Learning to Race through Coordinate Descent Bayesian Optimisation

In the automation of many kinds of processes, the observable outcome can often be described as the combined effect of an entire sequence of actions, or controls, applied throughout its execution. In these cases, strategies to optimise control policies for individual stages of the process might not be applicable, and instead the whole policy might have to be optimised at once. On the other hand, the cost to evaluate the policy's performance might also be high, being desirable that a solution can be found with as few interactions as possible with the real system. We consider the problem of optimising control policies to allow a robot to complete a given race track within a minimum amount of time. We assume that the robot has no prior information about the track or its own dynamical model, just an initial valid driving example. Localisation is only applied to monitor the robot and to provide an indication of its position along the track's centre axis. We propose a method for finding a policy that minimises the time per lap while keeping the vehicle on the track using a Bayesian optimisation (BO) approach over a reproducing kernel Hilbert space. We apply an algorithm to search more efficiently over high-dimensional policy-parameter spaces with BO, by iterating over each dimension individually, in a sequential coordinate descent-like scheme. Experiments demonstrate the performance of the algorithm against other methods in a simulated car racing environment.Comment: Accepted as conference paper for the 2018 IEEE International Conference on Robotics and Automation (ICRA

Flexible and user friendly tools for the incorporation of fluxomics data into metabolic models

The measurement of fluxes and the understanding of their control are at the core of Metabolic Engineering (ME). In this context, this work presents two integrated open- source software tools that allow to perform tasks of metabolic flux analysis (MFA). Both are platform independent, written in Java, and interact with the OptFlux framework [1], which also facilitates their communication (Figure 1). OptFlux is a modular open-source software that incorporates tools for strain optimization, i.e., the identification of ME targets. It also provides tools to use stoichiometric metabolic models for phenotype simulation of both wild-type and mutant organisms, using methods such as the well known Flux Balance Analysis (FBA). Graphical user interfaces are made available for every operation and to check the results that are obtained. Moreover, a network visualization system is offered, where simulation results can be added to overlap the network graph. The developed tools exploit OptFluxâ??s capabilities in terms of model interaction, simulation methods and visualization features. The first proposed software, named MetabolIc NEtwork Ratio AnaLysis (MiNeRAl) (Figure 1, bottom), aims at analyzing labeling experiments to infer flux constraints that for stoichiometric models. From a set of measurements of a 13C-labelling experiment, mass isotopomer distribution vectors (MDV) are calculated. If aminoacids are measured, the measured fragments, coupled with a carbon transition map provided by the user, are used to determine their precursors, and the corresponding MDVs are calculated. Based on the set of MDVs, the software uses the carbon transitions to determine the flux ratios that produce a given metabolite through the different pathways. These ratios are probabilistic equations that translate how the 13C-labeling pattern is distributed throughout the metabolic network [2]. Since the calculation of the flux ratios is independent of the flux distribution, this software can be used independently of other flux calculation processes, and the ratios can be further exploited to reduce the degrees of freedom of systems obtained in other MFA approaches [3,4]. The main differentiating characteristics of this tool are, besides being usr-friendly, the fact that it is generic for any type of metabolite fragmentation originating from GC-MS techniques and metabolic network topology. Furthermore, the software is also able to investigate what flux ratio constraints are possible to be inferred for a certain experiment beforehand. On the other hand, the second software application here described, jMFA (Figure 1, top), is focused on using different types of experimental flux data to constrain metabolic models and improve their predictions with a variety of tools. It allows users to define constraints associated with measured fluxes and/ or flux ratios, together with environmental conditions (e.g. media) and reaction/ gene knockouts. The application identifies the set of applicable methods based on the constraints defined from user inputs, allowing to select the desired approach, encompassing algebraic and constraint- based simulation methods (such as Flux Balance Analysis and its variants). Anytime a set of constraints is selected, the software calculates the degrees of freedom of the configured system, and updates the admissible methods depending on whether the system is underdetermined, determined or overdetermined, as shown in Figure 1. A method to perform robustness analysis is also implemented. The integration of jMFA within the OptFlux framework allows the use of different model formats and the integration with complementary methods for phenotype simulation and visualization of the results. Moreover, the flux ratio constraints can be obtained from previous calculations in MiNeRAl, or manually defined by the user. The first option provides a straightforward way to integrate both applications in a ME workflow

Uncovering the metabolic capacities of H. pylori 26695 using 13C labeling experiments

The determination of nutritional requirements of pathogenic organisms is of great significance for understanding host-pathogen interactions. Despite the knowledge obtained so far concerning amino acid requirements in H. pylori, it is still unclear which are the metabolic pathways used for biosynthesis and catabolism. Thus, information on the carbon flow in this organism is required. Glutamate is a very important metabolite in bacterial metabolism that can be used as a carbon and nitrogen source. 13C flux analysis has been largely applied to characterize phenotypes by quantifying in vivo the carbon fluxes. One of the most important applications of this approach is the identification of active pathways in less-studied organisms. Thus, in order to clarify the metabolic pathways used by H. pylori 26695, 13C labeling experiments with 13C-glutamate were conducted and labeled amino acids in biomass hydrolysates were analyzed by GC-MS. The obtained results confirmed L-glutamate as a potential sole and effective carbon source for H. pylori. Overall, all non-essential amino acids, except proline, presented a 13C labeling pattern. We hypothesized that L-proline is produced from L-arginine, while L-alanine is probably produced from pyruvate by alanine dehydrogenase. Additionally, the full usage of complete TCA cycle, under the conditions used, was also demonstrated

Ectopic ossification presenting as osteoid metaplasia in a salivary mucocele in a Shih Tzu dog

<p>Abstract</p> <p>Background</p> <p>Salivary mucocele is an accumulation of saliva in a single or multiloculated cavity lined by connective tissue that is contiguous to a salivary gland-duct complex and is the most common condition affecting the salivary glands in dogs. Occasionally, different types of metaplastic lesions, such as squamous and osseous metaplasia - which are rare lesions in animals - can be observed in association with salivary mucocele.</p> <p>Case presentation</p> <p>A right facial enlargement was suddenly observed in a 4-year-old non-spayed female Shih-Tzu dog. The lesion presented itself as a soft and fluctuant mass located in the right side of the face near to the neck. Histologically, the mass consisted of a cavitary formation without an epithelial lining. Additionally, microscopic examination revealed the presence of osteoid-producing cells which gave rise to areas of bone formation, probably induced by irritation due to the presence sialoliths. Such cells and bone formations were also present in the cavity wall, consequently leading us to classify the condition as a salivary mucocele with osseous metaplasia.</p> <p>Conclusions</p> <p>In the present case, the pathogenesis was probably associated with the presence of sialoliths, which can behave as etiological agents for the metaplastic lesion. The occurrence of osteoid metaplasia is a rare peculiar condition in the canine salivar y gland, and due to the rarity and lack of information about this specific disease, no clinical data can yet be associated with the development of salivary mucocele with osseous metaplasia in dogs.</p

Can large language models democratize access to dual-use biotechnology?

Large language models (LLMs) such as those embedded in 'chatbots' are accelerating and democratizing research by providing comprehensible information and expertise from many different fields. However, these models may also confer easy access to dual-use technologies capable of inflicting great harm. To evaluate this risk, the 'Safeguarding the Future' course at MIT tasked non-scientist students with investigating whether LLM chatbots could be prompted to assist non-experts in causing a pandemic. In one hour, the chatbots suggested four potential pandemic pathogens, explained how they can be generated from synthetic DNA using reverse genetics, supplied the names of DNA synthesis companies unlikely to screen orders, identified detailed protocols and how to troubleshoot them, and recommended that anyone lacking the skills to perform reverse genetics engage a core facility or contract research organization. Collectively, these results suggest that LLMs will make pandemic-class agents widely accessible as soon as they are credibly identified, even to people with little or no laboratory training. Promising nonproliferation measures include pre-release evaluations of LLMs by third parties, curating training datasets to remove harmful concepts, and verifiably screening all DNA generated by synthesis providers or used by contract research organizations and robotic cloud laboratories to engineer organisms or viruses.Comment: 6 pages, 0 figure

BioDR : semantic indexing networks for biomedical document retrieval

In Biomedical research, retrieving documents that match an interesting query is a task performed quite frequently. Typically, the set of obtained results is extensive containing many non-interesting documents and consists in a flat list, i.e., not organized or indexed in any way. This work proposes BioDR, a novel approach that allows the semantic indexing of the results of a query, by identifying relevant terms in the documents. These terms emerge from a process of Named Entity Recognition that annotates occurrences of biological terms (e.g. genes or proteins) in abstracts or full-texts. The system is based on a learning process that builds an Enhanced Instance Retrieval Network (EIRN) from a set of manually classified documents, regarding their relevance to a given problem. The resulting EIRN implements the semantic indexing of documents and terms, allowing for enhanced navigation and visualization tools, as well as the assessment of relevance for new documents.Fundação para a Ciência e a Tecnologia (FCT)Maria Barbeito” contract XuntaHUELLA financed by the Consellería de Sanidade (Xunta de Galicia de Galicia

Biomedical text mining applied to document retrieval and semantic indexing

In Biomedical research, the ability to retrieve the adequate information from the ever growing literature is an extremely important asset. This work provides an enhanced and general purpose approach to the process of document retrieval that enables the filtering of PubMed query results. The system is based on semantic indexing providing, for each set of retrieved documents, a network that links documents and relevant terms obtained by the annotation of biological entities (e.g. genes or proteins). This network provides distinct user perspectives and allows navigation over documents with similar terms and is also used to assess document relevance. A network learning procedure, based on previous work from e-mail spam filtering, is proposed, receiving as input a training set of manually classified documents

Extramedullary hematopoiesis in a case of benign mixed mammary tumor in a female dog: cytological and histopathological assessment

<p>Abstract</p> <p>Backgroud</p> <p>Extramedullary hematopoiesis (EMH) is defined as the presence of hematopoietic stem cells such as erythroid and myeloid lineage plus megakaryocytes in extramedullary sites like liver, spleen and lymph nodes and is usually associated with either bone marrow or hematological disorders. Mammary EMH is a rare condition either in human and veterinary medicine and can be associated with benign mixed mammary tumors, similarly to that described in this case.</p> <p>Case presentation</p> <p>Hematopoietic stem cells were found in a benign mixed mammary tumor of a 7-year-old female mongrel dog that presents a nodule in the left inguinal mammary gland. The patient did not have any hematological abnormalities. Cytological evaluation demonstrated two distinct cell populations, composed of either epithelial or mesenchymal cells, sometimes associated with a fibrillar acidophilic matrix, apart from megakaryocytes, osteoclasts, metarubricytes, prorubricytes, rubricytes, rubriblasts, promyelocytes, myeloblasts. Histological examination confirmed the presence of an active hematopoietic bone marrow within the bone tissue of a benign mammary mixed tumor.</p> <p>Conclusions</p> <p>EMH is a rare condition described in veterinary medicine that can be associated with mammary mixed tumors. It's detection can be associated with several neoplastic and non-neoplastic mammary lesions, i.e. osteosarcomas, mixed tumors and bone metaplasia.</p

A 1H-NMR-based metabolomic analysis of propolis from Santa Catarina state

16th IUFoST World Congress of Food Science and Technology: Addressing Global Food Security and Wellness through Food Science and TechnologyPropolis is a resinous biomass produced by honeybees from exudates of local flora. It has been used since ancient times in folk medicine and in recent years has been added to foods and beverages to improve health and prevent diseases. The chemical composition of propolis is highly variable and depends on the climate, season, specie of bee, and mainly the local flora visited by bees to collect resin. In order to identify groups of chemical similarity among samples (n=20 autumn, n=16 winter, n=19 spring, n=17 summer) of propolis produced in Santa Catarina (SC) state (southern Brazil - 2010), lyophilized ethanolic extracts (200 mg/ml, EtOH 70%, v/v) were solubilized in MeOD3 (700l) and analyzed by NMR spectroscopy. One-dimensional 1HNMR spectra were acquired at a magnetic field strength of 500,13/125,03 MHz using a Varian Inova 500 MHz equipment and standard conditions of data acquisition. The 1H-NMR peak list data set was processed under MetaboAnalyst 2.0. suite, computing the resonances at 0.80- 12ppm spectral window. Principal Components Analysis (PCA) score scatter plots (PC1 88.2% x PC2 2.2%) clearly demonstrated samples discriminated mainly according to the season of production. These results suggest that not only geographical origin is important for the classification of propolis, but the seasonal effects as well. Since seasons directly influence the flora available from where bees collect resin, the propolis chemical profile can be significantly modified over the seasons even from a same geographical origin.info:eu-repo/semantics/publishedVersio

A machine learning and chemometrics assisted interpretation of spectroscopic data: a NMR-based metabolomics platform for the assessment of Brazilian propolis

In this work, a metabolomics dataset from 1H nuclear magnetic resonance spectroscopy of Brazilian propolis was analyzed using machine learning algorithms, including feature selection and classification methods. Partial least square-discriminant analysis (PLS-DA), random forest (RF), and wrapper methods combining decision trees and rules with evolutionary algorithms (EA) showed to be complementary approaches, allowing to obtain relevant information as to the importance of a given set of features, mostly related to the structural fingerprint of aliphatic and aromatic compounds typically found in propolis, e.g., fatty acids and phenolic compounds. The feature selection and decision tree-based algorithms used appear to be suitable tools for building classification models for the Brazilian propolis metabolomics regarding its geographic origin, with consistency, high accuracy, and avoiding redundant information as to the metabolic signature of relevant compounds.The work is partially funded by ERDF -European Regional Development Fund through the COMPETE Programme (operational programme for competitiveness) and by National Funds through the FCT (Portuguese Foundation for Science and Technology) within projects ref. COMPETE FCOMP-01-0124-FEDER-015079 and PEstOE/ EEI/UI0752/2011. RC's work is funded by a PhD grant from the Portuguese FCT ( ref. SFRH/BD/66201/2009)