RadioLab project: knowledge of radon gas in Italy.

RadioLab is an Italian project, addressed to school-age people, and designed for the dissemination of scientific culture on the theme of environmental radioactivity, with particular regards to the importance of knowledge of radon gas exposure. The project is a nationwide initiative promoted by the National Institute of Nuclear Physics- INFN. First tool used by the project, and of immediate impact to assess the public awareness on radon, is the administration of the survey "do you know the radon gas?". In the survey, together with the knowledge of radon and of its sources, information on personal, cultural and territorial details regarding the interviewees are also taken. Reasonably, the survey invests not only young people, but also their relatives, school workers and, gradually, the public. The survey is administrated during exhibitions or outreach events devoted to schools, but also open to the public. The survey is in dual form: printed and online. The online mode clearly leads RadioLab project even outside the school environment. Based on the results of the survey, several statistical analyses have been performed and many conclusions are drawn about the knowledge of the population on the radon risk. The RadioLab benefit and the requirement to carry on the project goals, spreading awareness of environmental radioactivity from radon, emerge. The dataset involves all twenty Italian regions and consists of 28,612 entries covering the 5-year period 2018-2022.

Optimal growth of Lactobacillus casei in a Cheddar cheese ripening model system requires exogenous fatty acids.

Flavor development in ripening Cheddar cheese depends on complex microbial and biochemical processes that are difficult to study in natural cheese. Thus, our group has developed Cheddar cheese extract (CCE) as a model system to study these processes. In previous work, we found that CCE supported growth of Lactobacillus casei, one of the most prominent nonstarter lactic acid bacteria (NSLAB) species found in ripening Cheddar cheese, to a final cell density of 10(8) cfu/mL at 37°C. However, when similar growth experiments were performed at 8°C in CCE derived from 4-mo-old cheese (4mCCE), the final cell densities obtained were only about 10(6) cfu/mL, which is at the lower end of the range of the NSLAB population expected in ripening Cheddar cheese. Here, we report that addition of Tween 80 to CCE resulted in a significant increase in the final cell density of L. casei during growth at 8°C and produced concomitant changes in cytoplasmic membrane fatty acid (CMFA) composition. Although the effect was not as dramatic, addition of milk fat or a monoacylglycerol (MAG) mixture based on the MAG profile of milk fat to 4mCCE also led to an increased final cell density of L. casei in CCE at 8°C and changes in CMFA composition. These observations suggest that optimal growth of L. casei in CCE at low temperature requires supplementation with a source of fatty acids (FA). We hypothesize that L. casei incorporates environmental FA into its CMFA, thereby reducing its energy requirement for growth. The exogenous FA may then be modified or supplemented with FA from de novo synthesis to arrive at a CMFA composition that yields the functionality (i.e., viscosity) required for growth in specific conditions. Additional studies utilizing the CCE model to investigate microbial contributions to cheese ripening should be conducted in CCE supplemented with 1% milk fat.

Growth of Lactobacillus paracasei ATCC 334 in a cheese model system: a biochemical approach.

Growth of Lactobacillus paracasei ATCC 334, in a cheese-ripening model system based upon a medium prepared from ripening Cheddar cheese extract (CCE) was evaluated. Lactobacillus paracasei ATCC 334 grows in CCE made from cheese ripened for 2 (2mCCE), 6 (6mCCE), and 8 (8mCCE) mo, to final cell densities of 5.9×10(8), 1.2×10(8), and 2.1×10(7)cfu/mL, respectively. Biochemical analysis and mass balance equations were used to determine substrate consumption patterns and products formed in 2mCCE. The products formed included formate, acetate, and D-lactate. These data allowed us to identify the pathways likely used and to initiate metabolic flux analysis. The production of volatiles during growth of Lb. paracasei ATCC 334 in 8mCCE was monitored to evaluate the metabolic pathways utilized by Lb. paracasei during the later stages of ripening Cheddar cheese. The 2 volatiles detected at high levels were ethanol and acetate. The remaining detected volatiles are present in significantly lower amounts and likely result from amino acid, pyruvate, and acetyl-coenzyme A metabolism. Carbon balance of galactose, lactose, citrate, and phosphoserine/phosphoserine-containing peptides in terms of D-lactate, acetate, and formate are in agreement with the amounts of substrates observed in 2mCCE; however, this was not the case for 6mCCE and 8mCCE, suggesting that additional energy sources are utilized during growth of Lb. paracasei ATCC 334 in these CCE. This study provides valuable information on the biochemistry and physiology of Lb. paracasei ATCC 334 in ripening cheese.

Lactobacillus casei metabolic potential to utilize citrate as an energy source in ripening cheese: a bioinformatics approach.

AIMS: To identify potential pathways for citrate catabolism by Lactobacillus casei under conditions similar to ripening cheese. METHODS AND RESULTS: A putative citric acid cycle (PCAC) for Lact. casei was generated utilizing the genome sequence, and metabolic flux analyses. Although it was possible to construct a unique PCAC for Lact. casei, its full functionality was unknown. Therefore, the Lact. casei PCAC was evaluated utilizing end-product analyses of citric acid catabolism during growth in modified chemically defined media (mCDM), and Cheddar cheese extract (CCE). Results suggest that under energy source excess and limitation in mCDM this micro-organism produces mainly L-lactic acid and acetic acid, respectively. Both organic acids were produced in CCE. Additional end products include D-lactic acid, acetoin, formic acid, ethanol, and diacetyl. Production of succinic acid, malic acid, and butanendiol was not observed. CONCLUSIONS: Under conditions similar to those present in ripening cheese, citric acid is converted to acetic acid, L/D-lactic acid, acetoin, diacetyl, ethanol, and formic acid. The PCAC suggests that conversion of the citric acid-derived pyruvic acid into acetic acid, instead of lactic acid, may yield two ATPs per molecule of citric acid. Functionality of the PCAC reductive route was not observed. SIGNIFICANCE AND IMPACT OF THE STUDY: This research describes a unique PCAC for Lact. casei. Additionally, it describes the citric acid catabolism end product by this nonstarter lactic acid bacteria during growth, and under conditions similar to those present in ripening cheese. It provides insights on pathways preferably utilized to derive energy in the presence of limiting carbohydrates by this micro-organism.

Adaptive calibration of imaging array detectors.

We present two methods for nonuniformity correlation of imaging array detectors based on neural networks; both exploit image properties to supply lack of calibrations and maximize the entropy of the output. The first method uses a self-organizing net that produces a linear correction of the raw data with coefficients that adapt continuously. The second method employs a kind of contrast equalization curve to match pixel distributions. Our work originates from silicon detectors, but the treatment is general enough to be applicable to many kinds of array detectors like those used in infrared imaging or in high-energy physics.

A self-organizing neural network for the traveling salesman problem that is competitive with simulated annealing.

Unsupervised learning applied to an unstructured neural network can give approximate solutions to the traveling salesman problem. For 50 cities in the plane this algorithm performs like the elastic net of Durbin and Willshaw (1987) and it improves when increasing the number of cities to get better than simulated annealing for problems with more than 500 cities. In all the tests this algorithm requires a fraction of the time taken by simulated annealing.

Sorting with self-organizing maps.

A self-organizing feature map (Von der Malsburg 1973; Kohonen 1984) sorts n real numbers in O(n) time apparently violating the O(n log n) bound. Detailed analysis shows that the net takes advantage of the uniform distribution of the numbers and, in this case, sorting in O(n) is possible. There are, however, an exponentially small fraction of pathological distributions producing O(n2) sorting time. It is interesting to observe that standard learning produced a smart sorting algorithm.