Computing with viruses

In recent years, different computing models have emerged within the area of Unconven-tional Computation, and more specifically within Natural Computing, getting inspiration from mechanisms present in Nature. In this work, we incorporate concepts in virology and theoretical computer science to propose a novel computational model, called Virus Ma-chine. Inspired by the manner in which viruses transmit from one host to another, a virus machine is a computational paradigm represented as a heterogeneous network that con-sists of three subnetworks: virus transmission, instruction transfer, and instruction-channel control networks. Virus machines provide non-deterministic sequential devices. As num-ber computing devices, virus machines are proved to be computationally complete, that is, equivalent in power to Turing machines. Nevertheless, when some limitations are imposed with respect to the number of viruses present in the system, then a characterization for semi-linear sets is obtained

Hepatitis E virus infection in swine workers: A meta‐analysis

Hepatitis E virus (HEV) infects both humans and animals. Swine has been confirmed to be the principal natural reservoir, which raises a concern that HEV infection would be substantially increasing among swine workers. The present study calculated the pooled prevalence of IgG antibodies against HEV among swine workers and the general population in previous cross‐sectional studies. We conducted a meta‐analysis comparing the prevalence of HEV infection between swine workers and the general population, including local residents, blood donors and non‐swine workers. Through searches in three databases (PubMed and OVID in English, and CNKI in Chinese) and after study selection, a total of 32 studies from 16 countries (from 1999 through 2018) were included in the meta‐analysis. A random‐effect model was employed in the study; an I 2 statistic assessed heterogeneity, and the Egger’s test detected publication bias. The comparative prevalence of anti‐HEV IgG was pooled from the studies. Compared to the general population, the prevalence ratio (PR) for swine workers was estimated to be 1.52 (95% CI 1.38–1.76) with the I 2 being 71%. No publication bias was detected (p = 0.40). A subgroup analysis further indicated increased prevalence of anti‐HEV IgG in the swine workers in Asia (PR = 1.49, 95% CI: 1.35–1.64), in Europe (PR = 1.93, 95% CI: 1.49–2.50) and in all five swine‐related occupations, including swine farmers, butchers, meat processors, pork retailers and veterinarians (PR ranged between 1.19 and 1.75). In summary, swine workers have a relatively higher prevalence of past HEV infection, and this finding is true across swine‐related occupations, which confirms zoonotic transmission between swine and swine workers.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/147857/1/zph12548_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/147857/2/zph12548.pd

Embedded Based Miniaturized Universal Electrochemical Sensing Platform

We created an embedded sensing platform based on STM32 embedded system, with integrated carbon-electrode ionic sensor by using a self-made plug. Given ration of concentration-unknown nitrate liquid samples, this platform is able to measure the nitrate concentration in neutral environment. Response signals which were transmitted by the sensor can be displayed via a serial port to the computer screen or via Bluetooth to the smartphone. Processed by a fitting function, signals are transformed into related concentration. Through repeating the experiment many times, the accuracy and repeatability turned out to be excellent. The results can be automatically stored on smartphone via Bluetooth. We created this embedded sensing platform for field water quality measurement. This platform also can be applied for other micro sensors’ signal acquisition and data processing

LRBmat: A Novel Gut Microbial Interaction and Individual Heterogeneity Inference Method for Colorectal Cancer

Many diseases are considered to be closely related to the changes in the gut microbial community, including colorectal cancer (CRC), which is one of the most common cancers in the world. The diagnostic classification and etiological analysis of CRC are two critical issues worthy of attention. Many methods adopt gut microbiota to solve it, but few of them simultaneously take into account the complex interactions and individual heterogeneity of gut microbiota, which are two common and important issues in genetics and intestinal microbiology, especially in high-dimensional cases. In this paper, a novel method with a Binary matrix based on Logistic Regression (LRBmat) is proposed to deal with the above problem. The binary matrix can directly weakened or avoided the influence of heterogeneity, and also contain the information about gut microbial interactions with any order. Moreover, LRBmat has a powerful generalization, it can combine with any machine learning method and enhance them. The real data analysis on CRC validates the proposed method, which has the best classification performance compared with the state-of-the-art. Furthermore, the association rules extracted from the binary matrix of the real data align well with the biological properties and existing literatures, which are helpful for the etiological analysis of CRC. The source codes for LRBmat are available at https://github.com/tsnm1/LRBmat

Time variant natural frequencies of a roadway bridge under stochastic vehicle flow

A general framework for investigating the on-load frequencies of roadway bridges under stochastic traffic flows was developed. The cellular automaton (CA) model was adopted to develop the stochastic traffic flow. The on-load natural frequencies of the bridge are analyzed statistically. The results show that the on-load frequencies of bridge are less than the corresponding natural frequencies of the bridge. For higher or lower traffic occupancies, the fluctuation of on-load natural frequencies of the bridge becomes smaller than that under the middle range of traffic flow densities. A linear relationship exists between the mean frequency difference and the traffic flow density, with the mean slope of regression lines for the first four natural frequencies being 0.0844. There is a stronger linear relationship between the mean frequency difference and the total traffic weight, with the mean slope being 0.482. Based on traffic flow density or total traffic weight on the bridge, it is possible to make an estimate of the effect of traffic flows on the on-load frequency of a reinforced concrete continuous beam bridge using the regression relationships

Effectiveness of Traditional Chinese Medicine Compound JieDuTongLuoShengJin Granules Treatment in Primary Sjögren’s Syndrome: A Randomized, Double-Blind, Placebo-Controlled Clinical Trial

Objective. To evaluate the clinical therapeutic efficacy and safety of JieDuTongLuoShengJin granules + HCQ in patients with pSS. Methods. 40 patients with low-activity-level pSS and without visceral involvement participated in this study and were randomized to receive either JieDuTongLuoShengJin granules with HCQ or placebo with HCQ. Patients and investigators were blinded to treatment allocation. The primary endpoint was week 12 ESSPRI score, while secondary endpoints included ESSDAI, salivary and lacrimal gland function, and some laboratory variables. Safety-related data were also assessed. Results. Comparing with the placebo group, the treatment group experienced statistically significant improvement in the mean change from baseline for the primary endpoint of ESSPRI score and also in PGA. Moreover, in comparison with baseline values, the treatment group had significantly improved ESSDAI score, unstimulated saliva flow rate, and several laboratory variables. However, upon comparison of the two groups, there were no significant differences for them. The incidence of AEs was 10.0%, one in treatment group and three in placebo group. Conclusion. Treatment with a combination of JieDuTongLuoShengJin granules with HCQ is effective in improving patients’ subjective symptoms and some objective indicators of pSS. These results indicate that JieDuTongLuoShengJin is promising as a safe and effective treatment of pSS