A Biobrick Library for Cloning Custom Eukaryotic Plasmids

Researchers often require customised variations of plasmids that are not commercially available. Here we demonstrate the applicability and versatility of standard synthetic biological parts (biobricks) to build custom plasmids. For this purpose we have built a collection of 52 parts that include multiple cloning sites (MCS) and common protein tags, protein reporters and selection markers, amongst others. Importantly, most of the parts are designed in a format to allow fusions that maintain the reading frame. We illustrate the collection by building several model contructs, including concatemers of protein binding-site motifs, and a variety of plasmids for eukaryotic stable cloning and chromosomal insertion. For example, in 3 biobrick iterations, we make a cerulean-reporter plasmid for cloning fluorescent protein fusions. Furthermore, we use the collection to implement a recombinase-mediated DNA insertion (RMDI), allowing chromosomal site-directed exchange of genes. By making one recipient stable cell line, many standardised cell lines can subsequently be generated, by fluorescent fusion-gene exchange. We propose that this biobrick collection may be distributed peer-to-peer as a stand-alone library, in addition to its distribution through the Registry of Standard Biological Parts (http://partsregistry.org/)

Limits of use of social media for monitoring biosecurity events.

Compared to applications that trigger massive information streams, like earthquakes and human disease epidemics, the data input for agricultural and environmental biosecurity events (ie. the introduction of unwanted exotic pests and pathogens), is expected to be sparse and less frequent. To investigate if Twitter data can be useful for the detection and monitoring of biosecurity events, we adopted a three-step process. First, we confirmed that sightings of two migratory species, the Bogong moth (Agrotis infusa) and the Common Koel (Eudynamys scolopaceus) are reported on Twitter. Second, we developed search queries to extract the relevant tweets for these species. The queries were based on either the taxonomic name, common name or keywords that are frequently used to describe the species (symptomatic or syndromic). Third, we validated the results using ground truth data. Our results indicate that the common name queries provided a reasonable number of tweets that were related to the ground truth data. The taxonomic query resulted in too small datasets, while the symptomatic queries resulted in large datasets, but with highly variable signal-to-noise ratios. No clear relationship was observed between the tweets from the symptomatic queries and the ground truth data. Comparing the results for the two species showed that the level of familiarity with the species plays a major role. The more familiar the species, the more stable and reliable the Twitter data. This clearly presents a problem for using social media to detect the arrival of an exotic organism of biosecurity concern for which public is unfamiliar

Data from: Limits of use of social media for monitoring biosecurity events

Compared to applications that trigger massive information streams, like earthquakes and human disease epidemics, the data input for agricultural and environmental biosecurity events (ie. the introduction of unwanted exotic pests and pathogens), is expected to be sparse and less frequent. To investigate if Twitter data can be useful for the detection and monitoring of biosecurity events, we adopted a three-step process. First, we confirmed that sightings of two migratory species, the Bogong moth (Agrotis infusa) and the Common Koel (Eudynamys scolopaceus) are reported on Twitter. Second, we developed search queries to extract the relevant tweets for these species. The queries were based on either the taxonomic name, common name or keywords that are frequently used to describe the species (symptomatic or syndromic). Third, we validated the results using ground truth data. Our results indicate that the common name queries provided a reasonable number of tweets that were related to the ground truth data. The taxonomic query resulted in too small datasets, while the symptomatic queries resulted in large datasets, but with highly variable signal-to-noise ratios. No clear relationship was observed between the tweets from the symptomatic queries and the ground truth data. Comparing the results for the two species showed that the level of familiarity with the species plays a major role. The more familiar the species, the more stable and reliable the Twitter data. This clearly presents a problem for using social media to detect the arrival of an exotic organism of biosecurity concern for which public is unfamiliar

Welvaert et al. Bogong moth and Common Koel surveillance

The EXCEL datasheets "Common Moth", "Common Koel 1", "Common Koel 2", Common Koel 3", "Symp Moth 1", "Symp Moth2", "Symp Koel 1", "Symp Koel 2", "Symp Koel 3", and "Symp Koel 4" are relevance summaries (0=non-relevant, 1=relevant) of de-identified tweets (from Twitter). Tweets were produced using the Commonwealth Scientific & Industrial Research Organisations Emergency Situation Awareness (ESA) system. They are derived by the searches defined within the associated manuscript. The survey data of Bogong Moth data are field data collected from the summit ridge of Mount Gingera, Brindabella Ranges, Australia. Please contact the correspondence author, Peter Caley ([email protected]), for further information

Prevalence and Correlates of Food Insecurity among Palestinian Refugees in Lebanon: Data from a Household Survey.

Lebanon hosts the highest per capita refugee concentration worldwide. The Palestinian presence in Lebanon dates from 1948 and they remain a marginalized population. No information on their food security status has been reported previously. A survey of a representative sample of Palestinian refugee households in Lebanon (n = 2501) was conducted using a stratified two stage cluster sampling approach. We measured food insecurity using a modified USDA household food security module, locally validated. We collected data on household demographic, socioeconomic, health, housing, coping strategies and household intake of food groups and analysed these by food security status. About 41% (CI: 39-43) of households reported being food insecure and 20% (CI: 18-22) severely food insecure. Poor households were more likely to be severely food insecure (OR 1.41 (1.06-1.86)) while higher education of the head of household was significantly associated with protection against severe food insecurity (OR 0.66 (0.52-0.84)). Additionally, higher food expenditure and possession of food-related assets were significantly associated with food security (OR 0.93 (0.89-0.97) and OR 0.74 (0.59-0.92), respectively). After adjusting for confounders, households where at least one member suffered from an acute illness remained significantly more likely to be severely food insecure (OR 1.31(1.02-1.66)), as were households whose proxy respondent reported poor mental health (OR 2.64 (2.07-3.38)) and poor self-reported health (OR 1.62 (1.22-2.13). Severely food insecure households were more likely to eat cheaper foods when compared to non-severely food insecure households (p<0.001) and were more likely to rely on gifts (p<0.001) or welfare (p<0.001). They were also more likely to have exhausted all coping strategies, indicating significantly more frequently that they could not do anything (p = 0.0102). Food insecurity is a significant problem among Palestinian refugees in Lebanon and is likely to be exacerbated at this time when the Syrian crisis amplifies the problem

Phosphorus Supplementation Mitigated Food Intake and Growth of Rats Fed a Low-Protein Diet

BACKGROUND: Low protein intake is associated with various negative health outcomes at any life stage. When diets do not contain sufficient protein, phosphorus availability is compromised because proteins are the major sources of phosphorus. However, whether mineral phosphorus supplementation mitigates this problem is unknown, to our knowledge. OBJECTIVE: Our goal was to determine the impact of dietary phosphorus supplementation on food intake, weight gain, energy efficiency, body composition, blood metabolites, and liver histology in rats fed a low-protein diet for 9 wk. METHODS: Forty-nine 6-wk-old male Sprague-Dawley rats were randomly allocated to 5 groups and consumed 5 isocaloric diets ad libitum that varied only in protein (egg white) and phosphorus concentrations for 9 wk. The control group received a 20% protein diet with 0.3% P (NP-0.3P). The 4 other groups were fed a low-protein (10%) diet with a phosphorus concentration of 0.015%, 0.056%, 0.1%, or 0.3% (LP-0.3P). The rats' weight, body and liver composition, and plasma biomarkers were then assessed. RESULTS: The addition of phosphorus to the low-protein diet significantly increased food intake, weight gain, and energy efficiency, which were similar among the groups that received 0.3% P (LP-0.3P and NP-0.3P) regardless of dietary protein content. In addition, phosphorus supplementation of low-protein diets reduced plasma urea nitrogen and increased total body protein content (defatted). Changes in food intake and efficiency, body weight and composition, and plasma urea concentration were highly pronounced at a dietary phosphorus content &lt;0.1%, which may represent a critical threshold. CONCLUSIONS: The addition of phosphorus to low-protein diets improved growth measures in rats, mainly as a result of enhanced energy efficiency. A dietary phosphorus concentration of 0.3% mitigated detrimental effects of low-protein diets on growth parameters

CUSUM charts for the Koel Common name queries.

<p>Red dots indicate a deviation of the number of tweets, in particular the upper part of the chart points to an increase in tweets. UDB: Upper Decision Boundary; LDB: Lower Decision Boundary.</p

Validation of Koel Twitter data against historical monthly sightings.

<p>The Twitter data are represented as time series of the weekly counts, while the historical data are shown as monthly bars that are replicated for each migration season. The grey shaded area delimits the time period in which tweets couldn’t be reliably captured.</p

CUSUM charts for the Koel symptomatic queries.

<p>Red dots indicate a deviation of the number of tweets, in particular the upper part of the chart points to an increase in tweets. UDB: Upper Decision Boundary; LDB: Lower Decision Boundary.</p

Overview of the queries used in this study.

<p>Three types of queries are distinguished: (1) a taxonomic query using the taxonomic classification, (2) a common name query using the common name of the species, and (3) a symptomatic query that searches for tweets that indicate the presence of the species without mentioning either the taxonomic or common name.</p