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Inositol pyrophosphates (PP-IPs) such as 5-diphosphoinositol pentakisphosphate (5-IP7) are inositol metabolites containing high-energy phosphoanhydride bonds. Biosynthesis of PP-IPs is mediated by IP6 kinases (IP6Ks) and PPIP5 kinases (PPIP5Ks), which transfer phosphate to inositol hexakisphosphate (IP6). Pleiotropic actions of PP-IPs are involved in many key biological processes, including growth, vesicular remodeling, and energy homeostasis. PP-IPs function to regulate their target proteins through allosteric interactions or protein pyrophosphorylation. This review summarizes the current understanding of how PP-IPs control mammalian cellular signaling networks in physiology and disease.

Electrocatalysts for oxygen reduction are a critical component that may dramatically enhance the performance of fuel cells and metal-air batteries, which may provide the power for future electric vehicles. Here we report a novel bio-inspired composite electrocatalyst, iron phthalocyanine with an axial ligand anchored on single-walled carbon nanotubes, demonstrating higher electrocatalytic activity for oxygen reduction than the state-of-the-art Pt/C catalyst as well as exceptional durability during cycling in alkaline media. Theoretical calculations suggest that the rehybridization of Fe 3d orbitals with the ligand orbitals coordinated from the axial direction results in a significant change in electronic and geometric structure, which greatly increases the rate of oxygen reduction reaction. Our results demonstrate a new strategy to rationally design inexpensive and durable electrochemical oxygen reduction catalysts for metal-air batteries and fuel cells. The rational design of inexpensive and durable oxygen reduction catalysts may lead to enhanced fuel cell performance. Here, the authors report a bio-inspired catalyst in which hybridization of iron 3d electrons with a carbon nanotube alters its electronic structure and improves catalytic performance.

Porous metal-organic-frameworks (MOFs) are attractive materials for gas storage, separations, and catalytic reactions. A challenge exists, however, on how to introduce larger pores juxtaposed with the inherent micropores in different forms of MOFs, which would enable new functions and applications. Here we report the formation of heterogeneous pores within MOF particles, patterns, and membranes, using a discriminate etching chemistry, called silver-catalyzed decarboxylation. The heterogeneous pores are formed, even in highly stable MOFs, without altering the original structure. A decarboxylated MOF membrane is shown to have pH-responsive switchable selectivity for the flow-assisted separation of similarly sized proteins. We envision that our method will allow the use of heterogeneous pores for massive transfer and separation of complex and large molecules, and that the capability for patterning and positioning heterogeneous MOF films on diverse substrates bodes well for various energy and electronic device applications. Tailoring MOFs to allow access of complex and large molecules is a challenging task due to their inherent microporous nature. Here the authors engineer meso- and macroporous MOF patterns and membranes via a mild decarboxylation applicable to different substrates, demonstrating their potential in macromolecule separations.

Antidepressants play a crucial role in treating mental health disorders such as depression and anxiety. Understanding of patients' perspective on antidepressants is essential for improving treatment outcomes; however, year-to-year change in the public's perception of antidepressants remains unclear. We aimed to analyze changes in public sentiments and predominant perceptions regarding antidepressants using artificial intelligence pipeline. This study analyzed online discussions related to antidepressants on Reddit from January 1, 2009, to December 31, 2022. Antidepressant-associated communities were explored to collect a list of discussions relevant to antidepressant therapy. Discussion topics on antidepressants were identified using BERTopic, and the sentiments were analyzed using a RoBERTa model. Trends were assessed using the Mann-Kendall test to evaluate shifts in sentiments over time. We analyzed 429,510 antidepressant-related discourse over 14 years and found a predominance in negative sentiments. Key discussion topics include the benefits and side effects of antidepressants, experiences with drug switching, and specific concerns regarding bupropion therapy. In trend analyses, negative sentiments decreased, while neutral sentiments increased over time. This aligns with a decline in the annual proportion of topics associated with side effects within each cluster. Negative perceptions toward antidepressants are prevalent on social media, mainly focusing on efficacy and side effects. However, a decade-long analysis shows a decline in negative sentiments, with an increase in neutral sentiments with a downturn in yearly proportion of side-effected related topics within each cluster. These trends and information may help improve strategies to address barriers to antidepressant use and adherence.

Social robots in public cultural venues, such as science museums, must engage diverse visitors through brief, one-off encounters where long-term user modeling is infeasible. This research examines immediately interpretable behavioral cues of a robot that can evoke a sense of personalization without storing or profiling individual users. First, a video-based observational study of expert and novice museum docents identified service strategies that enable socially adaptive communication. Building on these insights, three controlled laboratory studies investigated how specific cues from robots influence user perception. A video-based controlled study examined how recognition accuracy shapes users’ social impressions of the robot’s intelligence. Additional studies based on the Wizard-of-Oz (WoZ) method tested whether explanatory content aligned with participants’ background knowledge and whether explicit preference inquiry and memory-based continuity strengthened perceptions of personalization. Results showed that recognition accuracy improved social impressions, whereas knowledge alignment, explicit preference inquiry, and memory-based continuity cues increased perceived personalization. These findings demonstrate that micro-level personalization cues, interpretable within a short-term encounter, can support user-centered interaction design for social robots in public environments.

The 3rd Generation Partnership Project (3GPP) narrowband Internet of Things (NB-IoT) over non-terrestrial networks (NTN) is the most promising candidate technology supporting 5G massive machine-type communication. Compared to geostationary earth orbit, low earth orbit (LEO) satellite communication has the advantage of low propagation loss, but suffers from high Doppler shift. The 3GPP proposes Doppler shift pre-compensation for each beam region of the satellite. However, user equipment farther from the beam center has significant residual Doppler shifts even after pre-compensation, which degrades link performance. This study proposes residual Doppler shift compensation by adding demodulation reference signal symbols and reducing satellite beam coverage. The block error rate (BLER) data are obtained using link-level simulation with the proposed technique. Since the communication time provided by a single LEO satellite moving fast is short, many LEO satellites are necessary for seamless 24-h communication. Therefore, with the BLER data, we analyze the link budget for actual three-dimensional orbits with a maximum of 162 LEO satellites. We finally investigate the effect of the proposed technique on performance metrics such as the per-day total service time and maximum persistent service time, considering the number of satellites and the satellite spacing. The results show that a more prolonged and continuous communication service is possible with significantly fewer satellites using the proposed technique.

This study proposes an efficient traffic classification model to address the growing threat of distributed denial-of-service (DDoS) attacks in 5th generation technology standard (5G) slicing networks. The proposed method utilizes an ensemble of encoder components from multiple autoencoders to compress and extract latent representations from high-dimensional traffic data. These representations are then used as input for a support vector machine (SVM)-based metadata classifier, enabling precise detection of attack traffic. This architecture is designed to achieve both high detection accuracy and training efficiency, while adapting flexibly to the diverse service requirements and complexity of 5G network slicing. The model was evaluated using the DDoS Datasets 2022, collected in a simulated 5G slicing environment. Experiments were conducted under both class-balanced and class-imbalanced conditions. In the balanced setting, the model achieved an accuracy of 89.33%, an F1-score of 88.23%, and an Area Under the Curve (AUC) of 89.45%. In the imbalanced setting (attack:normal = 7:3), the model maintained strong robustness, achieving a recall of 100% and an F1-score of 90.91%, demonstrating its effectiveness in diverse real-world scenarios. Compared to existing AI-based detection methods, the proposed model showed higher precision, better handling of class imbalance, and strong generalization performance. Moreover, its modular structure is well-suited for deployment in containerized network function (NF) environments, making it a practical solution for real-world 5G infrastructure. These results highlight the potential of the proposed approach to enhance both the security and operational resilience of 5G slicing networks.

Large language models (LLMs) have the ability to generate text by stringing together words from their extensive training data. The leading AI text generation tool built on LLMs, ChatGPT, has quickly grown a vast user base since its release, but the domains in which it is being heavily leveraged are not yet known to the public. To understand how generative AI is reshaping print media and the extent to which it is being implemented already, methods to distinguish human-generated text from that generated by AI are required. Since college students have been early adopters of ChatGPT, we sought to study the presence of generative AI in newspaper articles written by collegiate journalists. To achieve this objective, an accurate AI detection model is needed. Herein, we analyzed university newspaper articles from different universities to determine whether ChatGPT was used to write or edit the news articles. We developed a detection model using classical machine learning and used the model to detect AI usage in the news articles. The detection model showcased a 93% accuracy in the training data and had a similar performance in the test set, demonstrating effectiveness in AI detection above existing state-of-the-art detection tools. Finally, the model was applied to the task of searching for generative AI usage in 2023, and we found that ChatGPT was not used to revise articles to any appreciable measure to write university news articles at the schools we studied.

In the IoT (Internet of Things) domain, the increased use of encryption protocols such as SSL/TLS, VPN (Virtual Private Network), and Tor has led to a rise in attacks leveraging encrypted traffic. While research on anomaly detection using AI (Artificial Intelligence) is actively progressing, the encrypted nature of the data poses challenges for labeling, resulting in data imbalance and biased feature extraction toward specific nodes. This study proposes a reconstruction error-based anomaly detection method using an autoencoder (AE) that utilizes packet metadata excluding specific node information. The proposed method omits biased packet metadata such as IP and Port and trains the detection model using only normal data, leveraging a small amount of packet metadata. This makes it well-suited for direct application in IoT environments due to its low resource consumption. In experiments comparing feature extraction methods for AE-based anomaly detection, we found that using flow-based features significantly improves accuracy, precision, F1 score, and AUC (Area Under the Receiver Operating Characteristic Curve) score compared to packet-based features. Additionally, for flow-based features, the proposed method showed a 30.17% increase in F1 score and improved false positive rates compared to Isolation Forest and OneClassSVM. Furthermore, the proposed method demonstrated a 32.43% higher AUC when using packet features and a 111.39% higher AUC when using flow features, compared to previously proposed oversampling methods. This study highlights the impact of feature extraction methods on attack detection in imbalanced, encrypted traffic environments and emphasizes that the one-class method using AE is more effective for attack detection and reducing false positives compared to traditional oversampling methods.

A colonoscopy is a medical examination used to check disease or abnormalities in the large intestine. If necessary, polyps or adenomas would be removed through the scope during a colonoscopy. Colorectal cancer can be prevented through this. However, the polyp detection rate differs depending on the condition and skill level of the endoscopist. Even some endoscopists have a 90% chance of missing an adenoma. Artificial intelligence and robot technologies for colonoscopy are being studied to compensate for these problems. In this study, we propose a self-supervised monocular depth estimation using spatiotemporal consistency in the colon environment. It is our contribution to propose a loss function for reconstruction errors between adjacent predicted depths and a depth feedback network that uses predicted depth information of the previous frame to predict the depth of the next frame. We performed quantitative and qualitative evaluation of our approach, and the proposed FBNet (depth FeedBack Network) outperformed state-of-the-art results for unsupervised depth estimation on the UCL datasets.