9 , 258–275 (2018)]. Here, we define a mechanistic role for LMNA in preserving genome stability in small-cell lung cancer (SCLC), a malignancy marked by extreme genomic instability [N. Takahashi et al. , Cancer Res. Commun. 2 , 503–517 (2022)]. LMNA depletion promotes R-loop accumulation, transcription-replication conflicts, replication stress, DNA breaks, and micronuclei formation. Mechanistically, LMNA deficiency disrupts nuclear pore complex organization, specifically reducing phenylalanine-glycine (FG)-nucleoporin incorporation, resulting in impaired RNA export and nuclear retention of RNA. LMNA expression is repressed by EZH2 and reexpressed during SCLC differentiation from neuroendocrine (NE) to non-NE states, and low LMNA levels correlate with poor clinical outcomes. These findings establish LMNA as a key regulator of nuclear transport and genome integrity, linking nuclear architecture to SCLC progression and therapeutic vulnerability.