SELERA KONSUMEN JAGUNG REBUS DI PUSAT KULINER KELURAHAN OESAO, KECAMATAN KUPANG TIMUR KABUPATEN KUPANG

This study aims to determine consumer tastes in making decisions on purchasing boiled corn and to determine the factors that influence consumers in making purchasing decisions for boiled corn. This research was conducted in July 2020. Oesao Village was chosen to be the research location on purpose or purposive sampling, with the consideration that Oesao Village is a selling area for boiled corn that is visited by many buyers. Determination of respondents by chance (accidental sampling), namely finding consumers who are enjoying or buying boiled corn in the research location with the number of respondents as many as 50 people.             The results of the study are based on the Chi Square analysis, all the attributes examined in this study with a confidence level of 95%, there are three attributes that have differences in consumer tastes, namely attributes of type, size and volume, while the other two attributes, namely taste and cleanliness, have the same consumer taste There is no difference in taste for the attributes of boiled corn. And based on the results of the Fishbein Multi-attribute analysis, the attributes that become consumers' taste are consecutively the attributes with clean hygiene, sweetness, low purchase volume (<10 ears), with large / medium maize sizes and sweet corn types. Based on the results of multiple linear regression analysis, with simultaneous testing (F-test), it is found that the independent variables consisting of price (X1), location (X2), service (X3) and season (X4) together have an effect on the decision. purchase of boiled corn (Y). For partial testing (t-test), it was found that the independent variable (X) which affected the dependent variable (Y), namely the purchase decision, was the service variable (X3) and season (X4)

Tailored carrier/bacteria technology for rehabilitation of areas with pesticide-containing pollution – AQUAREHAB WP2

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Probabilistic reasoning with a bayesian DNA device based on strand displacement

We present a computing model based on the DNA strand displacement technique which performs Bayesian inference. The model will take single stranded DNA as input data, representing the presence or absence of a specific molecular signal (evidence). The program logic encodes the prior probability of a disease and the conditional probability of a signal given the disease playing with a set of different DNA complexes and their ratios. When the input and program molecules interact, they release a different pair of single stranded DNA species whose relative proportion represents the application of Bayes? Law: the conditional probability of the disease given the signal. The models presented in this paper can empower the application of probabilistic reasoning in genetic diagnosis in vitro

Caffeine vs. carbamazepine as indicators of wastewater pollution in a karst aquifer

This paper presents the analysis of caffeine and carbamazepine transport in the subsurface as a result of wastewater release in the Sorek creek over the outcrops of the carbonate, Yarkon-Taninim, aquifer in Israel. Both caffeine and carbamazepine were used as indicators of sewage contamination in the subsurface. While carbamazepine is considered conservative, caffeine is subject to sorption and degradation. The objective of the study was to quantify differences in their transport under similar conditions in the karst aquifer. Water flow and pollutant transport in a “vadose zone–aquifer” system were simulated by a quasi-3-D dual permeability numerical model. The results of this study show that each of these two pollutants can be considered effective tracers for characterization and assessment of aquifer contamination. Carbamazepine was found to be more suitable for assessing the contamination boundaries, while caffeine can be used as a contaminant tracer only briefly after contamination occurs. In instances where there are low concentrations of carbamazepine which appear as background contamination in an aquifer, caffeine might serve as a better marker for detecting new contamination events, given its temporal nature. The estimated caffeine degradation rate and the distribution coefficient of a linear sorption isotherm were 0.091&thinsp;d−1 and 0.1&thinsp;L&thinsp;kg−1, respectively, which imply a high attenuation capacity. The results of the simulation indicate that by the end of the year most of the carbamazepine mass (approximately 95&thinsp;%) remained in the matrix of the vadose zone, while all of the caffeine was completely degraded a few months after the sewage was discharged.</p

Computational genes: a tool for molecular diagnosis and therapy of aberrant mutational phenotype

<p>Abstract</p> <p>Background</p> <p>A finite state machine manipulating information-carrying DNA strands can be used to perform autonomous molecular-scale computations at the cellular level.</p> <p>Results</p> <p>We propose a new finite state machine able to detect and correct aberrant molecular phenotype given by mutated genetic transcripts. The aberrant mutations trigger a cascade reaction: specific molecular markers as input are released and induce a spontaneous self-assembly of a wild type protein or peptide, while the mutational disease phenotype is silenced. We experimentally demostrated in <it>in vitro </it>translation system that a viable protein can be autonomously assembled.</p> <p>Conclusion</p> <p>Our work demostrates the basic principles of computational genes and particularly, their potential to detect mutations, and as a response thereafter administer an output that suppresses the aberrant disease phenotype and/or restores the lost physiological function.</p

Gene and protein nomenclature in public databases

BACKGROUND: Frequently, several alternative names are in use for biological objects such as genes and proteins. Applications like manual literature search, automated text-mining, named entity identification, gene/protein annotation, and linking of knowledge from different information sources require the knowledge of all used names referring to a given gene or protein. Various organism-specific or general public databases aim at organizing knowledge about genes and proteins. These databases can be used for deriving gene and protein name dictionaries. So far, little is known about the differences between databases in terms of size, ambiguities and overlap. RESULTS: We compiled five gene and protein name dictionaries for each of the five model organisms (yeast, fly, mouse, rat, and human) from different organism-specific and general public databases. We analyzed the degree of ambiguity of gene and protein names within and between dictionaries, to a lexicon of common English words and domain-related non-gene terms, and we compared different data sources in terms of size of extracted dictionaries and overlap of synonyms between those. The study shows that the number of genes/proteins and synonyms covered in individual databases varies significantly for a given organism, and that the degree of ambiguity of synonyms varies significantly between different organisms. Furthermore, it shows that, despite considerable efforts of co-curation, the overlap of synonyms in different data sources is rather moderate and that the degree of ambiguity of gene names with common English words and domain-related non-gene terms varies depending on the considered organism. CONCLUSION: In conclusion, these results indicate that the combination of data contained in different databases allows the generation of gene and protein name dictionaries that contain significantly more used names than dictionaries obtained from individual data sources. Furthermore, curation of combined dictionaries considerably increases size and decreases ambiguity. The entries of the curated synonym dictionary are available for manual querying, editing, and PubMed- or Google-search via the ProThesaurus-wiki. For automated querying via custom software, we offer a web service and an exemplary client application

Scatter networks: a new approach for analysing information scatter

Information on any given topic is often scattered across the Web. Previously this scatter has been characterized through the inequality of distribution of facts (i.e. pieces of information) across webpages. Such an approach conceals how specific facts (e.g. rare facts) occur in specific types of pages (e.g. fact-rich pages). To reveal such regularities, we construct bipartite networks, consisting of two types of vertices: the facts contained in webpages and the webpages themselves. Such a representation enables the application of a series of network analysis techniques, revealing structural features such as connectivity, robustness and clustering. Not only does network analysis yield new insights into information scatter, but we also illustrate the benefit of applying new and existing analysis techniques directly to a bipartite network as opposed to its one-mode projection. We discuss the implications of each network feature to the users’ ability to find comprehensive information online. Finally, we compare the bipartite graph structure of webpages and facts with the hyperlink structure between the webpages.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/58170/2/njp7_7_231.pd