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We investigate the realization of quintom scenario for dynamical dark energy within modified gravity theories that can efficiently fit the recent observational data sets. Starting from a general effective field theory formulation of dark energy in metric-affine geometry, we derive the background action in unitary gauge and we demonstrate how both f(T) and f(Q) gravity can naturally realize quintom behavior through appropriate forms and parameter choices. Additionally, using the Gaussian process reconstruction of the latest Dark Energy Spectroscopic Instrument DR2 baryon acoustic oscillation data combined with Type Ia supernovae and cosmic microwave background observations, we extract the reconstructed dark-energy equation-of-state parameter, showing that it exhibits quintom-type evolution, crossing the phantom divide from below. Moreover, through detailed parameter estimations and application of information criteria, we compare the model with the quadratic one and the ΛCDM model. Our results show that, due to its rich structure, modified gravity stands as one of the main candidates for the realization of the data-favored dynamical dark energy.

The Kelvin–Helmholtz (K-H) instability is ubiquitous in space and astrophysical environments, playing a vital role in the transport of mass, momentum, and energy. Although it has been shown that the K-H instability can energize the electrons, the mechanisms of electron energization remain unclear. In this paper, two-dimensional full particle-in-cell simulations are used to explore the physical mechanisms governing electron energy variation during the evolution of the K-H instability. By tracking the temporal evolution of electron energy and incorporating the guiding-center theory, we quantitatively analyze the contributions from the parallel electric field, betatron, and Fermi mechanisms to electron energization. The results demonstrate that as the instability develops, vortices roll up and expand, with electron energization dominated by the betatron mechanism. Subsequently, small-scale magnetic reconnection disrupts the vortices, and the parallel electric field becomes the primary driver of electron energization. The vortices eventually merge into a larger vortex, and the betatron mechanism regains dominance due to the roll-up of the large-scale vortex. The Fermi mechanism contributes minimally to electron energization and even exhibits a cooling effect during vortices' expansion. This study elucidates the primary pathways of electron energization during the K-H instability evolution, providing theoretical insights into energy dissipation mechanisms in magnetized plasmas under velocity shear conditions.

Aim: We aimed to create a nano drug delivery system with tetracycline (TC)-grafted methoxy poly-(ethylene-glycol)‒poly-(D, L-lactic-co-glycolic acid) (mPEG‒PLGA) micelles (TC‒mPEG‒PLGA) with TC and mPEG‒PLGA for potential bone targeting. Prospectively, TC‒mPEG‒PLGA aims to deliver bioactive compounds, such as astragaloside IV (AS), for osteoporotic therapy. Methods: Preparation and evaluation of TC‒mPEG‒PLGA were accomplished via nano-properties, cytotoxicity, uptake by MC3T3-E1 cells, ability of hydroxyapatite targeting and potential bone targeting in vivo, as well as pharmacodynamics in a rat model. Results: The measured particle size of AS-loaded TC‒mPEG‒PLGA micelles was an average of 52.16 ± 2.44 nm, which exhibited a sustained release effect compared to that by free AS. The TC‒mPEG‒PLGA demonstrated low cytotoxicity and was easily taken by MC3T3-E1 cells. Through assaying of bone targeting in vitro and in vivo , we observed that TC‒mPEG‒PLGA could effectively increase AS accumulation in bone. A pharmacodynamics study in mice suggested potentially increased bone mineral density by AS-loaded TC‒mPEG‒PLGA in ovariectomized rats compared to that by free AS. Conclusion: The nano drug delivery system (TC‒mPEG‒PLGA) could target bone in vitro and in vivo , wherein it may be used as a novel delivery method for the enhancement of therapeutic effects of drugs with osteoporotic activity.

Inflammatory bowel disease (IBD) is a group of conditions characterized by chronic and recurrent intestinal inflammation, primarily including Crohn’s disease (CD) and ulcerative colitis (UC). The pathogenesis of IBD is closely linked to abnormal immune responses, particularly T-cell mediated immune reactions. Th17 cells promote persistent intestinal inflammation by secreting pro-inflammatory cytokines such as IL-17, while regulatory T (Treg) cells help maintain immune homeostasis by secreting anti-inflammatory cytokines like IL-10 and TGF-β. In patients with IBD, Th17 cell function is enhanced, whereas Treg cell function is impaired or their numbers are reduced, leading to an imbalance in the immune system and exacerbating intestinal inflammation. The gut microbiota plays a crucial role in the immune regulation of IBD. Dysbiosis can lead to excessive activation of Th17 cells and suppression of Treg cell function, further aggravating clinical symptoms. Studies have shown that restoring gut microbiota balance through probiotics, antibiotics, dietary interventions, or fecal microbiota transplantation can not only improve immune responses but also restore the balance between Th17 and Treg cells, which has a positive impact on IBD treatment. This review summarizes how gut microbiota modulates the Th17/Treg cell balance to influence IBD immune responses and explores therapeutic strategies targeting Th17/Treg balance, including cytokine antagonists and immunosuppressive agents, which provide new directions and approaches for clinical IBD treatment.

In the research of green vegetation coverage in the field of remote sensing image segmentation, crop planting area is often obtained by semantic segmentation of images taken from high altitude. This method can be used to obtain the rate of cultivated land in a region (such as a country), but it does not reflect the real situation of a particular farmland. Therefore, this paper takes low-altitude images of farmland to build a dataset. After comparing several mainstream semantic segmentation algorithms, a new method that is more suitable for farmland vacancy segmentation is proposed. Additionally, the Strip Pooling module (SPM) and the Mixed Pooling module (MPM), with strip pooling as their core, are designed and fused into the semantic segmentation network structure to better extract the vacancy features. Considering the high cost of manual data annotation, this paper uses an improved ResNet network as the backbone of signal transmission, and meanwhile uses data augmentation to improve the performance and robustness of the model. As a result, the accuracy of the proposed method in the test set is 95.6%, mIoU is 77.6%, and the error rate is 7%. Compared to the existing model, the mIoU value is improved by nearly 4%, reaching the level of practical application.

It is of great significance to study the porosity and permeability properties of medium and low-rank coal. The porosity and permeability in confining stress experiments were used to simulate the porosity and permeability variations of coal samples under different depth conditions. The pore structure of Baoqing coal samples is greatly affected by the confining pressure, and the pores and micro cracks are more easily compressed. Based on the experimental data of mercury intrusion porosimetry (MIP) and nitrogen adsorption (NA), the pore size distributions (PSDs) of medium and low-rank coals were studied. High mercury intrusion pressure would lead to coal matrix compression. Therefore, the pore volume calculated by MIP data was corrected by NA data. The PSDs characteristics of Jixi (JX) coal and Baoqing (BQ) coal samples are obtained from the revised pore volume, and the dominant pores of medium and low-rank coals are obtained. The results show that JX coal has higher spatial heterogeneity, connectivity and pore autocorrelation. Micro fractures have an influence on the autocorrelation and heterogeneity of coal samples, especially for BQ coal samples.

Selective harvesting of Hotan roses requires distinguishing between buds and blooms for different industrial uses. However, balancing detection accuracy and computational efficiency for edge deployment remains a challenge. This study proposes an integrated framework combining a lightweight detection model, Rose_YOLO, with an adaptive path-planning algorithm, the ROSE algorithm, to address these issues. The Rose_YOLO model optimizes the YOLOv8n architecture by incorporating the C2f-Faster-CGLU module and a Rose_Head detection head to enhance feature extraction while reducing redundancy. The ROSE algorithm integrates an improved genetic algorithm (GA) with a reciprocating search mechanism to dynamically optimize picking sequences based on scene complexity. Experimental results demonstrate that Rose_YOLO achieves a precision of 90.4% and a mAP@0.5 of 96.6% for blooms and a precision of 88.4% with a mAP@0.5 of 91.7% for buds. Compared to the baseline YOLOv8n, the model reduces parameters by 47.46% to 1.579 million, compresses the size to 3.19 MB, and lowers computational complexity to 4.6 GFLOPs. For path planning, the ROSE algorithm generates optimal paths with an average length of 2796.94 pixels, which is 73.1% shorter than the reciprocating algorithm and 51.6% shorter than the standard GA. Furthermore, it achieves an average runtime of only 7.33 ms, significantly outperforming traditional methods with respect to computational speed. In conclusion, the proposed framework achieves a superior balance between lightweight design and detection performance. The successful deployment on edge devices validates its effectiveness in providing real-time visual guidance and efficient path planning, offering a robust technical solution for the automated selective harvesting of roses in complex field environments.

Chimeric antigen receptor T (CAR-T) cell therapy has been a milestone breakthrough in the treatment of hematological malignancies, offering an effective therapeutic option for multi-line therapy-refractory patients. So far, abundant CAR-T products have been approved by the United States Food and Drug Administration or China National Medical Products Administration to treat relapsed or refractory hematological malignancies and exhibited unprecedented clinical efficiency. However, there were still several significant unmet needs to be progressed, such as the life-threatening toxicities, the high cost, the labor-intensive manufacturing process and the poor long-term therapeutic efficacy. According to the demands, many researches, relating to notable technical progress and the replenishment of alternative targets or cells, have been performed with promising results. In this review, we will summarize the current research progress in CAR-T eras from the “targets” to “alternative cells”, to “combinational drugs” in preclinical studies and clinical trials.

f ( Q ) and f ( T ) gravity are based on fundamentally different geometric frameworks, yet they exhibit many similar properties. This article provides a comprehensive summary and comparative analysis of the various theoretical branches of torsional gravity and non-metric gravity, which arise from different choices of affine connection. We identify two types of background-dependent and classical correspondences between these two theories of gravity. The first correspondence is established through their equivalence within the Minkowski spacetime background. To achieve this, we develop the tetrad-spin formulation of f ( Q ) gravity and derive the corresponding expression for the spin connection. The second correspondence is based on the equivalence of their equations of motion. Utilizing a metric-affine approach, we derive the general affine connection for static and spherically symmetric spacetime in f ( Q ) gravity and compare its equations of motion with those of f ( T ) gravity. Among others, our results reveal that, f ( T ) solutions are not simply a subset of f ( Q ) solutions; rather, they encompass a complex solution beyond f ( Q ) gravity in black hole background.

Object detection algorithms require compact structures, reasonable probability interpretability, and strong detection ability for small targets. However, mainstream second-order object detectors lack reasonable probability interpretability, have structural redundancy, and cannot fully utilize information from each branch of the first stage. Non-local attention can improve sensitivity to small targets, but most of them are limited to a single scale. To address these issues, we propose PNANet, a two-stage object detector with a probability interpretable framework. We propose a robust proposal generator as the first stage of the network and use cascade RCNN as the second stage. We also propose a pyramid non-local attention module that breaks the scale constraint and improves overall performance, especially in small target detection. Our algorithm can be used for instance segmentation after adding a simple segmentation head. Testing on COCO and Pascal VOC datasets as well as practical applications demonstrated good results in both object detection and instance segmentation tasks.