Almond Allergy: An Overview on Prevalence, Thresholds, Regulations and Allergen Detection

Food allergy has been on the increase for many years. The prevalence of allergy to different foods varies widely depending on type of food, frequency of consumption and geographic location. Data from the literature suggests that the prevalence of tree nut allergy is of the order of 1% in the general population. Almond is one such tree nut that is frequently eaten in many parts of the world and represents a potential allergenic hazard. Given the need to label products that contain allergens, a number of different methods of direct and indirect detection have been developed. However, in the absence of population-based threshold data, and given that almond allergy is rare, the sensitivity of the required detection is unknown and thus aims as low as possible. Typically, this is less than 1 ppm, which matches the thresholds that have been shown for other allergens. This review highlights the lack of quantitative data on prevalence and thresholds for almonds, which is limiting progress in consumer protection

Design, implementation and validation of a receiver-driven less-than-best-effort transport

LEDBAT++ is a congestion-control algorithm that implements a less-than-best-effort transport service. In this paper we present rLEDBAT, a purely receiver-based mechanism to implement LEDBAT++ for TCP. rLEDBAT enables a receiver to select some incoming traffic as less-than-best-effort, managing the capacity of the downlink. We describe the different mechanisms composing rLEDBAT that enable the execution of the LEDBAT++ congestion control algorithm at the receiver. We have implemented and experimentally tested rLEDBAT. We validate that the mechanisms incorporated by rLEDBAT at the receiver are indeed effective to implement a less-than-best-effort transport service at the receiver, as it performs similarly to the original sender-based LEDBAT++

Effect of food matrix and processing on release of almond protein during simulated digestion

Abstract The aims of the present work were to assess digestibility of almond protein in the upper gastrointestinal tract, evaluate the effects of food matrix on protein release and assess the persistence of immunoreactive polypeptides generated during simulated digestion. Prunin, the most abundant protein in almond flour, was sensitive to pepsin, with complete digestion after 20 min in the gastric phase. Addition of the surfactant phosphatidylcholine did not affect the rate and kinetic of digestion, as observed by SDS-PAGE analysis and HPLC, in the stomach and the small intestine of either natural or blanched almond flour. However, incorporation of almond flour into a food matrix, such as chocolate mousse and Victorian sponge cake, decreased the rate of almond protein degradation by pepsin and immunoreactivity of almond polypeptides detected by dot blots and sandwich ELISA retained better. Most of the almond protein identified by in-gel tryptic digestion and MALDI-TOF analysis corresponded to prunin, with pI values of 5–7. Further human sera studies are warranted to investigate the relationship between food matrix and almond allergy

Functionalization of Polyhydroxyalkanoates (PHA)-Based Bioplastic with Phloretin for Active Food Packaging: Characterization of Its Mechanical, Antioxidant, and Antimicrobial Activities

: The formulation of eco-friendly biodegradable packaging has received great attention during the last decades as an alternative to traditional widespread petroleum-based food packaging. With this aim, we designed and tested the properties of polyhydroxyalkanoates (PHA)-based bioplastics functionalized with phloretin as far as antioxidant, antimicrobial, and morpho-mechanic features are concerned. Mechanical and hydrophilicity features investigations revealed a mild influence of phloretin on the novel materials as a function of the concentration utilized (5, 7.5, 10, and 20 mg) with variation in FTIR e RAMAN spectra as well as in mechanical properties. Functionalization of PHA-based polymers resulted in the acquisition of the antioxidant activity (in a dose-dependent manner) tested by DPPH, TEAC, FRAR, and chelating assays, and in a decrease in the growth of food-borne pathogens (Listeria monocytogenes ATCC 13932). Finally, apple samples were packed in the functionalized PHA films for 24, 48, and 72 h, observing remarkable effects on the stabilization of apple samples. The results open the possibility to utilize phloretin as a functionalizing agent for bioplastic formulation, especially in relation to food packaging

Differential single nucleotide polymorphism-based analysis of an outbreak caused by Salmonella enterica serovar Manhattan reveals epidemiological details missed by standard pulsed-field gel electrophoresis

We retrospectively analyzed a rare Salmonella enterica serovar Manhattan outbreak that occurred in Italy in 2009 to evaluate the potential of new genomic tools based on differential single nucleotide polymorphism (SNP) analysis in comparison with the gold standard genotyping method, pulsed-field gel electrophoresis. A total of 39 isolates were analyzed from patients (n = 15) and food, feed, animal, and environmental sources (n = 24), resulting in five different pulsed-field gel electrophoresis (PFGE) profiles. Isolates epidemiologically related to the outbreak clustered within the same pulsotype, SXB-BS.0003, without any further differentiation. Thirty-three isolates were considered for genomic analysis based on different sets of SNPs, core, synonymous, nonsynonymous, as well as SNPs in different codon positions, by Bayesian and maximum likelihood algorithms. Trees generated from core and nonsynonymous SNPs, as well as SNPs at the second and first plus second codon positions detailed four distinct groups of isolates within the outbreak pulsotype, discriminating outbreak-related isolates of human and food origins. Conversely, the trees derived from synonymous and third-codon-position SNPs clustered food and human isolates together, indicating that all outbreak-related isolates constituted a single clone, which was in line with the epidemiological evidence. Further experiments are in place to extend this approach within our regional enteropathogen surveillance system

A haystack full of needles: scalable detection of IoT devices in the wild

Consumer Internet of Things (IoT) devices are extremely popular, providing users with rich and diverse functionalities, from voice assistants to home appliances. These functionalities often come with significant privacy and security risks, with notable recent large scale coordinated global attacks disrupting large service providers. Thus, an important first step to address these risks is to know what IoT devices are where in a network. While some limited solutions exist, a key question is whether device discovery can be done by Internet service providers that only see sampled flow statistics. In particular, it is challenging for an ISP to efficiently and effectively track and trace activity from IoT devices deployed by its millions of subscribers --all with sampled network data. In this paper, we develop and evaluate a scalable methodology to accurately detect and monitor IoT devices at subscriber lines with limited, highly sampled data in-the-wild. Our findings indicate that millions of IoT devices are detectable and identifiable within hours, both at a major ISP as well as an IXP, using passive, sparsely sampled network flow headers. Our methodology is able to detect devices from more than 77% of the studied IoT manufacturers, including popular devices such as smart speakers. While our methodology is effective for providing network analytics, it also highlights significant privacy consequences