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We present a multimodal approach for measuring the three-dimensional (3D) refractive index (RI) and fluorescence distributions of live cells by combining optical diffraction tomography (ODT) and 3D structured illumination microscopy (SIM). A digital micromirror device is utilized to generate structured illumination patterns for both ODT and SIM, which enables fast and stable measurements. To verify its feasibility and applicability, the proposed method is used to measure the 3D RI distribution and 3D fluorescence image of various samples, including a cluster of fluorescent beads, and the time-lapse 3D RI dynamics of fluorescent beads inside a HeLa cell, from which the trajectory of the beads in the HeLa cell is analyzed using spatiotemporal correlations.

How can we recommend items to users utilizing multiple types of user behavior data? Multi-behavior recommender systems leverage various types of user behavior data to enhance recommendation performance for the target behavior. These systems aim to provide personalized recommendations, thereby improving user experience, engagement, and satisfaction across different applications such as e-commerce platforms, streaming services, news websites, and content platforms. While previous approaches in multi-behavior recommendation have focused on incorporating behavioral order and dependencies into embedding learning, they often overlook the nuanced importance of individual behaviors in shaping user preferences during model training. We propose MBA (Multi-Behavior sequence-Aware recommendation via graph convolution networks), an accurate framework for multi-behavior recommendations. MBA adopts a novel approach by learning embeddings that capture both the dependencies between behaviors and their relative importance in influencing user preferences. Additionally, MBA employs sophisticated sampling strategies that consider the sequential nature of behaviors during model training, ensuring that the model effectively learns from the entire behavioral sequence. Through extensive experiments on real-world datasets, we demonstrate the superior performance of MBA compared to existing methods. MBA outperforms the best competitor, achieving improvements of up to 11.2% and 11.4% in terms of HR@10 and nDCG@10, respectively. These findings underscore the effectiveness of MBA in providing accurate and personalized recommendations tailored to individual user preferences.

The surface temperature response to greenhouse gas forcing displays a characteristic pattern of polar-amplified warming1–5, particularly in the Northern Hemisphere. However, the causes of this polar amplification are still debated. Some studies highlight the importance of surface-albedo feedback6–8, while others find larger contributions from longwave feedbacks4,9,10, with changes in atmospheric and oceanic heat transport also thought to play a role11–16. Here, we determine the causes of polar amplification using climate model simulations in which CO2 forcing is prescribed in distinct geographical regions, with the linear sum of climate responses to regional forcings replicating the response to global forcing. The degree of polar amplification depends strongly on the location of CO2 forcing. In particular, polar amplification is found to be dominated by forcing in the polar regions, specifically through positive local lapse-rate feedback, with ice-albedo and Planck feedbacks playing subsidiary roles. Extra-polar forcing is further shown to be conducive to polar warming, but given that it induces a largely uniform warming pattern through enhanced poleward heat transport, it contributes little to polar amplification. Therefore, understanding polar amplification requires primarily a better insight into local forcing and feedbacks rather than extra-polar processes.

Argonaute is the primary mediator of metazoan miRNA targeting (MT). Among the currently identified >1,500 human RNA-binding proteins (RBPs), there are only a handful of RBPs known to enhance MT and several others reported to suppress MT, leaving the global impact of RBPs on MT elusive. In this study, we have systematically analyzed transcriptome-wide binding sites for 150 human RBPs and evaluated the quantitative effect of individual RBPs on MT efficacy. In contrast to previous studies, we show that most RBPs significantly affect MT and that all of those MT-regulating RBPs function as MT enhancers rather than suppressors, by making the local secondary structure of the target site accessible to Argonaute. Our findings illuminate the unappreciated regulatory impact of human RBPs on MT, and as these RBPs may play key roles in the gene regulatory network governed by metazoan miRNAs, MT should be understood in the context of co-regulating RBPs. miRNAs are loaded into Argonaute protein and repress complementary mRNA targets. Here the authors show the unappreciated role of RNA binding proteins for efficient miRNA targeting and expand the current understanding of miRNA targeting.

COVID-19 is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which infected >200 million people resulting in >4 million deaths. However, temporal landscape of the SARS-CoV-2 translatome and its impact on the human genome remain unexplored. Here, we report a high-resolution atlas of the translatome and transcriptome of SARS-CoV-2 for various time points after infecting human cells. Intriguingly, substantial amount of SARS-CoV-2 translation initiates at a novel translation initiation site (TIS) located in the leader sequence, termed TIS-L. Since TIS-L is included in all the genomic and subgenomic RNAs, the SARS-CoV-2 translatome may be regulated by a sophisticated interplay between TIS-L and downstream TISs. TIS-L functions as a strong translation enhancer for ORF S, and as translation suppressors for most of the other ORFs. Our global temporal atlas provides compelling insight into unique regulation of the SARS-CoV-2 translatome and helps comprehensively evaluate its impact on the human genome. Here, Kim et al. apply various sequencing techniques (RPF-seq, QTI-seq, mRNA-seq, sRNA-seq) to unravel the high-resolution, longitudinal translatome and transcriptome of SARS-CoV-2. They identify a translation initiation site in the leader sequence of all genomic and subgenomic RNAs and show its relevance for the SARS-CoV-2 translatome.

Multidrug resistance (MDR) is one of the major barriers in chemotherapy. It is often related to the overexpression of efflux receptors such as P-glycoprotein (P-gp). Overexpressed efflux receptors inhibit chemotherapeutic efficacy by pumping out intracellularly delivered anticancer drugs. In P-gp-mediated MDR-related pathways, PI3K/Akt and NF-kB pathways are commonly activated signaling pathways, but these pathways are downregulated by melittin, a main component of bee venom. In this study, a polymersome based on a poly (lactic acid) (PLA)-hyaluronic acid (HA) (20k-10k) di-block copolymer and encapsulating melittin and doxorubicin was developed to overcome anticancer resistance and enhance chemotherapeutic efficacy. Through the simultaneous delivery of doxorubicin and melittin, PI3K/Akt and NF-κB pathways could be effectively inhibited, thereby downregulating P-gp and successfully enhancing chemotherapeutic efficacy. In conclusion, a polymersome carrying an anticancer drug and melittin could overcome MDR by regulating P-gp overexpression pathways.

Few studies have investigated the changes in patient state index (PSI) and bispectral index (BIS) in response to abrupt increase in electromyographic (EMG) activity. These were performed using intravenous anesthetics or reversal agents for neuromuscular blockade (NMB) other than sugammadex. We compared the changes in BIS and PSI values caused by the sugammadex reversal of NMB during steady-state sevoflurane anesthesia. We enrolled 50 patients with American Society of Anesthesiologists physical status 1 and 2. At the end of the surgery, we administered 2 mg kg −1 sugammadex while maintaining sevoflurane for a 10-min study period. The changes in BIS and PSI from baseline (T 0 ) to train of four ratio of 90% were not significantly different (median difference 0; 95% CI − 3 to 2; P = 0.83), neither were the changes in BIS and PSI values from T 0 to their maximum values (median difference 1; 95% CI − 1 to 4; P = 0.53). Maximum BIS and PSI were significantly higher than their baseline values (median difference 6; 95% CI 4–9; P < 0.001 and median difference 5; 95% CI 3–6; P < 0.001, respectively). We found weak positive correlations between BIS and BIS-EMG (r = 0.12, P = 0.01), as well as PSI and PSI-EMG (r = 0.25, P < 0.001). Both PSI and BIS were affected to some extent by EMG artifacts after sugammadex administration.

Background In the elderly, nutritional status and quality of life (QOL) could potentially affect food purchasing behaviors. We examined the association between combinations of nutritional status and QOL and food purchasing motives among the elderly. Methods A total of 143 community-dwelling elderly were recruited in Seoul, South Korea. Nutritional status and QOL were assessed and participants were divided into four groups according to those combinations. Binary logistic regression analysis was used to examine the odds of food purchasing motives according to combinations of nutritional status and QOL. Results As a result of comparing the scores (mean ± SD) of the overall important factors for food purchasing, health related factors such as Nutrition quality and Preventive of treatment effect were the highest score (4.4 ± 0.8), followed by Price (4.1 ± 0.9), Ease of purchase (3.8 ± 0.9), Ease of chewing (3.7 ± 0.9), and Taste (3.6 ± 0.9). Participants with a low nutritional status and low QOL had more eating-related problems (77.8%) including chewing difficulty (48.9%) and constipation (17.8%) than those with a high nutritional status and high QOL ( P  < 0.05). Participants who were in high nutritional status and low QOL were more likely to be motivated by Ease of chewing (OR: 6.72; 95% CI: 1.44–31.37; P  < 0.05), while those who were in low nutritional status and high QOL were less motivated by Taste (OR: 0.28; 95% CI: 0.08–0.94; P  < 0.05) compared to those who were in high nutritional status and high QOL. Conclusions There were differences in food purchasing motives such as Ease of chewing or Taste according to combinations of nutritional status and QOL. These data are important in demonstrating differing motives for food choice across nutritional status and QOL, and also provide indications of which care service and food development may be needed in promoting health for the elderly in South Korea.

Background: Decoding covert spatial attention (CSA) from dry, low-channel electroencephalography (EEG) is key for gaze-independent brain–computer interfaces (BCIs). Methods: We evaluate, on sixteen participants and three tasks (CSA, motor imagery (MI), Emotion), a four-electrode, subject-wise pipeline combining leak-safe preprocessing, multiresolution wavelets, and a compact Hybrid encoder (CNN-LSTM-MHSA) with robustness-oriented training (noise/shift/channel-dropout and supervised consistency). Results: Online, the Hybrid All-on-Wav achieved 0.695 accuracy with end-to-end latency ~2.03 s per 2.0 s decision window; the pure model inference latency is ≈185 ms on CPU and ≈11 ms on GPU. The same backbone without defenses reached 0.673, a CNN-LSTM 0.612, and a compact CNN 0.578. Offline subject-wise analyses showed a CSA median Δ balanced accuracy (BAcc) of +2.9%p (paired Wilcoxon p = 0.037; N = 16), with usability-aligned improvements (error 0.272 → 0.268; information transfer rate (ITR) 3.120 → 3.240). Effects were smaller for MI and present for Emotion. Conclusions: Even with simple hardware, compact attention-augmented models and training-time defenses support feasible, low-latency left–right CSA control above chance, suitable for embedded or laptop-class deployment.

Extracellular vesicles (EVs) composed of a lipid bilayer are released from various cell types, including animals, plants, and microorganisms, and serve as important mediators of cell-to-cell communication. EVs can perform a variety of biological functions through the delivery of bioactive molecules, such as nucleic acids, lipids, and proteins, and can also be utilized as carriers for drug delivery. However, the low productivity and high cost of mammalian-derived EVs (MDEVs) are major barriers to their practical clinical application where large-scale production is essential. Recently, there has been growing interest in plant-derived EVs (PDEVs) that can produce large amounts of electricity at a low cost. In particular, PDEVs contain plant-derived bioactive molecules such as antioxidants, which are used as therapeutic agents to treat various diseases. In this review, we discuss the composition and characteristics of PDEVs and the appropriate methods for their isolation. We also discuss the potential use of PDEVs containing various plant-derived antioxidants as replacements for conventional antioxidants.