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Meningiomas are among the most common intracranial tumors, and challenges still remain in terms of tumor classification, treatment, and management. With the popularization of artificial intelligence technology, radiomics has been further developed and more extensively applied in the study of meningiomas. This objective and quantitative technique has played an important role in the identification, classification, grading, pathology, treatment, and prognosis of meningiomas, although new problems have also emerged. This review examines the application of magnetic resonance imaging (MRI) techniques in meningioma research. A database search was conducted for articles published between November 2017 and April 2025, with a total of 87 studies included after screening. These studies were summarized in detail, and the risk of bias and the certainty of the evidence were assessed using the Quality Assessment of Diagnostic Accuracy Studies version 2 (QUADAS-2) and radiomics quality scores (RQS). All the studies were retrospective, with most being single-center studies. Contrast-enhanced T1-weighted imaging (T1C) and T2-weighted imaging (T2WI) are the most commonly used MRI sequences. Current research focuses on five topics, namely, differentiation, grade and subtypes, molecular pathology, biological behavior, treatment, and complications, with 14, 32, 14, 12, and 19 studies addressing these topics (some of which are multiple topics). Combined imaging features with clinical or pathological features often outperform traditional clinical models. Most studies show a low to moderate risk of bias. Large, prospective, multicenter studies are needed to validate the performance of radiomic models in diverse patient populations before their clinical implementation can be considered.

Objectives Atypical meningioma (AM) has a high recurrence rate. This study explored the factors associated with recurrence and built a predictive model for AM by combining radiomic and clinical features. Methods This retrospective cohort study enrolled 451 adult AM patients who underwent surgical treatment at three institutions between May 2012 and April 2024. The patients in institution 1 were randomly assigned to the training dataset ( n  = 246) or internal validation dataset ( n  = 164) at a ratio of 6:4, and patients in institutions 2 and 3 composed the external validation dataset ( n  = 41). The clinical and pathological characteristics of the patients were collected, and radiomics technology was used to extract image features from preoperative multiparametric MR images. After feature screening, three types of AM recurrence prediction models were constructed: the radiomic model, the clinical model and the combined model. Results The median follow-up time was 27 months, and 22.8% ( n  = 103) of the patients relapsed after surgery. A total of 23 radiomic features were included in the model. Compared with the radiomic model and clinical model, the combined model performed better in predicting recurrence, with C-index values of 0.8453, 0.7867 and 0.8125 in the training, internal validation and external validation datasets, respectively, and the AUC value remained above 0.85 within 5 years. The radiomics score plays the most important role in predicting the recurrence of AM, with features such as t1c_original_shape_MajorAxisLength and t1c_original_shape_Flatness being of high importance. Among the clinical features, secondary tumors, subtotal resection and high Ki-67 levels contribute to AM recurrence. Conclusions Radiomics has additional value for predicting AM tumor recurrence and has favorable predictive performance when combined with clinical features.

The carbon dioxide and carbon monoxide electroreduction reactions, when powered using low-carbon electricity, offer pathways to the decarbonization of chemical manufacture 1 , 2 . Copper (Cu) is relied on today for carbon–carbon coupling, in which it produces mixtures of more than ten C 2+ chemicals 3 – 6 : a long-standing challenge lies in achieving selectivity to a single principal C 2+ product 7 – 9 . Acetate is one such C 2 compound on the path to the large but fossil-derived acetic acid market. Here we pursued dispersing a low concentration of Cu atoms in a host metal to favour the stabilization of ketenes 10 —chemical intermediates that are bound in monodentate fashion to the electrocatalyst. We synthesize Cu-in-Ag dilute (about 1 atomic per cent of Cu) alloy materials that we find to be highly selective for acetate electrosynthesis from CO at high *CO coverage, implemented at 10 atm pressure. Operando X-ray absorption spectroscopy indicates in situ-generated Cu clusters consisting of <4 atoms as active sites. We report a 12:1 ratio, an order of magnitude increase compared to the best previous reports, in the selectivity for acetate relative to all other products observed from the carbon monoxide electroreduction reaction. Combining catalyst design and reactor engineering, we achieve a CO-to-acetate Faradaic efficiency of 91% and report a Faradaic efficiency of 85% with an 820-h operating time. High selectivity benefits energy efficiency and downstream separation across all carbon-based electrochemical transformations, highlighting the importance of maximizing the Faradaic efficiency towards a single C 2+ product 11 . A study using a copper-in-silver dilute alloy catalyst in a high-pressure gas flow reactor reports highly selective electrosynthesis of acetate from carbon monoxide.

An optical trap forms a restoring optical force field to immobilize and manipulate tiny objects. A fiber optical trap is capable of establishing the restoring optical force field using one or a few pieces of optical fiber, and it greatly simplifies the optical setup by removing bulky optical components, such as microscope objectives from the working space. It also inherits other major advantages of optical fibers: flexible in shape, robust against disturbance, and highly integrative with fiber-optic systems and on-chip devices. This review will begin with a concise introduction on the principle of optical trapping techniques, followed by a comprehensive discussion on different types of fiber optical traps, including their structures, functionalities and associated fabrication techniques. A brief outlook to the future development and potential applications of fiber optical traps is given at the end.

The renewable-energy-powered electrocatalytic conversion of carbon dioxide and carbon monoxide into carbon-based fuels provides a means for the storage of renewable energy. We sought to convert carbon monoxide—an increasingly available and low-cost feedstock that could benefit from an energy-efficient upgrade in value—into n -propanol, an alcohol that can be directly used as engine fuel. Here we report that a catalyst consisting of highly fragmented copper structures can bring C 1 and C 2 binding sites together, and thereby promote further coupling of these intermediates into n -propanol. Using this strategy, we achieved an n -propanol selectivity of 20% Faradaic efficiency at a low potential of −0.45 V versus the reversible hydrogen electrode (ohmic corrected) with a full-cell energetic efficiency of 10.8%. We achieved a high reaction rate that corresponds to a partial current density of 8.5 mA cm –2 for n -propanol. The upgrade of carbon monoxide to higher alcohols offers a route to renewable fuels. Now, Sinton, Sargent and co-workers report a highly fragmented, copper-based catalyst with engineered interfaces between the (111) and (100) facets that promote the coupling of C 1 and C 2 species, leading to enhanced production of n -propanol.

The carbon dioxide and carbon monoxide electroreduction reactions, when powered using low-carbon electricity, offer pathways to the decarbonization of chemical manufacture . Copper (Cu) is relied on today for carbon-carbon coupling, in which it produces mixtures of more than ten C chemicals : a long-standing challenge lies in achieving selectivity to a single principal C product . Acetate is one such C compound on the path to the large but fossil-derived acetic acid market. Here we pursued dispersing a low concentration of Cu atoms in a host metal to favour the stabilization of ketenes -chemical intermediates that are bound in monodentate fashion to the electrocatalyst. We synthesize Cu-in-Ag dilute (about 1 atomic per cent of Cu) alloy materials that we find to be highly selective for acetate electrosynthesis from CO at high *CO coverage, implemented at 10 atm pressure. Operando X-ray absorption spectroscopy indicates in situ-generated Cu clusters consisting of <4 atoms as active sites. We report a 12:1 ratio, an order of magnitude increase compared to the best previous reports, in the selectivity for acetate relative to all other products observed from the carbon monoxide electroreduction reaction. Combining catalyst design and reactor engineering, we achieve a CO-to-acetate Faradaic efficiency of 91% and report a Faradaic efficiency of 85% with an 820-h operating time. High selectivity benefits energy efficiency and downstream separation across all carbon-based electrochemical transformations, highlighting the importance of maximizing the Faradaic efficiency towards a single C product .

Neurospora crassa is an excellent model fungus for studies on molecular genetics, biochemistry, physiology, and molecular cell biology. Along with the rapid progress of Neurospora research, new tools facilitating more efficient and accurate genetic analysis are in high demand. Here, we tested whether the dominant selective makers widely used in yeasts are applicable in N. crassa . Among them, we found that the strains of N. crassa are sensitive to the aminoglycoside antibiotics, G418 and nourseothricin. 1000 μg/mL of G418 or 50 μg/mL of nourseothricin is sufficient to inhibit Neurospora growth completely. When the neomycin phosphotransferase gene ( neo ) used in mammalian cells is expressed, N. crassa shows potent resistance to G418. This establishes G418-resistant marker as a dominant selectable marker to use in N. crassa . Similarly, when the nourseothricin acetyltransferase gene ( nat ) from Streptomyces noursei is induced by qa-2 promoter in the presence of quinic acid (QA), N. crassa shows potent resistance to nourseothricin. When nat is constitutively expressed by full-length or truncated versions of the promoter from the N. crassa cfp gene (NCU02193), or by the trpC promoter of Aspergillus nidulans , the growth of N. crassa in the presence of nourseothricin is proportional to the expression levels of Nat. Finally, these two markers are used to knock-out wc-2 or al-1 gene from the N. crassa genome. The successful development of these two markers in this study expands the toolbox for N. crassa and very likely for other filamentous fungi as well.

Lonicera japonica Thunb. is a traditional Chinese medicinal herb whose floral buds are the primary source of pharmacological compounds that require manual harvesting. As a result, its floral bud duration, determined by the opening time, is a key determinant of both quality and economic value. However, the genetic mechanisms controlling floral bud duration remain poorly understood. In this study, we employed population structure analysis and molecular experiments to identify candidate genes associated with this trait. The improved cultivar Beihua No. 1 (BH1) opens its floral buds significantly later than the landrace Damaohua (DMH). Exogenous application of methyl jasmonate (MeJA) to BH1 indicated that jasmonate acts as a negative regulator of floral bud duration by accelerating floral bud opening. A genome-wide selection scan across 35 germplasms with varying floral bud durations identified the transcription factor LjWRKY50 as the causative gene influencing this trait. The dual-luciferase reporter assay and qRT-PCR experiments showed that LjWRKY50 activates the expression of the jasmonate biosynthesis gene, LjAOS. A functional variant within LjWRKY50 (Chr7:24636061) was further developed into a derived cleaved amplified polymorphic sequence (dCAPS) marker. These findings provide valuable insights into the jasmonate-mediated regulation of floral bud duration, offering genetic and marker resources for molecular breeding in L. japonica.

Robotic grasping is one of the most fundamental robotic manipulation tasks and has been the subject of extensive research. However, swiftly teaching a robot to grasp a novel target object in clutter remains challenging. This paper attempts to address the challenge by leveraging object attributes that facilitate recognition, grasping, and rapid adaptation to new domains. In this work, we present an end-to-end encoder-decoder network to learn attribute-based robotic grasping with data-efficient adaptation capability. We first pre-train the end-to-end model with a variety of basic objects to learn generic attribute representation for recognition and grasping. Our approach fuses the embeddings of a workspace image and a query text using a gated-attention mechanism and learns to predict instance grasping affordances. To train the joint embedding space of visual and textual attributes, the robot utilizes object persistence before and after grasping. Our model is self-supervised in a simulation that only uses basic objects of various colors and shapes but generalizes to novel objects in new environments. To further facilitate generalization, we propose two adaptation methods, adversarial adaption and one-grasp adaptation. Adversarial adaptation regulates the image encoder using augmented data of unlabeled images, whereas one-grasp adaptation updates the overall end-to-end model using augmented data from one grasp trial. Both adaptation methods are data-efficient and considerably improve instance grasping performance. Experimental results in both simulation and the real world demonstrate that our approach achieves over 81% instance grasping success rate on unknown objects, which outperforms several baselines by large margins.

Robotic grasping is one of the most fundamental robotic manipulation tasks and has been actively studied. However, how to quickly teach a robot to grasp a novel target object in clutter remains challenging. This paper attempts to tackle the challenge by leveraging object attributes that facilitate recognition, grasping, and quick adaptation. In this work, we introduce an end-to-end learning method of attribute-based robotic grasping with one-grasp adaptation capability. Our approach fuses the embeddings of a workspace image and a query text using a gated-attention mechanism and learns to predict instance grasping affordances. Besides, we utilize object persistence before and after grasping to learn a joint metric space of visual and textual attributes. Our model is self-supervised in a simulation that only uses basic objects of various colors and shapes but generalizes to novel objects and real-world scenes. We further demonstrate that our model is capable of adapting to novel objects with only one grasp data and improving instance grasping performance significantly. Experimental results in both simulation and the real world demonstrate that our approach achieves over 80\\% instance grasping success rate on unknown objects, which outperforms several baselines by large margins.