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Does anti-Müllerian hormone (AMH) differ between healthy European and Indian women, and are potential ethnic differences modified by infertility diagnosis?Cross-sectional analysis of three prospectively recruited cohorts (n = 2758); healthy European women (n = 758), healthy community cohort from Kolhapur, India (n = 400) and infertility cohort from Kolhapur, India (n = 1600). AMH was determined by assay. Ethnicity, age and cause of infertility were modelled using additive quantile regression models.Healthy Indian women had lower AMH than their healthy European counterparts (population estimates 20.0% lower [95% CI 7.2-36.5]), with increasing discordance with increasing age; at 25 years AMH was 11.9% lower (95% CI 9.4-14.1), increasing to 40.0% lower (95% CI 0-64.6) by age 45. Comparison of healthy and infertile Indian women revealed differences that were related to cause of infertility. Women whose male partner had severe oligoasthenoteratozoospermia (n = 95) had similar AMH to controls; women with polycystic ovary syndrome (n = 220) had higher AMH, especially in those30 years, and in women with a principal diagnosis of unexplained infertility (n = 757) AMH was lower (median difference 22.6% lower; 95% CI 9.1-37.7) than controls.AMH is substantially lower in healthy Indian women at all ages than their European counterparts. Infertile Indian women have variable differences in AMH from healthy Indian controls, with the extent and direction of differences primarily reflecting the underlying cause of infertility. Recognition of ethnic and cause-specific differences are critical to ensure accurate contextualizing of results and clinical outcomes for patients.

The search for alien life is hard because we do not know what signatures are unique to life. We show why complex molecules found in high abundance are universal biosignatures and demonstrate the first intrinsic experimentally tractable measure of molecular complexity, called the molecular assembly index (MA). To do this we calculate the complexity of several million molecules and validate that their complexity can be experimentally determined by mass spectrometry. This approach allows us to identify molecular biosignatures from a set of diverse samples from around the world, outer space, and the laboratory, demonstrating it is possible to build a life detection experiment based on MA that could be deployed to extraterrestrial locations, and used as a complexity scale to quantify constraints needed to direct prebiotically plausible processes in the laboratory. Such an approach is vital for finding life elsewhere in the universe or creating de-novo life in the lab., The search for life in the universe is difficult due to issues with defining signatures of living systems. Here, the authors present an approach based on the molecular assembly number and tandem mass spectrometry that allows identification of molecules produced by biological systems, and use it to identify biosignatures from a range of samples, including ones from outer space.

BackgroundThe fetus is vulnerable to maternal drug exposure. We determined associations of exposure to spinal, epidural, or general anesthesia on neonatal and childhood development outcomes during the first 1000 days of life.MethodsPopulation-based study of all singleton, cesarean livebirths of 24+0 to 43+6 weeks gestation between January 2007 and December 2016 in Scotland, stratified by urgency with follow-up to age 2 years. Models were adjusted for: maternal age, weight, ethnicity, socioeconomic status, smoking, drug-use, induction, parity, previous cesarean or abortion, pre-eclampsia, gestation, birth weight, and sex.Results140 866 mothers underwent cesarean section (41.2% (57,971/140,866) elective, 58.8% (82,895/140,866) emergency) with general anesthesia used in 3.2% (1877/57,971) elective and 9.8% (8158/82,895) of emergency cases. In elective cases, general anesthesia versus spinal was associated with: neonatal resuscitation (crude event rate 16.2% vs 1.9% (adjusted RR 8.20, 95% CI 7.20 to 9.33), Apgar ConclusionsGeneral anesthesia for cesarean section, irrespective of urgency, is associated with neonatal resuscitation, low Apgar, and neonatal unit admission. Associations were strongest in non-urgent cases and at term. Further evaluation of long-term outcomes is warranted.

There is a growing drive in the chemistry community to exploit rapidly growing robotic technologies along with artificial intelligence-based approaches. Applying this to chemistry requires a holistic approach to chemical synthesis design and execution. Here, we outline a universal approach to this problem beginning with an abstract representation of the practice of chemical synthesis that then informs the programming and automation required for its practical realization. Using this foundation to construct closed-loop robotic chemical search engines, we can generate new discoveries that may be verified, optimized, and repeated entirely automatically. These robots can perform chemical reactions and analyses much faster than can be done manually. As such, this leads to a road map whereby molecules can be discovered, optimized, and made on demand from a digital code.

STUDY QUESTION How did coronavirus disease 2019 (COVID-19) impact on medically assisted reproduction (MAR) services in Europe during the COVID-19 pandemic (March to May 2020)? SUMMARY ANSWER MAR services, and hence treatments for infertile couples, were stopped in most European countries for a mean of 7 weeks. WHAT IS KNOWN ALREADY With the outbreak of COVID-19 in Europe, non-urgent medical care was reduced by local authorities to preserve health resources and maintain social distancing. Furthermore, ESHRE and other societies recommended to postpone ART pregnancies as of 14 March 2020. STUDY DESIGN, SIZE, DURATION A structured questionnaire was distributed in April among the ESHRE Committee of National Representatives, followed by further information collection through email. PARTICIPANTS/MATERIALS, SETTING, METHODS The information was collected through the questionnaire and afterwards summarised and aligned with data from the European Centre for Disease Control on the number of COVID-19 cases per country. MAIN RESULTS AND THE ROLE OF CHANCE By aligning the data for each country with respective epidemiological data, we show a large variation in the time and the phase in the epidemic in the curve when MAR/ART treatments were suspended and restarted. Similarly, the duration of interruption varied. Fertility preservation treatments and patient supportive care for patients remained available during the pandemic. LARGE SCALE DATA N/A LIMITATIONS, REASONS FOR CAUTION Data collection was prone to misinterpretation of the questions and replies, and required further follow-up to check the accuracy. Some representatives reported that they, themselves, were not always aware of the situation throughout the country or reported difficulties with providing single generalised replies, for instance when there were regional differences within their country. WIDER IMPLICATIONS OF THE FINDINGS The current article provides a basis for further research of the different strategies developed in response to the COVID-19 crisis. Such conclusions will be invaluable for health authorities and healthcare professionals with respect to future similar situations. STUDY FUNDING/COMPETING INTEREST(S) There was no funding for the study, apart from technical support from ESHRE. The authors had no COI to disclose.

Research QuestionDiscontinuation of in vitro fertilisation (IVF) cycles has been part of the radical transformation of healthcare provision to enable reallocation of staff and resources to deal with the COVID-19 pandemic. We sought to estimate the impact of cessation of treatment on individual prognosis and United States population live-birth rates.DesignData from 271,438 ovarian stimulation UK IVF cycles was used to model the effect of age as a continuous, yet non-linear, function on cumulative live-birth rate. We recalibrated this model to cumulative live-birth rates reported for the 135,6733 stimulation cycles undertaken in the USA in 2016, with live-birth follow-up to October 2018. We calculated the effect of a one-month, three-month and six-month shutdown in IVF treatment as the effect of the equivalent increase in a woman’s age, stratified by age group. ResultsThe average reduction in cumulative live-birth rate would be 0.3% [95% CI: 0.3, 0.3], 0.8% [0.8, 0.8] and 1.6% [1.6, 1.6] for a one-month, three-month and six-month shutdown, respectively. This corresponds to a reduction of 369 [95% CI; 360, 378), 1,098 [1071, 1123] and 2,166 [2,116, 2,216] live-births in the cohort, respectively. The greatest contribution to this reduction was from older mothers. ConclusionsWe demonstrate that the discontinuation of fertility treatment for even 1 month in the USA could result in 369 fewer women having a live-birth, due to the increase in patients’ age during the shutdown. As a result of reductions in cumulative live-birth rate, more cycles may be required to overcome infertility at an individual and population level.

Although extending the reactivity of a given class of molecules is relatively straightforward, the discovery of genuinely new reactivity and the molecules that result is a wholly more challenging problem. If new reactions can be considered unpredictable using current chemical knowledge, then we suggest that they are not merely new but also novel. Such a classification, however, requires an expert judge to have access to all current chemical knowledge or risks a lack of information being interpreted as unpredictability. Here, we describe how searching chemical space using automation and algorithms improves the probability of discovery. The former enables routine chemical tasks to be performed more quickly and consistently, while the latter uses algorithms to facilitate the searching of chemical knowledge databases. Experimental systems can also be developed to discover novel molecules, reactions and mechanisms by augmenting the intuition of the human expert. In order to find new chemical laws, we must seek to question current assumptions and biases. Accomplishing that involves using two areas of algorithmic approaches: algorithms to perform searches, and more general machine learning and statistical modelling algorithms to predict the chemistry under investigation. We propose that such a chemical intelligence approach is already being used and that, in the not-too-distant future, the automated chemical reactor systems controlled by these algorithms and monitored by a sensor array will be capable of navigating and searching chemical space more quickly, efficiently and, importantly, without bias. This approach promises to yield not only new molecules but also unpredictable and thus novel reactivity. Automation can help in performing routine tasks quickly and consistently. Algorithms facilitate the searching of current knowledge. Combining the two could lead to a chemically intelligent approach to the discovery of not only new molecules but also novel and unpredictable reactivity.

The search for evidence of life elsewhere in the universe is hard because it is not obvious what signatures are unique to life. Here we postulate that complex molecules found in high abundance are universal biosignatures as they cannot form by chance. To explore this, we developed the first intrinsic measure of molecular complexity that can be experimentally determined, and this is based upon a new approach called assembly theory which gives the molecular assembly number (MA) of a given molecule. MA allows us to compare the intrinsic complexity of molecules using the minimum number of steps required to construct the molecular graph starting from basic objects, and a probabilistic model shows how the probability of any given molecule forming randomly drops dramatically as its MA increases. To map chemical space, we calculated the MA of ca. 2.5 million compounds, and collected data which showed the complexity of a molecule can be experimentally determined by using three independent techniques including infra-red spectroscopy, nuclear magnetic resonance, and by fragmentation in a mass spectrometer, and this data has an excellent corelation with the values predicted from our assembly theory. We then set out to see if this approach could allow us to identify molecular biosignatures with a set of diverse samples from around the world, outer space, and the laboratory including prebiotic soups. The results show that there is a non-living to living threshold in MA complexity and the higher the MA for a given molecule, the more likely that it had to be produced by a biological process. This work demonstrates it is possible to use this approach to build a life detection instrument that could be deployed on missions to extra-terrestrial locations to detect biosignatures, map the extent of life on Earth, and be used as a molecular complexity scale to quantify the constraints needed to direct prebiotically plausible processes in the laboratory. Such an approach is vital if we are going to find new life elsewhere in the universe or create de-novo life in the lab.

Research questionThe economic and reproductive medicine response to the coronavirus disease 2019 (COVID-19) pandemic in the USA has reduced the affordability and accessibility of fertility care. What is the impact of the 2008 financial recession and the COVID-19 recession on fertility treatments and cumulative live births?DesignThe study examined annual US natality, Centers for Disease Control and Prevention IVF cycle activity and live birth data from 1999 to 2018 encompassing 3,286,349 treatment cycles, to estimate the age-stratified reduction in IVF cycles undertaken after the 2008 financial recession, with forward quantitative modelling of IVF cycle activity and cumulative live births for 2020 to 2023.ResultsThe financial recession of 2008 caused a 4-year plateau in fertility treatments with a predicted 53,026 (95% confidence interval [CI] 49,581 to 56,471) fewer IVF cycles and 16,872 (95% CI 16,713 to 17,031) fewer live births. A similar scale of economic recession would cause 67,386 (95% CI 61,686 to 73,086) fewer IVF cycles between 2020 and 2023, with women younger than 35 years overall undertaking 22,504 (95% CI 14,320 to 30,690) fewer cycles, compared with 4445 (95% CI 3144 to 5749) fewer cycles in women over the age of 40 years. This equates to overall 25,143 (95% CI 22,408 to 27,877) fewer predicted live births from IVF, of which only 490 (95% CI 381 to 601) are anticipated to occur in women over the age of 40 years.ConclusionsThe COVID-19 recession could have a profound impact on US IVF live birth rates in young women, further aggravating pre-existing declines in total fertility rates.

BackgroundThe economic and reproductive medicine response to the COVID-19 pandemic in the United States has reduced the affordability and accessibility of fertility care. We sought to determine the impact of the 2008 financial recession and the COVID-19 recession on fertility treatments and cumulative live-births.MethodsWe examined annual US natality, CDC IVF cycle activity and live birth data from 1999 to 2018 encompassing 3,286,349 treatment cycles, to estimate the age-stratified reduction in IVF cycles undertaken after the 2008 financial recession, with forward quantitative modelling of IVF cycle activity and cumulative live-births for 2020 to 2023.ResultsThe financial recession of 2008 caused a four-year plateau in fertility treatments with a predicted 53,026 (95% CI 49,581 to 56,471) fewer IVF cycles and 16,872 (95% CI 16,713 to 17,031) fewer live births. A similar scale of economic recession would cause 67,386 (95% CI: 61,686 to 73,086) fewer IVF cycles between 2020 and 2023, with women younger than 35 years overall undertaking 22,504 (95% CI 14,320 to 30,690) fewer cycles, as compared to 4,445 (95% CI 3,144 to 5749) fewer cycles in women over the age of 40 years. This equates to overall 25,143 (95% CI: 22,408 to 27,877) fewer predicted live-births from IVF, of which only 490 (95% CI 381 to 601) are anticipated to occur in women over the age of 40 years.ConclusionsThe COVID-19 recession could have a profound impact on US IVF live-birth rates in young women, further aggravating pre-existing declines in total fertility rates.Trial registration numbernot applicable

STUDY QUESTIONWhat is the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral presence and seroconversion in staff members in European fertility units prior to recommencement of clinical activity?SUMMARY ANSWERA large proportion of fertility clinic staff remain susceptible to SARS-CoV-2 with no evidence of seroconversion, indicating that continued comprehensive risk mitigation strategies are essential.WHAT IS KNOWN ALREADYIn response to the coronavirus disease 2019 (COVID-19) pandemic, caused by SARS-CoV-2, routine fertility treatment was temporarily stopped in several European countries. The SARS-CoV-2 prevalence and seroconversion in fertility clinic staff, who are at potentially lower risk than routine healthcare workers, are unknown.STUDY DESIGN, SIZE, DURATIONThis cross-sectional study included 554 staff in 16 European IVF clinics, 13 ultrasound clinics, one diagnostic laboratory and one head office in four European countries (Austria, Denmark, Germany and the UK) between 15 April and 30 June 2020.PARTICIPANTS/MATERIALS, SETTING, METHODSThere were 554 staff members returning for resumption of clinical activity. Paired nucleic acid amplification tests of oropharyngeal swabs for SARS-CoV-2 and serological testing for SARS-CoV-2 IgG were performed.MAIN RESULTS AND THE ROLE OF CHANCEOf the 554 staff members tested, 0.19% (95% CI 0.03, 1.10%) had evidence of SARS-CoV-2 as detected by RT-PCR. In contrast, 23 staff members, i.e. 4.15% (95% CI 2.78, 6.15%), had antibodies against SARS-CoV-2, with a wide range of antibody titres. There was no evidence of differences in seroconversion between countries with estimates ranging from 2.78% (95% CI 0.77, 9.58) in Austria to 6.75% (95% CI 4.46, 10.1) for the UK. There was no strong evidence of clustering within the clinics, with 21 of the 30 facilities having no staff members affected (prevalence estimates ranging from 0% to 35%), and one clinic having seven staff members affected (35% (95% CI 18.1%, 56.7%)). The single staff member who tested positive for SARS-CoV-2 virus was in the pre-symptomatic phase and was isolated, with no contacts having evidence of infection on repeat testing.LIMITATIONS, REASONS FOR CAUTIONThis was a cross-sectional study prior to resumption of clinical activity, with repeat testing not undertaken.WIDER IMPLICATIONS OF THE FINDINGSThe low prevalence of seroconversion of fertility clinic staff highlights the need for continued comprehensive risk mitigation strategies and engagement with national endeavours to identify and isolate new cases and their contacts as we embark on the resumption of fertility services.STUDY FUNDING/COMPETING INTEREST(S)The Fertility Partnership funded the study. S.M.N. reports personal fees from Access Fertility, personal fees from Merck, personal fees from Ferring, grants and personal fees from Roche Diagnostics, personal fees from The Fertility Partnership and personal fees from Modern Fertility, outside the submitted work. T.C. reports personal fees from Merck and personal fees from Ferring, outside the submitted work. G.T. reports personal fees from Merck, personal fees from Ferring and personal fees from Roche Diagnostics, outside the submitted work. S.E. and P.S.G. report no conflicts of interest.TRIAL REGISTRATION NUMBERN/A.

Recently, automated robotic systems have become very efficient, thanks to improved coupling between sensor systems and algorithms, of which the latter have been gaining significance thanks to the increase in computing power over the past few decades. However, intelligent automated chemistry platforms for discovery orientated tasks need to be able to cope with the unknown, which is a profoundly hard problem. In this Outlook, we describe how recent advances in the design and application of algorithms, coupled with the increased amount of chemical data available, and automation and control systems may allow more productive chemical research and the development of chemical robots able to target discovery. This is shown through examples of workflow and data processing with automation and control, and through the use of both well-used and cutting-edge algorithms illustrated using recent studies in chemistry. Finally, several algorithms are presented in relation to chemical robots and chemical intelligence for knowledge discovery., Advances in algorithms and automatic data acquisition systems promise to help chemists improve productivity and to construct systems to work autonomously leading to new discoveries in chemical space.

Key Points Question Is the use of epidural analgesia during labor associated with adverse neonatal and childhood outcomes? Findings In this population-based cohort study of 435 281 mother-offspring pairs, the use of epidural analgesia in labor was not associated with adverse neonatal outcomes after adjustment for confounders and mediation by mode of delivery. Epidural analgesia was, however, associated with a small reduction in some adverse developmental outcomes at 2 years. Meaning In this study, epidural analgesia in labor was not associated with adverse immediate or longer-term offspring outcomes., This cohort study uses data on mother-infant pairs from the Scottish National Health Service to investigate the association of epidural anesthesia use during labor with neonatal and childhood outcomes., Importance Although use of epidural analgesia during labor is safe, detailed information about its association with neonatal and child outcomes is limited. Objective To investigate the association of labor epidural analgesia with neonatal outcomes and childhood development during the first 1000 days of life. Design, Setting, and Participants This population-based cohort study used Scottish National Health Service hospital administrative data of all 435 281 singleton live births in Scotland between January 1, 2007, and December 31, 2016, with follow-up over the first 1000 days of life. All 435 281 mother-infant pairs delivering between 24 weeks 0 days and 43 weeks 6 days’ gestation who were in active labor with cephalic presentation and who delivered vaginally or via unplanned cesarean delivery were included. Stillbirths and infants with known congenital anomalies were excluded. Data were analyzed between August 1, 2020, and July 23, 2021. Exposures Epidural analgesia in labor. Main Outcomes and Measures Neonatal outcomes included resuscitation, Apgar score less than 7 at 5 minutes, and neonatal unit admission. Childhood development measures (gross and fine motor function, communication, and social functioning) were obtained from standardized national childhood surveillance assessments performed at 2 years. Results This study included a total of 435 281 live births with cephalic presentation in labor (median gestational age at delivery, 40 weeks [IQR, 39-41 weeks]; 221 153 male infants [50.8%]), of which 94 323 (21.7%) had labor epidural. Epidural analgesia was associated with a reduction in spontaneous vaginal deliveries (confounder-adjusted [Cadj] relative risk [RR], 0.46; 95% CI, 0.42-0.50), an increased risk of neonatal resuscitation (Cadj RR, 1.07; 95% CI, 1.03-1.11), and an increased risk of neonatal unit admission (Cadj RR, 1.14; 95% CI, 1.11-1.17). With additional analysis for mediation by mode of delivery (CMadj), these associations were reversed (CMadj RR, 0.83; 95% CI, 0.79-0.86 for neonatal resuscitation and CMadj RR, 0.94; 95% CI, 0.91-0.97 for neonatal unit admission). Epidural analgesia was associated with a reduced risk of an Apgar score less than 7 at 5 minutes in both confounder and confounder/mediation analyses. Epidural analgesia was associated with a reduced risk of having developmental concern in any domain at 2 years in confounder and confounder/mediation analyses (CMadj RR, 0.96; 95% CI, 0.93-0.98), with specifically fewer concerns regarding communication (CMadj RR, 0.96; 95% CI, 0.93-0.99) and fine motor skills (CMadj RR, 0.89; 95% CI, 0.82-0.97). Conclusions and Relevance The results of this cohort study suggest that labor epidural analgesia is not independently associated with adverse neonatal or childhood development outcomes. Associations with neonatal resuscitation and admission were likely mediated by mode of delivery.

We designed and conducted a series of primordial‐soup Miller‐Urey style experiments with deuterated gases and reagents to compare the spark‐discharge products of a “deuterated world” with the standard reaction in the “hydrogenated world”. While the deuteration of the system has little effect on the distribution of amino acid products, significant differences are seen in other regions of the product‐space. Not only do we observe about 120 new species, we also see significant differences in their distribution if the two hydrogen isotope worlds are compared. Several isotopologue matches can be identified in both, but a large proportion of products have no equivalent in the corresponding isotope world with ca. 43 new species in the D world and ca. 39 new species in the H world. This shows that isotopic exchange (the addition of only one neutron) may lead to significant additional complexity in chemical space under otherwise identical reaction conditions.

The previously published version of this Article contained errors in Fig. 1. In Fig. 1a, an oxygen atom was omitted from one structure. In Fig. 1b, one structure was drawn as the incorrect enantiomer. These errors have been corrected in both the PDF and HTML versions of the Article.

Research on prebiotic chemistry and the origins of nucleic acids and proteins has traditionally been focussed on only one or the other. However, if nucleotides and amino acids co-existed on the early Earth, their mutual interactions and reactivity should be considered explicitly. Here we set out to investigate nucleotide/nucleoside formation by simple dehydration reactions of constituent building blocks (sugar, phosphate, and nucleobase) in the presence of different amino acids. We demonstrate the simultaneous formation of glycosidic bonds between ribose, purines, and pyrimidines under mild conditions without catalysts or activated reagents, as well as nucleobase exchange, in addition to the simultaneous formation of nucleotide and nucleoside isomers from several nucleobases. Clear differences in the distribution of glycosylation products are observed when glycine is present. This work demonstrates that reaction networks of nucleotides and amino acids should be considered when exploring the emergence of catalytic networks in the context of molecular evolution. The direct glycosylation of ribose by nucleobases offers an intuitive route to nucleosides, but is known to be challenging under prebiotically plausible reaction conditions. Here, the addition of amino acids is shown to influence the product distribution, and a dynamic exchange of nucleobases between nucleosides and nucleotides is observed.

Objective: The economic and reproductive medicine response to the COVID-19 pandemic in the United States has reduced the affordability and accessibility of fertility care We sought to determine the impact of the 2008 financial and the COVID-19 recession on fertility treatments and cumulative live-births Design: prospective projection modeling Materials and Methods: We examined annual US natality, CDC IVF cycle activity and live birth data from 1999 to 2018 encompassing 3,286,349 treatment cycles, to estimate the age-stratified reduction in IVF cycles undertaken after the 2008 financial recession, with forward quantitative modelling of IVF cycle activity and cumulative live-births for 2020 to 2023 Results: The financial recession of 2008 caused a four-year plateau in fertility treatments with a predicted 53,026 (95% CI 49,581 to 56,471) fewer IVF cycles and 16,872 (95% CI 16,713 to 17,031) fewer live births A similar scale of economic recession would cause 67,386 (95% CI: 61,686 to 73,086) fewer IVF cycles between 2020 and 2023, with women younger than 35 years overall undertaking 22,504 (95% CI 14,320 to 30,690) fewer cycles, as compared to 4,445 (95% CI 3,144 to 5749) fewer cycles in women over the age of 40 years This equates to overall 25,143 (95% CI: 22,408 to 27,877) fewer predicted live-births from IVF, of which only 490 (95% CI 381 to 601) are anticipated to occur in women over the age of 40 years Conclusions: The COVID-19 recession could have a profound impact on US IVF live-birth rates in young women, further aggravating pre-existing declines in total fertility rates

Research on the origin of nucleic acids and proteins has been approached by either multi-step synthesis or simple one-pot reactions, but exploration of their prebiotic chemistry is normally done separately. However, if nucleotides and amino acids co-existed on early Earth, their mutual interactions and reactivity should be considered in exploring the emergence of complex chemical systems that can ultimately evolve. To explore this idea, we set out to investigate nucleotide/nucleoside formation by a simple dehydration reaction of the constituent building blocks (sugar, phosphate, and nucleobase) in the presence of amino acids (i.e. glycine, arginine, glutamic acid, threonine, methionine, phenylalanine and tryptophan). Herein, we report the first example of simultaneous formation of glycosidic bonds between ribose, purines, and pyrimidines under mild conditions without a catalyst or activated reagents, as well as nucleobase exchange. We observed not only the simultaneous formation of nucleotide and nucleoside isomers from several nucleobases, but also the selection of distribution of glycosylation products when glycine was present. This work shows how reaction networks of nucleotides and amino acids should be considered when exploring the emergence of catalytic networks in the context of molecular evolution.

The predominance of partial least squares-discriminant analysis (PLS-DA) used to analyze metabolomics datasets (indeed, it is the most well-known tool to perform classification and regression in metabolomics), can be said to have led to the point that not all researchers are fully aware of alternative multivariate classification algorithms. This may in part be due to the widespread availability of PLS-DA in most of the well-known statistical software packages, where its implementation is very easy if the default settings are used. In addition, one of the perceived advantages of PLS-DA is that it has the ability to analyze highly collinear and noisy data. Furthermore, the calibration model is known to provide a variety of useful statistics, such as prediction accuracy as well as scores and loadings plots. However, this method may provide misleading results, largely due to a lack of suitable statistical validation, when used by non-experts who are not aware of its potential limitations when used in conjunction with metabolomics. This tutorial review aims to provide an introductory overview to several straightforward statistical methods such as principal component-discriminant function analysis (PC-DFA), support vector machines (SVM) and random forests (RF), which could very easily be used either to augment PLS or as alternative supervised learning methods to PLS-DA. These methods can be said to be particularly appropriate for the analysis of large, highly-complex data sets which are common output(s) in metabolomics studies where the numbers of variables often far exceed the number of samples. In addition, these alternative techniques may be useful tools for generating parsimonious models through feature selection and data reduction, as well as providing more propitious results. We sincerely hope that the general reader is left with little doubt that there are several promising and readily available alternatives to PLS-DA, to analyze large and highly complex data sets.

Correctly measured classification accuracy is an important aspect not only to classify pre-designated classes such as disease versus control properly, but also to ensure that the biological question can be answered competently. We recognised that there has been minimal investigation of pre-treatment methods and its influence on classification accuracy within the metabolomics literature. The standard approach to pre-treatment prior to classification modelling often incorporates the use of methods such as autoscaling, which positions all variables on a comparable scale thus allowing one to achieve separation of two or more groups (target classes). This is often undertaken without any prior investigation into the influence of the pre-treatment method on the data and supervised learning techniques employed. Whilst this is useful for deriving essential information such as predictive ability or visual interpretation in many cases, as shown in this study the standard approach is not always the most suitable option available. Here, a study has been conducted to investigate the influence of six pre-treatment methods—autoscaling, range, level, Pareto and vast scaling, as well as no scaling—on four classification models, including: principal components-discriminant function analysis (PC-DFA), support vector machines (SVM), random forests (RF) and k-nearest neighbours (kNN)—using three publically available metabolomics data sets. We have demonstrated that undertaking different pre-treatment methods can greatly affect the interpretation of the statistical modelling outputs. The results have shown that data pre-treatment is context dependent and that there was no single superior method for all the data sets used. Whilst we did find that vast scaling produced the most robust models in terms of classification rate for PC-DFA of both NMR spectroscopy data sets, in general we conclude that both vast scaling and autoscaling produced similar and superior results in comparison to the other four pre-treatment methods on both NMR and GC–MS data sets. It is therefore our recommendation that vast scaling is the primary pre-treatment method to use as this method appears to be more stable and robust across all the different classifiers that were conducted in this study.

RJAHE is supported by a Biotechnology and Biological Sciences Research Council – Doctoral Training Partnership (BBSRC-DTP) PhD-fellowship, and is grateful for receiving support from the Fundatie van de Vrijvrouwe van Renswoude and Scholten-Cordes scholarship foundations. All authors would like to thank the men who participated in the HUSERMET-project (10) and are grateful for funding of the HUSERMET project by the U.K. BBSRC (Grant number: BB/C519038/1) and U.K. Medical Research Council, with contributions from Astra-Zeneca and GlaxoSmithKline. Objectives: To assess ethnic differences in male reproductive hormone levels and to determine if any differences are explained by adiposity, insulin resistance (IR), or comorbidities in older men. Design: Multi-ethnic cross-sectional observational study. Participants: Community dwelling middle-aged and elderly men residing in the U.K. aged 40-84 years of South Asian (SA; n=180), White European (WE; n=328) or African Caribbean (AC; n=166) origin. Observations: Measured testosterone (T), calculated free T (cFT), SHBG, and LH in SA, WE and AC men along with an assessment of body composition, IR, life-style factors and medical conditions. Results: Age-adjusted mean T and cFT levels were lower in SA men when compared to WE and AC men (mean (SEM) T: SA: 14·0 ± 0·4; WE: 17·1 ± 0·3; AC: 17·2 ± 0·5 nmol/l, P < 0·001; cFT: SA: 283 ± 7; WE: 313 ± 5; AC: 314 ± 8 pmol/l, P < 0·006). Compared to WE and AC men, SA men had higher levels of body fat, IR, comorbidities and diabetes. After adjusting for body fat, IR and other confounders, T levels in SA men remained lower than in WE men (P = 0·04) but ethnic differences in cFT became nonsignificant. LH levels were higher in SA than WE men in age-adjusted and fully adjusted models. Conclusions: T and cFT are lower in SA men than in WE and AC men. Whether ethnic-specific reference ranges for T and cFT might be appropriate in clinical practice requires further investigation. Ethnic differences in cFT, but not T, appear to be, more readily, explained by ethnic differences in adiposity, thus providing insights into potential pathophysiological mechanisms. Postprint

Accurate detection of certain chemical vapours is important, as these may be diagnostic for the presence of weapons, drugs of misuse or disease. In order to achieve this, chemical sensors could be deployed remotely. However, the readout from such sensors is a multivariate pattern, and this needs to be interpreted robustly using powerful supervised learning methods. Therefore, in this study, we compared the classification accuracy of four pattern recognition algorithms which include linear discriminant analysis (LDA), partial least squares-discriminant analysis (PLS-DA), random forests (RF) and support vector machines (SVM) which employed four different kernels. For this purpose, we have used electronic nose (e-nose) sensor data (Wedge et al., Sensors Actuators B Chem 143:365-372, 2009). In order to allow direct comparison between our four different algorithms, we employed two model validation procedures based on either 10-fold cross-validation or bootstrapping. The results show that LDA (91.56% accuracy) and SVM with a polynomial kernel (91.66% accuracy) were very effective at analysing these e-nose data. These two models gave superior prediction accuracy, sensitivity and specificity in comparison to the other techniques employed. With respect to the e-nose sensor data studied here, our findings recommend that SVM with a polynomial kernel should be favoured as a classification method over the other statistical models that we assessed. SVM with non-linear kernels have the advantage that they can be used for classifying non-linear as well as linear mapping from analytical data space to multi-group classifications and would thus be a suitable algorithm for the analysis of most e-nose sensor data.

Many analytical approaches such as mass spectrometry generate large amounts of data (input variables) per sample analysed, and not all of these variables are important or related to the target output of interest. The selection of a smaller number of variables prior to sample classification is a widespread task in many research studies, where attempts are made to seek the lowest possible set of variables that are still able to achieve a high level of prediction accuracy; in other words, there is a need to generate the most parsimonious solution when the number of input variables is huge but the number of samples/objects are smaller. Here, we compare several different variable selection approaches in order to ascertain which of these are ideally suited to achieve this goal. All variable selection approaches were applied to the analysis of a common set of metabolomics data generated by Curie-point pyrolysis mass spectrometry (Py-MS), where the goal of the study was to classify the Gram-positive bacteria Bacillus. These approaches include stepwise forward variable selection, used for linear discriminant analysis (LDA); variable importance for projection (VIP) coefficient, employed in partial least squares-discriminant analysis (PLS-DA); support vector machines-recursive feature elimination (SVM-RFE); as well as the mean decrease in accuracy and mean decrease in Gini, provided by random forests (RF). Finally, a double cross-validation procedure was applied to minimize the consequence of overfitting. The results revealed that RF with its variable selection techniques and SVM combined with SVM-RFE as a variable selection method, displayed the best results in comparison to other approaches.

Significance We show that materials with different structure and function can emerge from the same starting materials under different environmental conditions, such as order of reactant addition or inclusion of minerals. The discoveries we report were made possible by using analytical tools more common in omics/systems biology for functional and structural characterization, retasked for exploring and manipulating complex reaction networks. We not only demonstrate that environments can differentiate fixed sets of starting materials (both mixtures of pure amino acids and the classic Miller–Urey “prebiotic soup” model), but that this has functional consequences. It has been often said that biology is “chemistry with history” and this work shows how this process can start., Many approaches to the origin of life focus on how the molecules found in biology might be made in the absence of biological processes, from the simplest plausible starting materials. Another approach could be to view the emergence of the chemistry of biology as process whereby the environment effectively directs “primordial soups” toward structure, function, and genetic systems over time. This does not require the molecules found in biology today to be made initially, and leads to the hypothesis that environment can direct chemical soups toward order, and eventually living systems. Herein, we show how unconstrained condensation reactions can be steered by changes in the reaction environment, such as order of reactant addition, and addition of salts or minerals. Using omics techniques to survey the resulting chemical ensembles we demonstrate there are distinct, significant, and reproducible differences between the product mixtures. Furthermore, we observe that these differences in composition have consequences, manifested in clearly different structural and functional properties. We demonstrate that simple variations in environmental parameters lead to differentiation of distinct chemical ensembles from both amino acid mixtures and a primordial soup model. We show that the synthetic complexity emerging from such unconstrained reactions is not as intractable as often suggested, when viewed through a chemically agnostic lens. An open approach to complexity can generate compositional, structural, and functional diversity from fixed sets of simple starting materials, suggesting that differentiation of chemical ensembles can occur in the wider environment without the need for biological machinery.

Summary: Multidrug-resistant bacteria are a major threat to human health, but broad-spectrum\ud antibiotics are losing efficacy. There is a need to screen a given drug against\ud a bacterial infection outside of the laboratory. To address this need, we have designed\ud and built an inexpensive and easy-to-use 3D-printer-based system that\ud allows easily readable quantitative tests for the performance of antibacterial\ud drugs. The platform creates a sterile diagnostic device by using 3D printing,\ud and the device is filled automatically with growth medium, and then antibiotics\ud are sprayed onto the medium by ink-jet technology. The sample for testing can\ud be introduced via a fluid port, and the printer hot bed is used to incubate the device,\ud allowing operation in the field. Combining advantages from various established\ud tests of antibacterial performance, this allows the comparison of a specific\ud antibiotics and bacteria. Also, this system can create and test several antibiotic\ud formulations for therapeutic use, and we demonstrate this potential by investigating\ud a mixture of pathogens that are only killed by a mixture of drugs.