Recent work has focused on explicating the relations among the current mood and anxiety disorders. This research has yielded some important findings (e.g., the very strong link between generalized ...anxiety disorder and the unipolar mood disorders). I discuss problems associated with disorder-based analyses, however, and I argue that they need to be supplemented by examining relations among the specific symptom dimensions within these diagnostic classes. I demonstrate that two quantitative elements need to be considered when analyzing the properties of symptoms-the level of specificity and the magnitude of the general distress variance. These quantitative elements can be used to organize relevant symptoms into four groups (i.e., a quadripartite model) that reflect varying combinations of distress and specificity. I illustrate the value of this approach by reviewing the properties of the major symptom dimensions within posttraumatic stress disorder, obsessive-compulsive disorder, and major depression.
High-throughput technologies such as next-generation sequencing allow biologists to observe cell function with unprecedented resolution, but the resulting datasets are too large and complicated for ...humans to understand without the aid of advanced statistical methods. Machine learning (ML) algorithms, which are designed to automatically find patterns in data, are well suited to this task. Yet these models are often so complex as to be opaque, leaving researchers with few clues about underlying mechanisms. Interpretable machine learning (iML) is a burgeoning subdiscipline of computational statistics devoted to making the predictions of ML models more intelligible to end users. This article is a gentle and critical introduction to iML, with an emphasis on genomic applications. I define relevant concepts, motivate leading methodologies, and provide a simple typology of existing approaches. I survey recent examples of iML in genomics, demonstrating how such techniques are increasingly integrated into research workflows. I argue that iML solutions are required to realize the promise of precision medicine. However, several open challenges remain. I examine the limitations of current state-of-the-art tools and propose a number of directions for future research. While the horizon for iML in genomics is wide and bright, continued progress requires close collaboration across disciplines.
As machine learning has gradually entered into ever more sectors of public and private life, there has been a growing demand for algorithmic explainability. How can we make the predictions of complex ...statistical models more intelligible to end users? A subdiscipline of computer science known as interpretable machine learning (IML) has emerged to address this urgent question. Numerous influential methods have been proposed, from local linear approximations to rule lists and counterfactuals. In this article, I highlight three conceptual challenges that are largely overlooked by authors in this area. I argue that the vast majority of IML algorithms are plagued by (1) ambiguity with respect to their true target; (2) a disregard for error rates and severe testing; and (3) an emphasis on product over process. Each point is developed at length, drawing on relevant debates in epistemology and philosophy of science. Examples and counterexamples from IML are considered, demonstrating how failure to acknowledge these problems can result in counterintuitive and potentially misleading explanations. Without greater care for the conceptual foundations of IML, future work in this area is doomed to repeat the same mistakes.
We introduce a new approach—acoustic restoration—focusing on the applied utility of soundscapes for restoration, recognising the rich ecological and social values they encapsulate. Broadcasting ...soundscapes in disturbed areas can accelerate recolonisation of animals and the microbes and propagules they carry; long duration recordings are also ideal sources of data for benchmarking restoration initiatives and evocative engagement tools.
We introduce a new approach—acoustic restoration—focusing on the applied utility of soundscapes for restoration, recognising the rich ecological and social values they encapsulate. Broadcasting soundscapes in disturbed areas can accelerate recolonisation of animals and the microbes and propagules they carry; long duration recordings are also ideal sources of data for benchmarking restoration initiatives and evocative engagement tools.
AbstractThe growth habit of mistletoes, the only woody, parasitic plants to infect host canopies, represents a key innovation. How this aerially parasitic habit originated is unknown; mistletoe ...macrofossils are relatively recent, from long after they adapted to canopy life and evolved showy, bird-pollinated flowers; sticky, bird-dispersed seeds; and woody haustoria diverting water and nutrients from host branches. Since the transition to aerial parasitism predates the origin of mistletoes' contemporary avian seed dispersers by 20-40 million years, this leaves unanswered the question of who the original mistletoe dispersers were. By integrating fully resolved phylogenies of mistletoes and aligning the timing of historic events, I identify two ancient mammals as likely candidates for planting Viscaceae and Loranthaceae in the canopy. Just as modern mouse lemurs and galagos disperse viscaceous mistletoe externally (grooming the sticky seeds from their fur), Cretaceous primates (e.g.,
) may have transported seeds of root-parasitic understory shrubs up into the canopy of Laurasian forests. In the Eocene, ancestors of today's mistletoe-dispersing marsupials,
, likely fed on the nutritious fruit of root-parasitic loranthaceous shrubs, depositing the seeds atop western Gondwanan forest crowns. Once mistletoes colonized the canopy, subsequent evolution and diversification coincided with the rise of nectar- and fruit-dependent birds.
Bifunctional molecules can be used to tether quantum dots to nanostructured and planar semiconductor and metal surfaces. Excited-state interfacial electron-transfer reactions at QD−molecule−substrate ...interfaces are of interest from a fundamental standpoint and may have applications in solar energy conversion and charge-transfer-based sensing. This Perspective highlights recent work and unanswered questions in two related areas, the linker-assisted assembly of QD−substrate architectures and the spectroscopic characterization of electron transfer at QD−molecule−substrate interfaces.
Community supported agriculture schemes are a prominent example of localized alternatives to the global food system. They are presented as alternative nodes of food production, where the consumer ...experiences a much closer relationship to the produce they are consuming and to the labour involved in producing it. They lift the commodity veil by inviting the consumer into the world of production – of labour. However, there has been little analysis of labour undertaken in the setting of community supported agriculture, particularly the labour of community supported agriculture consumers, or members. Marxian analysis of the food system at the macro level has underpinned powerful critiques of its shortcomings and highlighted inequalities of land and labour, but has rarely been employed to understand the possibilities of alternative food networks at a more micro level. In this article, I draw on Marx’s concept of alienation to explore the experience and organization of labour within a community supported agriculture scheme in the United Kingdom. In doing so, I present a case study of how labour in a community supported agriculture scheme counteracts experiences of alienation created by capitalism and consider how this might inform (re)organization of labour in the food system, more generally.
The necrophilous insect fauna on carcasses varies seasonally and geographically. The ecological succession of insects arriving to decaying neonate pig carcasses in central North Carolina during late ...summer was sampled using a novel vented-chamber collection method. We collected six blow fly species, flesh flies, house flies and 10 beetle taxa, including four species of scarab beetles. Necrophilous fly activity dominated the early decomposition stages, whereas beetle numbers remained low until day 4. By day 7, more than 50% of the pig carcasses were skeletonized and they attracted few insects. Differences in the taxa and successional patterns documented in this experiment and a previous study in the same location highlight the ecological variation in such investigations, and underscore the need for standardization, as well as for ecological succession studies on finer geographic scales.
We propose a formal framework for interpretable machine learning. Combining elements from statistical learning, causal interventionism, and decision theory, we design an idealised
explanation game
in ...which players collaborate to find the best explanation(s) for a given algorithmic prediction. Through an iterative procedure of questions and answers, the players establish a three-dimensional Pareto frontier that describes the optimal trade-offs between explanatory accuracy, simplicity, and relevance. Multiple rounds are played at different levels of abstraction, allowing the players to explore overlapping causal patterns of variable granularity and scope. We characterise the conditions under which such a game is almost surely guaranteed to converge on a (conditionally) optimal explanation surface in polynomial time, and highlight obstacles that will tend to prevent the players from advancing beyond certain explanatory thresholds. The game serves a descriptive and a normative function, establishing a conceptual space in which to analyse and compare existing proposals, as well as design new and improved solutions.
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