In this study, we sought to identify novel antimicrobial peptides (AMPs) in through bioinformatic analyses of publicly available genome information and experimental validation. In our analysis of the python genome, we identified 29 AMP-related candidate sequences. Of these, we selected five cathelicidin-like sequences and subjected them to further analyses. The results showed that these sequences likely have antimicrobial activity. The sequences were named Pb-CATH1 to Pb-CATH5 according to their sequence similarity to previously reported snake cathelicidins. We predicted their molecular structure and then chemically synthesized the mature peptide for three putative cathelicidins and subjected them to biological activity tests. Interestingly, all three peptides showed potent antimicrobial effects against Gram-negative bacteria but very weak activity against Gram-positive bacteria. Remarkably, ΔPb-CATH4 showed potent activity against antibiotic-resistant clinical isolates and also was observed to possess very low hemolytic activity and cytotoxicity. ΔPb-CATH4 also showed considerable serum stability. Electron microscopic analysis indicated that ΔPb-CATH4 exerts its effects via toroidal pore preformation. Structural comparison of the cathelicidins identified in this study to previously reported ones revealed that these Pb-CATHs are representatives of a new group of reptilian cathelicidins lacking the acidic connecting domain. Furthermore, Pb-CATH4 possesses a completely different mature peptide sequence from those of previously described reptilian cathelicidins. These new AMPs may be candidates for the development of alternatives to or complements of antibiotics to control multidrug-resistant pathogens.

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PurposeFreezing of gait (FOG), increasing the fall risk and limiting the quality of life, is common at the advanced stage of Parkinson’s disease, typically in old ages. A simple and unobtrusive FOG detection system with a small calculation load would make a fast presentation of on-demand cueing possible. The purpose of this study was to find a practical FOG detection system.Patients and methodsA sole-mounted sensor system was developed for an unobtrusive measurement of acceleration during gait. Twenty patients with Parkinson’s disease participated in this study. A simple and fast time-domain method for the FOG detection was suggested and compared with the conventional frequency-domain method. The parameters used in the FOG detection were optimized for each patient.ResultsThe calculation load was 1,154 times less in the time-domain method than the conventional method, and the FOG detection performance was comparable between the two domains (P=0.79) and depended on the window length (P<0.01) and dimension of sensor information (P=0.03).ConclusionA minimally constraining sole-mounted sensor system was developed, and the suggested time-domain method showed comparable FOG detection performance to that of the conventional frequency-domain method. Three-dimensional sensor information and 3–4-second window length were desirable. The suggested system is expected to have more practical clinical applications.

The net profit of investors can rapidly increase if they correctly decide to take one of these three actions: buying, selling, or holding the stocks. The right action is related to massive stock market measurements. Therefore, defining the right action requires specific knowledge from investors. The economy scientists, following their research, have suggested several strategies and indicating factors that serve to find the best option for trading in a stock market. However, several investors’ capital decreased when they tried to trade the basis of the recommendation of these strategies. That means the stock market needs more satisfactory research, which can give more guarantee of success for investors. To address this challenge, we tried to apply one of the machine learning algorithms, which is called deep reinforcement learning (DRL) on the stock market. As a result, we developed an application that observes historical price movements and takes action on real-time prices. We tested our proposal algorithm with three—Bitcoin (BTC), Litecoin (LTC), and Ethereum (ETH)—crypto coins’ historical data. The experiment on Bitcoin via DRL application shows that the investor got 14.4% net profits within one month. Similarly, tests on Litecoin and Ethereum also finished with 74% and 41% profit, respectively.

In today's era of aging society, people want to handle personal health care by themselves in everyday life. In particular, the evolution of medical and IT convergence technology and mobile smart devices has made it possible for people to gather information on their health status anytime and anywhere easily using biometric information acquisition devices. Healthcare information systems can contribute to the improvement of the nation's healthcare quality and the reduction of related cost. However, there are no perfect security models or mechanisms for healthcare service applications, and privacy information can therefore be leaked. In this paper, we examine security requirements related to privacy protection in u-healthcare service and propose an extended RBAC based security model. We propose and design u-healthcare service integration platform (u-HCSIP) applying RBAC security model. The proposed u-HCSIP performs four main functions: storing and exchanging personal health records (PHR), recommending meals and exercise, buying/selling private health information or experience, and managing personal health data using smart devices.