Clinical Risk Prediction with Temporal Probabilistic Asymmetric Multi-Task Learning

Although recent multi-task learning methods have shown to be effective in improving the generalization of deep neural networks, they should be used with caution for safety-critical applications, such as clinical risk prediction. This is because even if they achieve improved task-average performance, they may still yield degraded performance on individual tasks, which may be critical (e.g., prediction of mortality risk). Existing asymmetric multi-task learning methods tackle this negative transfer problem by performing knowledge transfer from tasks with low loss to tasks with high loss. However, using loss as a measure of reliability is risky since low loss could result from overfitting. In the case of time-series prediction tasks, knowledge learned for one task (e.g., predicting the sepsis onset) at a specific timestep may be useful for learning another task (e.g., prediction of mortality) at a later timestep, but lack of loss at each timestep makes it difficult to measure the reliability at each timestep. To capture such dynamically changing asymmetric relationships between tasks in time-series data, we propose a novel temporal asymmetric multi-task learning model that performs knowledge transfer from certain tasks/timesteps to relevant uncertain tasks, based on the feature-level uncertainty. We validate our model on multiple clinical risk prediction tasks against various deep learning models for time-series prediction, which our model significantly outperforms without any sign of negative transfer. Further qualitative analysis of learned knowledge graphs by clinicians shows that they are helpful in analyzing the predictions of the model

So-Ochim-Tang-Gamibang, a Traditional Herbal Formula, Ameliorates Depression by Regulating Hyperactive Glucocorticoid Signaling In Vitro and In Vivo

So-ochim-tang-gamibang (SOCG) is a Korean traditional medicine; it has previously been shown to be safe and effective against depression. Persistently increased levels of circulating glucocorticoids have been considered as a pathological mechanism for depression and associated with decreased neurotrophic factors in the hippocampus. This study investigated whether SOCG controls the hyperactivity of the hypothalamic-pituitary-adrenal (HPA) axis and the molecular mechanisms underlying its effects in vivo and in vitro. Wistar Kyoto (WKY) rats were subjected to restraint stress, where SOCG was orally administered to the animals for 2 weeks. An open field test (OFT), forced swimming test (FST), and sucrose preference test (SPT) were performed to explore the antidepressant activity of SOCG in WKY rats. Plasma levels of HPA axis hormones were measured by ELISA or western blotting analysis. The expression levels or activation of HPA axis-related signaling molecules such as brain-derived neurotrophic factor (BDNF), cAMP response element-binding protein (CREB), extracellular regulated kinase (ERK), and glucocorticoid receptors (GRs) in the brain were determined by real-time PCR and western blotting analysis. Furthermore, a corticosterone- (CORT-) induced cell injury model was established using SH-SY5Y cells to explore the antidepressive effects of SOCG in vitro. The results of the OFT, FST, and SPT revealed that SOCG ameliorated depressive-like behaviors in the WKY rats. The blood plasma levels of HPA axis hormones such as CORT, CORT-releasing hormone (CRH), and adrenocorticotrophic hormone were downregulated by SOCG. On the other hand, SOCG upregulated the phosphorylation of CREB and ERK in both the rat hippocampus and CORT-treated SH-SY5Y cells. Moreover, it also increased the GR expression. These results suggested that SOCG may improve depression by controlling hyperactive glucocorticoid signaling via the downregulation of HPA axis hormones and upregulation of GR

Self-Patterned Stretchable Electrode Based on Silver Nanowire Bundle Mesh Developed by Liquid Bridge Evaporation

A new strategy is required to realize a low-cost stretchable electrode while realizing high stretchability, conductivity, and manufacturability. In this study, we fabricated a self-patterned stretchable electrode using a simple and scalable process. The stretchable electrode is composed of a bridged square-shaped (BSS) AgNW bundle mesh developed by liquid bridge evaporation and a stretchable polymer matrix patterned with a microcavity array. Owing to the BSS structure and microcavity array, which effectively concentrate the applied strain on the deformable square region of the BSS structure under tensile stretching, the stretchable electrode exhibits high stretchability with a low ΔR/R0 of 10.3 at a strain of 40%. Furthermore, by exploiting the self-patterning ability—attributable to the difference in the ability to form liquid bridges according to the distance between microstructures—we successfully demonstrated a stretchable AgNW bundle mesh with complex patterns without using additional patterning processes. In particular, stretchable electrodes were fabricated by spray coating and bar coating, which are widely used in industry for low-cost mass production. We believe that this study significantly contributes to the commercialization of stretchable electronics while achieving high performance and complex patterns, such as stretchable displays and electronic skin

Dimensional and structural control of silica aerogel membranes for miniaturized motionless gas pumps

With growing public interest in portable electronics such as micro fuel cells, micro gas total analysis systems, and portable medical devices, the need for miniaturized air pumps with minimal electrical power consumption is on the rise. Thus, the development and downsizing of next-generation thermal transpiration gas pumps has been investigated intensively during the last decades. Such a system relies on a mesoporous membrane that generates a thermomolecular pressure gradient under the action of an applied temperature bias. However, the development of highly miniaturized active membrane materials with tailored porosity and optimized pumping performance remains a major challenge. Here we report a systematic study on the manufacturing of aerogel membranes using an optimized, minimal-shrinkage sol–gel process, leading to low thermal conductivity and high air conductance. This combination of properties results in superior performance for miniaturized thermomolecular air pump applications. The engineering of such aerogel membranes, which implies pore structure control and chemical surface modification, requires both chemical processing know-how and a detailed understanding of the influence of the material properties on the spatial flow rate density. Optimal pumping performance was found for devices with integrated membranes with a density of 0.062 g cm–3 and an average pore size of 142.0 nm. Benchmarking of such low-density hydrophobic active aerogel membranes gave an air flow rate density of 3.85 sccm·cm–2 at an operating temperature of 400 °C. Such a silica aerogel membrane based system has shown more than 50% higher pumping performance when compared to conventional transpiration pump membrane materials as well as the ability to withstand higher operating temperatures (up to 440 °C). This study highlights new perspectives for the development of miniaturized thermal transpiration air pumps while offering insights into the fundamentals of molecular pumping in three-dimensional open-mesoporous materials. Keywords: aerogel membrane; miniaturized gas pump; sodium silicate; low-temperature cofired ceramics; Knudsen flo

Unveiling the Role of Side Chain for Improving Nonvolatile Characteristics of Conjugated Polymers‐Based Artificial Synapse

Abstract Interest has grown in services that consume a significant amount of energy, such as large language models (LLMs), and research is being conducted worldwide on synaptic devices for neuromorphic hardware. However, various complex processes are problematic for the implementation of synaptic properties. Here, synaptic characteristics are implemented through a novel method, namely side chain control of conjugated polymers. The developed devices exhibit the characteristics of the biological brain, especially spike‐timing‐dependent plasticity (STDP), high‐pass filtering, and long‐term potentiation/depression (LTP/D). Moreover, the fabricated synaptic devices show enhanced nonvolatile characteristics, such as long retention time (≈102 s), high ratio of Gmax/Gmin, high linearity, and reliable cyclic endurance (≈103 pulses). This study presents a new pathway for next‐generation neuromorphic computing by modulating conjugated polymers with side chain control, thereby achieving high‐performance synaptic properties

Solution-Processable Semiconducting Conjugated Planar Network

After the emergence of graphene in the material science field, top-down and bottom-up studies to develop semiconducting organic materials with layered structures became highly active. However, most of them have suffered from poor processability, which hampers device fabrication and frustrates practical applications. Here, we suggest an unconventional approach to fabricating semiconducting devices, which avoids the processability issue. We designed a soluble amorphous network using a solution process to form a thin film on a substrate. We then employed heat treatment to develop a flattened organic structure in the thin film, as an active layer for organic thin-film transistors (TFTs). The fabricated TFTs showed good performance in both horizontal and vertical charge transport, suggesting a versatile and useful approach for the development of organic semiconductors.11Nsciescopu