Dual Adjuvant-Loaded Peptide Antigen Self-Assembly Potentiates Dendritic Cell-Mediated Tumor Immunotherapy.

Clinical translation of current cancer vaccine research has been hampered by limited antitumor immune responses due to inefficient antigen delivery and presentation, suboptimal DC and T cell activation. Biomaterial-based nanovaccine offers targeted antigen delivery, protection from degradation in vivo, and prolonged tumor therapeutic efficacy. This study introduces a lipid-coated deoxycholic acid-survivin nanoassembly (DA-L-DSA).

Engineering TGF-β 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.

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.

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.

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.

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.

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.

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.