Aristotle: stratified causal discovery for omics data

Background There has been a simultaneous increase in demand and accessibility across genomics, transcriptomics, proteomics and metabolomics data, known as omics data. This has encouraged widespread application of omics data in life sciences, from personalized medicine to the discovery of underlying pathophysiology of diseases. Causal analysis of omics data may provide important insight into the underlying biological mechanisms. Existing causal analysis methods yield promising results when identifying potential general causes of an observed outcome based on omics data. However, they may fail to discover the causes specific to a particular stratum of individuals and missing from others. Methods To fill this gap, we introduce the problem of stratified causal discovery and propose a method, Aristotle, for solving it. Aristotle addresses the two challenges intrinsic to omics data: high dimensionality and hidden stratification. It employs existing biological knowledge and a state-of-the-art patient stratification method to tackle the above challenges and applies a quasi-experimental design method to each stratum to find stratum-specific potential causes. Results Evaluation based on synthetic data shows better performance for Aristotle in discovering true causes under different conditions compared to existing causal discovery methods. Experiments on a real dataset on Anthracycline Cardiotoxicity indicate that Aristotle’s predictions are consistent with the existing literature. Moreover, Aristotle makes additional predictions that suggest further investigations.

Determinants of Sustainable Cross-Border Cooperation: A Structural Model for the Hungarian Context Using the PLS-SEM Methodology

The importance of this research stems from the need to ensure the sustainability of cross-border cooperation through a better understanding of its determinants and causal relationships. While having common features and patterns, cross-border cooperation is always expressed through the relations of specific countries and peoples. Therefore, based upon the PLS-SEM methodology, the authors consider the fundamental factors influencing the external cooperation of Hungary’s transboundary regions. The advantage of the PLS-SEM method is that it enables researchers to simultaneously identify and approximate hidden connections between input data and to construct a regression model describing the relationship between input data. Despite widespread application in economic studies, the authors have not found the use of PLS-SEM for studying cross-border cooperation issues in the current scientific literature. The authors have built a model to assess the hidden factors of cross-border cooperation and to identify the indirect influence of certain factors. The novelty of the research is to identify the determinants of sustainable cross-border cooperation and the relationship between them in a multi-level system of cross-border interaction between businesses, people, and the State. In the Hungarian context, transport infrastructure and business travel are shown to have a direct positive impact on cross-border cooperation. For the first time, tourism and socio-economic conditions have been shown to have powerful but indirect impacts. This work could be the beginning of gathering new evidence on the determinants and causation of cross-border cooperation in the context of other countries. An important finding of the study is the growing importance of indicators of the new, post-industrial economy. As for recommendations, the authors focus on state, regional, and municipal support measures, awareness of the possibilities of cross-border cooperation, the need to develop e-commerce, and alternative energy as a modern basis for converting Hungary’s cross-border position into a competitive advantage.

Micronutrients in Food Production: What Can We Learn from Natural Ecosystems?

Soil micronutrients limit crop productivity in many regions worldwide, and micronutrient deficiencies affect over two billion people globally. Microbial biofertilizers could combat these issues by inoculating arable soils with microorganisms that mobilize micronutrients, increasing their availability to crop plants in an environmentally sustainable and cost-effective manner. However, the widespread application of biofertilizers is limited by complex micronutrient–microbe–plant interactions, which reduce their effectiveness under field conditions. Here, we review the current state of seven micronutrients in food production. We examine the mechanisms underpinning microbial micronutrient mobilization in natural ecosystems and synthesize the state-of-knowledge to improve our overall understanding of biofertilizers in food crop production. We demonstrate that, although soil micronutrient concentrations are strongly influenced by soil conditions, land management practices can also substantially affect micronutrient availability and uptake by plants. The effectiveness of biofertilizers varies, but several lines of evidence indicate substantial benefits in co-applying biofertilizers with conventional inorganic or organic fertilizers. Studies of micronutrient cycling in natural ecosystems provide examples of microbial taxa capable of mobilizing multiple micronutrients whilst withstanding harsh environmental conditions. Research into the mechanisms of microbial nutrient mobilization in natural ecosystems could, therefore, yield effective biofertilizers to improve crop nutrition under global changes.

Mapping the broadband circular dichroism of copolymer films with supramolecular chirality in time and space

Measurements of the electronic circular dichroism (CD) are highly sensitive to the absolute configuration and conformation of chiral molecules and supramolecular assemblies and have therefore found widespread application in the chemical and biological sciences. Here, we demonstrate an approach to simultaneously follow changes in the CD and absorption response of photoexcited systems over the ultraviolet−visible spectral range with 100 fs time resolution. We apply the concept to chiral polyfluorene copolymer thin films and track their electronic relaxation in detail. The transient CD signal stems from the supramolecular response of the system and provides information regarding the recovery of the electronic ground state. This allows for a quantification of singlet−singlet annihilation and charge-pair formation processes. Spatial mapping of chiral domains on femtosecond time scales with a resolution of 50 μm and diffraction-limited steady-state imaging of the circular dichroism and the circularly polarised luminescence (CPL) of the films is demonstrated.

Application of copper(II)-based chemicals induces CH3Br and CH3Cl emissions from soil and seawater

Methyl bromide (CH3Br) and methyl chloride (CH3Cl) are major carriers of atmospheric bromine and chlorine, respectively, which can catalyze stratospheric ozone depletion. However, in our current understanding, there are missing sources associated with these two species. Here we investigate the effect of copper(II) on CH3Br and CH3Cl production from soil, seawater and model organic compounds: catechol (benzene-1,2-diol) and guaiacol (2-methoxyphenol). We show that copper sulfate (CuSO4) enhances CH3Br and CH3Cl production from soil and seawater, and it may be further amplified in conjunction with hydrogen peroxide (H2O2) or solar radiation. This represents an abiotic production pathway of CH3Br and CH3Cl perturbed by anthropogenic application of copper(II)-based chemicals. Hence, we suggest that the widespread application of copper(II) pesticides in agriculture and the discharge of anthropogenic copper(II) to the oceans may account for part of the missing sources of CH3Br and CH3Cl, and thereby contribute to stratospheric halogen load.

Bayesian updating and sequential testing: overcoming inferential limitations of screening tests

Background Bayes’ theorem confers inherent limitations on the accuracy of screening tests as a function of disease prevalence. Herein, we establish a mathematical model to determine whether sequential testing with a single test overcomes the aforementioned Bayesian limitations and thus improves the reliability of screening tests. Methods We use Bayes’ theorem to derive the positive predictive value equation, and apply the Bayesian updating method to obtain the equation for the positive predictive value (PPV) following repeated testing. We likewise derive the equation which determines the number of iterations of a positive test needed to obtain a desired positive predictive value, represented graphically by the tablecloth function. Results For a given PPV ($$\rho$$ ρ ) approaching k, the number of positive test iterations needed given a prevalence of disease ($$\phi$$ ϕ ) is: $$n_i =\lim _{\rho \rightarrow k}\left\lceil \frac{ln\left[ \frac{\rho (\phi -1)}{\phi (\rho -1)}\right] }{ln\left[ \frac{a}{1-b}\right] }\right\rceil \qquad \qquad (1)$$ n i = lim ρ → k l n ρ ( ϕ - 1 ) ϕ ( ρ - 1 ) l n a 1 - b ( 1 ) where $$n_i$$ n i = number of testing iterations necessary to achieve $$\rho$$ ρ , the desired positive predictive value, ln = the natural logarithm, a = sensitivity, b = specificity, $$\phi$$ ϕ = disease prevalence/pre-test probability and k = constant. Conclusions Based on the aforementioned derivation, we provide reference tables for the number of test iterations needed to obtain a $$\rho (\phi )$$ ρ ( ϕ ) of 50, 75, 95 and 99% as a function of various levels of sensitivity, specificity and disease prevalence/pre-test probability. Clinical validation of these concepts needs to be obtained prior to its widespread application.

Biological Stabilisers in Earthen Construction: A Mechanistic Understanding of their Response to Water-Ingress

Earthen construction is re-gaining popularity as an ecological and economical alternative to contemporary building materials. While building with earth offers several benefits, its performance due to water ingress is a concern for its widespread application. This limitation is often solved by adding chemical stabilisers such as Portland cement and hydraulic lime. Chemical stabilisers are a subject of widespread debate as they increase the cost and embodied energy of the structure, and reduce the desirable characteristics of raw or unstabilised earth. This along with perceived environmental performance, renewability, and proven effectiveness in traditional earthen construction has led to a growing interest in biological or organic stabilisers. Although the strengthening mechanism of biological stabilisers is widely covered in scientific studies, discussion regarding the water-resistance is limited. This review aggregates the research from the field of earthen construction and geotechnical engineering and extends it to explain the possible mechanism responsible for the water-resistance behaviour of biologically stabilised earthen materials. This study includes a wide range of traditional and industrial biological stabilisers derived from animals (cow-dung, casein, chitosan), plants (starch, guar gum, cactus mucilage, lignin, tannin) seaweeds (alginate, agar, carrageen) and microbes (xanthan gum, gellan gum). A conceptual model of water-ingress in unstabilised earthen blocks is proposed and the response of biological stabiliser to water ingress and related physico-chemical and physical factors is discussed using the model at microscale (stabiliser interaction with clay, sand) and macroscale (hydraulic conductivity of block). Properties of stabilisers such as hydrophobicity, stability under wet conditions or interaction with cations have a dominant effect on the overall response to water ingress. Key gaps have been identified in the existing knowledge that are necessary to investigate in order to understand the water-resistance behaviour comprehensively. The study concludes with a brief assessment of biological stabilisers based on their performance and feasibility to use in contemporary earthen construction.

Management-Oriented Upgrade and Construction of Urban Green Space Management System in Wuxi

With the widespread application of Geographic Information System (GIS) technology in urban green space management system, the refinement of greening management puts forward the demand for upgrading the system. Taking the construction of urban green space management system in Wuxi as an example, this paper reverses the conventional operation of building database before system, and puts forward the method of management-oriented system upgrading. Through the research on the status quo of urban greening management, starting from the analysis of management requirements, the management requirements are transformed into system design requirements, so that the system upgrade is guided by the urban greening management requirements. It solves the problems of large amount of and time-consuming data input during the upgrading and construction of the system, which leads to long process and data lag when it is put into use. After the rapid upgrade, along with the refinement of daily management, the system has been continuously improved, and has received good results in the depth and breadth of data.

Using Machine Learning to Greatly Accelerate Path Integral Ab Initio Molecular Dynamics

Ab initio molecular dynamics (AIMD) has become one of the most popular and robust approaches for modeling complicated chemical, liquid, and material systems. However, the formidable computational cost often limits its widespread application in simulations of the largest scale systems. The situation becomes even more severe in cases where the hydrogen nuclei may be better described as quantized particles using a path integral representation. Here, we present a computational approach that combines machine learning with recent advances in path integral contraction schemes, and we achieve a two-orders-of-magnitude acceleration over direct path integral AIMD simulation while at the same time maintaining its accuracy.

Generation of bright monomeric red fluorescent proteins via computational design of enhanced chromophore packing

Red fluorescent proteins (RFPs) have found widespread application in chemical and biological research due to their longer emission wavelengths. Here, we use computational protein design to increase the quantum yield...