Because the development of the internet of things (IoT) requires technology that transfers information between objects without human intervention, the core of IoT security will be secure authentication between devices or between devices and servers. Software-based authentication may be a security vulnerability in IoT, but hardware-based security technology can provide a strong security environment. A physical unclonable functions (PUFs) are a hardware security element suitable for lightweight applications. PUFs can generate challenge-response pairs(CRPs) that cannot be controlled or predicted by utilizing inherent physical variations that occur in the manufacturing process. In particular, pulse width memristive PUF (PWM-PUF) improves security performance by applying different write pulse widths and bank structures. Bloom filter (BF) is probabilistic data structures that answer membership queries using small memories. Bloom filter can improve search performance and reduce memory usage and are used in areas such as networking, security, big data, and IoT. In this paper, we propose a structure that applies Bloom filters based on the PWM-PUF to reduce PUF data transmission errors. The proposed structure uses two different Bloom filter types that store different information and that are located in front of and behind the PWM-PUF, improving security by removing challenges from attacker access. Simulation results show that the proposed structure decreases the data transmission error rate and reuse rate as the Bloom filter size increases, the simulation results also show that the proposed structure improves PWM-PUF security with a very small Bloom filter memory.

A cell filtration platform that affords accurate size separation and minimizes fouling was developed. The platform features an ultra-thin porous membrane (UPM) filter, a pumping head for backflushing (PHB), and cell size measurement (CSM) software. The UPM chip is an ultrathin free-standing membrane with a large window area of 0.68 mm2, a pore diameter of 5 to 9 μm, and a thickness of less than 0.9 μm. The PHB prevents filter fouling. The CSM software analyzes the size distributions of the supernatants and subnatants of isolated cells and presents the data visually. The D99 particle size of cells of the chronic myeloid leukemia (CML) line K562 decreased from 22.2 to 17.5 μm after passage through a 5-μm filter. K562 cells could be separated by careful selection of the pore size; the recovery rate attained 91.3%. The method was compared to conventional blocking models by evaluating the mean square errors (MSEs) between the measured and calculated filtering volumes. The filtering rate was fitted by a linear regression model with a significance that exceeded 0.99 based on the R2 value. The platform can be used to separate various soft biomaterials and affords excellent stability during filtration.

This study was to investigate and assess salivary biomarkers as a means of diagnosing periodontitis. A total of 121 subjects were included: 28 periodontally healthy subjects, 24 with stage I, 24 with stage II, 23 with stage III, and 22 with stage IV periodontitis. Salivary proteins including active matrix metalloproteinase-8 (MMP-8), pro-MMP-8, total MMP-8, C-reactive protein, secretory immunoglobulin A and planktonic bacteria including Aggregatibacter actinomycetemcomitans, Porphyromonas gingivalis, Tannerella forsythia, Treponema denticola, Fusobacterium nucleatum, Prevotella intermedia, Porphyromonas nigrescens, Parvimonas micra, Campylobacter rectus, Eubacterium nodatum, Eikenella corrodens, Streptococcus mitis, Streptococcus mutans, Staphylococcus aureus, Enterococcus faecalis, and Actinomyces viscosus were measured from salivary samples. The performance of the diagnostic models was assessed by receiver operating characteristics (ROC) and area under the ROC curve (AUC) analysis. The diagnostic models were constructed based on the subjects’ proteins and/or microbial profiles, resulting in two potential diagnosis models, which achieved better diagnostic powers with an AUC value > 0.750 for the diagnosis of stage II, III, and IV periodontitis (Model PC-I; AUC: 0.796, sensitivity: 0.754, specificity: 0.712) and for the diagnosis of stage III and IV periodontitis (Model PC-II; AUC: 0.796, sensitivity: 0.756, specificity: 0.868). This study can contribute to screening for periodontitis based on salivary biomarkers.