Extracting abundance information from DNA-based data

The accurate extraction of species-abundance information from DNA-based data (metabarcoding, metagenomics) could contribute usefully to diet reconstruction and quantitative food webs, the inference of species interactions, the modelling of population dynamics and species distributions, the biomonitoring of environmental state and change, and the inference of false positives and negatives. However, capture bias, capture noise, species pipeline biases, and pipeline noise all combine to inject error into DNA-based datasets. We focus on methods for correcting the latter two error sources, as the first two are addressed extensively in the ecological literature. To extract abundance information, it is useful to distinguish two concepts. (1) Across-species quantification describes relative species abundances within one sample. (2) In contrast, within-species quantification describes how the abundance of each individual species varies from sample to sample, as in a time series, an environmental gradient, or different experimental treatments. Firstly, we review methods to remove species pipeline biases and pipeline noise. Secondly, we demonstrate experimentally (with a detailed protocol) how to use a 'DNA spike-in' to remove pipeline noise and recover within-species abundance information. We also introduce a statistical estimator that can partially remove pipeline noise from datasets that lack a physical DNA spike-in.

Categories or dimensions? Towards a transdiagnostic treatment of eating psychopathology / ¿Categorías o dimensiones? Hacia un tratamiento transdiagnóstico de la psicopatología alimentaria

The purpose of this research was to contrast the categorial and dimensional approaches within the eating disorders area. Research on the eating problems categorical model reveals vast evidence against its validity: excessive comorbidity, inadequate coverage, diagnostic migration, residual categories, false positives and negatives, etc. The dimensional conceptualization of the eating psychopathology study would achieve more accurate findings by considering eating problems according to the degree in which they manifest, avoiding diagnostics based on arbitrary cut-off points and facilitating the analysis of eating psychopathology at early age, as well as following symptom evolution throughout development. Based on the dimensional model, transdiagnostic perspective has received empirical support, which endorses the use of the transdiagnostic treatment aimed to underlying psychological mechanisms, such as negative affect and emotional dysregulation.ResumenEl propósito de la presente investigación fue contrastar los enfoques categorial y dimensional dentro del área de los trastornos alimentarios. La investigación sobre el modelo categorial de los problemas alimentarios revela un amplio cúmulo de pruebas en contra de su validez, como comorbilidad excesiva, cobertura inadecuada, migración diagnóstica, categorías residuales, falsos positivos y negativos, etc. El estudio de la psicopatología alimentaria desde una conceptuación dimensional permitiría obtener hallazgos más precisos, al considerar a los problemas alimentarios según el grado en el que se presentan, evitar diagnósticos basados en puntos de corte arbitrarios, facilitar su análisis a edad temprana, así como seguir la evolución de los síntomas a lo largo del desarrollo. Apoyada en el modelo dimensional, la perspectiva transdiagnóstica ha recibido respaldo empírico que fundamenta su uso en el tratamiento de los mecanismos psicológicos subyacentes a las problemáticas alimentarias, como el afecto negativo y la desregulación emocional.

Improving Salience Retention and Identification in the Automated Filtering of Event Log Messages

<p>Event log messages are currently the only genuine interface through which computer systems administrators can effectively monitor their systems and assemble a mental perception of system state. The popularisation of the Internet and the accompanying meteoric growth of business-critical systems has resulted in an overwhelming volume of event log messages, channeled through mechanisms whose designers could not have envisaged the scale of the problem. Messages regarding intrusion detection, hardware status, operating system status changes, database tablespaces, and so on, are being produced at the rate of many gigabytes per day for a significant computing environment. Filtering technologies have not been able to keep up. Most messages go unnoticed; no filtering whatsoever is performed on them, at least in part due to the difficulty of implementing and maintaining an effective filtering solution. The most commonly-deployed filtering alternatives rely on regular expressions to match pre-defi ned strings, with 100% accuracy, which can then become ineffective as the code base for the software producing the messages 'drifts' away from those strings. The exactness requirement means all possible failure scenarios must be accurately anticipated and their events catered for with regular expressions, in order to make full use of this technique. Alternatives to regular expressions remain largely academic. Data mining, automated corpus construction, and neural networks, to name the highest-profi le ones, only produce probabilistic results and are either difficult or impossible to alter in any deterministic way. Policies are therefore not supported under these alternatives. This thesis explores a new architecture which utilises rich metadata in order to avoid the burden of message interpretation. The metadata itself is based on an intention to improve end-to-end communication and reduce ambiguity. A simple yet effective filtering scheme is also presented which fi lters log messages through a short and easily-customisable set of rules. With such an architecture, it is envisaged that systems administrators could signi ficantly improve their awareness of their systems while avoiding many of the false-positives and -negatives which plague today's fi ltering solutions.</p>

Improving Salience Retention and Identification in the Automated Filtering of Event Log Messages

<p>Event log messages are currently the only genuine interface through which computer systems administrators can effectively monitor their systems and assemble a mental perception of system state. The popularisation of the Internet and the accompanying meteoric growth of business-critical systems has resulted in an overwhelming volume of event log messages, channeled through mechanisms whose designers could not have envisaged the scale of the problem. Messages regarding intrusion detection, hardware status, operating system status changes, database tablespaces, and so on, are being produced at the rate of many gigabytes per day for a significant computing environment. Filtering technologies have not been able to keep up. Most messages go unnoticed; no filtering whatsoever is performed on them, at least in part due to the difficulty of implementing and maintaining an effective filtering solution. The most commonly-deployed filtering alternatives rely on regular expressions to match pre-defi ned strings, with 100% accuracy, which can then become ineffective as the code base for the software producing the messages 'drifts' away from those strings. The exactness requirement means all possible failure scenarios must be accurately anticipated and their events catered for with regular expressions, in order to make full use of this technique. Alternatives to regular expressions remain largely academic. Data mining, automated corpus construction, and neural networks, to name the highest-profi le ones, only produce probabilistic results and are either difficult or impossible to alter in any deterministic way. Policies are therefore not supported under these alternatives. This thesis explores a new architecture which utilises rich metadata in order to avoid the burden of message interpretation. The metadata itself is based on an intention to improve end-to-end communication and reduce ambiguity. A simple yet effective filtering scheme is also presented which fi lters log messages through a short and easily-customisable set of rules. With such an architecture, it is envisaged that systems administrators could signi ficantly improve their awareness of their systems while avoiding many of the false-positives and -negatives which plague today's fi ltering solutions.</p>

DIAGNÓSTICO COMPARATIVO DE BRUCELOSIS MEDIANTE MÉTODOS SEROLÓGICOS Y MOLECULARES

Brucellosis is an infectious disease that limits livestock development and greatly affects the livestock economy, being considered one of the most important and widely distributed zoonoses worldwide. Early diagnosis of this disease is an essential tool in its control and eradication. The methods recognized by NOM-041-ZOO-1995, such as the card test and complement fixation, present limitations in the diagnosis, compared to the PCR molecular technique. In the present work, a comparative diagnosis of Brucella spp. was performed by PCR amplification of the gene coding for a protein located in the outer membrane (Omp2a) of Brucella spp. and serological tests in blood, milk and cheese samples from goats and cattle. The results showed a higher sensitivity in the detection by PCR technique, while the card test and complement fixation showed inconsistencies due to the occurrence of false positives and negatives. Based on the results, it is suggested to include the PCR technique in the Mexican Official Standard as an objective alternative in the routine diagnosis of brucellosis.

The Misreporting Trade-Off Between List Experiments and Direct Questions in Practice: Partition Validation Evidence from Two Countries

To reduce strategic misreporting on sensitive topics, survey researchers increasingly use list experiments rather than direct questions. However, the complexity of list experiments may increase nonstrategic misreporting. We provide the first empirical assessment of this trade-off between strategic and nonstrategic misreporting. We field list experiments on election turnout in two different countries, collecting measures of respondents’ true turnout. We detail and apply a partition validation method which uses true scores to distinguish true and false positives and negatives for list experiments, thus allowing detection of nonstrategic reporting errors. For both list experiments, partition validation reveals nonstrategic misreporting that is: undetected by standard diagnostics or validation; greater than assumed in extant simulation studies; and severe enough that direct turnout questions subject to strategic misreporting exhibit lower overall reporting error. We discuss how our results can inform the choice between list experiment and direct question for other topics and survey contexts.

Can a quantitative approach be mitigated? Proposals for the application of the "early warnings" required by the new Italian Insolvency Code

In compliance with European regulations, the new Italian "Insolvency Code" introduced new tools to prevent future financial crises in businesses ("early warn-ings"). Their aim is to highlight future insolvency issues, to enable timely action in order to avert the potential crisis for as long as possible.V This mechanism will come into force on 15 August 2020. Based on a previous investigation that identified the most sensitive financial ratios for evaluating a go-ing concern, this study proposes and tests a possible approach which combines generic quantitative indicators with a case-by-case solution. A discriminant analysis was made on a pairwise sample of Italian non-listed small and medium-sized companies (SMEs). The proposed model overcomes the problem that arose from a combined interpretation of the indicators, and also it acts as a tool that can deter-mine the level of risk within each situation. This approach aims to limit the rigidity produced by common quantitative thresholds, thereby reducing false positives and negatives, ensuring an automatic reporting process that can preserve the efficiency of the early warning mechanism. Furthermore, our proposal is better suited to SMEs, since it is based on financial statements rather than forecasts.

Effects of Obstructions on the Accuracy of Bluetooth Contact Tracing

During the COVID-19 pandemic, the idea of facilitating contact tracing using Bluetooth is becoming widespread due to the prevalence of smartphones. The automatic classification of encounters between smartphones is challenging due to variations in signal strength – resulting in high false positive and false negative rates. For example, obstructions between two smartphones can reduce the strength of received signals and thus increase the calculated distance. This can benefit contact tracing by preventing a contact from being added to the database when people are on opposite side of a wall, but can also harm tracing efforts if two people are close together but their bodies reduce the signal between devices. A Raspberry Pi Bluetooth emitter and a phone receiver were placed 1 meter apart, and various obstacles were placed between them to simulate normal obstacles. Drywall and stud walls were shown to be ineffective at reducing Bluetooth signal strength. Cinder block, and especially the human body, were found to effectively lower Bluetooth strength so that the distance estimate was higher. The results of these experiments imply Bluetooth contact tracing will involve many false positives and negatives.

Noisy Pooled PCR for Virus Testing

Fast testing can help mitigate the coronavirus disease 2019 (COVID-19) pandemic. Despite their accuracy for single sample analysis, infectious diseases diagnostic tools, like RT-PCR, require substantial resources to test large populations. We develop a scalable approach for determining the viral status of pooled patient samples. Our approach converts group testing to a linear inverse problem, where false positives and negatives are interpreted as generated by a noisy communication channel, and a message passing algorithm estimates the illness status of patients. Numerical results reveal that our approach estimates patient illness using fewer pooled measurements than existing noisy group testing algorithms. Our approach can easily be extended to various applications, including where false negatives must be minimized. Finally, in a Utopian world we would have collaborated with RT-PCR experts; it is difficult to form such connections during a pandemic. We welcome new collaborators to reach out and help improve this work!