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in Architecture & Planning
Periodicals

Aluminum, a Friend or Foe of Higher Plants in Acid Soils

Aluminum (Al) is the most abundant metal in the earth's crust, but its availability depends on soil pH. Despite this abundance, Al is not considered an essential element and so far no experimental evidence has been put forward for a biological role. In plants and other organisms, Al can have a beneficial or toxic effect, depending on factors such as, metal concentration, the chemical form of Al, growth conditions and plant species. Here we review recent advances in the study of Al in plants at physiological, biochemical and molecular levels, focusing mainly on the beneficial effect of Al in plants (stimulation of root growth, increased nutrient uptake, the increase in enzyme activity, and others). In addition, we discuss the possible mechanisms involved in improving the growth of plants cultivated in soils with acid pH, as well as mechanisms of tolerance to the toxic effect of Al.

Architecture & Planning• 2017• Frontiers Media
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Travel and Subjective Well-Being: A Focus on Findings, Methods and Future Research Needs

Subjectively experienced well-being has recently attracted increased attention in transport and mobility studies. However, these studies are still in their infancy and many of the multifarious links between travel behaviour and well-being are still under-examined; most studies only focus on one aspect of this link (i.e. travel satisfaction). In this paper, we give an overview of studies concerning travel and well-being, focusing on results, methods and gaps in present research. We suggest that travel behaviour affects well-being through experiences during (destination-oriented) travel, activity participation enabled by travel, activities during (destination-oriented) travel, trips where travel is the activity and through potential travel (or motility). The majority of empirical studies to date have been based on hedonic views of well-being, where pleasure and satisfaction are seen as the ultimate goal in life. They have paid little attention to eudaimonic views of well-being, which emphasise the realisation of one's true potential, although this form of well-being can also be influenced by travel behaviour. We also argue that longer-term decisions, such as residential location choices, can affect well-being through travel. Travel options differ between different kinds of neighbourhoods, which can result in different levels of (feelings of) freedom and consequently different levels of subjective well-being. Since studies at present only show a subset of the travel behaviour–well-being interactions, we conclude the paper with an agenda for future research.

Architecture & Planning• 2013• Taylor & Francis
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Hybrid CNN-LSTM Model for Short-Term Individual Household Load Forecasting

Power grids are transforming into flexible, smart, and cooperative systems with greater dissemination of distributed energy resources, advanced metering infrastructure, and advanced communication technologies. Short-term electric load forecasting for individual residential customers plays a progressively crucial role in the operation and planning of future grids. Compared to the aggregated electrical load at the community level, the prediction of individual household electric loads is legitimately challenging because of the high uncertainty and volatility involved. Results from previous studies show that prediction using machine learning and deep learning models is far from accurate, and there is still room for improvement. We herein propose a deep learning framework based on a combination of a convolutional neural network (CNN) and long short-term memory (LSTM). The proposed hybrid CNN-LSTM model uses CNN layers for feature extraction from the input data with LSTM layers for sequence learning. The performance of our developed framework is comprehensively compared to state-of-the-art systems currently in use for short-term individual household electric load forecasting. The proposed model achieved significantly better results compared to other competing techniques. We evaluated our proposed model with the recently explored LSTM-based deep learning model on a publicly available electrical load data of individual household customers from the Smart Grid Smart City (SGSC) project. We obtained an average mean absolute percentage error (MAPE) of 40.38% for individual household electric load forecasts in comparison with the LSTM-based model that obtained an average MAPE of 44.06%. Furthermore, we evaluated the effectiveness of the proposed model on different time horizons (up to 3 h ahead). Compared to the recently developed LSTM-based model tested on the same dataset, we obtained 4.01%, 4.76%, and 5.98% improvement for one, two, and six look-forward time steps, respectively (with 2 lookback time steps). Additionally, we have performed clustering analysis based on the power consumption behavior of the energy users, which indicate that prediction accuracy could be improved by grouping and training the representative model using large amount of data. The results indicated that the proposed model outperforms the LSTM-based model for both 1 h ahead and 3 h ahead in forecasting individual household electric loads.

Architecture & Planning• 2020• Institute of Electrical and Electronics Engineers
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Improving Nitrogen Use Efficiency in Crops for Sustainable Agriculture

In this review, we present the recent developments and future prospects of improving nitrogen use efficiency (NUE) in crops using various complementary approaches. These include conventional breeding and molecular genetics, in addition to alternative farming techniques based on no-till continuous cover cropping cultures and/or organic nitrogen (N) nutrition. Whatever the mode of N fertilization, an increased knowledge of the mechanisms controlling plant N economy is essential for improving NUE and for reducing excessive input of fertilizers, while maintaining an acceptable yield and sufficient profit margin for the farmers. Using plants grown under agronomic conditions, with different tillage conditions, in pure or associated cultures, at low and high N mineral fertilizer input, or using organic fertilization, it is now possible to develop further whole plant agronomic and physiological studies. These can be combined with gene, protein and metabolite profiling to build up a comprehensive picture depicting the different steps of N uptake, assimilation and recycling to produce either biomass in vegetative organs or proteins in storage organs. We provide a critical overview as to how our understanding of the agro-ecophysiological, physiological and molecular controls of N assimilation in crops, under varying environmental conditions, has been improved. We have used combined approaches, based on agronomic studies, whole plant physiology, quantitative genetics, forward and reverse genetics and the emerging systems biology. Long-term sustainability may require a gradual transition from synthetic N inputs to legume-based crop rotation, including continuous cover cropping systems, where these may be possible in certain areas of the world, depending on climatic conditions. Current knowledge and prospects for future agronomic development and application for breeding crops adapted to lower mineral fertilizer input and to alternative farming techniques are explored, whilst taking into account the constraints of both the current world economic situation and the environment.

Architecture & Planning• 2011• Multidisciplinary Digital Publishing Institute
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Brian: a simulator for spiking neural networks in Python

"Brian" is a new simulator for spiking neural networks, written in Python (http://brian. di.ens.fr). It is an intuitive and highly flexible tool for rapidly developing new models, especially networks of single-compartment neurons. In addition to using standard types of neuron models, users can define models by writing arbitrary differential equations in ordinary mathematical notation. Python scientific libraries can also be used for defining models and analysing data. Vectorisation techniques allow efficient simulations despite the overheads of an interpreted language. Brian will be especially valuable for working on non-standard neuron models not easily covered by existing software, and as an alternative to using Matlab or C for simulations. With its easy and intuitive syntax, Brian is also very well suited for teaching computational neuroscience.

Architecture & Planning• 2008• Frontiers Media
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The Parallelized Large-Eddy Simulation Model (PALM) version 4.0 for atmospheric and oceanic flows: model formulation, recent developments, and future perspectives

Abstract. In this paper we present the current version of the Parallelized Large-Eddy Simulation Model (PALM) whose core has been developed at the Institute of Meteorology and Climatology at Leibniz Universität Hannover (Germany). PALM is a Fortran 95-based code with some Fortran 2003 extensions and has been applied for the simulation of a variety of atmospheric and oceanic boundary layers for more than 15 years. PALM is optimized for use on massively parallel computer architectures and was recently ported to general-purpose graphics processing units. In the present paper we give a detailed description of the current version of the model and its features, such as an embedded Lagrangian cloud model and the possibility to use Cartesian topography. Moreover, we discuss recent model developments and future perspectives for LES applications.

Architecture & Planning• 2015• Copernicus Publications
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Periodicals

Applications of artificial neural networks in health care organizational decision-making: A scoping review

Health care organizations are leveraging machine-learning techniques, such as artificial neural networks (ANN), to improve delivery of care at a reduced cost. Applications of ANN to diagnosis are well-known; however, ANN are increasingly used to inform health care management decisions. We provide a seminal review of the applications of ANN to health care organizational decision-making. We screened 3,397 articles from six databases with coverage of Health Administration, Computer Science and Business Administration. We extracted study characteristics, aim, methodology and context (including level of analysis) from 80 articles meeting inclusion criteria. Articles were published from 1997-2018 and originated from 24 countries, with a plurality of papers (26 articles) published by authors from the United States. Types of ANN used included ANN (36 articles), feed-forward networks (25 articles), or hybrid models (23 articles); reported accuracy varied from 50% to 100%. The majority of ANN informed decision-making at the micro level (61 articles), between patients and health care providers. Fewer ANN were deployed for intra-organizational (meso- level, 29 articles) and system, policy or inter-organizational (macro- level, 10 articles) decision-making. Our review identifies key characteristics and drivers for market uptake of ANN for health care organizational decision-making to guide further adoption of this technique.

Architecture & Planning• 2019• Public Library of Science
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Pathway Based Analysis of Mutation Data Is Efficient for Scoring Target Cancer Drugs

Despite the significant achievements in chemotherapy, cancer remains one of the leading causes of death. Target therapy revolutionized this field, but efficiencies of target drugs show dramatic variation among individual patients. Personalization of target therapies remains, therefore, a challenge in oncology. Here, we proposed molecular pathway-based algorithm for scoring of target drugs using high throughput mutation data to personalize their clinical efficacies. This algorithm was validated on 3,800 exome mutation profiles from The Cancer Genome Atlas (TCGA) project for 128 target drugs. The output values termed Mutational Drug Scores (MDS) showed positive correlation with the published drug efficiencies in clinical trials. We also used MDS approach to simulate all known protein coding genes as the putative drug targets. The model used was built on the basis of 18,273 mutation profiles from COSMIC database for eight cancer types. We found that the MDS algorithm-predicted hits frequently coincide with those already used as targets of the existing cancer drugs, but several novel candidates can be considered promising for further developments. Our results evidence that the MDS is applicable to ranking of anticancer drugs and can be applied for the identification of novel molecular targets.

Architecture & Planning• 2019• Frontiers Media
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The evolutionary neuroscience of musical beat perception: the Action Simulation for Auditory Prediction (ASAP) hypothesis

EVERY HUMAN CULTURE HAS SOME FORM OF MUSIC WITH A BEAT: a perceived periodic pulse that structures the perception of musical rhythm and which serves as a framework for synchronized movement to music. What are the neural mechanisms of musical beat perception, and how did they evolve? One view, which dates back to Darwin and implicitly informs some current models of beat perception, is that the relevant neural mechanisms are relatively general and are widespread among animal species. On the basis of recent neural and cross-species data on musical beat processing, this paper argues for a different view. Here we argue that beat perception is a complex brain function involving temporally-precise communication between auditory regions and motor planning regions of the cortex (even in the absence of overt movement). More specifically, we propose that simulation of periodic movement in motor planning regions provides a neural signal that helps the auditory system predict the timing of upcoming beats. This "action simulation for auditory prediction" (ASAP) hypothesis leads to testable predictions. We further suggest that ASAP relies on dorsal auditory pathway connections between auditory regions and motor planning regions via the parietal cortex, and suggest that these connections may be stronger in humans than in non-human primates due to the evolution of vocal learning in our lineage. This suggestion motivates cross-species research to determine which species are capable of human-like beat perception, i.e., beat perception that involves accurate temporal prediction of beat times across a fairly broad range of tempi.

Architecture & Planning• 2014• Frontiers Media
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Operations for Learning with Graphical Models

This paper is a multidisciplinary review of empirical, statistical learning from a graphical model perspective. Well-known examples of graphical models include Bayesian networks, directed graphs representing a Markov chain, and undirected networks representing a Markov field. These graphical models are extended to model data analysis and empirical learning using the notation of plates. Graphical operations for simplifying and manipulating a problem are provided including decomposition, differentiation, andthe manipulation of probability models from the exponential family. Two standard algorithm schemas for learning are reviewed in a graphical framework: Gibbs sampling and the expectation maximizationalgorithm. Using these operations and schemas, some popular algorithms can be synthesized from their graphical specification. This includes versions of linear regression, techniques for feed-forward networks, and learning Gaussian and discrete Bayesian networks from data. The paper concludes by sketching some implications for data analysis and summarizing how some popular algorithms fall within the framework presented. The main original contributions here are the decompositiontechniques and the demonstration that graphical models provide a framework for understanding and developing complex learning algorithms.

Architecture & Planning• 1994• AI Access Foundation
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Servitization of the manufacturing firm

Purpose: This paper aims to explore practices and technologies successfully servitised manufacturers employ in the delivery of advanced services. Design/methodology/approach: A case study methodology is applied across four manufacturing organisations successful in servitization. Through interviews with personnel across host manufacturers, their partners, and key customers, extensive data are collected about service delivery systems. Analyses identify convergence in their practices and technologies. Findings: Six distinct technologies and practices are revealed: facilities and their location, micro-vertical integration and supplier relationships, information and communication technologies (ICTs), performance measurement and value demonstration, people deployment and their skills, and business processes and customer relationships. These are then combined in an integrative framework that illustrates how operations are configured to successfully deliver advanced services. Research limitations/implications: The analyses are reductive and rationalising. Future studies could identify other technologies and practices. Case study as a method is inherently limited in the extent to which findings can be generalised. Practical implications: Awareness and interest in servitization is growing, yet adoption of a servitization strategy requires particular organisational capabilities on the part of the manufacturer. This study identifies technologies and practices that underpin these capabilities. Originality/value: This paper contributes to the understanding of the servitization process and, in particular, the implications to broader operations of the firm.

Architecture & Planning• 2013• Emerald Publishing Limited
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Cervical cancer in low and middle‑income countries (Review)

Cervical cancer is a malignant tumour that occurs in the cervix and is classified into two histological types, adenocarcinoma and squamous cell carcinoma (SCC); SCC is more common and accounts for 70% of all cases. In 2018 there were ~569,000 new cases of cervical cancer diagnosed worldwide and ~311,000 deaths were attributed to cervical cancer. Of these, between 84 and 90% occurred in low- and middle-income countries (LMICs) such as South Africa, India, China and Brazil. The most common cause of cervical cancer is persistent infection caused by the sexually transmitted human papilloma virus. Other factors that contribute to the incidence of cervical cancer include geography, traditional practices and beliefs, the screening levels, socioeconomic status, healthcare access, public awareness, use of oral contraceptives, smoking and co-infection with HIV. An estimated 11 million women from LMICs will be diagnosed with cervical cancer in the next 10-20 years. The aim of this review was to explore various types of genetic and epigenetic factors that influence the development, progression or suppression of cervical cancer.

Architecture & Planning• 2020• Spandidos Publishing
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Antibiotic development challenges: the various mechanisms of action of antimicrobial peptides and of bacterial resistance

Antimicrobial peptides (AMPs) are natural antibiotics produced by various organisms such as mammals, arthropods, plants, and bacteria. In addition to antimicrobial activity, AMPs can induce chemokine production, accelerate angiogenesis, and wound healing and modulate apoptosis in multicellular organisms. Originally, their antimicrobial mechanism of action was thought to consist solely of an increase in pathogen cell membrane permeability, but it has already been shown that several AMPs do not modulate membrane permeability in the minimal lethal concentration. Instead, they exert their effects by inhibiting processes such as protein and cell wall synthesis, as well as enzyme activity, among others. Although resistance to these molecules is uncommon several pathogens developed different strategies to overcome AMPs killing such as surface modification, expression of efflux pumps, and secretion of proteases among others. This review describes the various mechanisms of action of AMPs and how pathogens evolve resistance to them.

Architecture & Planning• 2013• Frontiers Media
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Flood Prediction Using Machine Learning Models: Literature Review

Floods are among the most destructive natural disasters, which are highly complex to model. The research on the advancement of flood prediction models contributed to risk reduction, policy suggestion, minimization of the loss of human life, and reduction of the property damage associated with floods. To mimic the complex mathematical expressions of physical processes of floods, during the past two decades, machine learning (ML) methods contributed highly in the advancement of prediction systems providing better performance and cost-effective solutions. Due to the vast benefits and potential of ML, its popularity dramatically increased among hydrologists. Researchers through introducing novel ML methods and hybridizing of the existing ones aim at discovering more accurate and efficient prediction models. The main contribution of this paper is to demonstrate the state of the art of ML models in flood prediction and to give insight into the most suitable models. In this paper, the literature where ML models were benchmarked through a qualitative analysis of robustness, accuracy, effectiveness, and speed are particularly investigated to provide an extensive overview on the various ML algorithms used in the field. The performance comparison of ML models presents an in-depth understanding of the different techniques within the framework of a comprehensive evaluation and discussion. As a result, this paper introduces the most promising prediction methods for both long-term and short-term floods. Furthermore, the major trends in improving the quality of the flood prediction models are investigated. Among them, hybridization, data decomposition, algorithm ensemble, and model optimization are reported as the most effective strategies for the improvement of ML methods. This survey can be used as a guideline for hydrologists as well as climate scientists in choosing the proper ML method according to the prediction task.

Architecture & Planning• 2018• Queensland University of Technology
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Optical coherence tomography: a review of clinical development from bench to bedside

Since its introduction, optical coherence tomography (OCT) technology has advanced from the laboratory bench to the clinic and back again. Arising from the fields of low coherence interferometry and optical time- and frequency-domain reflectometry, OCT was initially demonstrated for retinal imaging and followed a unique path to commercialization for clinical use. Concurrently, significant technological advances were brought about from within the research community, including improved laser sources, beam delivery instruments, and detection schemes. While many of these technologies improved retinal imaging, they also allowed for the application of OCT to many new clinical areas. As a result, OCT has been clinically demonstrated in a diverse set of medical and surgical specialties, including gastroenterology, dermatology, cardiology, and oncology, among others. The lessons learned in the clinic are currently spurring a new set of advances in the laboratory that will again expand the clinical use of OCT by adding molecular sensitivity, improving image quality, and increasing acquisition speeds. This continuous cycle of laboratory development and clinical application has allowed the OCT technology to grow at a rapid rate and represents a unique model for the translation of biomedical optics to the patient bedside. This work presents a brief history of OCT development, reviews current clinical applications, discusses some clinical translation challenges, and reviews laboratory developments poised for future clinical application.

Architecture & Planning• 2007• SPIE
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A framework for expressing the relationships between multiple views in requirements specification

Composite systems are generally comprised of heterogeneous components whose specifications are developed by many development participants. The requirements of such systems are invariably elicited from multiple perspectives that overlap, complement, and contradict each other. Furthermore, these requirements are generally developed and specified using multiple methods and notations, respectively. It is therefore necessary to express and check the relationships between the resultant specification fragments. We deploy multiple ViewPoints that hold partial requirements specifications, described and developed using different representation schemes and development strategies. We discuss the notion of inter-ViewPoint communication in the context of this ViewPoints framework, and propose a general model for ViewPoint interaction and integration. We elaborate on some of the requirements for expressing and enacting inter-ViewPoint relationships-the vehicles for consistency checking and inconsistency management. Finally, though we use simple fragments of the requirements specification method CORE to illustrate various components of our work, we also outline a number of larger case studies that we have used to validate our framework. Our computer-based ViewPoints support environment, The Viewer, is also briefly described.>

Architecture & Planning• 1994• IEEE Computer Society
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Prefrontal Control and Internet Addiction: A Theoretical Model and Review of Neuropsychological and Neuroimaging Findings

Most people use the Internet as a functional tool to perform their personal goals in everyday-life such as making airline or hotel reservations. However, some individuals suffer from a loss of control over their Internet use resulting in personal distress, symptoms of psychological dependence, and diverse negative consequences. This phenomenon is often referred to as Internet addiction. Only Internet Gaming Disorder has been included in the appendix of the DSM-5, but it has already been argued that Internet addiction could also comprise problematic use of other applications with cybersex, online relations, shopping, and information search being Internet facets at risk for developing an addictive behavior. Neuropsychological investigations have pointed out that certain prefrontal functions in particular executive control functions are related to symptoms of Internet addiction, which is in line with recent theoretical models on the development and maintenance of the addictive use of the Internet. Control processes are particularly reduced when individuals with Internet addiction are confronted with Internet-related cues representing their first choice use. For example, processing Internet-related cues interferes with working memory performance and decision making. Consistent with this, results from functional neuroimaging and other neuropsychological studies demonstrate that cue-reactivity, craving, and decision making are important concepts for understanding Internet addiction. The findings on reductions in executive control are consistent with other behavioral addictions, such as pathological gambling. They also emphasize the classification of the phenomenon as an addiction, because there are also several similarities with findings in substance dependency. The neuropsychological and neuroimaging results have important clinical impact, as one therapy goal should enhance control over the Internet use by modifying specific cognitions and Internet use expectancies.

Architecture & Planning• 2014• Frontiers Media
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Genetic Diversity, Conservation, and Utilization of Plant Genetic Resources

Plant genetic resources (PGRs) are the total hereditary material, which includes all the alleles of various genes, present in a crop species and its wild relatives. They are a major resource that humans depend on to increase farming resilience and profit. Hence, the demand for genetic resources will increase as the world population increases. There is a need to conserve and maintain the genetic diversity of these valuable resources for sustainable food security. Due to environmental changes and genetic erosion, some valuable genetic resources have already become extinct. The landraces, wild relatives, wild species, genetic stock, advanced breeding material, and modern varieties are some of the important plant genetic resources. These diverse resources have contributed to maintaining sustainable biodiversity. New crop varieties with desirable traits have been developed using these resources. Novel genes/alleles linked to the trait of interest are transferred into the commercially cultivated varieties using biotechnological tools. Diversity should be maintained as a genetic resource for the sustainable development of new crop varieties. Additionally, advances in biotechnological tools, such as next-generation sequencing, molecular markers, in vitro culture technology, cryopreservation, and gene banks, help in the precise characterization and conservation of rare and endangered species. Genomic tools help in the identification of quantitative trait loci (QTLs) and novel genes in plants that can be transferred through marker-assisted selection and marker-assisted backcrossing breeding approaches. This article focuses on the recent development in maintaining the diversity of genetic resources, their conservation, and their sustainable utilization to secure global food security.

Architecture & Planning• 2023• Multidisciplinary Digital Publishing Institute
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Black-Box vs. White-Box: Understanding Their Advantages and Weaknesses From a Practical Point of View

Nowadays, in the international scientific community of machine learning, there exists an enormous discussion about the use of black-box models or explainable models; especially in practical problems. On the one hand, a part of the community defends that black-box models are more accurate than explainable models in some contexts, like image preprocessing. On the other hand, there exist another part of the community alleging that explainable models are better than black-box models because they can obtain comparable results and also they can explain these results in a language close to a human expert by using patterns. In this paper, advantages and weaknesses for each approach are shown; taking into account a state-of-the-art review for both approaches, their practical applications, trends, and future challenges. This paper shows that both approaches are suitable for solving practical problems, but experts in machine learning need to understand the input data, the problem to solve, and the best way for showing the output data before applying a machine learning model. Also, we propose some ideas for fusing both, explainable and black-box, approaches to provide better solutions to experts in real-world domains. Additionally, we show one way to measure the effectiveness of the applied machine learning model by using expert opinions jointly with statistical methods. Throughout this paper, we show the impact of using explainable and black-box models on the security and medical applications.

Architecture & Planning• 2019• Institute of Electrical and Electronics Engineers
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Mesenchymal Stem/Stromal Cell-Derived Exosomes for Immunomodulatory Therapeutics and Skin Regeneration

Exosomes are nano-sized vesicles that serve as mediators for cell-to-cell communication. With their unique nucleic acids, proteins, and lipids cargo compositions that reflect the characteristics of producer cells, exosomes can be utilized as cell-free therapeutics. Among exosomes derived from various cellular origins, mesenchymal stem cell-derived exosomes (MSC-exosomes) have gained great attention due to their immunomodulatory and regenerative functions. Indeed, many studies have shown anti-inflammatory, anti-aging and wound healing effects of MSC-exosomes in various in vitro and in vivo models. In addition, recent advances in the field of exosome biology have enabled development of specific guidelines and quality control methods, which will ultimately lead to clinical application of exosomes. This review highlights recent studies that investigate therapeutic potential of MSC-exosomes and relevant mode of actions for skin diseases, as well as quality control measures required for development of exosome-derived therapeutics.

Architecture & Planning• 2020• Multidisciplinary Digital Publishing Institute
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Bile resistance mechanisms in Lactobacillus and Bifidobacterium

Probiotics are live microorganisms which when administered in adequate amounts confer a health benefit on the host. Most of the probiotic bacteria currently available in the market belong to the genera Lactobacillus and Bifidobacterium, and specific health-promoting activities, such as treatment of diarrhea or amelioration of gastrointestinal discomfort, have been attributed to them. In order to be able to survive the gastrointestinal transit and transiently colonize our gut, these bacteria must be able to counteract the deleterious action of bile salts, which are the main components of bile. Bile salts are detergent-like biological substances synthesized in the liver from cholesterol. Host enzymes conjugate the newly synthesized free bile acids in the liver with the amino acids glycine or taurine, generating conjugated bile salts. These compounds are stored in the gall bladder and they are released into the duodenum during digestion to perform their physiological function, which is the solubilization of fat coming from diet. These bile salts possess strong antimicrobial activity, since they are able to disorganize the structure of the cell membrane, as well as trigger DNA damage. This means that bacteria inhabiting our intestinal tract must have intrinsic resistance mechanisms to cope with bile salts. To do that, Lactobacillus and Bifidobacterium display a variety of proteins devoted to the efflux of bile salts or protons, to modify sugar metabolism or to prevent protein misfolding. In this manuscript, we review and discuss specific bile resistance mechanisms, as well as the processes responsible for the adaptation of bifidobacteria and lactobacilli to bile.

Architecture & Planning• 2013• Frontiers Media
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