A meeting report on the 7th Wellcome Trust conference on Exploring Human Host–Microbiome Interactions in Health and Disease, held at Hinxton, UK, 5–7 December 2018. Exploring Human Host–Microbiome Interactions in Health and Disease was held in December 2018 for the seventh time in Hinxton, Cambridge, UK. The Scientific Programme Committee once again did an outstanding job in selecting excellent keynote, invited and abstract speakers. The meeting started with a keynote lecture by John Cryan  on the link between the gut microbiome, stress and brain development. The various routes of communication between the gut and brain include the vagus nerve, the immune system, tryptophan metabolism, the enteric nervous system and microbial metabolites such as short-chain fatty acids (SCFAs). These mechanisms also impinge on neuroendocrine function at multiple levels. Multifactorial modes of interaction between the microbiome and human physiological functions were a key aspect addressed at this meeting for different human body sites. This is a crucial factor to consider when translating the increased knowledge about the human microbiome into tangible human health applications. It also represents a key difference in comparison with classic approaches in medicine, where single molecules or compounds are typically selected to target single pathways in attempts to treat diseases or alleviate symptoms.The meeting titled Most studies in humans are still focused on (gut) microbiome comparisons of healthy and diseased or unbalanced conditions. Such comparisons  have suggested that the gut microbiota is altered in a variety of conditions such as obesity, schizophrenia, autism and Parkinson’s disease, but these relationships are—until now—mainly associations, without substantiation of real causality. Studies in animal models have the power to reveal causal relations. They have thus been key in delineating that neurodevelopment and the programming of an appropriate stress response are dependent on the microbiota. For example, Cryan presented data from an animal model that mice delivered through C-section behaved differently in acute stress situations compared with vaginal-birth controls. This opens up possibilities for studying the impact of prebiotics and probiotics and other microbiome interventions. A more extreme situation is when all microbes are eliminated, such as in germfree mice, and this model is thus increasingly being applied to study causal links between the microbiome and social behaviour. Yet, also for mice maintained in conventional nonsterile conditions, interesting causal links can be established. For instance, Cryan presented novel data showing that oral supplementation of a mixture of the three principle SCFA microbiome metabolites  alleviated psychosocial stress-induced behaviour. In addition, SCFAs exhibited behavioral-test-specific antidepressant and anxiolytic effects, which were not present when mice had undergone psychosocial stress without impacting on the microbiome.Lactobacillus rhamnosus (JB-1), which has been shown to reduce stress-related behaviour and corticosterone release and alter central expression of GABA receptors in anxious mouse models. Based on these mouse experiments, this L. rhamnosus strain was one of the first strains shown to be highly promising as a psychobiotic—that is, a live microorganism with a potential mental health benefit. In humans, however, this strain failed to modulate stress or cognitive performance in healthy male subjects. Thus, translating psychobiotic- and microbiota-related animal data to a healthy human population remains challenging, owing to the physiological, microbiota, immunological and social differences between humans and the stress-susceptible mouse models.Yet, mice are not men. A paramount example is Clostridium difficile infections through fecal microbiota transplants, for which now various follow-up trials are ongoing with more defined fecal microbial strain mixtures or formulations. More recently, Gilbert described how adjusting a patient’s diet before gut surgery could lead to a more healthy and robust microbial response to surgery-related stress factors and better surgery outcomes through the reduced presence of collagen-degrading microbes. Currently, according to ClinicalTrials.gov, more than 650 clinical trials ongoing globally involve the microbiome as a biomarker or treatment option.Effective translation from bench to bedside can be facilitated by a better understanding of the microbiome and probiotic and prebiotic mechanisms of action responsible for the observed health benefits. In his talk, Jack Gilbert  encouraged the field to move from identifying correlations towards a more mechanistic understanding of the relationships between the human microbiome and disease through intervention trials. Recent developments in sequencing, metabolomics, proteomics and bioinformatics in combination with longitudinal sampling and multiple molecular perspectives have paved the way for microbiome-wide association studies (MWASs). Such MWASs, although highly complex in nature Fig. , allow uTranslation of the potential of microbiome studies is also supported by the emerging evidence that microbiota members can have a profound impact on therapy efficacy and pharmacodynamics of various drugs. Laurence Zitvogel  gave a convincing talk on the pathways through which the gastrointestinal microbiota and microbial factors can impact colon cancer immunotherapy and immunosurveillance. Accumulating data show that antibiotic administration can negatively alter the course of cancer immunotherapy, whereas specific microorganisms can promote positive therapy outcomes. Zitvogel described how cell-death inducers and gut microbiota members both play a role in facilitating immunogenic cell death in the ileum, which promotes immune responses against proximal colon cancer. Importantly, natural adjuvants from gut commensal microorganisms are crucial for stimulating the anticancer responses of the immune system. In particular, recent unpublished data from the Zitvogel group show that particular microbe-associated molecular patterns (MAMPs) and damage-associated molecular patterns (DAMPs) present in the ileum are highly relevant during oxaliplatin-induced death of ileal intestinal epithelial cells.While certain microbiota can influence pharmacological treatment efficacy, there is an increasing understanding that regularly administered drugs can in turn also alter the microbiota composition. Nassos Typas  described a high-throughput microbiomics system that was developed as a collaborative effort of several European Molecular Biology Laboratories to screen for the interactions between representative gut bacteria and pharmacological compounds and xenobiotics. More than 1000 commercially available non-antibiotic drugs have been tested, and 24% of pharmacological compounds with human targets have been demonstrated to have an inhibitory effect on at least one of the 40 tested bacterial strains. Future research should focus on the mechanisms through which non-antibiotic drugs might promote antibiotic resistance and on determining how gut bacteria can influence the bioavailability of regularly administered drugs.In addition to the impact of gut microbiota on exogenously administered compounds, resident microorganisms also play an important role in the metabolism of endogenous host signaling molecules. In her talk, Susan Joyce  emphasized the importance of gut microbial enzymes for generating the range and variety of bile acids and salts. These bile moieties engage in local and systemic cross-talk with the host processes linked to health or disease. Consequently, metabolic  and microbial markers can serve as read-outs for the health status of the host. Susan Joyce presented data from a patient cohort with inflammatory bowel diseases (IBDs), in which secondary bile acids generated by the host microbiota were linked to bile acid diarrhea and incidence of Crohn’s disease. These results demonstrate both marker correlations and a mechanistic understanding of how microbial activity can influence the host health status through alteration of host signaling molecules.In the final part of the meeting, Julie Segre  once again emphasized that humans are ecosystems constantly undergoing beneficial and potentially harmful microbe–host interactions. She used the complexity of the human skin microbiome as an example of the various multi-kingdom functional interactions that involve not only bacteria but also fungi and viruses. Interestingly, the presented longitudinal data demonstrated that skin microbial communities were specific for individuals and largely stable over months and even years of sampling. These findings are highly relevant for studies in which microbiome alterations in disease are explored and suggest that both disease development and therapeutic outcomes might be highly individualized from the microbiome perspective. In addition, Julie Segre presented data on how host genetics can define skin microbial communities, which adds another layer to the complexity of microbe–host interactions. For example, drastically increased eukaryotic viral colonization was detected in patients with the dedicator of cytokinesis-8 (DOCK8) primary immune deficiency. Hundreds of previously undescribed human papillomavirus genomes were detected in these patients through deep metagenomic sequencing, shedding light on the ‘microbial dark matter’ of the human microbiome.In the future, sequencing and culturing data should be combined with translational microbiome approaches for a thorough characterization of how the microbiota can shape host health and disease. Detailed knowledge on the causative links between microbiota composition and functionality, and host physiology and genetics, will pave the way for personalized and precision medicine.


None of the machine learning models with only BCRAT inputs were significantly stronger than the BCRAT. However, the logistic regression, linear discriminant analysis, and neural network models with the broader set of inputs were all significantly stronger than the BCRAT. These results suggest that relative to the BCRAT, additional easy-to-obtain personal health inputs can improve five-year breast cancer risk prediction. Our models could be used as non-invasive and cost-effective risk stratification tools to increase early breast cancer detection and prevention, motivating both immediate actions like screening and long-term preventative measures such as hormone replacement therapy and chemoprevention.Among women, breast cancer is a leading cause of death. Breast cancer risk predictions can inform screening and preventative actions. Previous works found that adding inputs to the widely-used Gail model improved its ability to predict breast cancer risk. However, these models used simple statistical architectures and the additional inputs were derived from costly and / or invasive procedures. By contrast, we developed machine learning models that used highly accessible personal health data to predict five-year breast cancer risk. We created machine learning models using only the Gail model inputs and models using both Gail model inputs and additional personal health data relevant to breast cancer risk. For both sets of inputs, six machine learning models were trained and evaluated on the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial data set. The area under the receiver operating characteristic curve metric quantified each model’s performance. Since this data set has a small percentage of positive breast cancer cases, we also reported sensitivity, specificity, and precision. We used Delong tests ( Among women, breast cancer is the most frequently diagnosed cancer and a leading cause of death . In the Many previous works used the Gail model to assess breast cancer risk. The Gail model is a statistical model that estimates breast cancer risk for women with no personal history of the disease and no known mutations in high-risk genes . This moThe Gail model, however, is far from perfect. This model has failed to effectively predict breast cancer risk for some population segments , 9. AddiThere is a plethora of previous research that demonstrates how the addition of costly inputs to the Gail model can improve its predictive power. Previous papers found that when validated on the same data set, models that were fed one or more informative inputs in addition to the Gail model inputs better predicted breast cancer risk than the Gail model itself. These works implemented simple statistical models and incorporated inputs derived from costly and / or invasive procedures. These inputs included breast density –13, geneThis study examines whether complex machine learning models can better predict five-year breast cancer risk than the BCRAT  does. CoThis study also analyzes whether models that are fed both the BCRAT  inputs aAll inputs to our machine learning models can be easily and cheaply obtained from electronic health records. Thus, if stronger than the BCRAT , our modThe goal of our research is to develop machine learning models that predict five-year breast cancer risk better than the BCRAT  does, anModels were trained and evaluated on the PLCO data set . This daWe initially downloaded data for all of the women in the PLCO data set. This data set consists of 78,215 women ages 50-78. We chose to exclude women who met any of the following conditions: 1) were missing data regarding whether they had been diagnosed with breast cancer and / or the timing of the diagnosis, 2) were diagnosed with breast cancer before the baseline questionnaire, 3) did not self-identify as White, Black, or Hispanic, 4) identified as Hispanic but did not have information available about where they were born, or 5) were missing data for any of the thirteen selected predictors. We excluded women who were diagnosed with breast cancer before the baseline questionnaire because the BCRAT , we assuWe trained a set of machine learning models that were fed five of the usual seven inputs to the BCRAT . These fOur inputs to the models with the broader set of predictors included the five BCRAT  inputs aWe made a limited number of modifications to the predictor variables in order to facilitate the training and testing of our models. First, we appropriately assigned values to categorical variables. The PLCO data set presented the age at menarche, age at first live birth, age at menopause, years of hormone usage, and years of birth control usage data as categorical variables . For exaAfter modifying the categorical variables, we made a few adjustments to the versions of the age at first live birth and the race / ethnicity variables fed into the machine learning models. For the BCRAT  model, wix is the original thi value of the variable x, and iz is the rescaled value [Finally, we ensured that the versions of all variables fed into the machine learning models were scaled to lie between 0 and 1. Rescaling machine learning model inputs is important because doing so helps the models to train more quickly and to better generalize to all predictors . We chosed value .zi=xi-mFor all models, the outcome variable indicated those who had breast cancer within five years  of their PLCO  baselineWe randomly split the PLCO data set  into 80%We implemented the BCRAT  using thWe evaluated the effectiveness of six machine learning binary classifiers in predicting the probabilities that individuals develop breast cancer during the five years following their PLCO  baselineThe logistic regressions classify data by using maximum likelihood functions to predict the probabilities of outcome classes . LogistiNaive Bayes models are probabilistic classifiers . They arDecision trees classify data by posing recursive questions about predictor variables . DecisioLinear discriminant analysis models determine a linear decision boundary between classes  by maximSupport vector machines, first presented in Cortes & Vapnik , find thNeural networks are models loosely based on the structure of the brain . These mThe Python programming language (version 3.6.7)  was usedWe developed our neural networks using Python’s TensorFlow (version 1.8.0) . All neuFor the BCRAT  model anWe determined the appropriate hyperparameters for the neural networks through 10-fold cross-validation on the training portion of the PLCO data set . We testAs our classification problem is unbalanced (roughly 2% positive cases), we report each machine learning model’s sensitivity , specificity, and precision in addition to the model’s AUC. Breast cancer data sets are inherently unbalanced as population breast cancer prevalence (in the United States in 2016), like the PLCO data set five-year breast cancer prevalence, is roughly 2% , 42. SenWe calculated these metrics by first obtaining risk predictions for the training data set subjects from the Python machine learning models. All further calculations of these metrics were completed in R due to the availability of the “pROC”  package Our examined BCRAT , implemen was the number of breast cancer cases for sensitivity and the number of non-breast cancer cases for specificity. In order to take into account its dependencies on both sensitivity and specificity, we calculated Steinberg [Since the Delong paper  did not teinberg  95% confp-value less than 0.05. For each set of input predictors, we compared the performance of each of the machine learning models to that of the BCRAT [We compared the performance of two models using a Delong  test. Wehe BCRAT . Also foFor each set of input predictors, we plotted the receiver operating characteristic (ROC) curves of the BCRAT  and of eFor both sets of inputs, the selected neural network hyperparameters were those that produced the highest mean minus standard deviation AUC across 10 iterations of neural networks trained using 10-fold cross-validation on the training data set. For neural networks with only BCRAT  inputs, None of the machine learning models with only BCRAT  predictoThe logistic regression, linear discriminant analysis, and neural network models using the broader set of predictors were each significantly stronger than the BCRAT  see Tab and 6. TComparisons between our machine learning models with the broader set of inputs and the BCRAT  suggest We did not perform rigorous statistical comparisons between the BCRAT  and machWe hypothesized that the neural network would be the best machine learning model for both sets of inputs. Unexpectedly, when only BCRAT  inputs wAmong machine learning models with only BCRAT  inputs, Among the top machine learning models fed the broader set of inputs, the linear discriminant analysis had by far the highest sensitivity, but the lowest specificity. On the other hand, the logistic regression had a very low sensitivity and a high specificity. The neural network had a moderately high sensitivity and specificity. The importance of sensitivity highlights the value of the linear discriminant analysis model and undercuts the potential of the logistic regression.One limitation was that models were trained and tested on separate portions of the same data set. Ideally, models would be trained on one data set and validated on another data set from a separate study. Such external validation can prove the generalizability of models. We did not have access to another data set that we could use to model prospective five-year breast cancer risk and thus were forced to split the PLCO data set  into traAnother potential limitation was the lack of available biopsy or atypical hyperplasia data in the PLCO data set . It is pAs our logistic regression, linear discriminant analysis, and neural network models with the broader set of inputs effectively predicted five-year breast cancer risk, these models could be used to inform and guide screening and preventative measures. Our models could easily be incorporated into phone application or website breast cancer risk prediction tools. Using our models as such would be convenient and cost-effective as our personal health data inputs are easy and inexpensive to obtain from electronic health records or an office visit. While the purpose of such a tool would not be to replace physician advice or mammograms, risk predictions derived from our models could contribute to both early breast cancer detection and breast cancer prevention. Women who receive high estimated risks could be motivated to seek out a doctor or take other preventative actions. Models could be used to guide immediate personal decisions such as screening. Doctors could also use these models to inform decisions on whether and when to recommend long-term preventative actions such as chemoprevention and hormone replacement therapy.Our logistic regression, linear discriminant analysis, and neural network models with the broader set of inputs predicted five-year breast cancer risk better than the BCRAT did. Whereas previous works have demonstrated that inputs derived from costly and / or invasive procedures can improve the Gail model’s performance, these results suggest that additional easy-to-obtain inputs can also improve the Gail model’s ability to predict breast cancer risk. Our models could serve as the bases of new cost-effective and non-invasive tools to inform and prompt screening and immediate and long-term preventative actions with the potential to increase early detection and reduce the incidence of breast cancer.


Breast cancer is an important cause of morbidity and mortality for women, yet a significant proportion of variation in individual risk is unexplained. It is reasonable to infer that unexplained breast cancer risks are caused by a myriad of exposures and their interactions with genetic factors. Most epidemiological studies investigating environmental contribution to breast cancer risk have focused on a limited set of exposures and outcomes based on a priori knowledge. We hypothesize that by measuring a rich set of molecular information with omics  and comparing these profiles using a case-control design we can pinpoint novel environmental risk factors. Specifically, exposome-wide association study approaches can be used to compare molecular profiles between controls and either breast cancer cases or participants with phenotypic measures associated with breast cancer . Current challenges in annotating compound peaks from biological samples can be addressed by creating libraries of environmental chemicals that are breast cancer relevant using publicly available high throughput exposure and toxicity data, and by mass spectra fragmentation. This line of discovery and innovation will extend understanding of how environmental exposures interact with genetics to affect health, and provide evidence to support new breast cancer prevention strategies. Breast cancer is the most common cancer in women worldwide, and is a major cause of death for women in mid-life . In factIncreasing evidence suggests that environmental factors, rather that inherited genetic factors, are the major causes of chronic diseases, including breast cancer. Studies of cohorts of 44,788 pairs of Western European monozygotic twins estimated that the genetic population attributable fraction for breast cancer was 31% (95% CI: 11–51%) . In otheWhile several environmental factors, including history of hormone replacement therapy, reproductive history, alcohol consumption, and physical activity are associated with breast cancer risk ,8, risk Animal studies and epidemiology suggest that environmental chemicals likely play diverse biological roles in the development of breast cancer, with some exposures important early in life and others important in mid- or late-life. For example, our recent review of biological evidence and epidemiologic studies found a higher risk of breast cancer for exposures to dioxins, air pollution, dichlorodiphenyltrichloroethane (DDT), and perflurooctanesulfonamide (PFOSA) during breast development, and for occupational exposures to solvents and other mammary carcinogens such as gasoline components . However, most epidemiological studies investigating environmental contribution to breast cancer risk have focused on a limited set of exposures and outcomes based on a priori knowledge and, as a result, are likely to miss important relationships. In addition, these studies have almost exclusively assessed the risks of exposure to single chemicals. In order to reduce the burden of breast cancer, there is an urgent need to find the undiscovered environmental risk factors. One promising approach is to conduct exposome-wide association studies (EWAS) that measure a rich set of molecular information from both exogenous and endogenous sources in biospecimens and identify associations with breast cancer ,12. SystThe objective of this research program is to measure a myriad of environmental exposures , evaluate their interactions with genetic factors, and assess their associations with health outcomes and/or biological pathways related to breast cancer  . We hypoN = 621 cases) and nineteen with estrogen receptor positive breast cancer (N = 418 cases), including saturated fatty acids (from fats/oils), vitamin E derivatives (from desserts or vitamin supplements) and androgens , with ORs ranging from 0.6 to 2.2.Metabolomics is recognized as a powerful platform for detecting small molecules in biospecimens ,20. ThesThe main challenge with the use of non-targeted metabolomics based on high-resolution mass spectrometry (HRMS) is the annotation  of small molecules. The first step for the annotation of metabolomics features relies on matching accurate mass of detected features to known small molecules present in public databases such as the Human Metabolome Database (HMDB)  or the MData resources that are now available from the US Environmental Protection Agency (EPA) and other sources provide valuable information to help build environmental chemical databases that are relevant to breast cancer. To start, very large exogenous chemical libraries are available such as the EPA’s Distributed Structure-Searchable Toxicity   or PubChAdditional opportunities to develop relevant chemical databases for annotating tentative chemical matches would use available data from EPA on expected population exposures and mechanisms of toxicity. For example, EPA has used high throughput modeling along with information on production volumes, chemical properties, and some chemical use information to predict population exposures for almost 500,000 chemicals. These data may be used to prioritize chemicals for a database. For example EPA’s modeling predicted 1880 chemicals would have median population exposures above 0.1 mg/kg-day . ChemicaAnother challenge with annotation of metabolomics features is that it is often not possible to identify compounds simply based on accurate mass since several molecules with different structures exhibit similar mass. Therefore, successful studies should utilize HRMS to produce MS/MS fragmentation spectra. The generated MS/MS spectra will provide structural information specific to each molecule to guide identification of compounds. It is also important to note that non-targeted metabolomics analysis using HRMS is less sensitive, compared to targeted HRMS analysis, and therefore exposures to very low levels of environmental factors may be missed. New tools and approaches to improve the sensitivity of non-targeted HRMS metabolomics analysis would be greatly encouraged. Adductomics is another top-down technique that measures modifications of blood proteins like hemoglobin (Hb) or human serum albumin (HSA) to characterize exposures to reactive electrophiles that are inherently toxic but cannot be measured directly in biospecimens . By charThe integration of data from multiple omics platforms would provide a more holistic understanding of gene-environment interactions in breast cancer pathogenesis compared to single omics approaches analyzed in isolation. However, multi-omics integration faces several challenges such as data harmonization, large number of false positive results, inadequate data visualization tools, and metabolic pathway databases. This research program would need to be carried out by a multidisciplinary team with expertise in omics measurements, bioinformatics, breast cancer biology, toxicology , and epidemiology. EPA data on short term toxicity testing and exposure modeling are publicly available and can be used to build libraries of breast cancer-relevant chemicals. The proposed research could leverage existing cohorts, which have archived biological specimens and have collected extensive health information, including known risk factors for breast cancer and breast cancer outcomes or phenotypes relevant to breast cancer. California is well positioned to carry out this research idea, with several state-of-the-art omics platforms, researchers with relevant expertise, and access to existing cohorts of adult women and young girls with archived biological specimens suitable for omics and information on breast cancer risk factors . Projects designed to meet these objectives will need budgets in the range of $1 M/year for 4–5 years.We anticipate that this research program will build and demonstrate a new approach combining omics and phenotypic measurements to investigate associations of an unprecedentedly wide spectrum of exposures  with phenotypic measures, which will provide unique opportunities to discover novel environmental risk factors for breast cancer and bring new insights into molecular mechanisms of these relationships. Two key innovative elements are application and integration of omics approaches to identify differences between participants with and without breast cancer or breast cancer risk factors, and the creation of a new breast cancer-related chemical library of pharmaceuticals, food and consumer product chemicals, and pollutants that have biological activities similar to known breast carcinogens. The use of MS/MS fragmentation profiles will provide critical structural information to get more confident identification of compounds. Use of newly available databases of short-term toxicity and exposure predictions for thousands of chemicals to identify breast cancer-related chemicals is also novel as an approach for building a metabolomics compound database. Findings from this program will provide a newly extended list of breast cancer-related environmental factors and strengthen scientific support for reducing exposure to such factors by providing novel data about human exposures from diet, lifestyle factors, and chemicals that are associated with health outcomes and/or pathways related to breast cancer. We also expect that this program will identify endogenous mediators of the relationships between exposures and breast cancer-related phenotypes, providing new insights about molecular mechanisms underlying exposure–outcome relationships. This will further our understanding of how environmental exposures interact with genetics and impact women’s health, and will ultimately help develop prevention strategies and lower the incidence of breast cancer. Projects that would stratify women based on menopausal status, race/ethnicity and socio-economic level would provide an opportunity to also evaluate disparities in estimated breast cancer risk from exposure to environmental factors and identify disproportionately affected populations.We hypothesize that by measuring a rich set of molecular information with omics  and comparing these profiles using a case-control design we can pinpoint novel environmental risk factors for breast cancer. Specifically, exposome-wide association study (EWAS) approaches can be used to compare molecular profiles between controls and either breast cancer cases or participants with phenotypic measures associated with breast cancer . Current challenges in annotating compound peaks from biological samples can be addressed by creating libraries of environmental chemicals that are breast cancer relevant using publicly available high throughput exposure and toxicity data, and by mass spectra fragmentation. This line of discovery and innovation will extend understanding of how environmental exposures interact with genetics to affect health, and provide evidence to support new breast cancer prevention strategies.


The unique combination of mechanical, optical and electrical properties offered by carbon nanotubes has fostered research for their use in many kinds of applications, including the biomedical field. However, due to persisting outstanding questions regarding their potential toxicity when considered as free particles, the research is now focusing on their immobilization on substrates for interface tuning or as biosensors, as load in nanocomposite materials where they improve both mechanical and electrical properties or even for direct use as scaffolds for tissue engineering. After a brief introduction to carbon nanotubes in general and their proposed applications in the biomedical field, this review will focus on nanocomposite materials with hydrogel-based matrices and especially their potential future use for diagnostics, tissue engineering or targeted drug delivery. The toxicity issue will also be briefly described in order to justify the safe(r)-by-design approach offered by carbon nanotubes-based hydrogels. If they are designed to be released, their size also allows a much faster distribution in the body. Among nanoparticles in general, carbon nanomaterials combine interesting properties such as a very high chemical resistance (no dissolution even in aggressive environments), excellent mechanical properties and a very light weight. The most used carbon nanomaterials include nanodiamonds (ND), carbon nanotubes (CNT) and graphene and its related materials (GRM: few-layer graphene (FLG), graphene oxide (GO), reduced graphene oxide (rGO)) [Biomaterials are a key element of medical devices. Due to their specific properties related to the nanoscale , nanopare (rGO))  and are 3 alone or mixed with H2SO4), leading to the grafting of oxygen-containing functional groups  at the surface of the outer wall, as well as to the opening of the CNT [Carbon nanotubes are an allotropic form of carbon identified in 1991 by Iijima and since widely studied and used for a wide range of applications such as materials reinforcement, electrode materials and/or components for nanoelectronics (biosensors) or even (which could be remotely activated in some cases) drug carriers in biomedicine. They can be synthesized by different methods which will not be described in detail here but include the historical electric-arc discharge, laser ablation and the wide family of catalytic chemical vapour deposition (CCVD) techniques . CNT can the CNT . However the CNT ,9,10.Application of CNT in the biomedical field requires a few challenges to be met. The first one is related to safety and implies to use very high purity CNT in order to limit potential release of toxic ions during operation in any biological environment. This is a real challenge because high purity CNT samples usually cannot be prepared in very large scale and a compromise between quality and quantity often has to be made. The other main challenges are more related to formulation issues, not only in the biomedical field. Being able to achieve good dispersions of CNT in solvents and especially in water is one of the major ones. The strong hydrophobicity of CNT make them not only difficult to separate/individualize in a solvent but also to stabilize the suspension. This can be achieved through functionalization , as has already been discussed. Another strategy, because CNT are always difficult to disperse when starting from a dry powder, is to limit or avoid drying steps during their processing. Another challenge is the typical viscosity increase related to the proper dispersion of CNT in a fluid, even at low concentration , which c2 g−1 in the case of DWCNT [Depending on their electronic structure, SWCNT can behave either as semiconductors or as metals. Within a random SWCNT population, there is a statistical distribution of 2/3 of semiconducting ones and 1/3 metallic of metallic ones . When thof DWCNT , a very of DWCNT  as well of DWCNT , CNT areAs early diagnostic is a key point for an efficient treatment, the improvement of detection methods is especially relevant. In vitro analysis of biomarkers is already possible with good accuracy thanks to the detection immune complexes but can be time consuming and also require large amounts of biological samples when using classical dosage strategies. Due to their electronic properties, several teams have considered using CNTs as the key element of electrochemical sensors and different kind of label-free CNT-based biosensors were developed.CNT can also act as contrast agents in different bio imaging methods . FunctioIn the biosensors field, CNT have been proposed as sensing element to detect and monitor several diseases, especially diabetes but also bacterial infection. For instance, Punbusayakul et al. used electrochemical monitoring of immune complexes for salmonella detection, reducing thus the detection time and facilitating the sample preparation compared to existing methods . An immuGenerally speaking, the presence of CNT at the surface of the electrodes allows faster electron transfer and improves the sensitivity for electrochemical detection . Field-EAnother recent approach demonstrated the interest of porosity as a way to immobilize molecules on biosensor electrode. Zhang et al. described a non-enzymatic glucose detector composed of porous nickel-based metal oxide framework (Ni-MOF), where the electrical conductivity was enhanced by addition of CNT. These electrodes exhibit a high glucose selectivity and this method is proposed as a relevant alternative to detection through immune complexes immobilisation .Various technologies based on CNT are available in the imaging field. For example, photoluminescent imaging takes advantage of the fluorescence of excited SWCNT in the Near InfraRed-I (700–900 nm) and NIR-II (1100–1400 nm) ranges, wavelengths at which tissues and water are almost transparent, making possible to reach deeper penetration depths. Welsher et al. used this property to optically track in real time the biodistribution of injected SCWNTs in mice deep tissues and vessels ,25. SWCNThe photoacoustic effect is another interesting way to use CNT NIR absorption. Photoacoustic imaging measures the ultrasounds produced by tissues expansion around heat emitting CNT exposed to NIR stimulation. De La Zerda et al. obtained improved contrast on targeted tumour cells thanks to SWCNT conjugated with Arg-Gly-Asp peptides as well as an enhanced sensitivity and detection threshold, compare to pristine SWCNT ,30.−1. Liu et al. demonstrated the use of the combination of peak shifting for SWCNT depending on the 13C/12C isotopic ratio with the labelling by different targeting ligands, leading to five-colour multiplexed Raman imaging [Raman scattering is also an imaging option, thanks to the G band exhibited by SWCNT around 1580 cm imaging . Further imaging . MWCNT uThe radioprobes presented by Hong et al. used for single-photon emission computed tomography imaging (SPECT) and composed of metal halides encapsulated within CNT, illustrate another application of CNT in bioimaging . Further2 surface, due to a favourable surface texture and better adsorption of culture medium proteins, allowing patterning of neurons networks as illustrated in Influence of CNT on living cells draws more and more attention as utilisation of CNT as base material for biomedical applications is increasing. Lovat et al. shown that CNT represent a good surface for cellular growth and have a promoting effect on neural signal transmission . MoreoveThe ability of CNT to form 3D architecture described by Correa-Duarte et al. is equally interesting for cell proliferation enhancement and tissue engineering. The proposed MWCNT network could be shaped to fit best natural tissue morphology but also remained stable in vivo . AbarratEfficient drug administration is a real concern in the medical field. Indeed, low selectivity and low half-life time may lead to multiply the administrations, resulting in secondary effects and sometimes, fatal issues. Due to their expected biocompatibility and suitable size, CNT are widely studied as nanocarriers for drug and gene delivery as well as cancer treatment.The use of CNT as nanocarriers has drawn more and more attention in last years since CNT exhibit exceptional cell transfection capabilities. Liu et al. have shown that drugs such as doxorubicin (DOX) can be loaded onto the surface of poly(ethylene glycol) (PEG) conjugated SWCNT thanks to non-covalent bounds and can reach the tumorous tissue thanks to the enhanced permeability and retention (EPR) effect. A pH - release dependency would also permit delivery close to the tumour tissues ,43. WellCNT have also the capacity to enter cells , indepenKaboudin et al. demonstrated transfection of nucleic acid through another mechanism than endocytosis in a recent work. MWCNT functionalised with pyridine and magnetic particles transported nucleic acids bound by π-π interactions through the cell membrane and released them in the cytoplasm. A magnetic field was then applied to remove the nanocarriers from the cell, thus limiting their cytotoxicity . MagnetiUsing another strategy, Kong et al. considered the use of NIR light irradiation to help the DNA delivery. Laser stimulation of SWCNT coated by poly(ethyleneimine)-cholesterol (PEI-Chol) induced membrane permeabilization due to SWCNT photothermal activity, promoting cellular uptake and DNA release . HoweverTaking advantage of CNT photothermal properties with NIR laser stimulation was considered as an elegant way for directly treating cancer. Burke et al. showed that intra-tumoral injection of MWCNT suspension, followed by short laser excitation resulted in tumour ablation in mice and enhanced survival . On the Most of the works presented in this section have determined the toxicity of CNT on cells during their utilisation but the fate and possible degradation of those objects were not thoroughly examined. This concern, still controversial, could be the most important drawback of CNT application in biomedicine when the nanotubes are not trapped within or at the surface of a device.Hydrogels are 3D polymeric cross-linked edifices where water absorption is made possible thanks to hydrophilic functional groups. Those materials can be classified in two groups depending of the nature of the cross-linking interactions. In chemical hydrogel, obtained for instance by radical polymerization or UV irradiation, the cross-linking is permanent and of covalent nature. Physical cross-linking is about electrostatic interactions and thus induces a possible reversibility aspect . HydrogeHydrogels are widely used in the biomedical field due to their biocompatibility and the low inflammatory responses they induce . Their pSeveral polymers are suitable for hydrogel synthesis but natural polymers are more often selected for biomedical application because most are biocompatible and renewable. Some synthetic polymer also fit biocompatibility criteria (nontoxic and inducing low inflammation). Those polymers or monomers are often used combined with others, Most biosensors developed using CNT based-hydrogel focused on glucose detection, as it is well documented. Several of them consist of electrodes allowing the monitoring of electron transfers within the redox couple glucose oxidase (GOx)-glucose. Conductive Bacterial cellulose (BC)-CNT-GOx films were directly used as electrodes by Kim et al., offering a simple and biocompatible material for cyclic voltammetry monitoring . Comba e2 drying [The 3D architecture and mechanical properties of CNT-based hydrogels allow the design of different types of sensing methods. The optical glucose sensor presented by Barone et al. was based on a shift of photoluminescence (PL) emission of CNT embedded in poly (PVA) hydrogel, induced by apo-glucose oxidase (apoGOx) and glucose interaction. ApoGOx attached to PVA contributed to the cross-linking of the hydrogel and its interaction with glucose modified PVA conformation and hydrostatic pressure, resulting in CNT lattice deformation and change of the environment . This wo2 drying .Maintaining the recolonization of cells in order to replace dead or harmed tissues requires scaffolding materials that respect certain properties. They should be able to form cross-linked networks, be mechanically resistant, biocompatible and should allow good cell adhesion. For such purposes, hydrogels based on biopolymers are often preferred. Furthermore, the inclusion of nanofillers such as CNT in those matrices enhance the mechanical properties  but alsoIn the field of cancer engineering, intensified cell proliferation linked to the presence of CNT can also be exploited for patterning cells growth, as shown by cancer spheroid formation in the report by Sheikholeslam et al. .The use of CNT-based hydrogels for drug delivery is quite new but was investigated by many authors, as reported in the extensive review of Cirillo et al. . The porN,N′-ethylenebisacrylamide. The drug, diclofenac sodium salt, was loaded by soaking of microspheres into a concentrated solution. The authors shown that the application of a 9V tension on the beads induced a shrinkage and thus increased the drug release [Several teams investigated the possibility of electro-responsive CNT-based hydrogels for drug delivery. For instance, Spizzirri et al. synthesised microspheres through the polymerisation of gelatine containing MWCNT with the help of sodium methacrylate and  release . This te release . This wa release ,95.As for CNT-based drug carriers, pH-controlled drug delivery systems based on CNT/hydrogel nanocomposites were also considered. Peng et al. reported a different swelling of MWCNT/chitosan hydrogel depending of the pH of buffers mimicking two gastrointestinal environments, impacting the release rate . Ye et aWei et al. designed a dual release carrier with CNT/polycaprolactone (PCL)-PEG-PCL thermosensitive hydrogel, where DOX was loaded on CNT while Rhod was within the hydrogel. The difference of drug loading is pointed out as the source of the observed release rate. Furthermore, pH dependency was also noticed . CNT basAs Kuche et al. explained in their review work, the major drawbacks of existing transdermal delivery patches are the low drug release rate and the poor skin permeability . CNT basstratum corneum, which large molecules like insulin, carried by electrophoretic forces, may use to cross this natural barrier. The developed agarose/DWCNT hydrogel was use directly on mice skin as both drug storage system and electrode and demonstrated preserved mechanical properties but most importantly enhanced drug penetration into the skin, as shown in The recent work of Guillet et al. opens a new route to solving both problems at once using CNT-based hydrogels directly as an electrode for skin electroporation. The application of high voltage electrical pulses through the skin was shown to temporarily create pores within the The toxicity of CNT is a complex subject to address as CNT actually gather a family of nanoparticles, which properties and behaviour are related non-exhaustively to their structure, number of walls, chirality, diameter and length . HoweverAs described earlier in this work, CNT can transfect cells. Thanks to labelled MWCNT and further imaging it was proved that once inside the body, they could cross barriers, reach blood circulation and thus, spread within the organism ,118. CNTA study of Allen et al. on horseradish peroxidase (HRP) degradation effect on carboxylated SWCNT suggested that polycyclic aromatic hydrocarbons (PAHs) may be found among the degradation products . MoreoveThe use of different kinds of CNT with different morphologies and purities, with different biological models  at different doses using different dispersion/exposure/characterisation protocols certainly explains why the answer about the potential toxicity of CNT is still not available. The question of suitable doses for toxicity assessment is also a delicate matter. The actual exposure to CNT in real conditions is hard to quantify and concentrations used for testing the toxicity of CNT is generally far from reality. Finally the use of the weight concentration to express the exposure dose, although very practical to use, is rather irrelevant especially for comparison purposes and should be replaced by the surface concentration which is much more relevant .In regard of those observations, the most cautious way to use CNT would be to prevent them from entering freely the organism. As such, trapping of CNT inside hydrogels contributes to limit the risk of direct interaction with CNT, promoting a safe-by-design development direction, while improving hydrogel properties . HoweverCNT are promising materials in the biomedical field. Once functionalised to ensure their biocompatibility and conjugated with organic compounds or metallic nanoparticles, they can be used as efficient biocompatible biosensors or contrast agent for imaging but could also increase drugs lifetime within the body and facilitate their direct delivery within cells. Their usefulness in cell growth and recolonization is well illustrated, in particular for nervous cells. The use of CNT as additives inside matrices, especially biocompatible hydrogels, is an encouraging alternative way of taking advantage of their extraordinary properties while limiting the risk of direct exposure. It was shown that mechanical and electrical properties of hydrogels could be greatly improved by embedding CNT and that those nanocomposites could be used for biosensing, tissue engineering and drug delivery without the risk of CNT release into cells. Furthermore, the 3D porous structure of hydrogel widens the possible range of applications, offering more versatility, as shown by the recent development of injectable tissue scaffolds or drug delivery patches.If the investigation of CNT local cytotoxicity is often included in recent in vivo biomedical application works and CNT generally shown not to be harmful for neighbouring cells, their fate on long term within the body is unfortunately the subject of less attention. CNT situation after they completed their intended task depends of many parameters including concentration, size and functionalisation and if most of them are generally quickly excreted, the remaining part is to stay in the body and accumulate leading to unknown long-term effects. However, as described in this work, CNT confinement inside hydrogels is a good way to limit their scattering inside the organism (as long as the hydrogel itself is not biodegradable), representing a promising way of taking advantages of those carbon nanoparticles for biomedical applications.


Next generation sequencing technologies have allowed the study of microbial ecosystems at previously unseen depths. In both ecology and human biology, there is a pressing quest to advance our understanding of how microbial communities impact their host and their environment. In particular, the majority of microbiome studies are aimed at identifying specific microbial taxa, community profiles, genes, or metabolites which may be predictive of specific outcomes, functions, or disease states. However, due to the complexity of microbiome data, the statistical and computational analysis of these data present many challenges which may affect the validity of commonly employed methods. Therefore, despite the fact that microbiome and bioinformatic researchers often use widely accepted pipelines, the field remains wide open for improvement. In this Research Topic, a few researchers have responded to the task of reviewing or describing novel methodologies aimed at tackling the challenges of microbiome data and the respective metadata. Only with the development of improved statistical and computational models can one really hope to exploit microbiome based research to understand biological mechanisms, identify biomarkers of disease, or delineate microbial interactions with their environment.Gloor et al. reviews a number of recently proposed compositional data analyses methods for microbiome data, and provides some caveats against a naïve use of statistical models if the data are not treated as compositional.The largest challenge investigators face in developing statistical approaches to study microbiome data is considering all of the constraints of microbiome data fully. Multiple researchers support regarding microbiome data as compositional, meaning the data are usually described as relative quantitative descriptions as parts of some whole, such as proportions or relative abundance. Of course, this view is also partial since important information may be lost when adopting a compositional perspective. However, intrinsic complications arise among commonly employed techniques if microbiome data are examined using a non-compositional paradigm. Kaul et al. propose a novel method (ANCOM-II) for handling zeros in microbiome data by first identifying the types of zeros in your data, then comparing the abundance of taxa relative to a background or reference value which is present in all specimens. Simulations of the authors' methodology show improved control for false discovery rate and higher statistical power compared to pseudo-counts. Another dilemma is that rare and low abundant taxa naturally exist among microbiomes. Karpinets et al. attempt to reduce the burden of filtering for the rare OTUs and overcome the difficulty of compositionality by treating the OTUs as qualitative variables. They explore the biological role of the rare low abundance OTUs and analyze them by using association networks (Anets) and show Anets have the potential to serve as a unsupervised methodology for linking rare OTUs to associations with environment or phenotypes.One common problem among compositional microbiome data is that it is sparse and zero-inflated. This compositional bias leads to false positives as well as underpowered statistical associations when conducting multiple comparisons. A common strategy to handle excess zeros is to add a small number called a pseudo count. Pinto et al. construct an ordinary differential equation (ODE) -based kinetic model incorporating microbial growth equations and metabolic interactions among bacteria using experimental data from gut microbe cultures. Their model accurately predicted bacterial abundance as well as metabolite consumption and production in a bioreactor experiment.Many methodologies naïvely separate themselves from the biologic aspect of the data inasmuch that these are complex interacting ecosystems with intertwined metabolic pathways, different rates of growth etc. Zhai et al. suggest a variance component selection scheme, or VC-lasso, for sparse and high-dimensional taxonomic data analysis. They disperse individual OTUs into clusters at phylogenetic levels, and translate the phylogenetic distance information to kernel matrices, where they treat the taxonomic clusters as multiple random effects in a variance component model. Similarly, Xiao et al. also develop a methodology for capturing clustered microbiome signals dependent on phylogeny. “glmmTree” is their novel prediction method based on a generalized linear mixed model, which captures clustered microbiome signals. In this framework, the effects of the bacterial taxa are modeled as random with the correlation structure dependent on a phylogenetic tree, whereas the effects of predictive variables are treated as fixed. Another conundrum is the concern that methodologies based on binning mapped sequences can still be riddled with error due to subpar databases. Currently OTU binning is the well accepted methodology, but group specific signatures can be just as important for biomarker discovery or disease association. Wang et al. recommend using K-mers which provides an alignment free method to characterize microbial communities.Furthermore, many approaches do not take into consideration phylogeny or relatedness of the organisms in order to make associations. McNally et al. is a web based tool for interactive visualization of microbiome multi-omic data combined with taxonomic and functional information. BURRITO visualizes the taxonomic and functional compositions of multiple samples and underlines relationships between taxa and function. Baksi et al. present a web based framework called “TIME” . TIME allows for predicting taxa that might have a higher influence on community structure in different conditions.There is a definite shortage of visualization or web based tools that support the integration of taxonomic and functional profiles. BURRITO, described by Baksi et al.). In this program, longitudinal time points, and respective metadata can be used to visualize temporal variations. Lee and Sison-Mangus developed a Bayesian semiparametric generalized linear regression model to investigate the effects of physical and biological variables the abundance of microbes. This model allows for borrowing information across OTUs, across samples and across time points. Shields-Cutler et al. introduce splinectomeR, an R package that uses smoothing splines to summarize categorical variables for hypothesis testing in longitudinal microbiome studies. Lastly, Wagner et al. propose the use of a bi-exponential function to summarize and compare diversity curves over time using hierarchical modeling. This approach accounts for repeated measures on each subject in order to compare and model alpha diversity indices over time.As substantial variability in microbiota communities can be seen across subjects, and across time, the improvement of longitudinal study design, and causal models is paramount to associate a dynamic ecosystem with complicated environmental and host factors. Many of the papers in this Research Topic offer methods which address different issues that arise when handling longitudinal data. The previously discussed web based application, TIME, was developed specifically to identify potential taxonomic markers from time series data (Together, these original research articles and reviews emphasize the difficulties faced when analyzing microbiome data and the shortcomings of current statistical, computational and visualization tactics. Currently, many researchers perform all of their own coding and individual analyses without well-defined descriptions of their methods or sharing of analysis pipelines between laboratories. Thus, there is a pressing need for consistent and harmonious data analysis procedures. As a scientific community, microbiome researchers should not be content with “status quo” when it comes to widely accepted practices in microbiome analyses as they have many faults and limitations. In the modern era of data sharing and web-based tools scientists should be working together to compare results between sites and cohorts, improve current techniques, as well as validate methodologies. Only when the research community ensures that novel approaches hold up to independent validation across populations can we truly develop a microbiome analysis paradigm which allows for reliable reproducibility of findings across multiple institutions all over the world.All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


An intervention that successfully reduced colonization and infection with carbapenemase-producing Enterobacteriaceae (CPE) in Chicago-area long-term acute-care hospitals included active surveillance and contact precautions. However, the specific effects of contact precautions applied to surveillance-detected carriers on patient-to-patient transmission are unknown, as other, concurrent intervention components or changes in facility patient dynamics also could have affected the observed outcomes.Using previously published data from before and after the CPE intervention, we designed a mathematical model with an explicit representation of postintervention surveillance. We estimated preintervention to postintervention changes of 3 parameters: Assuming that CPE carriers under contact precautions transmit carriage to other patients at half the rate of undetected carriers, the model produced no convincing evidence for a postintervention change in the baseline transmission rate Our results suggest that contact precautions for surveillance-detected CPE carriers could potentially explain the observed decrease in colonization by itself, even under conservative assumptions for the effectiveness of those precautions for reducing cross-transmission. Other intervention components such as daily chlorhexidine gluconate bathing of all patients and hand-hygiene education and adherence monitoring may have contributed primarily to reducing rates of colonized patients progressing to bacteremia. We mathematically modeled a bundled intervention that successfully reduced carbapenemase-producing Enterobacteriaceae in long-term acute care hospitals. Results show that contact precautions for surveillance-detected carriers plausibly can explain the observed reduction in carriage prevalence without substantial contribution from other intervention components. Infections caused by multidrug-resistant organisms have limited available treatments and pose a significant health threat . Some drInterventions to interrupt the transmission of organisms among patients in healthcare facilities often take a “bundled” approach, comprising multifaceted efforts that may include, for example, instituting active surveillance for asymptomatic carriers; contact precautions such as glove and gown use, isolation, and/or cohorting for identified carriers; healthcare worker education/monitoring for hygiene behaviors; decontamination procedures applied to patients and/or environmental surfaces; and antibiotic stewardship , 5. ApplActive surveillance combined with contact precautions applied to detected carriers is an intervention component of high interest due to its high cost and the difficulty in directly assessing its effectiveness. While healthcare facilities instituting new surveillance programs have seen decreases in healthcare-associated infections , it has The specific scenario we model here is an intervention targeted at carbapenemase-producing Enterobacteriaceae (CPE) in a long-term acute care hospital (LTACH) . Due to Specifically, we calibrate our model to an intervention that was initiated in Chicago-area LTACHs with nearly 50% prevalence of CPE carriers and significant rates of CPE clinical detection and bacteremia  . After tWe developed a mathematical model representing the portion of LTACH inpatients in different states of CPE colonization, infection, and contact precaution status. Each model carries an assumption of a constant patient death rate and a live-discharge hazard that depends on the time since the patient’s admission, using a mixture of gamma distributions . These sLTACH inpatients were classified into 6 possible states :Susceptible , or calibration targets  in the model. Here, we relied on the reported uncertainty ranges from the source paper  Table 1Table 1. Under our assumed values for the 3 independently based parameters  to convincingly conclude that other intervention components reduced transmission.If it is true that our assumption for contact precaution effectiveness of omponent .The intervention bundle also included “healthcare-worker education and adherence monitoring, with a focus on hand hygiene” . A postim entry 2.4%, therWhile the above describe plausible explanations for the intervention results if contact precaution effects were much weaker than our 50% assumption, our findings also suggest that assuming highly effective contact precautions could also be consistent with the data. However, a high value of Our findings strongly suggest a postintervention decrease in the per-capita rate of CPE carriers progressing to CPE bacteremia, that is, the decrease in the overall LTACH bacteremia incidence was not just due to the lower prevalence of CPE carriers at risk of infection, but also due to a decreased per-capita risk of infection among carriers. It is plausible that this was a result of chlorhexidine bathing or other hygiene-oriented components of the intervention, which could have reduced the risk of carried organisms entering the bloodstream.Conversely, our findings do not conclusively suggest that the per-capita progression rate to nonbacteremia clinical detection changed after the intervention. While the overall incidence of CPE clinical detections decreased postintervention, the magnitude of decrease can be explained by the decrease in prevalence of carriers at risk of progression, and does not require a substantial change in the per-capita progression rate.We did not model the possibility that the intervention caused an increase in the rate that CPE carriers cleared colonization. If intervention efforts did increase the clearance rate, then it is possible our model overestimated the intervention’s impact on transmission. There is evidence that chlorhexidine gluconate bathing might reduce skin colonization with CPE , but a rIf the intervention efforts reduced skin contamination on carriers but did not directly eliminate rectal carriage, then our finding that the intervention reduced the bacteremia progression rate but perhaps not the baseline transmission rate could have implications for inferring the sources of those events—that is, skin contamination may be an important source of organisms that initiate a bloodstream infection in that patient but may be less important as a source of organisms posing risk to other patients in the LTACH. Organisms carried rectally and in other internal sites could contaminate the hospital environment intermittently, for example, via stool or invasive devices, which could create sources of organisms that pose risk to other patients and would not be removed via skin decontamination. More research focused on these possibilities would be beneficial.Staphylococcus aureus or vancomycin-resistant Enterococcus, for which the effectiveness of contact precautions have been questioned [Insights derived from this work may or may not apply to other drug-resistant organisms. CPE are gram-negative bacteria that may have different contamination and transmission patterns in healthcare facilities than gram-positive bacterial strains such as methicillin-resistant estioned . HoweverInsights about the effect of contact precautions in LTACHs may not extend to other types of healthcare facilities. It is possible that atypical care required by LTACH patients could create unusual transmission pathways that lead to unusual effects of contact precautions. Also, long lengths of stay for LTACH patients  affects the way that transmission reduction manifests in observable outcomes. In short-stay hospitals, effects of prevented acquisitions may not be apparent if acquiring patients tend to be discharged before progressing to clinical infection, testing positive via surveillance, or transmitting to other hospital patients. Thus, the effects of reducing transmission in short-stay hospitals might be more difficult to observe without data from patients and their contacts at postdischarge locations.Our model-based approach has several advantages. Its representation of patient states allows us to quantify the relationship between observable clinical events and unobservable colonization events, which makes it particularly well suited to examine the mechanisms of interventions that could affect both types of events simultaneously. Our model also flexibly accounts for length of stay and death rate and retains the realism that a patient’s discharge hazard over the course of a stay is nonconstant. The effects of those length of stay details on the relationship between facility prevalence, infection incidence, and nonlinear transmission processes are difficult to account for without a mathematical model.Our approach to solving for key unknown model parameters by assuming an equilibrium state for pre- and postintervention models allowed us to make use of equation-based results that can be efficiently solved and do not require the use of extensive, time-consuming simulations. The equilibrium assumption is consistent with the observation that CPE prevalence was stable during the preintervention period and then restabilized a new level several months after the intervention efforts were in place . Other sThe use of a multifaceted “bundled” approach to intervening in healthcare facilities seeking to reduce the presence of drug-resistant organisms is understandable; the primary goal is to reduce risk to facility patients from dangerous infections, and if the projected effects of particular intervention components are uncertain then it makes sense to maximize the probability of success by combining them. For the secondary but still highly important goal of understanding the effect that each intervention component had on the outcomes, our work demonstrates that pairing mathematical transmission models with the data can be a powerful tool.Staphylococcus aureus [Clostridioides difficile [Our model-based findings also suggest additional data collection efforts that would help isolate the effects of contact precautions. Data that can suggest the source of patient acquisitions via genetic comparisons of patient samples would be particularly powerful. Combined with data on detection timing and contact precaution status, such genetic data could be used to quantify the relative rates of transmission from carriers under contact precautions vs other carriers, which would help constrain the possible scenarios depicted in s aureus  and Closifficile ; we beliClinical Infectious Diseases online. Consisting of data provided by the authors to benefit the reader, the posted materials are not copyedited and are the sole responsibility of the authors, so questions or comments should be addressed to the corresponding author.Supplementary materials are available at ciz557_suppl_Supplementary_InformationClick here for additional data file.


N = 105 314) in the study area and identified 26 typical crop portfolios. We show that portfolio composition is related to farm characteristics  and location. Currently, diversification of crop portfolios fails to promote stability of portfolio revenue in the study area, where policy still indirectly influences market prices of energy crops. We conclude that the portfolio effect as a natural insurance was less important in recent years due to high market prices for specific crops. This low need for natural insurances probably favoured simplified portfolios leading to decreased agricultural diversity.Diversity of agricultural landscapes is important to maintain the provision of ecosystem services. In face of decreasing support measures for agricultural markets in the European Union, diversified crop portfolios could also offer a possibility to stabilize revenue at farm level (portfolio effect). We hypothesize that (i) diversity of crop portfolios changes along spatial gradients in the study area , (ii) the composition of portfolios depends on farm parameters, and (iii) more diverse portfolios on arable land provide higher revenue stability. We analysed agricultural census data comprising all farms ( Crop diversity is an important part of agrobiodiversity and is related to ecosystem services provided by agroecosystems . For exaOn the spatial scale, a diverse crop portfolio provides an insurance effect to mitigate revenue variability at farm level –11. ThisBesides rationally composing portfolios to adjust for market risks, personal preferences of the farm holder influence portfolio composition , 18. TheDirect political guidelines also influence farmers’ decision-making. In particular in organic framing, the Common Agricultural Policy (CAP) of the EU demands increased portfolio diversity . FurtherSince crop diversity and portfolio composition are essential for ecosystem services in the agricultural landscape, it is important to analyse how the physical and socio-economic environments constrain the portfolio composition. Only thus can we understand how policy could enhance portfolio diversity and how clear policy schemes for enhancing ecosystem services in agricultural systems could be developed. To our knowledge, most studies that tried to find the connection between crop portfolio composition and ecological, economic and farm-specific factors concentrated on a small number of sample farms. Given the multitude of interactions, however, this link might be better understood from a larger geographic perspective—an approach we pursue in this work.N = 105 314) of the complete Free State of Bavaria were incorporated in one analysis. We determine typical crop portfolios for farms in Bavaria and analyse their diversity on the spatial scale. We further evaluate the study-area specific relationship between portfolio diversity on arable land and temporal revenue stability of those portfolios. Since the whole analysis is based on the diversity of these farm-specific crop portfolios, the work is clearly focussed on the farm scale. We test the hypothesis that portfolio diversity changes along spatial gradients (H1). This is based on the assumption that agriculture is adequately adapted to local geographic  conditions. Moreover, we hypothesize that portfolio composition of individual farms depends on socio-economic parameters and that higher diversity is found on larger farms, in full-time compared to part-time farming, on farms integrated arable farming and animal husbandry, in farming without intra-annual crop rotation, and on farms where whole farm revenue largely originates from arable land use (H2). Testing the portfolio theory in Bavarian agriculture, we hypothesize that more diverse portfolios offer a more stable risk–revenue relationship (H3).Our goal was to analyse regionally specific crop portfolios and to give further insights into how the choice of crops and composition of crop portfolios is influenced by geographical and socio-economic farm parameters in Bavaria (Germany). In an approach combining both descriptive and correlative techniques for analysis of large data sets, all farms (2) almost half is used for agriculture and one third for forestry  of each crop within each portfolio. The number of farms that cultivated each portfolio is given as (PDF)Click here for additional data file.S1 Appendix(PDF)Click here for additional data file.


They populate the lowest level of the phonological hierarchy, the segmental tier, and both classes are obligatory across spoken languages. But only some languages also make use of lexical tones, contrastive sub-syllabic fundamental frequency (pitch) variations referred to as tonemes . These two classes of phonemes are used to differentiate between words, whose meanings can be categorically changed by switching even a single vowel or consonant, as in <Unlike consonants and vowels, lexical tones are optionalMoreover, languages that lack lexical tones (non-tone languages) are far from devoid of systematic pitch variations. All spoken languages use pitch and contour paralinguistically, e.g., to convey information about emotions and talker gender and age. More importantly for our discussion of lexical tones, all languages also use pitch variation linguistically to mark intonation distinctions at supra-syllabic  levels of the phonological hierarchy: prosodic word, phonological phrase, intonational phrase, and utterance tiers  prosodic distinctions. For example, they may perceive non-native lexical tones as L1 intonational phrase  to assimilating them as native prosodic patterns, and on the other hand to better understand how and when young learners of tone languages begin to tease apart lexical tones  from not only paralinguistic indexical information  but also linguistic prosodic information in their language.Understanding the phonological status of lexical tones could provide an important linguistic basis for predicting and interpreting both native and non-native tone perception and early learning. However, it has not yet been resolved whether the lexical tones of tone languages serve suprasegmental or segmental functions, and in the latter case whether they constitute a third class of phonological segments or serve as phonological features of vowels or of consonants. As briefly summarized in the following paragraphs, certain sources of evidence and/or theoretical analyses appear to be consistent with each of these possibilities. Unfortunately, the nature of the evidence differs among them, making it difficult to decide among them. Further research and theoretical analyses will be needed to tease them apart. It is likely that the answer will depend on whether the approach focuses on tone production and phonological processes in lexical tone languages, or whether the approach focuses instead on native or non-native perception. With the former approach the answer may vary depending on what types of tone systems the target languages have, whereas with the latter approach the answer should vary according to whether the listener groups have tone or non-tone L1s.The question of the suprasegmental vs. segmental status of lexical tones in tone languages has been addressed primarily via phonological analysis of diachronic and synchronic data on tones as produced in a range of languages. In classic generative phonology tones were considered to be segmental in nature  in tone languages. Firstly, the emergence of lexical tones during the historical evolution of a language (tonogenesis) is much more likely to arise via diachronic changes in laryngeal features of consonants, e.g., through trans-phonologization of voicing contrasts, than from diachronic changes in vowels (see Maddieson, This leads us to the next question: do lexical tones constitute a third class of phonological segments in addition to consonants and vowels in tone languages, or do they instead serve as optional phonological features of vowels or consonants? In the classic generative phonology framework of (Chomsky and Halle, Can we form a clearer picture based on existing cross-language tone perception studies? On the one hand, many reports on early developmental changes in non-native lexical tone perception appear compatible with the idea that tones are phonologically associated with consonants. For example, English-learning infants have been found to discriminate non-native Mandarin tone contrasts at 6 months but not at 9 months (e.g., Mattock and Burnham, Further complicating things are other developmental findings suggesting that language-specific changes in consonant perception appear somewhat earlier, by 8 months, in French-learning than English-learning infants (Hoonhorst et al., The challenge for further research is how to design tests of whether young children, or adults for that matter, perceive tones as features of consonants or vowels or as different from both, and of how that pattern may differ for native listeners vs. non-native listeners/learners of different types of tone languages or non-tone languages. Future research will also need to take into account that all languages, whether or not they use lexical tones, employ prosodic pitch distinctions at higher tiers of the phonological hierarchy. This means that speakers of so-called non-tone languages are not lacking entirely in experience with phonological information being conveyed by pitch variations, and can refer to native pitch settings at a higher tier of the prosodic hierarchy when perceiving non-native lexical tones. Conversely, it also means that for speakers or learners of a tone language there is potential for ambiguity or confusion over which phonological tier is being represented by a given tonal pattern. Such confusions could be the root cause of apparent developmental “dips” in tone sensitivity even in children whose native language uses lexical tones.A key unanswered question for listeners from non-tone L1s is whether and how assimilating tones to native prosodic contrasts may help or hinder learning the lexical tones of words in a tone language. More specifically, it is an open question whether and how cross-tier perceptual influences differ quantitatively and/or qualitatively from perceiving non-native consonant and vowel contrasts with reference to same-tier native contrasts (for an excellent step toward addressing this see Braun and Johnson, The author confirms being the sole contributor of this work and has approved it for publication.The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Intracortical brain-machine interfaces are a promising technology for allowing people with chronic and severe neurological disorders that resulted in loss of function to potentially regain those functions through neuroprosthetic devices. The penetrating microelectrode arrays used in almost all previous studies of intracortical brain-machine interfaces in people had a limited recording life , as well as a limited number of recording electrodes with limited distribution in the brain. Significant advances are required in this array interface to deal with the issues of long-term biocompatibility and lack of distributed recordings. The Musk and Neuralink manuscript proposes a novel and potentially disruptive approach to advancing the brain-electrode interface technology, with the potential of addressing many of these hurdles. Our commentary addresses the potential advantages of the proposed approach, as well as the remaining challenges to be addressed. Over the past two decades, several academic research laboratories have advanced the science and implementation of intracortical brain-machine interfaces from exclusive use in nonhuman primates to investigative use in human volunteers with chronic neurological impairments under pilot clinical trials approved through the US Food and Drug Administration (FDA). Researchers in these labs have successfully demonstrated that these persons can use intracortical brain-machine interfaces to command movements of a computer cursor, a robotic arm, their own arm reanimated through functional electrical stimulation, and recently speech articulation. Our own group has developed a system that uses an intracortical brain-machine interface to command a functional electrical stimulation system that coordinates activation of paralyzed upper limb muscles to restore useful function. Our current study is the Reconnecting the Hand and Arm with Brain (ReHAB) clinical trial, which uses Blackrock recording and stimulation arrays.As noted in the paper by Musk and Neuralink , the samBased on our experiences, any significant step forward in human brain recording will require electrode technologies that provide orders of magnitude more information. This information will be derived from: (1) a much higher number of recording/stimulation contacts; (2) recording both surface and deep brain structures , as well as from multiple brain areas with different functionality; and (3) different signal types . These electrodes should be able to be inserted safely and within a reasonable surgical time window, and should be well tolerated by the brain . Hardware that records signals from these electrodes should extract relevant neural information from the high numbers of channels with high fidelity and in real-time, avoid percutaneous interfaces, and always be available for use.The authors of this paper described a novel approach involving ultrafine polymer threads, each containing a dense linear array of 32 high-impedance electrodes, that are individually implanted into the cortex using a robotic device. The implantation robot can be controlled intraoperatively with micron precision to avoid small surface blood vessels and is able to implant up to six threads (192 electrodes) per minute. A small (23x18.5 millimeter) custom printed circuit board with onboard power is able to connect to up to 96 of the threads for a total of 3072 electrodes per implanted array, and digitized high-bandwidth neural data is streamed from the device using a single USB-C cable. Two different versions of the system were developed, one maximizing reliable manufacturing  and another maximizing channel count. Testing in an awake rat demonstrated the ability to record signals interpreted as neural spike data from 43% of the channels. The authors conclude that this strategy may revolutionize brain-machine interfaces by recording from an unprecedented number of neurons.Clinical application of brain-machine interfaces may require a very high channel count to allow recording from many neurons, so the relatively small number of contacts available using currently available platforms has hindered its development. The technology and processes presented in the Musk and Neuralink paper can certainly increase the channel count by an order of magnitude, providing a more detailed sampling of relevant signals as well as some welcome redundancy. Perhaps even more attractive about the proposed approach is the possibility of placing electrodes into areas of the brain that have been difficult or impossible to reach with existing intracortical arrays . These new locations could potentially provide different types of information for enhancing brain-machine interface performance , as well as better elucidate the interactions between different brain structures . Such information is likely to be critical for successful performance in more complex brain-machine interface applications, such as multidimensional arm movement involving complex physical dynamics.The use of individually implanted threads of electrodes is a clever way to allow an exponential increase of channel count over existing brain-machine interface technology, and the authors’ approach is well conceived with a good consideration of both physical and technical characteristics. However, the potential clinical application of this strategy is unclear since it has only been tested in a small number of rodents, with no comparison to existing approaches or verification of safety using histological analysis after implantation. The authors claim that their implants will have greater longevity than other options because of less immune response related to electrode stiffness and microvascular disruption, but no evidence is presented to support either of these assumptions, and improved durability was not verified using long-term implantation. It is not clear that blood vessels below the surface can be avoided, potentially critical for immune responses. The paper does not address the use of the thread electrodes for larger brains with more complex cortical structures . The potentially implantable recording system as presented does not include hermetic sealing, a relevant power source , or a technique  for transmitting high bandwidth data out of the body without a percutaneous interface. Furthermore, the potential impact of noise and artifact has not been unequivocally established: It is uncertain whether the signals recorded in the rodent study actually represent meaningful neural data since the measured impedance was relatively low compared with penetrating electrodes typically used to measure single-unit neural activity, similar signals were seen on many adjacent channels, and no attempt was made to validate that the spike data had physiological characteristics typical of neural spiking patterns in the regions that were implanted. The technology is very innovative, but better validation will be necessary to establish its clinical potential.Overall, this new technology is exciting because it attempts to directly address a number of bottlenecks that have hindered true clinical translation of intracortical arrays for use in brain-machine interfaces. The amount of high-resolution information that could be simultaneously recorded from cortical neurons may lead to new advances in the application of data mining and machine learning approaches to better understand cortical electrophysiological activity at the macro-, meso-, and microscale levels. Ultimately these multi-prong advances will likely result in advances in brain-controlled neuroprosthetics for addressing impaired function in neurologically compromised individuals. However, a responsible stance of cautious optimism must be taken. There is a long road between showing single neuron recordings in a handful of rodents to clinically translated human use, including proof  of longevity, efficacy, and safety through an FDA-approved human clinical trial. If successful, the proposed technology (and future derived technologies) could pave the way for more widespread translation of intracortical brain-machine interfaces for medical applications in people with chronic neurological impairments.