Here is the evidence, now what is the hypothesis? The complementary roles of inductive and hypothesis-driven science in the post-genomic era

Intersection of Immunology and Metabolism in Myocardial Disease

Immunometabolism is an emerging field at the intersection of immunology and metabolism. Immune cell activation plays a critical role in the pathogenesis of cardiovascular diseases and is integral for regeneration during cardiac injury. We currently possess a limited understanding of the processes governing metabolic interactions between immune cells and cardiomyocytes. The impact of this intercellular crosstalk can manifest as alterations to the steady state flux of metabolites and impact cardiac contractile function. Although much of our knowledge is derived from acute inflammatory response, recent work emphasizes heterogeneity and flexibility in metabolism between cardiomyocytes and immune cells during pathological states, including ischemic, cardiometabolic, and cancer-associated disease. Metabolic adaptation is crucial because it influences immune cell activation, cytokine release, and potential therapeutic vulnerabilities. This review describes current concepts about immunometabolic regulation in the heart, focusing on intercellular crosstalk and intrinsic factors driving cellular regulation. We discuss experimental approaches to measure the cardio-immunologic crosstalk, which are necessary to uncover unknown mechanisms underlying the immune and cardiac interface. Deeper insight into these axes holds promise for therapeutic strategies that optimize cardioimmunology crosstalk for cardiac health.

A Perspective on How Fibrinaloid Microclots and Platelet Pathology May be Applied in Clinical Investigations

Microscopy imaging has enabled us to establish the presence of fibrin(ogen) amyloid (fibrinaloid) microclots in a range of chronic, inflammatory diseases. Microclots may also be induced by a variety of purified substances, often at very low concentrations. These molecules include bacterial inflammagens, serum amyloid A, and the S1 spike protein of severe acute respiratory syndrome coronavirus 2. Here, we explore which of the properties of these microclots might be used to contribute to differential clinical diagnoses and prognoses of the various diseases with which they may be associated. Such properties include distributions in their size and number before and after the addition of exogenous thrombin, their spectral properties, the diameter of the fibers of which they are made, their resistance to proteolysis by various proteases, their cross-seeding ability, and the concentration dependence of their ability to bind small molecules including fluorogenic amyloid stains. Measuring these microclot parameters, together with microscopy imaging itself, along with methodologies like proteomics and imaging flow cytometry, as well as more conventional assays such as those for cytokines, might open up the possibility of a much finer use of these microclot properties in generative methods for a future where personalized medicine will be standard procedures in all clotting pathology disease diagnoses.

Fibrinaloid Microclots and Atrial Fibrillation

Atrial fibrillation (AF) is a comorbidity of a variety of other chronic, inflammatory diseases for which fibrinaloid microclots are a known accompaniment (and in some cases, a cause, with a mechanistic basis). Clots are, of course, a well-known consequence of atrial fibrillation. We here ask the question whether the fibrinaloid microclots seen in plasma or serum may in fact also be a cause of (or contributor to) the development of AF. We consider known 'risk factors' for AF, and in particular, exogenous stimuli such as infection and air pollution by particulates, both of which are known to cause AF. The external accompaniments of both bacterial (lipopolysaccharide and lipoteichoic acids) and viral (SARS-CoV-2 spike protein) infections are known to stimulate fibrinaloid microclots when added in vitro, and fibrinaloid microclots, as with other amyloid proteins, can be cytotoxic, both by inducing hypoxia/reperfusion and by other means. Strokes and thromboembolisms are also common consequences of AF. Consequently, taking a systems approach, we review the considerable evidence in detail, which leads us to suggest that it is likely that microclots may well have an aetiological role in the development of AF. This has significant mechanistic and therapeutic implications.

Mycobacterium tuberculosis: immune response, biomarkers, and therapeutic intervention

Although tuberculosis (TB) is an infectious disease, the progression of the disease following Mycobacterium tuberculosis (MTB) infection is closely associated with the host's immune response. In this review, a comprehensive analysis of TB prevention, diagnosis, and treatment was conducted from an immunological perspective. First, we delved into the host's immune response mechanisms against MTB infection as well as the immune evasion mechanisms of the bacteria. Addressing the challenges currently faced in TB diagnosis and treatment, we also emphasized the importance of protein, genetic, and immunological biomarkers, aiming to provide new insights for early and personalized diagnosis and treatment of TB. Building upon this foundation, we further discussed intervention strategies involving chemical and immunological treatments for the increasingly critical issue of drug-resistant TB and other forms of TB. Finally, we summarized TB prevention, diagnosis, and treatment challenges and put forward future perspectives. Overall, these findings provide valuable insights into the immunological aspects of TB and offer new directions toward achieving the WHO's goal of eradicating TB by 2035.

Correlations for untargeted GC × GC-HRTOF-MS metabolomics of colorectal cancer

INTRODUCTION

Modern comprehensive instrumentations provide an unprecedented coverage of complex matrices in the form of high-dimensional, information rich data sets.

OBJECTIVES

In addition to the usual biomarker research that focuses on the detection of the studied condition, we aimed to define a proper strategy to conduct a correlation analysis on an untargeted colorectal cancer case study with a data set of 102 variables corresponding to metabolites obtained from serum samples analyzed with comprehensive two-dimensional gas chromatography coupled to high-resolution time-of-flight mass spectrometry (GC × GC-HRTOF-MS). Indeed, the strength of association existing between the metabolites contains potentially valuable information about the molecular mechanisms involved and the underlying metabolic network associated to a global perturbation, at no additional analytical effort.

METHODS

Following Anscombe's quartet, we took particular attention to four main aspects. First, the presence of non-linear relationships through the comparison of parametric and non-parametric correlation coefficients: Pearson's r, Spearman's rho, Kendall's tau and Goodman-Kruskal's gamma. Second, the visual control of the detected associations through scatterplots and their associated regressions and angles. Third, the effect and handling of atypical samples and values. Fourth, the role of the precision of the data on the attribution of the ranks through the presence of ties.

RESULTS

Kendall's tau was found the method of choice for the data set at hand. Its application highlighted 17 correlations significantly altered in the active state of colorectal cancer (CRC) in comparison to matched healthy controls (HC), from which 10 were specific to this state in comparison to the remission one (R-CRC) investigated on distinct patients. 15 metabolites involved in the correlations of interest, on the 25 unique ones obtained, were annotated (Metabolomics Standards Initiative level 2).

CONCLUSIONS

The metabolites highlighted could be used to better understand the pathology. The systematic investigation of the methodological aspects that we expose allows to implement correlation analysis to various fields and many specific cases.

Accurate Prediction of Momentary Cognition From Intensive Longitudinal Data

BACKGROUND

Deficits in cognitive performance are implicated in the development and maintenance of psychopathology. Emerging evidence further suggests that within-person fluctuations in cognitive performance may represent sensitive early markers of neuropsychiatric decline. Incorporating routine cognitive assessments into standard clinical care-to identify between-person differences and monitor within-person fluctuations-has the potential to improve diagnostic screening and treatment planning. In support of these goals, it is critical to understand to what extent cognitive performance varies under routine, remote assessment conditions (i.e., momentary cognition) in relation to a wide range of possible predictors.

METHODS

Using data-driven, high-dimensional methods, we ranked strong predictors of momentary cognition and evaluated out-of-sample predictive accuracy. Our approach leveraged innovations in digital technology, including ambulatory assessment of cognition and behavior 1) at scale (n = 122 participants, n = 94 females), 2) in naturalistic environments, and 3) within an intensive longitudinal study design (mean = 25.5 assessments/participant).

RESULTS

Reaction time (R2 > 0.70) and accuracy (0.56 >R2 > 0.35) were strongly predicted by age, between-person differences in mean performance, and time of day. Effects of self-reported, intraindividual fluctuations in environmental (e.g., noise) and internal (e.g., stress) states were also observed.

CONCLUSIONS

Our results provide robust estimates of effect size to characterize sources of cognitive variability, to support the identification of optimal windows for psychosocial interventions, and to possibly inform clinical evaluation under remote neuropsychological assessment conditions.

Slowing down decay: biological clocks in personalized medicine

This article discusses so-called biological clocks. These technologies, based on aging biomarkers, trace and measure molecular changes in order to monitor individuals' "true" biological age against their chronological age. Drawing on the concept of decay, and building on ethnographic fieldwork in an academic laboratory and a commercial firm, we analyze the implications of the development and commercialization of biological clocks that can identify when decay is "out of tempo." We show how the building of biological clocks rests on particular forms of knowing decay: In the academic laboratory, researchers focus on endo-processes of decay that are internal to the person, but when the technology moves to the market, the focus shifts as staff bracket decay as exo-processes, which are seen as resulting from a person's lifestyle. As the technology of biological clocks travels from the laboratory to the market of online testing of the consumer's biological age, we observe shifting visions of aging: from an inevitable trajectory of decline to a malleable and plastic one. While decay is an inevitable trajectory starting at birth and ending with death, the commercialization of biological clocks points to ways of stretching time between birth and death as individuals "optimize" their biological age through lifestyle changes. Regardless of admitted uncertainties about what is measured and the connection between maintenance and future health outcomes, the aging person is made responsible for their decaying body and for enacting maintenance to slow down decay. We show how the biological clock's way of "knowing" decay turns aging and its maintenance into a life-long concern and highlight the normative implications of framing decay as malleable and in need of intervention.

Study on Proteomics-Based Aortic Dissection Molecular Markers Using iTRAQ Combined With Label Free Techniques

Background: Aortic dissection refers to the separation of aortic media and extension along the long axis to form the true and false chambers of the aortic wall. 65–70% of the patients died of cardiac tamponade, arrhythmia, dissection rupture, etc. At present, echocardiography, computed tomography angiography (CTA), etc. are the main diagnosis tools for aortic dissection. To date, there is no rapid serum molecular marker that can be used for differential diagnosis and risk assessment.

Objectives: To screen serum molecular markers systematically amid aortic dissection and acute coronary syndrome and to preliminarily identify the pathogenesis of acute aortic dissection.

Methods: Related disputes cases of all hospitals were statistically analyzed for the AAD medical disputes ratio, early death ratio and misdiagnosis ratio from the database of Guangdong Province Medical Disputes Coordination Committee from 2013 to 2017. Serum and Aortic tissues samples were respectively quantified by iTRAQ and label-free analysis, further validated by ELISA and protein verified by immunofluorescence and Western blot from AAD and control patients enrolled from the Zhujiang Hospital of Southern Medical University and Guangdong Province people's Hospital from 2016 to 2018.

Results: AAD cases ratio accounted for 15.29% in all 150 cardiovascular disputes, 59.26% in all cardiovascular death less than 24 h, and 88.89% in the patients who remained undiagnosed at the time of death, 84 proteins (66 and 18 upregulated and downregulated, respectively) were identified by iTRAQ and 16 proteins (9 and 7 upregulated and downregulated, respectively) by Label-free. Nine proteins (Lumican, FGL1, PI16, MMP9, FBN1, MMP2, VWF, MMRN1, and PF4) related to the pathogenesis of aortic dissection were identified by David /Ease and String techniques as candidate biomarkers for verification test. Four proteins (Lumican, FGL1, PI16, and MMP9) were found to be statistically different after ELISA verification. The expression of FGL1, PI16, and MMP9 proteins was pathologically significantly increased except for Lumican. Histologically, TGF-β1, α-SMA, and Collagen1 were also significantly higher in the aortic group.

Conclusion: Lumican, FGL1, PI16, and MMP9 may be potential biomarkers in AAD patients, and the Lumican-mediated TGF-β1 pathway is likely to be involved in the pathogenesis of aortic dissection.

Mechanistic Research for the Student or Educator (Part I of II)

Many discoveries in the biological sciences have emerged from observational studies, but student researchers also need to learn how to design experiments that distinguish correlation from causation. For example, identifying the physiological mechanism of action of drugs with therapeutic potential requires the establishment of causal links. Only by specifically interfering with the purported mechanisms of action of a drug can the researcher determine how the drug causes its physiological effects. Typically, pharmacological or genetic approaches are employed to modify the expression and/or activity of the biological drug target or downstream pathways, to test if the salutary properties of the drug are thereby abolished. However, experimental techniques have caveats that tend to be underappreciated, particularly for newer methods. Furthermore, statistical effects are no guarantor of their biological importance or translatability across models and species. In this two-part series, the caveats and strengths of mechanistic preclinical research are briefly described, using the intuitive example of pharmaceutical drug testing in experimental models of human diseases. Part I focuses on technical practicalities and common pitfalls of cellular and animal models designed for drug testing, and Part II describes in simple terms how to leverage a full-factorial ANOVA, to test for causality in the link between drug-induced activation (or inhibition) of a biological target and therapeutic outcomes. Upon completion of this series, students will have forehand knowledge of technical and theoretical caveats in mechanistic research, and comprehend that “a model is just a model.” These insights can help the new student appreciate the strengths and limitations of scientific research.

The Notable Achievements and the Prospects of Bacterial Pathogen Genomics

Throughout the entirety of human history, bacterial pathogens have played an important role and even shaped the fate of civilizations. The application of genomics within the last 27 years has radically changed the way we understand the biology and evolution of these pathogens. In this review, we discuss how the short- (Illumina) and long-read (PacBio, Oxford Nanopore) sequencing technologies have shaped the discipline of bacterial pathogen genomics, in terms of fundamental research (i.e., evolution of pathogenicity), forensics, food safety, and routine clinical microbiology. We have mined and discuss some of the most prominent data/bioinformatics resources such as NCBI pathogens, PATRIC, and Pathogenwatch. Based on this mining, we present some of the most popular sequencing technologies, hybrid approaches, assemblers, and annotation pipelines. A small number of bacterial pathogens are of very high importance, and we also present the wealth of the genomic data for these species (i.e., which ones they are, the number of antimicrobial resistance genes per genome, the number of virulence factors). Finally, we discuss how this discipline will probably be transformed in the near future, especially by transitioning into metagenome-assembled genomes (MAGs), thanks to long-read sequencing.

Question-driven stepwise experimental discoveries in biochemistry: two case studies

Philosophers of science diverge on the question what drives the growth of scientific knowledge. Most of the twentieth century was dominated by the notion that theories propel that growth whereas experiments play secondary roles of operating within the theoretical framework or testing theoretical predictions. New experimentalism, a school of thought pioneered by Ian Hacking in the early 1980s, challenged this view by arguing that theory-free exploratory experimentation may in many cases effectively probe nature and potentially spawn higher evidence-based theories. Because theories are often powerless to envisage workings of complex biological systems, theory-independent experimentation is common in the life sciences. Some such experiments are triggered by compelling observation, others are prompted by innovative techniques or instruments, whereas different investigations query big data to identify regularities and underlying organizing principles. A distinct fourth type of experiments is motivated by a major question. Here I describe two question-guided experimental discoveries in biochemistry: the cyclic adenosine monophosphate mediator of hormone action and the ubiquitin-mediated system of protein degradation. Lacking underlying theories, antecedent data bases, or new techniques, the sole guides of the two discoveries were respective substantial questions. Both research projects were similarly instigated by theory-free exploratory experimentation and continued in alternating phases of results-based interim working hypotheses, their examination by experiment, provisional hypotheses again, and so on. These two cases designate theory-free, question-guided, stepwise biochemical investigations as a distinct subtype of the new experimentalism mode of scientific enquiry.

The "'Crisis' Crisis" in psychology

The recent trend to label dilemmas in psychology as "crises" is insidious. The "'Crisis' Crisis" in psychology can distract us from actionable practices. As a case in point, "The Generalizability Crisis" offers the valuable central thesis that verbal-quantitative gaps imperil psychological science. Focusing on the key issues rather than crisis narratives can lead to progress in our discourse and research.

Metabolomics as an Important Tool for Determining the Mechanisms of Human Skeletal Muscle Deconditioning

Muscle deconditioning impairs both locomotor function and metabolic health, and is associated with reduced quality life and increased mortality rates. Despite an appreciation of the existence of phenomena such as muscle anabolic resistance, mitophagy, and insulin resistance with age and disease in humans, little is known about the mechanisms responsible for these negative traits. With the complexities surrounding these unknowns and the lack of progress to date in development of effective interventions, there is a need for alternative approaches. Metabolomics is the study of the full array of metabolites within cells or tissues, which collectively constitute the metabolome. As metabolomics allows for the assessment of the cellular metabolic state in response to physiological stimuli, any chronic change in the metabolome is likely to reflect adaptation in the physiological phenotype of an organism. This, therefore, provides a holistic and unbiased approach that could be applied to potentially uncover important novel facets in the pathophysiology of muscle decline in ageing and disease, as well as identifying prognostic markers of those at risk of decline. This review will aim to highlight the current knowledge and potential impact of metabolomics in the study of muscle mass loss and deconditioning in humans and will highlight key areas for future research.

MassGenie: A Transformer-Based Deep Learning Method for Identifying Small Molecules from Their Mass Spectra

The 'inverse problem' of mass spectrometric molecular identification ('given a mass spectrum, calculate/predict the 2D structure of the molecule whence it came') is largely unsolved, and is especially acute in metabolomics where many small molecules remain unidentified. This is largely because the number of experimentally available electrospray mass spectra of small molecules is quite limited. However, the forward problem ('calculate a small molecule's likely fragmentation and hence at least some of its mass spectrum from its structure alone') is much more tractable, because the strengths of different chemical bonds are roughly known. This kind of molecular identification problem may be cast as a language translation problem in which the source language is a list of high-resolution mass spectral peaks and the 'translation' a representation (for instance in SMILES) of the molecule. It is thus suitable for attack using the deep neural networks known as transformers. We here present MassGenie, a method that uses a transformer-based deep neural network, trained on ~6 million chemical structures with augmented SMILES encoding and their paired molecular fragments as generated in silico, explicitly including the protonated molecular ion. This architecture (containing some 400 million elements) is used to predict the structure of a molecule from the various fragments that may be expected to be observed when some of its bonds are broken. Despite being given essentially no detailed nor explicit rules about molecular fragmentation methods, isotope patterns, rearrangements, neutral losses, and the like, MassGenie learns the effective properties of the mass spectral fragment and valency space, and can generate candidate molecular structures that are very close or identical to those of the 'true' molecules. We also use VAE-Sim, a previously published variational autoencoder, to generate candidate molecules that are 'similar' to the top hit. In addition to using the 'top hits' directly, we can produce a rank order of these by 'round-tripping' candidate molecules and comparing them with the true molecules, where known. As a proof of principle, we confine ourselves to positive electrospray mass spectra from molecules with a molecular mass of 500Da or lower, including those in the last CASMI challenge (for which the results are known), getting 49/93 (53%) precisely correct. The transformer method, applied here for the first time to mass spectral interpretation, works extremely effectively both for mass spectra generated in silico and on experimentally obtained mass spectra from pure compounds. It seems to act as a Las Vegas algorithm, in that it either gives the correct answer or simply states that it cannot find one. The ability to create and to 'learn' millions of fragmentation patterns in silico, and therefrom generate candidate structures (that do not have to be in existing libraries) directly, thus opens up entirely the field of de novo small molecule structure prediction from experimental mass spectra.

A pioneer study on human 3-nitropropionic acid intoxication: Contributions from metabolomics

The neurotoxin 3-nitropropionic acid (3-NPA) is an inhibitor of succinate dehydrogenase, an enzyme participating both in the citric acid cycle and the mitochondrial respiratory chain. In human intoxications, it produces symptoms such as vomiting and stomach ache in mild cases, and dystonia, coma, and sometimes death in severe cases. We report the results from a liquid chromatography-Orbitrap mass spectrometry metabolomics study mapping the metabolic impacts of 3-NPA intoxication in plasma, urine, and cerebrospinal fluid (CSF) samples of a Norwegian boy initially suspected to suffer from a mitochondrial disease. In addition to the identification of 3-NPA, our findings included a large number of annotated/identified altered metabolites (80, 160, and 62 in plasma, urine, and CSF samples, respectively) belonging to different compound classes, for example, amino acids, fatty acids, and purines and pyrimidines. Our findings indicated protective mechanisms to attenuate the toxic effects of 3-NPA (e.g., decreased oleamide), occurrence of increased oxidative stress in the patient (such as increased free fatty acids and hypoxanthine) and energy turbulence caused by the intoxication (e.g., increased succinate). To our knowledge, this is the first case of 3-NPA intoxication reported in Norway and the first published metabolomics study of human 3-NPA intoxication worldwide. The unexpected identification of 3-NPA illustrates the importance for health care providers to consider intake-related intoxications during diagnostic evaluations, treatment and follow-up examinations for neurotoxicity and a wide range of metabolic derangements.

Intelligent host engineering for metabolic flux optimisation in biotechnology

Optimising the function of a protein of length N amino acids by directed evolution involves navigating a 'search space' of possible sequences of some 20N. Optimising the expression levels of P proteins that materially affect host performance, each of which might also take 20 (logarithmically spaced) values, implies a similar search space of 20P. In this combinatorial sense, then, the problems of directed protein evolution and of host engineering are broadly equivalent. In practice, however, they have different means for avoiding the inevitable difficulties of implementation. The spare capacity exhibited in metabolic networks implies that host engineering may admit substantial increases in flux to targets of interest. Thus, we rehearse the relevant issues for those wishing to understand and exploit those modern genome-wide host engineering tools and thinking that have been designed and developed to optimise fluxes towards desirable products in biotechnological processes, with a focus on microbial systems. The aim throughput is 'making such biology predictable'. Strategies have been aimed at both transcription and translation, especially for regulatory processes that can affect multiple targets. However, because there is a limit on how much protein a cell can produce, increasing kcat in selected targets may be a better strategy than increasing protein expression levels for optimal host engineering.

The Transporter-Mediated Cellular Uptake and Efflux of Pharmaceutical Drugs and Biotechnology Products: How and Why Phospholipid Bilayer Transport Is Negligible in Real Biomembranes

Over the years, my colleagues and I have come to realise that the likelihood of pharmaceutical drugs being able to diffuse through whatever unhindered phospholipid bilayer may exist in intact biological membranes in vivo is vanishingly low. This is because (i) most real biomembranes are mostly protein, not lipid, (ii) unlike purely lipid bilayers that can form transient aqueous channels, the high concentrations of proteins serve to stop such activity, (iii) natural evolution long ago selected against transport methods that just let any undesirable products enter a cell, (iv) transporters have now been identified for all kinds of molecules (even water) that were once thought not to require them, (v) many experiments show a massive variation in the uptake of drugs between different cells, tissues, and organisms, that cannot be explained if lipid bilayer transport is significant or if efflux were the only differentiator, and (vi) many experiments that manipulate the expression level of individual transporters as an independent variable demonstrate their role in drug and nutrient uptake (including in cytotoxicity or adverse drug reactions). This makes such transporters valuable both as a means of targeting drugs (not least anti-infectives) to selected cells or tissues and also as drug targets. The same considerations apply to the exploitation of substrate uptake and product efflux transporters in biotechnology. We are also beginning to recognise that transporters are more promiscuous, and antiporter activity is much more widespread, than had been realised, and that such processes are adaptive (i.e., were selected by natural evolution). The purpose of the present review is to summarise the above, and to rehearse and update readers on recent developments. These developments lead us to retain and indeed to strengthen our contention that for transmembrane pharmaceutical drug transport "phospholipid bilayer transport is negligible".

Who cares for women with children? Crossing the bridge between disciplines

It has long been acknowledged that women with children require social support to promote their health and wellbeing, as well as that of their children. However, the dominant conceptualizations of support have been heavily influenced by Western family norms. The consequence, at best, has been to stifle our understanding of the nature and consequences of support for mothers and children. At worst, it has led to systematic discrimination negatively impacting maternal-child health. To fully engage with the complexities of social support, we must take multidisciplinary or interdisciplinary approaches spanning diverse cultural and geographical perspectives. However, multidisciplinary knowledge-processing can be challenging, and it is often unclear how different studies from different disciplines relate. To address this, we outline two epistemological frameworks-the scientific approach and Tinbergen's four questions-that can be useful tools in connecting research across disciplines. In this theme issue on 'Multidisciplinary perspectives on social support and maternal-child health', we attempt to foster multidisciplinary thinking by presenting work from a diverse range of disciplines, populations and cultures. Our hope is that these tools, along with papers in this issue, help to build a holistic understanding of social support and its consequences for mothers and their children. Overall, a multidisciplinary perspective points to how the responsibility of childrearing should not fall solely onto mothers. Indeed, this multidisciplinary issue demonstrates that successful childrearing is consistently an activity shared beyond the mother and the nuclear family: an insight that is crucial to harnessing the potential of social support to improve maternal-child health. This article is part of the theme issue 'Multidisciplinary perspectives on social support and maternal-child health'.

A Protocol for Teaching Basic Next Generation Sequencing (NGS) Analysis Skills to Undergraduate Students Using Bash and R

Next generation sequencing (NGS) is routinely used to study crucial aspects of biological systems, including differentially expressed genes identification, microbiome taxonomic composition and structure, enrichment of specific cellular functions in a given environment, and so on. Current research laboratories are facing a serious lack in the availability of properly trained researchers capable of carrying out basic NGS analysis computational pipelines. This reflects a gap in most academic curricula concerning the basics of NGS data management, analysis, and interpretation. Indeed, most of the times, the knowledge necessary to undertake these tasks is acquired through the use of one-shot tutorial, without a thorough explanation of the concepts behind the practical steps. With this protocol we aim to fill this gap by providing teachers with a hands-on protocol to guide bachelor and master students in a more focused analysis of NGS data, from basic and standard operations on sequencing reads (e.g., quality check and trimming) to more advanced analysis techniques (e.g., data normalization).

Bio-inspired evaporation from shaped interfaces: an experimental study

Evaporative interfaces help process heat and substances in a variety of technical realms, from electronic to architectural applications. Because geometry affects the hydraulics, thermal properties and aerodynamics of evaporative devices, their performance can be tuned through design. While non-smooth interfaces are widely exploited to enhance transfer passively, surface area extension in packed volumes is a predominant line of research. This leaves aerodynamic structure-transfer relations and the impact of geometry itself unclear. Meanwhile, protrusions in leaves such as lobes and toothed margins have been associated with enhanced vapor dissipation. This experimental study explores the design space of leaf-inspired structures with evaporating protrusions. Three sets of water-absorbing models with fixed evaporating surface area and unlimited hydraulic supply were tested: (1) paper strips with dimension-equivalent protrusions of varied shape and degree of elongation; (2) cellulose sponges with the same designs as their cross-sectional profile, extruded three-dimensionally; (3) ceramic tiles with grooves of varied cross-section, conceived as building elements for evaporative cooling. Overall, results demonstrate that protrusions affect mass transfer rate and surface temperatures and can be integrated in the design of evaporative exchangers with non-smooth geometries. For the paper models, evaporation rate correlated with protrusion aspect ratio, supporting a functional interpretation of leaf design and its utilization in low-wind plate-fin exchangers. However, the same transfer enhancement was not regained from simply extruding an effective design into three-dimensions. For the ceramic tiles, geometry-driven differences in evaporation depended on the aerodynamic roughness and size of the grooved pattern, and on ventilation. Their outdoor thermal behavior was complex due to a multifaceted interaction with the environment and geometry-related factors such as self-shading and thermal mass. Ultimately, this design effort illustrates the potential of structured interfaces for evaporative exchange and thermoregulating the built environment.

Anthracyclins Increase PUFAs: Potential Implications in ER Stress and Cell Death

Metabolic and personalized interventions in cancer treatment require a better understanding of the relationship between the induction of cell death and metabolism. Consequently, we treated three primary liver cancer cell lines with two anthracyclins (doxorubicin and idarubin) and studied the changes in the lipidome. We found that both anthracyclins in the three cell lines increased the levels of polyunsaturated fatty acids (PUFAs) and alkylacylglycerophosphoethanolamines (etherPEs) with PUFAs. As PUFAs and alkylacylglycerophospholipids with PUFAs are fundamental in lipid peroxidation during ferroptotic cell death, our results suggest supplementation with PUFAs and/or etherPEs with PUFAs as a potential general adjuvant of anthracyclins. In contrast, neither the markers of de novo lipogenesis nor cholesterol lipids presented the same trend in all cell lines and treatments. In agreement with previous research, this suggests that modulation of the metabolism of cholesterol could be considered a specific adjuvant of anthracyclins depending on the type of tumor and the individual. Finally, in agreement with previous research, we found a relationship across the different cell types between: (i) the change in endoplasmic reticulum (ER) stress, and (ii) the imbalance between PUFAs and cholesterol and saturated lipids. In the light of previous research, this imbalance partially explains the sensitivity to anthracyclins of the different cells. In conclusion, our results suggest that the modulation of different lipid metabolic pathways may be considered for generalized and personalized metabochemotherapies.

Computer Vision, Machine Learning, and the Promise of Phenomics in Ecology and Evolutionary Biology

For centuries, ecologists and evolutionary biologists have used images such as drawings, paintings and photographs to record and quantify the shapes and patterns of life. With the advent of digital imaging, biologists continue to collect image data at an ever-increasing rate. This immense body of data provides insight into a wide range of biological phenomena, including phenotypic diversity, population dynamics, mechanisms of divergence and adaptation, and evolutionary change. However, the rate of image acquisition frequently outpaces our capacity to manually extract meaningful information from images. Moreover, manual image analysis is low-throughput, difficult to reproduce, and typically measures only a few traits at a time. This has proven to be an impediment to the growing field of phenomics – the study of many phenotypic dimensions together. Computer vision (CV), the automated extraction and processing of information from digital images, provides the opportunity to alleviate this longstanding analytical bottleneck. In this review, we illustrate the capabilities of CV as an efficient and comprehensive method to collect phenomic data in ecological and evolutionary research. First, we briefly review phenomics, arguing that ecologists and evolutionary biologists can effectively capture phenomic-level data by taking pictures and analyzing them using CV. Next we describe the primary types of image-based data, review CV approaches for extracting them (including techniques that entail machine learning and others that do not), and identify the most common hurdles and pitfalls. Finally, we highlight recent successful implementations and promising future applications of CV in the study of phenotypes. In anticipation that CV will become a basic component of the biologist’s toolkit, our review is intended as an entry point for ecologists and evolutionary biologists that are interested in extracting phenotypic information from digital images.

A protet-based, protonic charge transfer model of energy coupling in oxidative and photosynthetic phosphorylation

Textbooks of biochemistry will explain that the otherwise endergonic reactions of ATP synthesis can be driven by the exergonic reactions of respiratory electron transport, and that these two half-reactions are catalyzed by protein complexes embedded in the same, closed membrane. These views are correct. The textbooks also state that, according to the chemiosmotic coupling hypothesis, a (or the) kinetically and thermodynamically competent intermediate linking the two half-reactions is the electrochemical difference of protons that is in equilibrium with that between the two bulk phases that the coupling membrane serves to separate. This gradient consists of a membrane potential term Δψ and a pH gradient term ΔpH, and is known colloquially as the protonmotive force or pmf. Artificial imposition of a pmf can drive phosphorylation, but only if the pmf exceeds some 150-170mV; to achieve in vivo rates the imposed pmf must reach 200mV. The key question then is 'does the pmf generated by electron transport exceed 200mV, or even 170mV?' The possibly surprising answer, from a great many kinds of experiment and sources of evidence, including direct measurements with microelectrodes, indicates it that it does not. Observable pH changes driven by electron transport are real, and they control various processes; however, compensating ion movements restrict the Δψ component to low values. A protet-based model, that I outline here, can account for all the necessary observations, including all of those inconsistent with chemiosmotic coupling, and provides for a variety of testable hypotheses by which it might be refined.

The Importance of Childhood for Adult Health and Development-Study Protocol of the Zurich Longitudinal Studies

Evidence is accumulating that individual and environmental factors in childhood and adolescence should be considered when investigating adult health and aging-related processes. The data required for this is gathered by comprehensive long-term longitudinal studies. This article describes the protocol of the Zurich Longitudinal Studies (ZLS), a set of three comprehensive cohort studies on child growth, health, and development that are currently expanding into adulthood. Between 1954 and 1961, 445 healthy infants were enrolled in the first ZLS cohort. Their physical, motor, cognitive, and social development and their environment were assessed comprehensively across childhood, adolescence, and into young adulthood. In the 1970s, two further cohorts were added to the ZLS and assessed with largely matched study protocols: Between 1974 and 1979, the second ZLS cohort included 265 infants (103 term-born and 162 preterm infants), and between 1970 and 2002, the third ZLS cohort included 327 children of participants of the first ZLS cohort. Since 2019, the participants of the three ZLS cohorts have been traced and invited to participate in a first wave of assessments in adulthood to investigate their current health and development. This article describes the ZLS study protocol and discusses opportunities, methodological and conceptual challenges, and limitations arising from a long-term longitudinal cohort recruited from a study about development in early life. In the future, the ZLS will provide data to investigate childhood antecedents of adult health outcomes and, ultimately, will help respond to the frequent call of scientists to shift the focus of aging research into the first decades of life and, thus, to take a lifespan perspective on aging.

Metabolic impact of weight variations in Icelandic horses

Background

Insulin dysregulation (ID) is an equine endocrine disorder, which is often accompanied by obesity and various metabolic perturbations. The relationship between weight variations and fluctuations of the insulin response to oral glucose tests (OGT) as well as the metabolic impact of ID have been described previously. The present study seeks to characterize the concomitant metabolic impact of variations in the insulin response and bodyweight during repeated OGTs using a metabolomics approach.

Methods

Nineteen Icelandic horses were subjected to five OGTs over one year and their bodyweight, insulin and metabolic response were monitored. Analysis of metabolite concentrations depending on time (during the OGT), relative bodyweight (rWeight; defined as the bodyweight at one OGT divided by the mean bodyweight across all OGTs) and relative insulin response (rAUC_ins; defined accordingly from the area under the insulin curve during OGT) was performed using linear models. Additionally, the pathways significantly associated with time, rWeight and rAUC_ins were identified by rotation set testing.

Results

The results suggested that weight gain and worsening of ID activate distinct metabolic pathways. The metabolic profile associated with weight gain indicated an increased activation of arginase, while the pathways associated with time and rAUC_ins were consistent with the expected effect of glucose and insulin, respectively. Overall, more metabolites were significantly associated with rWeight than with rAUC_ins.

Physicochemical and metabolic constraints for thermodynamics-based stoichiometric modelling under mesophilic growth conditions

Metabolic engineering in the post-genomic era is characterised by the development of new methods for metabolomics and fluxomics, supported by the integration of genetic engineering tools and mathematical modelling. Particularly, constraint-based stoichiometric models have been widely studied: (i) flux balance analysis (FBA) (in silico), and (ii) metabolic flux analysis (MFA) (in vivo). Recent studies have enabled the incorporation of thermodynamics and metabolomics data to improve the predictive capabilities of these approaches. However, an in-depth comparison and evaluation of these methods is lacking. This study presents a thorough analysis of two different in silico methods tested against experimental data (metabolomics and ¹³C-MFA) for the mesophile Escherichia coli. In particular, a modified version of the recently published matTFA toolbox was created, providing a broader range of physicochemical parameters. Validating against experimental data allowed the determination of the best physicochemical parameters to perform the TFA (Thermodynamics-based Flux Analysis). An analysis of flux pattern changes in the central carbon metabolism between ¹³C-MFA and TFA highlighted the limited capabilities of both approaches for elucidating the anaplerotic fluxes. In addition, a method based on centrality measures was suggested to identify important metabolites that (if quantified) would allow to further constrain the TFA. Finally, this study emphasised the need for standardisation in the fluxomics community: novel approaches are frequently released but a thorough comparison with currently accepted methods is not always performed.

Biotechnology has benefitted from the development of high throughput methods characterising living systems at different levels (e.g. concerning genes or proteins), allowing the industrial production of chemical commodities. Recently, focus has been placed on determining reaction rates (or metabolic fluxes) in the metabolic network of certain microorganisms, in order to identify bottlenecks hindering their exploitation. Two main approaches are commonly used, termed metabolic flux analysis (MFA) and flux balance analysis (FBA), based on measuring and estimating fluxes, respectively. While the influence of thermodynamics in living systems was accepted several decades ago, its application to study biochemical networks has only recently been enabled. In this sense, a multitude of different approaches constraining well-established modelling methods with thermodynamics has been suggested. However, physicochemical parameters are generally not properly adjusted to the experimental conditions, which might affect their predictive capabilities. In this study, we have explored the reliability of currently available tools by investigating the impact of varying said parameters in the simulation of metabolic fluxes and metabolite concentration values. Additionally, our in-depth analysis allowed us to highlight limitations and potential solutions that should be considered in future studies.

An Overview of the Computational Models Dealing with the Regulatory ceRNA Mechanism and ceRNA Deregulation in Cancer

Pools of RNA molecules can act as competing endogenous RNAs (ceRNAs) and indirectly alter their expression levels by competitively binding shared microRNAs. This ceRNA cross talk yields an additional posttranscriptional regulatory layer, which plays key roles in both physiological and pathological processes. MicroRNAs can act as decoys by binding multiple RNAs, as well as RNAs can act as ceRNAs by competing for binding multiple microRNAs, leading to many cross talk interactions that could favor significant large-scale effects in spite of the weakness of single interactions. Identifying and studying these extended ceRNA interaction networks could provide a global view of the fine-tuning gene regulation in a wide range of biological processes and tumor progressions. In this chapter, we review current progress of predicting ceRNA cross talk, by summarizing the most up-to-date databases, which collect computationally predicted and/or experimentally validated miRNA-target and ceRNA-ceRNA interactions, as well as the widespread computational methods for discovering and modeling possible evidences of ceRNA-ceRNA interaction networks. These methods can be grouped in two categories: statistics-based methods exploit multivariate analysis to build ceRNA networks, by considering the miRNA expression levels when evaluating miRNA sponging relationships; mathematical methods build deterministic or stochastic models to analyze and predict the behavior of ceRNA cross talk.

Deep learning and generative methods in cheminformatics and chemical biology: navigating small molecule space intelligently

The number of 'small' molecules that may be of interest to chemical biologists - chemical space - is enormous, but the fraction that have ever been made is tiny. Most strategies are discriminative, i.e. have involved 'forward' problems (have molecule, establish properties). However, we normally wish to solve the much harder generative or inverse problem (describe desired properties, find molecule). 'Deep' (machine) learning based on large-scale neural networks underpins technologies such as computer vision, natural language processing, driverless cars, and world-leading performance in games such as Go; it can also be applied to the solution of inverse problems in chemical biology. In particular, recent developments in deep learning admit the in silico generation of candidate molecular structures and the prediction of their properties, thereby allowing one to navigate (bio)chemical space intelligently. These methods are revolutionary but require an understanding of both (bio)chemistry and computer science to be exploited to best advantage. We give a high-level (non-mathematical) background to the deep learning revolution, and set out the crucial issue for chemical biology and informatics as a two-way mapping from the discrete nature of individual molecules to the continuous but high-dimensional latent representation that may best reflect chemical space. A variety of architectures can do this; we focus on a particular type known as variational autoencoders. We then provide some examples of recent successes of these kinds of approach, and a look towards the future.

The biology of ergothioneine, an antioxidant nutraceutical

Ergothioneine (ERG) is an unusual thio-histidine betaine amino acid that has potent antioxidant activities. It is synthesised by a variety of microbes, especially fungi (including in mushroom fruiting bodies) and actinobacteria, but is not synthesised by plants and animals who acquire it via the soil and their diet, respectively. Animals have evolved a highly selective transporter for it, known as solute carrier family 22, member 4 (SLC22A4) in humans, signifying its importance, and ERG may even have the status of a vitamin. ERG accumulates differentially in various tissues, according to their expression of SLC22A4, favouring those such as erythrocytes that may be subject to oxidative stress. Mushroom or ERG consumption seems to provide significant prevention against oxidative stress in a large variety of systems. ERG seems to have strong cytoprotective status, and its concentration is lowered in a number of chronic inflammatory diseases. It has been passed as safe by regulatory agencies, and may have value as a nutraceutical and antioxidant more generally.

Toxicity of airborne particles-established evidence, knowledge gaps and emerging areas of importance

Epidemiological research has taught us a great deal about the health effects of airborne particulate matter (PM), particularly cardiorespiratory effects of combustion-related particles. This has been matched by toxicological research to define underlying mechanistic pathways. To keep abreast of the substantial challenges that air pollution continues to throw at us requires yet more strides to be achieved. For example, being aware of the most toxic components/sources and having a definitive idea of the range of associated disease outcomes. This review discusses approaches designed to close some of these knowledge gaps. These include a focus on particles arising from non-exhaust PM at the roadside and microplastics-both of which are becoming more relevant in the light of a shift in PM composition in response to global pressure to reduce combustion emissions. The application of hypothesis-free approaches in both mechanistic studies and epidemiology in unveiling unexpected relationships and generating novel insights is also discussed. Previous work, strengthening the evidence for both the adverse effects and benefits of intervention tell us that the sooner we act to close knowledge gaps, increase awareness and develop creative solutions, the sooner we can reduce the public health burden attributable to these complex and insidious environmental pollutants. This article is part of a discussion meeting issue 'Air quality, past present and future'.

Caring for data: Value creation in a data-intensive research laboratory

Drawing upon ethnographic observations of staff working within a research laboratory built around research and clinical data from twins, this article analyzes practices underlying the production and maintenance of a research database. While critical data studies have discussed different forms of 'data work' through which data are produced and turned into effective research resources, in this paper we foreground a specific form of data work, namely the affective and attentive relationships that humans build with data. Building on STS and feminist scholarship that highlights the importance of care in scientific work, we capture this specific form of data work as care. Treating data as relational entities, we discuss a set of caring practices that staff employ to produce and maintain their data, as well as the hierarchical and institutional arrangements within which these caring practices take place. We show that through acts of caring, that is, through affective and attentive engagements, researchers build long-term relationships with the data they help produce, and feel responsible for its flourishing and growth. At the same time, these practices of care - which we found to be gendered and valued differently from other practices within formal and informal reward systems - help to make data valuable for the institution. In this manner, care for data is an important practice of valuation and valorisation within data-intensive research that has so far received little explicit attention in scholarship and professional research practice.

Stress hypothesis overload: 131 hypotheses exploring the role of stress in tradeoffs, transitions, and health

Stress is ubiquitous and thus, not surprisingly, many hypotheses and models have been created to better study the role stress plays in life. Stress spans fields and is found in the literature of biology, psychology, psychophysiology, sociology, economics, and medicine, just to name a few. Stress, and the hypothalamic-pituitaryadrenal/interrenal (HPA/I) axis and sympathetic nervous system (SNS), are involved in a multitude of behaviors and physiological processes, including life-history and ecological tradeoffs, developmental transitions, health, and survival. The goal of this review is to highlight and summarize the large number of available hypotheses and models, to aid in comparative and interdisciplinary thinking, and to increase reproducibility by a) discouraging hypothesizing after results are known (HARKing) and b) encouraging a priori hypothesis testing. For this review I collected 214 published hypotheses or models dealing broadly with stress. In the main paper, I summarized and categorized 131 of those hypotheses and models which made direct connections among stress and/or HPA/I and SNS, tradeoffs, transitions, and health. Of those 131, the majority made predictions about reproduction (n = 43), the transition from health to disease (n = 38), development (n = 23), and stress coping (n = 18). Additional hypotheses were classified as stage-spanning or models (n = 37). The additional 83 hypotheses found during searches were tangentially related, or pertained to immune function or oxidative stress, and these are listed separately. Many of the hypotheses share underlying rationale and suggest similar, if not identical, predictions, and are thus not mutually exclusive; some hypotheses spanned classification categories. Some of the hypotheses have been tested multiple times, whereas others have only been examined a few times. It is the hope that multi-disciplinary stress researchers will begin to harmonize their naming of hypotheses in the literature so as to build a clearer picture of how stress impacts various outcomes across fields. The paper concludes with some considerations and recommendations for robust testing of stress hypotheses.

Getting Beyond the Toy Domain. Meditations on David Deamer's "Assembling Life"

David Deamer has written another book, Assembling Life, on the origin of life. It is unapologetically polemic, presenting Deamer's view that life originated in fresh water hydrothermal fields on volcanic islands on early Earth, arguing that this provided a unique environment not just for organic chemistry but for the self-assembling structure that drive that chemistry and form the basis of structure in life. It is worth reading, it is an advance in the field, but is it convincing? I argue that the Origin of Life field as a whole is unconvincing, generating results in Toy Domains that cannot be scaled to any real world scenario. I suggest that, by analogy with the history of artificial intelligence and solar astronomy, we need much more scale, and fundamentally new ideas, to take the field forward.

Problem formulation and phenotypic characterisation for the development of novel crops

Phenotypic characterisation provides important information about novel crops that helps their developers to make technical and commercial decisions. Phenotypic characterisation comprises two activities. Product characterisation checks that the novel crop has the qualities of a viable product-the intended traits have been introduced and work as expected, and no unintended changes have been made that will adversely affect the performance of the final product. Risk assessment evaluates whether the intended and unintended changes are likely to harm human health or the environment. Product characterisation follows the principles of problem formulation, namely that the characteristics required in the final product are defined and criteria to decide whether the novel crop will have these properties are set. The hypothesis that the novel crop meets the criteria are tested during product development. If the hypothesis is corroborated, development continues, and if the hypothesis is falsified, the product is redesigned or its development is halted. Risk assessment should follow the same principles. Criteria that indicate the crop poses unacceptable risk should be set, and the hypothesis that the crop does not possess those properties should be tested. However, risk assessment, particularly when considering unintended changes introduced by new plant breeding methods such as gene editing, often ignores these principles. Instead, phenotypic characterisation seeks to catalogue all unintended changes by profiling methods and then proceeds to work out whether any of the changes are important. This paper argues that profiling is an inefficient and ineffective method of phenotypic characterisation for risk assessment. It discusses reasons why profiling is favoured and corrects some misconceptions about problem formulation.

Juvenile-onset and adult-onset demodicosis in dogs in the UK: prevalence and breed associations

OBJECTIVES

To explore epidemiological features of demodicosis relevant to UK veterinary general practitioners. Breed risk factors were proposed as distinct between juvenile-onset and adult-onset disease.

MATERIALS AND METHODS

The study used anonymised clinical data on dogs under primary veterinary care at practices enrolled in the UK VetCompass Programme. Case inclusion required recording of a final demodicosis diagnosis for a dermatological condition that was present during the 2013 study period. Risk factor analysis used multivariable logistic regression modelling.

RESULTS

In dogs aged <2 years (juvenile-onset), the 1-year period prevalence was 0.48% (95% confidence interval: 0.45 to 0.52). Compared with crossbred dogs, seven breeds showed increased odds of diagnosis with demodex: British bulldog, Staffordshire bull terrier, Chinese shar-pei, dogue de Bordeaux, pug, French bulldog and boxer. Additionally, six breeds showed reduced odds of juvenile demodicosis: Lhasa apso, bichon frise, Labrador retriever, German shepherd dog, shih-tzu and Chihuahua. In dogs aged >4 years (adult-onset), the 1-year period prevalence was 0.05% (95% confidence interval: 0.0.04 to 0.06). Six breeds showed increased odds of demodicosis compared with crossbred dogs: Chinese shar-pei, shih-tzu, West Highland white terrier, pug, boxer and Border terrier.

CLINICAL SIGNIFICANCE

Juvenile-onset demodicosis is much more common (about 10 times higher) than the adult-onset form. Knowledge of the predisposed breeds for these two presentations can assist with diagnosis and support the concept of distinct aetiopathogenetic phenotypes.

Immune Biomarkers for Diagnosis and Treatment Monitoring of Tuberculosis: Current Developments and Future Prospects

Tuberculosis (TB) treatment monitoring is paramount to clinical decision-making and the host biomarkers appears to play a significant role. The currently available diagnostic technology for TB detection is inadequate. Although GeneXpert detects total DNA present in the sample regardless live or dead bacilli present in clinical samples, all the commercial tests available thus far have low sensitivity. Humoral responses against Mycobacterium tuberculosis (Mtb) antigens are generally low, which precludes the use of serological tests for TB diagnosis, prognosis, and treatment monitoring. Mtb-specific CD4⁺ T cells correlate with Mtb antigen/bacilli burden and hence might serve as good biomarkers for monitoring treatment progress. Omics-based techniques are capable of providing a more holistic picture for disease mechanisms and are more accurate in predicting TB disease outcomes. The current review aims to discuss some of the recent advances on TB biomarkers, particularly host biomarkers that have the potential to diagnose and differentiate active TB and LTBI as well as their use in disease prognosis and treatment monitoring.

The role and robustness of the Gini coefficient as an unbiased tool for the selection of Gini genes for normalising expression profiling data

We recently introduced the Gini coefficient (GC) for assessing the expression variation of a particular gene in a dataset, as a means of selecting improved reference genes over the cohort ('housekeeping genes') typically used for normalisation in expression profiling studies. Those genes (transcripts) that we determined to be useable as reference genes differed greatly from previous suggestions based on hypothesis-driven approaches. A limitation of this initial study is that a single (albeit large) dataset was employed for both tissues and cell lines. We here extend this analysis to encompass seven other large datasets. Although their absolute values differ a little, the Gini values and median expression levels of the various genes are well correlated with each other between the various cell line datasets, implying that our original choice of the more ubiquitously expressed low-Gini-coefficient genes was indeed sound. In tissues, the Gini values and median expression levels of genes showed a greater variation, with the GC of genes changing with the number and types of tissues in the data sets. In all data sets, regardless of whether this was derived from tissues or cell lines, we also show that the GC is a robust measure of gene expression stability. Using the GC as a measure of expression stability we illustrate its utility to find tissue- and cell line-optimised housekeeping genes without any prior bias, that again include only a small number of previously reported housekeeping genes. We also independently confirmed this experimentally using RT-qPCR with 40 candidate GC genes in a panel of 10 cell lines. These were termed the Gini Genes. In many cases, the variation in the expression levels of classical reference genes is really quite huge (e.g. 44 fold for GAPDH in one data set), suggesting that the cure (of using them as normalising genes) may in some cases be worse than the disease (of not doing so). We recommend the present data-driven approach for the selection of reference genes by using the easy-to-calculate and robust GC.

Language of ADHD in Adults on Social Media

Objective: We computationally analyze the language of social media users diagnosed with ADHD to understand what they talk about, and how their language is correlated with users' characteristics such as personality and temporal orientation. Method: We analyzed approximately 1.3 million tweets written by 1,399 Twitter users with self-reported diagnoses of ADHD, comparing their posts with those used by a control set matched by age, gender, and period of activity. Results: Users with ADHD are found to be less agreeable, more open, to post more often, and to use more negations, hedging, and swear words. Posts are suggestive of themes of emotional dysregulation, self-criticism, substance abuse, and exhaustion. A machine learning model can predict which of these Twitter users has ADHD with an out-of-sample AUC of .836. Conclusion: Based on this emerging technology, conjectures of future uses of social media by researchers and clinicians to better understand the naturalistic manifestations and sequelae of ADHD.

Perspective: Dimensions of the scientific method

The scientific method has been guiding biological research for a long time. It not only prescribes the order and types of activities that give a scientific study validity and a stamp of approval but also has substantially shaped how we collectively think about the endeavor of investigating nature. The advent of high-throughput data generation, data mining, and advanced computational modeling has thrown the formerly undisputed, monolithic status of the scientific method into turmoil. On the one hand, the new approaches are clearly successful and expect the same acceptance as the traditional methods, but on the other hand, they replace much of the hypothesis-driven reasoning with inductive argumentation, which philosophers of science consider problematic. Intrigued by the enormous wealth of data and the power of machine learning, some scientists have even argued that significant correlations within datasets could make the entire quest for causation obsolete. Many of these issues have been passionately debated during the past two decades, often with scant agreement. It is proffered here that hypothesis-driven, data-mining-inspired, and "allochthonous" knowledge acquisition, based on mathematical and computational models, are vectors spanning a 3D space of an expanded scientific method. The combination of methods within this space will most certainly shape our thinking about nature, with implications for experimental design, peer review and funding, sharing of result, education, medical diagnostics, and even questions of litigation.

Tear Metabolomics in Dry Eye Disease: A Review

Dry eye disease (DED) is a multifactorial syndrome that can be caused by alteration in the quality or quantity of the precorneal tear film. It is considered one of the most common ocular conditions leading patients to seek eye care. The current method for diagnostic evaluations and follow-up examinations of DED is a combination of clinical signs and symptoms determined by clinical tests and questionnaires, respectively. The application of powerful omics technologies has opened new avenues toward analysis of subjects in health and disease. Metabolomics is a new emerging and complementary research discipline to all modern omics in the comprehensive analysis of biological systems. The identification of distinct metabolites and integrated metabolic profiles in patients can potentially inform clinicians at an early stage or during monitoring of disease progression, enhancing diagnosis, prognosis, and the choice of therapy. In ophthalmology, metabolomics has gained considerable attention over the past decade but very limited such studies have been reported on DED. This paper aims to review the application of tear metabolomics in DED.