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Edible insects are a promising source of bioactive compounds with diverse health benefits. This study investigated the immunomodulatory properties of polysaccharides isolated from Protaetia brevitarsis seulensis larvae (PBSL). Polysaccharides were fractionated using a molecular weight cutoff membrane, and among these fractions, the higher molecular weight fraction (H-PBSL, 666.84 kDa) demonstrated significant immunomodulatory activity. Methylation and NMR structural analyses revealed H-PBSL to be an acidic heteroglycan with a complex structure, consisting of a backbone of repeating (1→4)-linked α-glucan units highly branched at O-6 positions with T-α-Glcp, T-α-GlcpA, and T-β-Galp. The immunomodulatory effects of H-PBSL were evaluated using RAW264.7 macrophage cells. H-PBSL treatment enhanced phagocytic activity and increased the expression of immune modulators and macrophage activation surface molecules. In addition, H-PBSL activated the NF-κB signaling pathway, evidenced by increased phosphorylation of IκB and NF-κB, and translocation of NF-κB from cytosol to nucleus. Moreover, inhibition of Toll-like receptor 4 (TLR4) reduced H-PBSL-induced nitric oxide synthesis, indicating TLR4′s pivotal role in H-PBSL-mediated macrophage activation. These findings highlight the potential of H-PBSL as a novel immunomodulatory agent derived from an edible insect, with possible applications in functional foods and nutraceuticals.

Abstract Direct methane protonic ceramic fuel cells are promising electrochemical devices that address the technical and economic challenges of conventional ceramic fuel cells. However, Ni, a catalyst of protonic ceramic fuel cells exhibits sluggish reaction kinetics for CH4 conversion and a low tolerance against carbon-coking, limiting its wider applications. Herein, we introduce a self-assembled Ni-Rh bimetallic catalyst that exhibits a significantly high CH4 conversion and carbon-coking tolerance. It enables direct methane protonic ceramic fuel cells to operate with a high maximum power density of ~0.50 W·cm−2 at 500 °C, surpassing all other previously reported values from direct methane protonic ceramic fuel cells and even solid oxide fuel cells. Moreover, it allows stable operation with a degradation rate of 0.02%·h−1 at 500 °C over 500 h, which is ~20-fold lower than that of conventional protonic ceramic fuel cells (0.4%·h−1). High-resolution in-situ surface characterization techniques reveal that high-water interaction on the Ni-Rh surface facilitates the carbon cleaning process, enabling sustainable long-term operation.

Abstract Background Humidifier disinfectants (HDs) were commonly used household chemicals to prevent microbial growth in a humidifier water tank in South Korea. A growing body of evidence has indicated that its airborne exposure can induce severe lung injury. However, there has been low awareness of other health outcomes in HD users. This study aimed to evaluate health conditions appealed by claimants for compensation in relation with an increased exposure to HD. Methods From survey data of personal HD exposure assessment of claimants for compensation in Korea, we included a total of 4,179 subjects [cases in each dataset were defined by nine reported health conditions, i.e., pneumonia, asthma, cardiovascular disease, respiratory disease, otorhinolaryngologic disease, brain disease (including cerebrovascular disease), dermatological disease, lung cancer, and all cancers]. HD exposures was considered as the following exposure criteria: exposure duration, exposure proximity, exposure direction, chemical type, cumulative exposure time, indoor air concentration, and cumulative exposure level. Logistic regression models were used to evaluate the associations between HD exposure and health conditions. Results After adjusting for sociodemographic and health behavioral factors and other chemical exposures (households, environmental, and occupational exposures), an increase in cumulative HD exposure time was significantly associated with risks of all nine diseases (all p-trends

Abstract The hydrogel of biomolecule-assisted metal/organic complex has the superior ability to form a uniform, continuous, and densely integrated structure, which is necessary for fine thin film fabrication. As a representative of nature-originated polymers with abundant reactive side chains, we select the gelatin molecule as an element for weaving the metal cations. Here, we demonstrate the interaction between the metal cation and gelatin molecules, and associate it with coating quality. We investigate the rheological property of gelatin solutions interacting with metal cation from the view of cross-linking and denaturing of gelatin molecules. Also, we quantitatively compare the corresponding interactions by monitoring the absorbance spectrum of the cation. The coated porous structure is systematically investigated from the infiltration of gelatin-mediated Gd0.2Ce0.8O2−δ (GDC) precursor into Sm0.5Sr0.5CoO3−δ (SSC) porous scaffold. By applying the actively interacting gelatin–GDC system, we achieve a thin film of GDC on SSC with excellent uniformity. Compare to the discrete coating from the typical infiltration process, the optimized thin film coated structure shows enhanced performance and stability.

The chemical instability of perovskite oxides containing Sr is a critical issue for the long-term operation of solid oxide fuel cells. In this study, we demonstrate a remarkable improvement in the chemical and electrochemical stability of a heterostructured La0.6Sr0.4CoO3-δ (LSC)-Ce0.9Gd0.1O1.95 (GDC) electrode. Electrostatic spray deposition was employed to fabricate heterostructured nanoparticles in a single step with a coaxial nozzle supplying the LSC powders in the core nozzle and the GDC precursors in the shell nozzle. Moreover, the reducing fuel added to the GDC precursor solution induced the sol-gel combustion reaction in the droplet to form a uniform nanocrystalline GDC coating with high surface coverage. The high surface coverage of GDC on the LSC more significantly improved long-term stability compared with than of the bare LSC cathode at a constant current density of 1 A/cm2 at 600°C for 100 h.

Sluggish oxygen reduction reaction (ORR) kinetics are a major obstacle to developing intermediate-temperature solid-oxide fuel cells (IT-SOFCs). In particular, engineering the anion defect concentration at an interface between the cathode and electrolyte is important for facilitating ORR kinetics and hence improving the electrochemical performance. We developed the yttria-stabilized zirconia (YSZ) nanofiber (NF)-based composite cathode, where the oxygen vacancy concentration is controlled by varying the dopant cation (Y _2 O _3 ) ratio in the YSZ NFs. The composite cathode with the optimized oxygen vacancy concentration exhibits maximum power densities of 2.66 and 1.51 W cm ^−2 at 700 and 600 °C, respectively, with excellent thermal stability at 700 °C over 500 h under 1.0 A cm ^−2 . Electrochemical impedance spectroscopy and distribution of relaxation time analysis revealed that the high oxygen vacancy concentration in the NF-based scaffold facilitates the charge transfer and incorporation reaction occurred at the interfaces between the cathode and electrolyte. Our results demonstrate the high feasibility and potential of interface engineering for achieving IT-SOFCs with higher performance and stability.

Advancements in convolutional neural network (CNN) have resulted in remarkable success in various computing fields. However, the need to protect data against external security attacks has become increasingly important because inference process in CNNs exploit sensitive data. Secure Memory is a hardware-based protection technique that can protect the sensitive data of CNNs. However, naively applying secure memory to a CNN application causes significant performance and energy overhead. Furthermore, ensuring secure memory becomes more difficult in environments that require area efficiency and low-power execution, such as the Internet of Things (IoT). In this paper, we investigated memory access patterns for CNN workloads and analyzed their effects on secure memory performance. According to our observations, most CNN workloads intensively write to narrow memory regions, which can cause a considerable number of counter overflows. On average, 87.6% of total writes occur in 6.8% of the allocated memory space; in the extreme case, 93.9% of total writes occur in 1.4% of the allocated memory space. Based on our observations, we propose an efficient integrity-tree structure called Countermark-tree that is suitable for CNN workloads. The proposed technique reduces overall energy consumption by 48%, shows a performance improvement of 11.2% compared to VAULT-128, and requires a similar integrity-tree size to VAULT-64, a state-of-the-art technique.

In recent years, there have been frequent reports on the adverse effects of synthetic cannabinoid (SC) abuse. SCs cause psychoactive effects, similar to those caused by marijuana, by binding and activating cannabinoid receptor 1 (CB1R) in the central nervous system. The aim of this study was to establish a reliable quantitative structure–activity relationship (QSAR) model to correlate the structures and physicochemical properties of various SCs with their CB1R-binding affinities. We prepared tetrahydrocannabinol (THC) and 14 SCs and their derivatives (naphthoylindoles, naphthoylnaphthalenes, benzoylindoles, and cyclohexylphenols) and determined their binding affinity to CB1R, which is known as a dependence-related target. We calculated the molecular descriptors for dataset compounds using an R/CDK (R package integrated with CDK, version 3.5.0) toolkit to build QSAR regression models. These models were established, and statistical evaluations were performed using the mlr and plsr packages in R software. The most reliable QSAR model was obtained from the partial least squares regression method via Y-randomization test and external validation. This model can be applied in vivo to predict the addictive properties of illicit new SCs. Using a limited number of dataset compounds and our own experimental activity data, we built a QSAR model for SCs with good predictability. This QSAR modeling approach provides a novel strategy for establishing an efficient tool to predict the abuse potential of various SCs and to control their illicit use.