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ABSTRACT The relationship between the host gut microbiota and obesity has been well documented in humans and mice; however, few studies reported the association between the gut microbiota and fat deposition in pigs. In a previous study, we generated uncoupling protein 1 (UCP1) knock-in pigs (UCP1 pigs), which exhibited a lower fat deposition phenotype. Whether the gut microbiota was reshaped in these pigs and whether the reshaped gut microbiota contributes to the lower fat content remain unknown. Here, we revealed that the fecal microbiota composition and metabolites were significantly altered under both chow diet (CD) and high-fat/high-cholesterol (HFHC) diet conditions in UCP1 pigs compared to those in wild-type (WT) pigs. The abundance of Oscillospira and Coprococcus and the level of metabolite hyodeoxycholic acid (HDCA) from feces were observed to be significantly increased in UCP1 pigs. An association analysis revealed that Oscillospira and Coprococcus were significantly negatively related to backfat thickness. In addition, after fecal microbiota transplantation (FMT), the mice that were orally gavaged with feces from UCP1 pigs exhibited less fat deposition under both CD and high-fat diet (HFD) conditions, suggesting that the fecal microbes of UCP1 pigs participate in regulating host lipid metabolism. Consistently, HDCA-treated mice also exhibited reduced fat content. Mechanistically, we found that UCP1 expression in white adipose tissue alters the gut microbiota via the adipose-liver-gut axis in pigs. Our study provides new data concerning the cross talk between host genetic variations and the gut microbiota and paves the way for the potential application of microbes or their metabolites in the regulation of fat deposition in pigs. IMPORTANCE This article investigated the effect of the ectopic expression of UCP1 on the regulation of fecal microbiota composition and metabolites and which alters the fat deposition phenotype. Bacteria, including Oscillospira and Coprococcus, and the metabolite HDCA were found to be significantly increased in feces of UCP1 pigs and had a negative relationship with backfat thickness. Mice with fecal microbiota transplantation phenocopied the UCP1 pigs under both CD and HFD conditions, suggesting that the fecal microbes of UCP1 pigs participate in regulating host lipid metabolism. Our study provides new data regarding the cross talk between host genetic variations and the gut microbiota and paves the way for the potential application of microbes or their metabolic production in the regulation of fat deposition in pigs.

Abstract Background Histamine is a key mediator of the anti-inflammatory activity conferred by the probiotic organism Lactobacillus reuteri ATCC PTA 6475 in animal models of colitis and colorectal cancer. In L. reuteri, histamine synthesis and secretion requires l-histidine decarboxylase and a l-histidine/histamine exchanger. Chloride channel (ClC)-family proton/chloride antiporters have been proposed to act as electrochemical shunts in conjunction with amino acid decarboxylase systems, correcting ion imbalances generated by decarboxylation through fixed ratio exchange of two chloride ions for one proton. This family is unique among transporters by facilitating ion flux in either direction. Here we examine the histidine decarboxylase system in relation to ClC antiporters in the probiotic organism Lactobacillus reuteri. Results In silico analyses reveal that L. reuteri possesses two ClC transporters, EriC and EriC2, as well as a complete histidine decarboxylase gene cluster (HDC) for the synthesis and export of histamine. When the transport activity of either proton/chloride antiporter is disrupted by genetic manipulation, bacterial histamine output is reduced. Using fluorescent reporter assays, we further show that ClC transporters affect histamine output by altering intracellular pH and membrane potential. ClC transport also alters the expression and activity of two key HDC genes: the histidine decarboxylase (hdcA) and the histidine/histamine exchanger (hdcP). Conclusions Histamine production is a potentially beneficial feature for intestinal microbes by promoting long-term colonization and suppression of inflammation and host immune responses. ClC transporters may serve as tunable modulators for histamine production by L. reuteri and other gut microbes.

Histamine poisoning is a significant public health and safety concern. Intoxication from ingestion of food containing high amounts of histamine may cause mild or severe symptoms that can even culminate in cardiac arrest. Nonetheless, although histamine levels in dairy products are not subject to any regulation, important outbreaks and severe adverse health effects have been reported due to intake of dairy products with a high histamine content, especially ripened cheeses. Histamine, a biogenic amine, can accumulate in dairy products as a result of the metabolism of starter and nonstarter lactic acid bacteria, as well as yeasts that contribute to the ripening or flavoring of the final product, or even as a result of spoilage bacteria. The aim of this review is to describe the microbiological causes of the presence of histamine in fermented milk products, and to propose control measures and potential methods for obtaining histamine‐free dairy products. Thus, this manuscript focuses on histamine‐producing microbiota in dairy products, highlighting the detection of histamine‐producing bacteria through traditional and novel techniques. In addition, this review aims to explore control measures to prevent the access of histamine‐producing microbiota to raw materials, as well as the formation of histamine in dairy products, such as a careful selection of starter cultures lacking the ability to produce histamine, or even the implementation of effective food processing technologies to reduce histamine‐producing microbiota. Finally, the removal of histamine already formed in dairy products through histamine‐degrading microorganisms or by enzymatic degradation will also be explored. [ABSTRACT FROM AUTHOR]

The earthquake magnitude prediction is a task of utmost difficulty that has been addressed by using many different strategies, with no further transformation thus far. This work evaluates the Haskell based deterministic dendritic cell algorithm (hDCA)'s accuracy when used to predict earthquake magnitude in Sichuan and surroundings. First, eight seismicity indicators have been retrieved from the literature and used as input for the algorithms, and they are calculated from the earthquake catalog of the Sichuan and surroundings by well-known geophysical theory, named Gutenberg-Richter inverse power-law, and characteristic earthquake magnitude distribution and also conclusions drawn by recent related studies. Then, the hDCA is used to predict earthquakes with magnitude larger than 4.5 in the next month. In this work, the proposed method has been compared to the well-known machine learning algorithms, such as Dendritic Cell Algorithm (DCA), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Back Propagation Neural Network (BPNN), Recurrent Neural Network (RNN), Probabilistic Neural Network (PNN) and Neural Dynamic Classification (NDC). Overall our method yields the promising results in terms of all qualify parameters evaluated. [ABSTRACT FROM AUTHOR]

Background: Histamine is a key mediator of the anti-inflammatory activity conferred by the probiotic organism Lactobacillus reuteri ATCC PTA 6475 in animal models of colitis and colorectal cancer. In L. reuteri, histamine synthesis and secretion requires l-histidine decarboxylase and a l-histidine/histamine exchanger. Chloride channel (ClC)-family proton/chloride antiporters have been proposed to act as electrochemical shunts in conjunction with amino acid decarboxylase systems, correcting ion imbalances generated by decarboxylation through fixed ratio exchange of two chloride ions for one proton. This family is unique among transporters by facilitating ion flux in either direction. Here we examine the histidine decarboxylase system in relation to ClC antiporters in the probiotic organism Lactobacillus reuteri. Results: In silico analyses reveal that L. reuteri possesses two ClC transporters, EriC and EriC2, as well as a complete histidine decarboxylase gene cluster (HDC) for the synthesis and export of histamine. When the transport activity of either proton/chloride antiporter is disrupted by genetic manipulation, bacterial histamine output is reduced. Using fluorescent reporter assays, we further show that ClC transporters affect histamine output by altering intracellular pH and membrane potential. ClC transport also alters the expression and activity of two key HDC genes: the histidine decarboxylase (hdcA) and the histidine/histamine exchanger (hdcP). Conclusions: Histamine production is a potentially beneficial feature for intestinal microbes by promoting long-term colonization and suppression of inflammation and host immune responses. ClC transporters may serve as tunable modulators for histamine production by L. reuteri and other gut microbes. [ABSTRACT FROM AUTHOR]

Image classification is one of the most common methods of information extraction from satellite images. In this paper, a novel algorithm for image classification based on gravity theory was developed, which was called “homogeneity distance classification algorithm (HDCA)”. The proposed HDCA used texture and spectral information for classifying images in two iterative supplementary computing stages: (1) merging, (2) traveling and escaping operators. The HDCA was equipped by a new concept of distance, the weighted Manhattan distance (WMD). Moreover, an improved gravitational search algorithm (IGSA) was applied for selecting features and determining optimal feature space scale in HDCA. In the case of multispectral satellite image classification, the proposed method was compared with two well-known classification methods, Maximum Likelihood classifier (MLC) and Support Vector Machine (SVM). The results of the comparison indicated that overall accuracy values for HDCA, MLC, and SVM are 95.99, 93.15, and 95.00, respectively. Furthermore, the proposed HDCA method was also used for classifying hyperspectral reference datasets (Indian Pines, Salinas and Salinas-A scene). The classification results indicated substantial improvement over previous algorithms and studies by 2% in Indian Pines dataset, 0.7% in the Salinas dataset and 1.2% in the Salinas-A scene. These experimental results demonstrate that the proposed algorithm can classify both multispectral and hyperspectral remote sensing images with reliable accuracy because this algorithm uses the WMD in the classification process and the IGSA to select automatically optimal features for image classification based on spectral and texture information.

Histamine is the biogenic amine (BA) most frequently involved in food poisoning. Cheese is among the foods in which it is most commonly found, and in some of the highest concentrations. Its accumulation in cheese is mainly due to the presence of lactic acid bacteria (LAB) that produce histidine decarboxylase, an enzyme coded by the gene hdcA . This gene has been sequenced in several histamine-producing LAB. This paper reports a new culture-independent method based on PCR-DGGE for detecting and identifying, at the species level, the histaminogenic bacteria present in cheese. Primers were designed based on the hdcA gene sequences available for Gram positive bacteria, and PCR and DGGE optimized in order to differentiate between amplicons corresponding to different histamine-producing species. The proposed method provides a rapid and simple means of detecting and identifying histamine-producing Gram positive bacteria in foods with complex microbial communities, such as cheese. [ABSTRACT FROM AUTHOR]

In food, the biogenic amine (BA) histamine is mainly produced by histidine decarboxylation catalysed by microbial histidine decarboxylase. The consumption of foods containing high concentrations of histamine can trigger adverse neurological, gastrointestinal and respiratory reactions. Indeed, histamine is one of the most toxic of all BAs, and is often detected in high concentration in cheese. However, little is known about the microorganisms responsible for its accumulation in this food. In the present work, 25 histamine-producing Lactobacillus vaginalis strains were isolated from a blue-veined cheese (the first time that histamine-producing strains of this species have been isolated from any food). The restriction profiles of their genomes were analysed by PFGE, and seven lineages identified. The presence of the histidine decarboxylase gene ( hdcA ) was confirmed by PCR. The nucleotide sequence and genetic organisation of the histamine biosynthesis gene cluster (HDC) and its flanking regions are described for a representative strain ( L. vaginalis IPLA11050). [ABSTRACT FROM AUTHOR]

Outbreaks of histamine-related food poisoning are mainly caused when histidine-rich red fish are not preserved at an optimal temperature, which triggers the bacterial production of histamines. However, it has been found that food poisoning occurs even when the temperature is managed by refrigeration, thus drawing our attention to the histamine-producing bacteria, which can proliferate at lower temperatures. In the present study, we investigated the effects of environmental factors (temperature and pH) on the histamine production of the psychrophilic bacterial species, Photobacterium iliopiscarium . The results of culturing this bacterium in a histidine broth suggested that there was a risk of histamine food poisoning at all of the investigated culturing temperatures (5 °C, 10 °C, and 20 °C). The amount of histamine produced exceeded 500 ppm after a certain period, and high levels of histamine were produced at low pH. Moreover, this trend was also seen in the level of expression of the hdcA gene. The present study suggests that the risk of bacterial food poisoning is particularly high in low-pH food products. [ABSTRACT FROM AUTHOR]

This paper first presents the requirements for and the architectural framework of a type of real-time parallel dataflow computer architecture, specifically, a Hybrid Data/Command Driven Architecture (HDCA). Application of such an architecture is discussed. Goals, strategies, and algorithms for both static load-balancing and dynamic resource allocation on an HDCA system are presented. A parallel simulation of an HDCA system employing the presented static load-balancing and dynamic resource allocation algorithms is developed and presented. The parallel simulation was executed on a parallel computer system for increased fidelity. Analysis of simulation results for two example applications of the architecture allowed verification of design goals and correct performance of the HDCA architectural framework and associated static load-balancing and dynamic resource allocation strategies and algorithms. [ABSTRACT FROM PUBLISHER]

Image classification is one of the most common methods of information extraction from satellite images. In this paper, a novel algorithm for image classification based on gravity theory was developed, which was called "homogeneity distance classification algorithm (HDCA)". The proposed HDCA used texture and spectral information for classifying images in two iterative supplementary computing stages: (1) merging, (2) traveling and escaping operators. The HDCA was equipped by a new concept of distance, the weighted Manhattan distance (WMD). Moreover, an improved gravitational search algorithm (IGSA) was applied for selecting features and determining optimal feature space scale in HDCA. In the case of multispectral satellite image classification, the proposed method was compared with two well-known classification methods, Maximum Likelihood classifier (MLC) and Support Vector Machine (SVM). The results of the comparison indicated that overall accuracy values for HDCA, MLC, and SVM are 95.99, 93.15, and 95.00, respectively. Furthermore, the proposed HDCA method was also used for classifying hyperspectral reference datasets (Indian Pines, Salinas and Salinas-A scene). The classification results indicated substantial improvement over previous algorithms and studies by 2% in Indian Pines dataset, 0.7% in the Salinas dataset and 1.2% in the Salinas-A scene. These experimental results demonstrate that the proposed algorithm can classify both multispectral and hyperspectral remote sensing images with reliable accuracy because this algorithm uses the WMD in the classification process and the IGSA to select automatically optimal features for image classification based on spectral and texture information. [ABSTRACT FROM AUTHOR]