Effect of Probiotics in Stress-Associated Constipation Model in Zebrafish (Danio rerio) Larvae.

The pathophysiology of functional bowel disorders is complex, involving disruptions in gut motility, visceral hypersensitivity, gut-brain-microbiota interactions, and psychosocial factors. Light pollution, as an environmental stressor, has been associated with disruptions in circadian rhythms and the aggravation of stress-related conditions. In this study, we investigated the effects of environmental stress, particularly continuous light exposure, on intestinal motility and inflammation using zebrafish larvae as a model system. We also evaluated the efficacy of probiotics, specifically Bifidobacterium longum (B. longum), at alleviating stress-induced constipation. Our results showed that continuous light exposure in zebrafish larvae increased the cortisol levels and reduced the intestinal motility, establishing a stress-induced-constipation model. We observed increased inflammatory markers and decreased intestinal neural activity in response to stress. Furthermore, the expressions of aquaporins and vasoactive intestinal peptide, crucial for regulating water transport and intestinal motility, were altered in the light-induced constipation model. Administration of probiotics, specifically B. longum, ameliorated the stress-induced constipation by reducing the cortisol levels, modulating the intestinal inflammation, and restoring the intestinal motility and neural activity. These findings highlight the potential of probiotics to modulate the gut-brain axis and alleviate stress-induced constipation. Therefore, this study provides a valuable understanding of the complex interplay among environmental stressors, gut function, and potential therapeutic strategies.

Design Method for a Wideband Non-Uniformly Spaced Linear Array Using the Modified Reinforcement Learning Algorithm.

In this paper, we present a design method for a wideband non-uniformly spaced linear array (NUSLA), with both symmetric and asymmetric geometries, using the modified reinforcement learning algorithm (MORELA). We designed a cost function that provided freedom to the beam pattern by setting limits only on the beam width (BW) and side-lobe level (SLL) in order to satisfy the desired BW and SLL in the wide band. We added the scan angle condition to the cost function to design the scanned beam pattern, as the ability to scan a beam in the desired direction is important in various applications. In order to prevent possible pointing angle errors for asymmetric NUSLA, we employed a penalty function to ensure the peak at the desired direction. Modified reinforcement learning algorithm (MORELA), which is a reinforcement learning-based algorithm used to determine a global optimum of the cost function, is applied to optimize the spacing and weights of the NUSLA by minimizing the proposed cost function. The performance of the proposed scheme was verified by comparing it with that of existing heuristic optimization algorithms via computer simulations.

Multi-Target Localization Based on Unidentified Multiple RSS/AOA Measurements in Wireless Sensor Networks.

All existing hybrid target localization algorithms using received signal strength (RSS) and angle of arrival (AOA) measurements in wireless sensor networks, to the best of our knowledge, assume a single target such that even in the presence of multiple targets, the target localization problem is translated to multiple single-target localization problems by assuming that multiple measurements in a node are identified with their originated targets. Herein, we first consider the problem of multi-target localization when each anchor node contains multiple RSS and AOA measurement sets of unidentified origin. We propose a computationally efficient method to cluster RSS/AOA measurement sets that originate from the same target and apply the existing single-target linear hybrid localization algorithm to estimate multiple target positions. The complexity analysis of the proposed algorithm is presented, and its performance under various noise environments is analyzed via simulations.

Target Localization with Unknown Transmit Power and Path-Loss Exponent Using a Kalman Filter in WSNs.

We present a novel hybrid localization algorithm for wireless sensor networks in the absence of knowledge regarding the transmit power and path-loss exponent. Transmit power and the path-loss exponent are critical parameters for target localization algorithms in wireless sensor networks, which help extract target position information from the received signal strength. In the absence of information on transmit power and path-loss exponent, it is critical to estimate them for reliable deployment of conventional target localization algorithms. In this paper, we propose a simultaneous estimation of transmit power and path-loss exponent based on Kalman filter. The unknown transmit power and path-loss exponent are estimated using a Kalman filter with the tentatively estimated target position based solely on angle information. Subsequently, the target position is refined using a hybrid method incorporating received signal strength measurements based on the estimated transmit power and path-loss exponent. Our proposed algorithm accurately estimates transmit power and path-loss exponent and yields almost the same target position accuracy as the simulation results confirm, as the hybrid target localization algorithms with known transmit power and path-loss exponent. Simulation results confirm the proposed algorithm achieves 99.7% accuracy of the target localization performance with known transmit power and path-loss exponent, even in the presence of severe received signal strength measurement noise.

Hybrid RSS/AOA Localization using Approximated Weighted Least Square in Wireless Sensor Networks.

We present a target localization method using an approximated error covariance matrix based weighted least squares (WLS) solution, which integrates received signal strength (RSS) and angle of arrival (AOA) data for wireless sensor networks. We approximated linear WLS errors via second-order Taylor approximation, and further approximated the error covariance matrix using a least-squares solution and the variance in measurement noise over the sensor nodes. The algorithm does not require any prior knowledge of the true target position or noise variance. Simulations validated the superior performance of our new method.

Engineering TGF-ß inhibitor-encapsulated macrophage-inspired multi-functional nanoparticles for combination cancer immunotherapy.

BACKGROUND: The emergence of cancer immunotherapies, notably immune checkpoint inhibitors, has revolutionized anti-cancer treatments. These treatments, however, have been reported to be effective in a limited range of cancers and cause immune-related adverse effects. Thus, for a broader applicability and enhanced responsiveness to solid tumor immunotherapy, immunomodulation of the tumor microenvironment is crucial. Transforming growth factor-ß (TGF-ß) has been implicated in reducing immunotherapy responsiveness by promoting M2-type differentiation of macrophages and facilitating cancer cell metastasis. METHODS: In this study, we developed macrophage membrane-coated nanoparticles loaded with a TGF-ßR1 kinase inhibitor, SD-208 (M[Formula: see text]-SDNP). Inhibitions of M2 macrophage polarization and epithelial-to-mesenchymal transition (EMT) of cancer cells were comprehensively evaluated through in vitro and in vivo experiments. Bio-distribution study and in vivo therapeutic effects of M[Formula: see text]-SDNP were investigated in orthotopic breast cancer model and intraveneously injected metastasis model. RESULTS: M[Formula: see text]-SDNPs effectively inhibited cancer metastasis and converted the immunosuppressive tumor microenvironment (cold tumor) into an immunostimulatory tumor microenvironment (hot tumor), through specific tumor targeting and blockade of M2-type macrophage differentiation. Administration of M[Formula: see text]-SDNPs considerably augmented the population of cytotoxic T lymphocytes (CTLs) in the tumor tissue, thereby significantly enhancing responsiveness to immune checkpoint inhibitors, which demonstrates a robust anti-cancer effect in conjunction with anti-PD-1 antibodies. CONCLUSION: Collectively, responsiveness to immune checkpoint inhibitors was considerably enhanced and a robust anti-cancer effect was demonstrated with the combination treatment of M[Formula: see text]-SDNPs and anti-PD-1 antibody. This suggests a promising direction for future therapeutic strategies, utilizing bio-inspired nanotechnology to improve the efficacy of cancer immunotherapy.

Drosophila ppk19 encodes a proton-gated and mechanosensitive ion channel.

In Drosophila larvae, nociceptive mdIV sensory neurons detect diverse noxious stimuli and prompt a nociceptive rolling response. Intriguingly, the same neurons also regulate stereotyped larval movement. The channels responsible for transducing these stimuli into electric signals are not yet fully identified. Here we undertook genetic and electrophysiological analysis of Ppk19, a member of the Deg/ENaC family of cationic channels. ppk19 mutants exhibited an impaired nociceptive rolling response upon mechanical force and acid, but no impairment in response to noxious temperature and gentle touch. Mutants also exhibited defective larval movement. RNAi against ppk19 in mdIV neurons likewise produced larvae with defects in mechanical and acid nociception and larval movement, but no impairment in detection of heat and gentle touch. Cultured cells transfected with ppk19 produced currents in acid and hypotonic solution, suggesting that ppk19 encodes an ion channel that responds to acid and cell swelling. Taken together, these findings suggest that Ppk19 acts in mdIV neurons as a proton- and mechano-gated ion channel to mediate acid- and mechano-responsive nociception and larval movement.

Highly transparent poly(glycidyl methacrylate-co-acryloisobutyl POSS) for 100 µm-thick submicron patterns with an aspect ratio over 100.

This is the first report on the fabrication of defect-free submicron structures with more than 100 µm thickness and an aspect ratio over 100. Highly transparent poly(glycidyl methacrylate-co-acryloisobutyl POSS) (PGP) was synthesized via radical polymerization. The mechanical properties of the PGP submicron structure displayed a Young's modulus of 6.09 GPa and a hardness of 0.16 GPa, 4.2 and 8 times, respectively, than those of SU8 nanopatterns. These enhancements enable the utilization of ultrathick 2D-/3D-submicron structures as an ideal platform for microelectromechanical systems, big data storage systems, energy devices, etc.