Falls are a very dangerous situation especially among elderly people, because they may lead to fractures, concussion, and other injuries. Without timely rescue, falls may even endanger their lives. The existing optical sensor-based fall monitoring systems have some disadvantages, such as limited monitoring range and inconvenience to carry for users. Furthermore, the fall detection system based only on an accelerometer often mistakenly determines some activities of daily living (ADL) as falls, leading to low accuracy in fall detection. We propose a human fall monitoring system consisting of a highly portable sensor unit including a triaxis accelerometer, a triaxis gyroscope, and a triaxis magnetometer, and a mobile phone. With the data from these sensors, we obtain the acceleration and Euler angle (yaw, pitch, and roll), which represents the orientation of the user’s body. Then, a proposed fall detection algorithm was used to detect falls based on the acceleration and Euler angle. With this monitoring system, we design a series of simulated falls and ADL and conduct the experiment by placing the sensors on the shoulder, waist, and foot of the subjects. Through the experiment, we re-identify the threshold of acceleration for accurate fall detection and verify the best body location to place the sensors by comparing the detection performance on different body segments. We also compared this monitoring system with other similar works and found that better fall detection accuracy and portability can be achieved by our system.

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When optimizing nanocarriers, structural motifs that are beneficial for the respective type of cargo need to be identified. Here, succinoyl tetraethylene pentamine (Stp)-based lipo-oligoaminoamides (OAAs) were optimized for the delivery of plasmid DNA (pDNA). Structural variations comprised saturated fatty acids with chain lengths between C2 and C18 and terminal cysteines as units promoting nanoparticle stabilization, histidines for endosomal buffering, and disulfide building blocks for redox-sensitive release. Biophysical and tumor cell culture screening established clear-cut relationships between lipo-OAAs and characteristics of the formed pDNA complexes. Based on the optimized alternating Stp-histidine backbones, lipo-OAAs containing fatty acids with chain lengths around C6 to C10 displayed maximum gene transfer with around 500-fold higher gene expression than that of C18 lipo-OAA analogues. Promising lipo-OAAs, however, showed only moderate in vivo efficiency. In vitro testing in 90% full serum, revealing considerable inhibition of lytic and gene-transfer activity, was found as a new screening model predictive for intravenous applications in vivo.

Sequence-defined lipo-oligomers generated via solid-phase assisted synthesis have been developed as siRNA delivery systems for RNA-interference (RNAi) based gene silencing. Here, novel siRNA lipo-polyplexes were established, which were postmodified with monovalent or bivalent DBCO-PEG24 agents terminated with peptide GE11 (YH­WYG­YTP­QN­VI) for epidermal growth factor receptor (EGFR)-targeted siRNA delivery into EGFR-positive tumor cells. Lipo-oligomers containing eight cationizable succinoyltetraethylene-pentamine (Stp) units mediated higher siRNA nanoparticle core stability than those containing four Stp units, and the incorporation of histidines for enhanced endosomal buffer capacity resulted in an improved gene silencing efficiency. Lipo-polyplexes modified with monovalent or bivalent PEG-GE11 via the copper-free click reaction possessed significantly enhanced cellular internalization and transfection efficiency in EGF receptor-positive human cervical KB and hepatoma Huh7 cells in comparison with the corresponding lipo-polyplexes shielded with PEG24 without targeting. Furthermore, modification with the bivalent DBCO-PEG24-GE11 ligand resulted in higher gene silencing efficiency than modification with the same equivalents of the monovalent DBCO-PEG24-GE11 ligand.

Targeted delivery remains the major limitation in the development of small interfering RNA (siRNA) therapeutics. The successful siRNA multistep delivery requires precise carriers of substantial complexity. To achieve this, a monodisperse carrier is presented, synthesized by solid‐phase supported chemistry. The sequence‐defined assembly contains two oleic acids attached to a cationizable oligoaminoamide backbone in T‐shape configuration, and a terminal azide functionality for coupling to the atherosclerotic plaque‐specific peptide‐1 (AP‐1) as the cell targeting ligand for interleukin‐4 receptor (IL‐4R) which is overexpressed in a variety of solid cancers. For combined cytosolic delivery with siRNA, different apoptotic peptides (KLK, BAK, and BAD) are covalently conjugated via bioreversible disulfide linkage to the 5′‐end of the siRNA sense strand. siRNA‐KLK conjugates provide the highest antitumoral potency. The optimized targeted carrier is complexed with dual antitumoral siEG5‐KLK conjugates. The functionality of each subdomain is individually confirmed. The lipo‐oligomer confers stable assembly of siRNA conjugates into spherical 150–250 nm sized nanoparticles. Click‐shielding with dibenzocyclootyne‐PEG‐AP‐1 (DBCO‐PEG‐AP‐1) mediates an IL‐4R‐specific cell targeting and gene silencing in tumor cells. Most importantly, formulation of the siEG5‐KLK conjugate displays enhanced apoptotic tumor cell killing due to the combined effect of mitotic arrest by EG5 gene silencing and mitochondrial membrane disruption by KLK.

This Article addresses the problem of integrating subspace-based model identification with first-principles modeling for handling scenarios where the subspace model identifies spurious relationships between inputs and outputs. The key motivation is to suitably synergize the two approaches while retaining the simplicity of subspace-based model identification. In the proposed methodology, as is done with traditional subspace identification, state trajectories that best describe the input−output data are first computed (which implicitly correspond to an underlying linear time invariant model). In computing the system matrices using the state trajectories, constraints derived from first-principles understanding are incorporated into the optimization problem. To reconcile the resulting mismatch between the state trajectories and the system matrices, an iterative process is utilized. First, the system matrices computed from the optimization problem are utilized to re-estimate the state trajectories (this time utilizing a state estimator and the input and output trajectories). The state trajectories are, in turn, utilized to resolve the system matrices using the input−output data. The process is repeated until convergence occurs between successive state trajectories, thus yielding state trajectories and "consistent" system matrices. The efficacy of the proposed approach is shown via simulations using a nonlinear process example.

This paper presents the use of relevance feedback to the problem of content-based sub-image retrieval (CBsIR). Relevance feedback is used to improve the accuracy of successive retrievals via a tile re-weighting scheme that assigns penalties to each tile of database images and updates the tile penalties for all relevant images retrieved at each iteration using both the relevant (positive) and irrelevant (negative) images identified by the user. Performance evaluation on a dataset of over 10,000 images shows the effectiveness and efficiency of the proposed framework. Using 64 quantized colors in the RGB color space, the system can achieve a stable average recall value of 70% within the top 20 retrieved (and presented) images after only 5 iterations, with each such iteration taking about 2 seconds.