Most cyberattacks use malicious codes, and according to AV-TEST, more than 1 billion malicious codes are expected to emerge in 2020. Although such malicious codes have been widely seen around the PC environment, they have been on the rise recently, focusing on IoT devices such as smartphones, refrigerators, irons, and various sensors. As is known, Linux/embedded environments support various architectures, so it is difficult to identify the architecture in which malware operates when analyzing malware. This paper proposes an AI-based malware analysis technology that is not affected by the operating system or architecture platform. The proposed technology works intuitively. It uses platform-independent binary data rather than features based on the structured format of the executable files. We analyzed the strings from binary data to classify malware. The experimental results achieved 94% accuracy on Windows and Linux datasets. Based on this, we expect the proposed technology to work effectively on other platforms and improve through continuous operation/verification.

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Background:The association between chronic rhinosinusitis (CRS) and chronic rhinitis remains unclear. The aim of this study was to estimate the predictive factors for sinus opacification in chronic rhinitis patients without obvious CRS. Methods:We retrospectively studied a total of 332 adult patients with chronic rhinitis who visited our clinic from January 2015 to December 2017. All of the patients underwent endoscopic examination, allergy test, and osteomeatal-unit computed tomography. The subjects were assigned to the normal sinus (NS) group (Lund-Mackay score [LMS] <5) and sinus opacification (SO) group (LMS ࣙ5). Results:A total of 288 patients were eligible for this study. Of them, 183 (63.5%) were classified in the NS group and 105 (36.5%) in the SO group. Total immunoglobulin E (IgE) level and peripheral blood eosinophil count were significantly higher in the SO than NS group (p = 0.031 and p < 0.0001, respectively). Using Pearson correlation coefficients, we determined that eosinophil count had a positive correlation with the LMS (r = 0.282). In logistic analysis, the interquartile range increase (0.19 × 10 9 /L) of the eosinophil count was significantly associated with SO (odds ratio [OR] 1.76; 95% confidence interval [CI], 1.30 to 2.39). A er adjusting for age, gender, smoking, drinking, and underlying disease, the interquartile range increase of the eosinophil count increased the odds of SO to 1.69-fold; this increase was statistically significant (p = 0.007; 95% CI, 1.17 to 2.43). Conclusion:Peripheral blood eosinophil count is an independent predictor of CRS in patients with chronic rhinitis. C 2018 ARS-AAOA, LLC. How to Cite this Article:Oh SY, Hwang J, Ryu Y-J, Won JY, Kwon SO, Lee WH. Blood eosinophils may predict radiographic sinus opacification in patients with chronic rhinitis. Int Forum Allergy Rhinol. 2019;9:522-527.

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This study aimed to evaluate the impact of different surface treatments (machined; sandblasted, large grit, and acid-etched (SLA); hydrophilic; and hydrophobic) on dental titanium (Ti) implant surface morphology, roughness, and biofilm formation. Four groups of Ti disks were prepared using distinct surface treatments, including femtosecond and nanosecond lasers for hydrophilic and hydrophobic treatments. Surface morphology, wettability, and roughness were assessed. Biofilm formation was evaluated by counting the colonies of Aggregatibacter actinomycetemcomitans (Aa), Porphyromonas gingivalis (Pg), and Prevotella intermedia (Pi) at 48 and 72 h. Statistical analysis was conducted to compare the groups using the Kruskal–Wallis H test and the Wilcoxon signed-rank test (α = 0.05). The analysis revealed that the hydrophobic group had the highest surface contact angle and roughness (p < 0.05), whereas the machined group had significantly higher bacterial counts across all biofilms (p < 0.05). At 48 h, the lowest bacterial counts were observed in the SLA group for Aa and the SLA and hydrophobic groups for Pg and Pi. At 72 h, low bacterial counts were observed in the SLA, hydrophilic, and hydrophobic groups. The results indicate that various surface treatments affect implant surface properties, with the hydrophobic surface using femtosecond laser treatment exerting a particularly inhibitory effect on initial biofilm growth (Pg and Pi).