A recombinant vector vaccine uses an attenuated virus, bacterium, or parasite as the carrier to express a heterologous antigen(s). Many recombinant vaccine vectors and related vaccines have been developed and extensively investigated. To compare and better understand recombinant vectors and vaccines, we have generated Vaxvec (http://www.violinet.org/vaxvec), the first web-based database that stores various recombinant vaccine vectors and those experimentally verified vaccines that use these vectors. Vaxvec has now included 59 vaccine vectors that have been used in 196 recombinant vector vaccines against 66 pathogens and cancers. These vectors are classified to 41 viral vectors, 15 bacterial vectors, 1 parasitic vector, and 1 fungal vector. The most commonly used viral vaccine vectors are double-stranded DNA viruses, including herpesviruses, adenoviruses, and poxviruses. For example, Vaxvec includes 63 poxvirus-based recombinant vaccines for over 20 pathogens and cancers. Vaxvec collects 30 recombinant vector influenza vaccines that use 17 recombinant vectors and were experimentally tested in 7 animal models. In addition, over 60 protective antigens used in recombinant vector vaccines are annotated and analyzed. User-friendly web-interfaces are available for querying various data in Vaxvec. To support data exchange, the information of vaccine vectors, vaccines, and related information is stored in the Vaccine Ontology (VO). Vaxvec is a timely and vital source of vaccine vector database and facilitates efficient vaccine vector research and development.
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Background and objectivePatients' demographics (race, age, gender, and ethnicity) have been determined to affect patients' health status. It has been established that chronic disease prevalence varies by race, age, gender, and ethnicity; however, not much is known about how these demographic factors influence presenting conditions or complaints within a student-run clinic (SRC). This study aimed to investigate how demographic factors in the Apopka community in Florida determine what internal medicine (IM) conditions or complaints patients present with at a student-run free clinic. MethodsElectronic medical record (EMR) data for adult patients seen at the clinic from February 2019 to February 2020 were reviewed to collect information on patient demographics, IM presenting conditions or complaints, and body mass index (BMI). Binary logistic regressions were employed to investigate the relationship between demographic factors and presenting conditions or complaints. ResultsThe majority of the patients were female (62.2%), with an almost equal representation of Hispanic (50.3%) and non-Hispanic individuals. About half of the patients visiting the clinic were either overweight or obese. Of the 167 patients, the average age was 44.17 and 44.32 years for males and females respectively. The most common presenting conditions or complaints included cardiac conditions (25.07%), diabetes (9.64%), gastric pain (9.21%), and upper respiratory infection (URI)/allergies (6.15%). Cardiac conditions were further broken down into hypertension (18.94%), dyslipidemia (3.94%), and palpitations (2.19%). Patient age was a contributing factor to the incidence of diabetes (p=0.002), hypertension (p<0.0001), and cardiovascular conditions excluding hypertension (p=0.021). There was a significant relationship between obesity and diabetes (p=0.036) and hypertension (p=<0.001). ConclusionSRCs can make use of the information obtained from this study to advocate for coverage of medications to treat diabetes and hypertension in this undocumented population to prevent morbidity rates. We believe our findings can also provide guidance in terms of instituting screening programs for these illnesses among the broader population and SRCs with different patient makeups.
Data mining technique has been considered as useful means for recognize patterns and accumulate of large set of data. This method is basically used to extract the unknown pattern from the large set of data as real time applications. It is an approximate intellect discipline which has appeared valuable tool for data analysis, new knowledge recognition and decision making. The speech recognition is also most important research area to find the speech signa by the computer. To evolve the recognition of the continuous speech signal, a speech segmentation, feature extraction and clustering techniques are used. The unlabelled data from the large dataset can be categorized initially in an unaided fashion by usi cluster analysis. The result of the clustering process and efficiency of its application are generally resolved through algorithms. There are various algorithms which are used to solve this problem. In this research paper two important clustering algori canter points based K-Means and representative object based FCM (Fuzzy C-Means) clustering algorithms are compared. The Hidden morkov model and Gaussian mixture model are the most suitable acoustic models are used to scale the continuous speech signal and recognize the corresponding text data.Keywords: Hidden Markov Model (HMM), Gaussian Mixture Model, k means and Fuzzy c means (FCM) clustering.
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