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The attention network test (ANT), developed based on the triple‐network taxonomy by Posner and colleagues, has been widely used to examine the efficacy of alerting, orienting and executive control in clinical and developmental neuroscience studies. Recent research suggests the imperfect reliability of the behavioural ANT and its variants. However, the classical ANT fMRI task's test–retest reliability has received little attention. Moreover, it remains ambiguous whether the attention‐related intrinsic network components, especially the dorsal attention, ventral attention and frontoparietal network, manifest acceptable reliability. The present study approaches these issues by utilizing an openly available ANT fMRI dataset for participants with Parkinson's disease and healthy elderly. The reproducibility of group‐level activations across sessions and participant groups and the test–retest reliability at the individual level were examined at the voxel, region and network levels. The intrinsic network was defined using the Yeo‐Schaefer atlas. Our results reveal three critical facets: (1) the overlapping of the group‐level contrast map between sessions and between participant groups was unsatisfactory; (2) the reliability of alerting, orienting and executive, defined as a contrast between conditions, was worse than estimates of specific conditions. (3) Dorsal attention, ventral attention, visual and somatomotor networks showed acceptable reliability for the congruent and incongruent conditions. Our results suggest that specific condition estimates might be used instead of the contrast map for individual or group‐difference studies. [ABSTRACT FROM AUTHOR]

Environmental changes can trigger endoplasmic reticulum (ER) stress and misfolded protein accumulation, potentially leading to pre-eclampsia (PE). Amyloid-β (Aβ) is a crucial misfolded protein that can overactivate autophagy. Our study assessed the expression of Aβ1−42 and autophagic activity in PE placental tissues and trophoblasts under ER stress. Placental tissues were surgically collected from normal pregnant women (NP) and pregnant women with late-onset PE (LOPE) delivering through cesarean section. The expression levels of Aβ1−42 were detected in both PE and NP placental tissues, as well as in tunicamycin (TM)-induced HTR-8/SVneo cells. Autophagy-related proteins, such as Beclin-1, the ratio of LC3-II to LC3-I, ATG5, and SQSTM1/p62 in the placental tissues and HTR-8/SVneo cells were measured by Western blot. The number and morphology of autophagosomes were observed using transmission electron microscopy (TEM). Potential targets associated with the unfolded protein response (UPR) in the placental tissues of NP and PE cases were screened using PCR Arrays. The misfolded protein was significantly upregulated in the PE group. In both PE placental tissues and TM-induced HTR-8/SVneo cells, not only was Aβ1−42 upregulated, but also Beclin-1, ATG5, and LC3BII/I were significantly increased, accompanied by an increase in autophagosome count, while SQSTM1/P62 was downregulated. A total of 17 differentially expressed genes (DEGs) associated with the UPR were identified, among which elevated calnexin (CANX) was validated in the placenta from both PE and TM-induced HTR-8/SVneo cells. Autophagy is significantly upregulated in PE cases due to ER stress-induced Aβ1−42 accumulation, likely mediated by autophagy-related proteins involved in the UPR. [ABSTRACT FROM AUTHOR]

High-entropy carbides with controlled carbon content have the advantages of high thermal stability and good oxidation resistance, which give them potential for application as high-temperature microwave absorbing materials. In this study, (Co0.2Mo0.2V0.2Nb0.2Ta0.2)Cx high-entropy ceramic (HEC) powders are successfully prepared using a mechanical alloying method. Herein, reduction of the conductive graphite content leads to an increase in the concentration of carbon vacancies and a decrease in conductivity, which coordinates the polarization loss and conductivity loss capabilities. The dielectric loss capacity is enhanced by the combined effect of enhanced polarization loss and weakened conductivity loss. Simultaneously, the saturation magnetization intensity increases with the increase of carbon vacancy concentration, which leads to an enhanced magnetic loss capability. Ultimately, the sample (Co0.2Mo0.2V0.2Nb0.2Ta0.2)C0.8 with good impedance matching and attenuation ability achieves microwave absorption optimization at 293–573 K. The minimum reflection loss of (Co0.2Mo0.2V0.2Nb0.2Ta0.2)C0.8 reaches −57.77 dB (473 K) and the effective absorption bandwidth reaches 3.34 GHz (573 K), covering nearly 80% of the X-band with a thickness of only 2.2 mm. This work provides valuable insights into optimizing the high-temperature microwave absorption performance of high entropy carbides. [ABSTRACT FROM AUTHOR]

To address the increasing demand for high microwave‐absorbing performance, new polarization‐response wave‐absorbing metamaterials are developed herein inspired by the microstructure of locust compound eyes by employing bionics and optimization principles. The materials overcome a common limitation of wave‐absorbing metamaterials, wherein the performance substantially decreases with changes in the polarization mode, enabling the broadband absorption for x and y polarizations. The designed bioinspired materials have various shapes of structural units, triggering a multifrequency point response mechanism that effectively improves the wave‐absorbing performance. Experimental and simulation results show that the designed materials can form different equivalent circuits based on structural and polarization differences, influencing the overall wave‐absorbing performance. They exhibit an effective absorption bandwidth of ≈12 GHz (reflection loss < −10 dB) for x or y polarizations at a thickness of 2 mm. This study provides a typical example for future research on wave‐absorbing metamaterials with polarization‐response characteristics. [ABSTRACT FROM AUTHOR]

Diagnosing malignant skin tumors accurately at an early stage can be challenging due to ambiguous and even confusing visual characteristics displayed by various categories of skin tumors. To improve diagnosis precision, all available clinical data from multiple sources, particularly clinical images, dermoscopy images, and medical history, could be considered. Aligning with clinical practice, we propose a novel Transformer model, named Remix-Former++ that consists of a clinical image branch, a dermoscopy image branch, and a metadata branch. Given the unique characteristics inherent in clinical and dermoscopy images, specialized attention strategies are adopted for each type. Clinical images are processed through a top-down architecture, capturing both localized lesion details and global contextual information. Conversely, dermoscopy images undergo a bottom-up processing with two-level hierarchical encoders, designed to pinpoint fine-grained structural and textural features. A dedicated metadata branch seamlessly integrates non-visual information by encoding relevant patient data. Fusing features from three branches substantially boosts disease classification accuracy. RemixFormer++ demonstrates exceptional performance on four single-modality datasets (PAD-UFES-20, ISIC 2017/2018/2019). Compared with the previous best method using a public multi-modal Derm7pt dataset, we achieved an absolute 5.3% increase in averaged F1 and 1.2% in accuracy for the classification of five skin tumors. Furthermore, using a large-scale in-house dataset of 10,351 patients with the twelve most common skin tumors, our method obtained an overall classification accuracy of 92.6%. These promising results, on par or better with the performance of 191 dermatologists through a comprehensive reader study, evidently imply the potential clinical usability of our method.

With the exponential growth of digital data, there is a pressing need for innovative storage media and techniques. DNA molecules, due to their stability, storage capacity, and density, offer a promising solution for information storage. However, DNA storage also faces numerous challenges, such as complex biochemical constraints and encoding efficiency. This paper presents Explorer, a high-efficiency DNA coding algorithm based on the De Bruijn graph, which leverages its capability to characterize local sequences. Explorer enables coding under various biochemical constraints, such as homopolymers, GC content, and undesired motifs. This paper also introduces Codeformer, a fast decoding algorithm based on the transformer architecture, to further enhance decoding efficiency. Numerical experiments indicate that, compared with other advanced algorithms, Explorer not only achieves stable encoding and decoding under various biochemical constraints but also increases the encoding efficiency and bit rate by ¿10%. Additionally, Codeformer demonstrates the ability to efficiently decode large quantities of DNA sequences. Under different parameter settings, its decoding efficiency exceeds that of traditional algorithms by more than two-fold. When Codeformer is combined with Reed-Solomon code, its decoding accuracy exceeds 99%, making it a good choice for high-speed decoding applications. These advancements are expected to contribute to the development of DNA-based data storage systems and the broader exploration of DNA as a novel information storage medium., (© The Author(s) 2024. Published by Oxford University Press.)

Non-line-of-sight (NLOS) imaging aims to reconstruct the three-dimensional hidden scenes by using time-of-flight photon information after multiple diffuse reflections. The under-sampled scanning data can facilitate fast imaging. However, the resulting reconstruction problem becomes a serious ill-posed inverse problem, the solution of which is highly likely to be degraded due to noises and distortions. In this paper, we propose novel NLOS reconstruction models based on curvature regularization, i.e., the object-domain curvature regularization model and the dual (signal and object)-domain curvature regularization model. In what follows, we develop efficient optimization algorithms relying on the alternating direction method of multipliers (ADMM) with the backtracking stepsize rule, for which all solvers can be implemented on GPUs. We evaluate the proposed algorithms on both synthetic and real datasets, which achieve state-of-the-art performance, especially in the compressed sensing setting. Based on GPU computing, our algorithm is the most effective among iterative methods, balancing reconstruction quality and computational time. All our codes and data are available at https://github.com/Duanlab123/CurvNLOS.

What Is Already Known About This Topic?: Individuals who initially contract severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) lack significant mixed immunity. Therefore, it is crucial to monitor the clinical characteristics and associated factors of these individuals in order to inform policy-making., What Is Added by This Report?: The common symptoms reported were fever, cough, and sore throat. Reinfections and receiving four vaccination doses within a 6-month period were found to be associated with a shorter duration of virus shedding, decreased hospitalization rate, and reduced risk of pneumonia. Individuals aged 60 years and older, as well as those with underlying medical conditions, had a higher risk of developing pneumonia., What Are the Implication for Public Health Practices?: Online surveys conducted through social media platforms have the potential to complement disease surveillance and data collection efforts. In terms of vaccination prioritization, it is recommended to prioritize older individuals and those with underlying diseases., Competing Interests: No conflicts of interest., (Copyright and License information: Editorial Office of CCDCW, Chinese Center for Disease Control and Prevention 2024.)