Antidepressants for treatment of depression in primary care:A systematic review and meta-analysis

published_or_final_versio

Machine Learning to Quantitate Neutrophil NETosis

We introduce machine learning (ML) to perform classifcation and quantitation of images of nuclei from human blood neutrophils. Here we assessed the use of convolutional neural networks (CNNs) using free, open source software to accurately quantitate neutrophil NETosis, a recently discovered process involved in multiple human diseases. CNNs achieved \u3e94% in performance accuracy in diferentiating NETotic from non-NETotic cells and vastly facilitated dose-response analysis and screening of the NETotic response in neutrophils from patients. Using only features learned from nuclear morphology, CNNs can distinguish between NETosis and necrosis and between distinct NETosis signaling pathways, making them a precise tool for NETosis detection. Furthermore, by using CNNs and tools to determine object dispersion, we uncovered diferences in NETotic nuclei clustering between major NETosis pathways that is useful in understanding NETosis signaling events. Our study also shows that neutrophils from patients with sickle cell disease were unresponsive to one of two major NETosis pathways. Thus, we demonstrate the design, performance, and implementation of ML tools for rapid quantitative and qualitative cell analysis in basic science

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Eco-evolutionary dynamics in high dimensions

With the advent of many new and revolutionary technologies (e.g. cheap high-throughput sequencing, precise gene editing, and more), biology has become a much more quantitative science over the past few decades. With these technologies in hand, we find ourselves able to probe some of the “fundamental” questions and assumptions that undergird the fields of evolution and ecology, both in controlled laboratory environments and in natural settings. However, with these new, quantitative observations, it is clear that some of the frameworks we use to think about biological populations are insufficient to describe relatively simple scenarios.One of the key assumptions shared by population genetics and theoretical ecology is that these two fields are distinct. In other words, it has been taken for granted that ecological and evolutionary processes act separately and on disparate timescales. However, this may not necessarily be the case, where even in controlled laboratory evolution experiments, ecological structure frequently evolves on human observable times. This leads to an interesting (and broad) set of questions – what are the new timescales to consider in a joint eco-evolutionary process? How does eco-evolutionary feedback affect known observables? What new observables might be relevant? And, importantly, what should we find surprising in such a setting?The contents of this dissertation hope to start to answer some of these questions by proposing and analyzing relatively simple models of eco-evolutionary dynamics. Since the task involves combining models that fall under the distinct classes of population genetics and ecology, the resulting joint models are necessarily more complex. However, by taking cues from statistical physics, I study these models in an explicitly high-dimensional setting, finding some forms of simplification.First, inspired by experimental observations of diversification resulting from the evolution of novel resource preferences, I (in joint work with Benjamin Good and Oskar Hallatschek) propose a minimal model of evolution in the setting of resource consumption with trade-offs. This model combines aspects of niche construction theory from the realm of ecology, with directional selection from the realm of population genetics. We study the low and high dimensional behavior of the model and describe its relatively simple steady state behavior which is dominated by resource generalists.Second, I extend this model to include epistasis, or ‘rugged’ trade-offs. I show that the simple behavior of the non-epistatic model yields to a richer phase diagram when there is even weak epistasis. I show that in the many resource limit, the resource generalist state becomes ‘fragile’ to small epistatic fitness differences. This results in a transition in which the steady state gives way to a state of ‘punctuated equilibrium’ in which the ecosystem spends long times waiting for fitness mutations which bring about rapid rearrangement of the resource strategies of resident strains. This can be understood in light of the form of the Lyapunov function, which naturally separates into fitness specific and ecology specific components.Finally, I propose a simple model of predator-prey co-evolution in a high dimensional setting. Using a combination of stochastic and deterministic simulations and theory inspired by the physics of disordered spin systems, I show that co-evolution stabilizes such populations for sufficiently variable interactions and for sufficiently high mutation rates, which stands in sharp contrast to expectations from ecological models alone. I also derive the phase boundary between a stable eco-evolutionary phase and an extinct phase, showing the dependence on relevant parameter combinations