Two Optimal Strategies for Active Learning of Causal Models from Interventional Data

From observational data alone, a causal DAG is only identifiable up to Markov equivalence. Interventional data generally improves identifiability; however, the gain of an intervention strongly depends on the intervention target, that is, the intervened variables. We present active learning (that is, optimal experimental design) strategies calculating optimal interventions for two different learning goals. The first one is a greedy approach using single-vertex interventions that maximizes the number of edges that can be oriented after each intervention. The second one yields in polynomial time a minimum set of targets of arbitrary size that guarantees full identifiability. This second approach proves a conjecture of Eberhardt (2008) indicating the number of unbounded intervention targets which is sufficient and in the worst case necessary for full identifiability. In a simulation study, we compare our two active learning approaches to random interventions and an existing approach, and analyze the influence of estimation errors on the overall performance of active learning

Repeat or not repeat?—Statistical validation of tandem repeat prediction in genomic sequences

Tandem repeats (TRs) represent one of the most prevalent features of genomic sequences. Due to their abundance and functional significance, a plethora of detection tools has been devised over the last two decades. Despite the longstanding interest, TR detection is still not resolved. Our large-scale tests reveal that current detectors produce different, often nonoverlapping inferences, reflecting characteristics of the underlying algorithms rather than the true distribution of TRs in genomic data. Our simulations show that the power of detecting TRs depends on the degree of their divergence, and repeat characteristics such as the length of the minimal repeat unit and their number in tandem. To reconcile the diverse predictions of current algorithms, we propose and evaluate several statistical criteria for measuring the quality of predicted repeat units. In particular, we propose a model-based phylogenetic classifier, entailing a maximum-likelihood estimation of the repeat divergence. Applied in conjunction with the state of the art detectors, our statistical classification scheme for inferred repeats allows to filter out false-positive predictions. Since different algorithms appear to specialize at predicting TRs with certain properties, we advise applying multiple detectors with subsequent filtering to obtain the most complete set of genuine repeat

Multi-metal electrohydrodynamic redox 3d printing at the submicron scale: Microstructure – geometrical gradients – chemical gradients and the resulting mechanical properties

An extensive range of metals can be dissolved and re-deposited in liquid solvents using electrochemistry. We harness this concept for additive manufacturing, demonstrating the focused electrohydrodynamic ejection of metal ions dissolved from sacrificial anodes and their subsequent reduction to elemental metals on the substrate. This technique, termed electrohydrodynamic redox printing (EHD-RP), enables the direct, ink-free fabrication of polycrystalline multi-metal 3D structures without the need for post-print processing. On- the-fly switching and mixing of two or more metals printed from a single multichannel nozzle facilitates a chemical feature size of \u3c400 nm with a spatial resolution of 250 nm at printing speeds of up to 10 voxels per second. The additive control of the chemical architecture of materials provided by EHD-RP unlocks the synthesis of 3D bi-metal structures with programmed local properties and opens new avenues for the direct fabrication of chemically architected materials and devices. Mechanical properties can be locally controlled by alloying, dealloying (resulting in controlled porosity) and grain-size tuning via process control. The properties of EHD-RP are put into perspective by comparing with the most prominent current technologies for metal 3D printing at the nanoscale (Fig. 1). Please click Additional Files below to see the full abstract

Two optimal strategies for active learning of causal models from interventions

Abstract From observational data alone, a causal DAG is in general only identifiable up to Markov equivalence. Interventional data generally improves identifiability; however, the gain of an intervention strongly depends on the intervention target, i.e., the intervened variables. We present active learning strategies calculating optimal interventions for two different learning goals. The first one is a greedy approach using single-vertex interventions that maximizes the number of edges that can be oriented after each intervention. The second one yields in polynomial time a minimum set of targets of arbitrary size that guarantees full identifiability. This second approach proves a conjecture of Eberhard

Near-optimal experimental design for model selection in systems biology

Motivation: Biological systems are understood through iterations of modeling and experimentation. Not all experiments, however, are equally valuable for predictive modeling. This study introduces an efficient method for experimental design aimed at selecting dynamical models from data. Motivated by biological applications, the method enables the design of crucial experiments: it determines a highly informative selection of measurement readouts and time points. Results: We demonstrate formal guarantees of design efficiency on the basis of previous results. By reducing our task to the setting of graphical models, we prove that the method finds a near-optimal design selection with a polynomial number of evaluations. Moreover, the method exhibits the best polynomial-complexity constant approximation factor, unless P = NP. We measure the performance of the method in comparison with established alternatives, such as ensemble non-centrality, on example models of different complexity. Efficient design accelerates the loop between modeling and experimentation: it enables the inference of complex mechanisms, such as those controlling central metabolic operation. Availability: Toolbox ‘NearOED' available with source code under GPL on the Machine Learning Open Source Software Web site (mloss.org). Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics onlin

Bisphosphonates reduce biomaterial turnover in healing of critical-size rat femoral defects.

Treatment of osteoporotic patients with bisphosphonates (BPs) preserves bone mass and microarchitecture. The high prescription rate of the drugs brings about increases in the numbers of fractures and bone defects requiring surgical interventions in these patients. Currently, critical-size defects are filled with biomaterials and healing is supported with bone morphogenetic proteins (BMP). It is hypothesized that BPs interfere with biomaterial turnover during BMP-supported repair of defects filled with β-tricalcium phosphate (βTCP) ceramics. To test this hypothesis, retired breeder rats were ovariectomized ( OVX). After 8 weeks, treatment with alendronate (ALN) commenced. Five weeks later, 6 mm diaphyseal femoral defects were applied and stabilized with locking plates. βTCP cylinders loaded with 1 μg and 10 μg BMP2, 10 μg L51P, an inhibitor of BMP antagonists and 1 μg BMP2/10 μg L51P were fitted into the defects. Femora were collected 16 weeks post-implantation. In groups receiving calcium phosphate implants loaded with 10 μg BMP2 and 1 μg BMP2/10 μg L51P, the volume of bone was increased and βTCP was decreased compared to groups receiving implants with 1 μg BMP2 and 10 μg L51P. Treatment of animals with ALN caused a decrease in βTCP turnover. The results corroborate the synergistic effects of BMP2 and L51P on bone augmentation. Administration of ALN caused a reduction in implant turnover, demonstrating the dependence of βTCP removal on osteoclast activity, rather than on chemical solubility. Based on these data, it is suggested that in patients treated with BPs, healing of biomaterial-filled bone defects may be impaired because of the failure to remove the implant and its replacement by authentic bone

Urinary And Breast Milk Biomarkers To Assess Exposure Ro Naphthalene In Pregnant Women: An Investigation Of Personal And Indoor Air Sources

Naphthalene exposures for most non-occupationally exposed individuals occur primarily indoors at home. Residential indoor sources include pest control products (specifically moth balls), incomplete combustion such as cigarette smoke, woodstoves and cooking, some consumer and building products, and emissions from gasoline sources found in attached garages. The study aim was to assess naphthalene exposure in pregnant women from Canada, using air measurements and biomarkers of exposure