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A bstract We develop a formalism to extract triple crossing symmetric positivity bounds for effective field theories with multiple degrees of freedom, by making use of su symmetric dispersion relations supplemented with positivity of the partial waves, st null constraints and the generalized optical theorem. This generalizes the convex cone approach to constrain the s 2 coefficient space to higher orders. Optimal positive bounds can be extracted by semi-definite programs with a continuous decision variable, compared with linear programs for the case of a single field. As an example, we explicitly compute the positivity constraints on bi-scalar theories, and find all the Wilson coefficients can be constrained in a finite region, including the coefficients with odd powers of s , which are absent in the single scalar case.

A bstract We derive soft theorems for on-shell scattering amplitudes from non-linearly realised global space-time symmetries, arising from the flat space and decoupling limits of the effective field theories (EFTs) of inflation, while taking particular care of on-shell limits, soft limits, time-ordered correlations, momentum derivatives, energy-momentum conserving delta functions and iε prescriptions. Intriguingly, contrary to common belief, we find with a preferred soft hierarchy among the soft momentum q , on-shell residue p a 0 ± E a , and ε , the soft theorems do not have dependence on unconstrained off-shell interactions, even in the presence of cubic vertices. We also argue that the soft hierarchy is a natural choice, ensuring the soft limit and on-shell limit commute. Our soft theorems depend solely on on-shell data and hold to all orders in perturbation theory. We present various examples including polynomial shift symmetries, non-linear realisation of Lorentz boosts and dilatations on how the soft theorems work. We find that the collection of exchange diagrams whose soft momenta are associated with cubic vertices, that are indeterminate in the soft limit, exhibits an enhanced soft scaling. The enhanced soft scaling explains why the sum of such diagrams do not enter the soft theorems non-trivially. We further apply the soft theorems to bootstrap the scattering amplitudes of the superfluid and scaling superfluid EFTs, finding agreement with the Hamiltonian analysis.

A bstract We consider effective field theories (EFTs) of scalar fields with broken Lorentz boosts, which arise by taking the decoupling and flat-space limits of the EFT of inflation, and derive constraints that must be satisfied by the corresponding scattering amplitudes if there is an underlying non-linearly realised symmetry. We primarily concentrate on extended shift symmetries which depend on the space-time coordinates, and find that combinations of scattering amplitudes obey enhanced Adler zeros. That is, such combinations vanish as one external momentum is taken soft, with the rate at which they vanish dictated by the corresponding symmetry. In our soft theorem derivation, we pay particular care to the energy and momentum-conserving delta functions that arise due to space-time translations, and show that when acted upon by derivatives with respect to spatial momenta, they yield a tower of soft theorems which are ultimately required for closure of the underlying symmetry algebra. All of our soft theorems correspond to constraints that must be satisfied by on-shell amplitudes and, even for symmetries that depend on the time coordinate, our soft theorems only require derivatives to be taken with respect to spatial momenta. We perform a soft bootstrap procedure to find solutions to our soft theorems, and compare these solutions to what we find from an off-shell analysis using the coset construction.

Quantitative real-time PCR of phylogenetic and functional marker genes is among the most commonly used techniques to quantify the abundance of microbial taxa in environmental samples. However, in most environmental applications, the approach is a rough assessment of population abundance rather than an exact absolute quantification method because of PCR-based estimation biases caused by multiple factors. Previous studies on these technical issues have focused on primer or template sequence features or PCR reaction conditions. However, how target gene sequence characteristics (e.g., evenness and dominance) in environmental samples affect qPCR quantifications has not been well studied. Here, we compared three primer sets targeting the beta subunit of the dissimilatory sulfite reductase ( dsrB ) to investigate qPCR quantification performance under different target gene sequence evenness and dominance conditions using artificial gBlock template mixtures designed accordingly. Our results suggested that the qPCR quantification performance of all tested primer sets was determined by the comprehensive effect of the target gene sequence evenness and dominance in environmental samples. Generally, highly degenerate primer sets have equivalent or better qPCR quantification results than a more target-specific primer set. Low template concentration in this study (~10 5 copies/L) will exaggerate the qPCR quantification results difference among tested primer sets. Improvements to the accuracy and reproducibility of qPCR assays for gene copy number quantification in environmental microbiology and microbial ecology studies should be based on prior knowledge of target gene sequence information acquired by metagenomic analysis or other approaches, careful selection of primer sets, and proper reaction conditions optimization.

Due to the frequent occurrence and elevated concentrations of emerging contaminants (ECs) in water environments, as well as their high toxicity, these compounds have become a growing concern, threatening water safety, human health, and environmental health. Stricter regulations and routine monitoring are required to control EC pollution in water. Analytical chemistry-based techniques are the most widely used approach for quantifying ECs in environmental samples. However, high costs, complex sample preparation, time-consuming protocols, and labor-intensive processes limit their application for the routine and rapid detection of ECs. Biosensors are a promising biotechnological alternative that has received increased attention in recent years for the quantification of ECs. This review provides a comprehensive overview of the main types of biosensors used for monitoring ECs in aquatic environments, highlighting their underlying detection mechanisms and recent technological advancements. It also discusses key challenges associated with different biosensor platforms, such as stability, sensitivity, and development complexity. Potential future research directions to address these limitations and enhance the performance of biosensors include immobilization on hybrid nanomaterials, and the development of portable and multifunctional biosensors for on-site and real-time monitoring. By summarizing current progress and identifying future directions, this review will broaden the awareness and recognition of biosensors for monitoring ECs in water environments, contributing to water safety, sanitation, and sustainability.

•The high-risk individuals with IGD among young adults were identified.•The high-risk individuals have abnormal functional connectome gradient at baseline.•The role of impulsivity was highlighted in the development of IGD. Adolescents and young adults are considered a high-risk group for internet gaming disorder (IGD). Early screening for high-risk individuals with IGD and exploring the underlying neural mechanisms is an effective strategy to reduce the harm of IGD. We recruited 219 non-internet gaming addicted college students and evaluated them with magnetic resonance imaging, followed by a two-year longitudinal follow-up. We used functional connectome gradient (FCG) to capture the macroscopic hierarchical organization of human brain. Canonical correlation analysis was employed to identify components mapping relationships between FCG and behavioral scores. Consequently, K-means clustering was used to define distinct subtypes. The risk of developing IGD and FCG patterns were compared among the subtypes. Three subtypes were identified and subtype 3 exhibited the highest risk for developing IGD according to the occurrence rates of IGD two years later: (1) subtype 1 (5.3 %, 4 participants), (2) subtype 2 (10.8 %, 9 participants), (3) subtype 3 (20 %, 12 participants). The abnormal FCG in the inferior frontal gyrus and posterior cingulate cortex at baseline were observed in subtype 3, which were correlated with impulsivity. These findings advanced understanding of the biological and behavioral heterogeneity associated with developing of IGD, and represented a promising step toward the prediction of high-risk individuals.

The color variations present in histopathological images pose a significant challenge to computational pathology and, consequently, negatively affect the performance of certain pathological image analysis methods, especially those based on deep learning techniques. To date, several methods have been proposed to mitigate this issue. However, these methods either produce images with low texture retention, perform poorly when trained with small datasets, or have low generalization capabilities. In this paper, we propose a Deep Supervised Two-stage Generative Adversarial Network known as DSTGAN for stain-normalization. Specifically, we introduce deep supervision to generative adversarial networks in an innovative way to enhance the learning capacity of the model, benefiting from different model regularization methods. To make fuller use of source domain images for training the model, we drew upon semi-supervised concepts to design a novel two-stage staining strategy. Additionally, we construct a generator that can capture long-distance semantic relationships, enabling the model to retain more abundant texture information in the generated images. In the evaluation of the quality of generated images, we have achieved state-of-the-art performance on TUPAC-2016, MITOS-ATYPIA-14, ICIAR-BACH-2018 and MICCAI-16-GlaS datasets, improving the precision of classification and segmentation by 5.2% and 4.2%, respectively. Not only has our model significantly improved the quality of the stained images compared to existing stain normalization methods, but it also has a positive impact on the execution of downstream classification and segmentation tasks. Our method has further reduced the effect that staining differences have on computational pathology, thereby improving the accuracy of histopathological image analysis to some extent.

Hematological malignancies (HMs) pose a severe threat to human health and contribute substantially to the disease burden in mainland China and Taiwan. Therefore, understanding their burden is crucial for informed decision-making and the effective allocation of healthcare resources. This study utilized the latest data from the Global Burden of Disease 2021 study to describe the epidemiological indices of HMs in mainland China and Taiwan from 1990 to 2021. The future disease burden was projected for the next decade using the Bayesian age-period cohort (BAPC) model. Between 1990 and 2021, mainland China experienced an increase in the prevalence and incidence of leukemia and lymphoma, while the mortality and disability-adjusted life years (DALYs) for these diseases declined. Conversely, Taiwan witnessed an overall increase in the prevalence, incidence, mortality, and DALYs of leukemia over the same period. Additionally, multiple myeloma (MM), myelodysplastic/myeloproliferative neoplasms, and other hematopoietic neoplasms have shown significant increases in prevalence, incidence, mortality, and DALYs in China. While the disease burden of myeloid leukemia decreased in mainland China, that of lymphoid neoplasms (including leukemia, lymphoma, and MM) increased, which was not observed in Taiwan. Predictions from the BAPC model suggest that the incidence of several lymphoid neoplasms and MM is expected to increase in mainland China and Taiwan. Taiwan continues to face greater challenges in managing HMs compared to mainland China. MM imposes a significant burden on the Chinese population. The findings of this study provide valuable epidemiological insights for optimizing the allocation of medical resources.

In order to improve the detection efficiency and accuracy of microfluidic chip, a magnetic beads preset technology were designed by using double permanent magnets as external magnetic field and the motion characteristics of preset magnetic beads were studied. The control principle of magnetic beads preset technology was introduced in detail, and the control structure was designed. The coupled field characteristics for magnetic beads in microchannels were analyzed, and the motion models of magnetic beads were established based on the magnetic beads preset technology, including capture motion and mixing motion. The relationship between the magnetic field force and the flow velocity for capturing magnetic bead, and the mixing time under the influence of flow field and magnetic field were derived. The magnetic beads preset technology effect was verified by experiments and numerical simulations were developed to analyze the influence of aspect ratio of permanent magnet on magnetic field. The study showed that the accuracy and efficiency of the magnetic bead control in the microchannel could be better realized by the magnetic beads preset technology. The derivation of the magnetic bead motion model can understand the motion characteristics of the magnetic bead more clearly, facilitate accurate control of the magnetic bead, and improve the success rate of the microfluidic detection.

The quality of agricultural products relates to the internal structure, which has long been a matter of interest in agricultural scientists. However, inspection methods of the opaque nature of internal information on agricultural products are usually destructive and require sample separation or preparation. X‐ray computed tomography (X‐ray CT) technology is one of the important nondestructive testing (NDT) technologies without sample separation and preparation. In this study, X‐ray CT technology is used to obtain two‐dimensional slice images and three‐dimensional tomographic images of samples. The purpose of the review was to provide an overview of the working principle of X‐ray CT technology, image processing, and analysis. This review aims to focus on the development of the agricultural products (e.g., wheat, maize, rice, apple, beef) and its applications (e.g., internal quality evaluation, microstructure observation, mechanical property measurement, and others) using CT scanner. This paper covers the aspects regarding the advantages and disadvantages of NDT technology, especially the unique advantages and limitations of X‐ray CT technology on the quality inspection of agricultural products. Future prospects of X‐ray CT technology are also put forward to become indispensable to the quality evaluation and product development on agricultural products. X‐ray computed tomography technology is an innovative technique for quality inspection of agricultural products. X‐ray computed tomography technology is one of the important nondestructive testing technologies without sample separation and preparation.