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Understanding the mechanisms of major depressive disorder (MDD) improvement is a key challenge to determining effective personalized treatments.
Methods
To identify a data-driven pattern of clinical improvement in MDD and to quantify neural-to-symptom relationships according to antidepressant treatment, we performed a secondary analysis of the publicly available dataset EMBARC (Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care). In EMBARC, participants with MDD were treated either by sertraline or placebo for 8 weeks (Stage 1), and then switched to bupropion according to clinical response (Stage 2). We computed a univariate measure of clinical improvement through a principal component (PC) analysis on the variations of individual items of four clinical scales measuring depression, anxiety, suicidal ideas, and manic-like symptoms. We then investigated how initial clinical and neural factors predicted this measure during Stage 1 by running a linear model for each brain parcel’s resting-state global brain connectivity (GBC) with individual improvement scores during Stage 1.
Results
The first PC (PC1) was similar across treatment groups at stages 1 and 2, suggesting a shared pattern of symptom improvement. PC1 patients’ scores significantly differed according to treatment, whereas no difference in response was evidenced between groups with the Clinical Global Impressions Scale. Baseline GBC correlated with Stage 1 PC1 scores in the sertraline but not in the placebo group.
Using data-driven reduction of symptom scales, we identified a common profile of symptom improvement with distinct intensity between sertraline and placebo.
Conclusions
Mapping from data-driven symptom improvement onto neural circuits revealed treatment-responsive neural profiles that may aid in optimal patient selection for future trials.
From early on, infants show a preference for infant-directed speech (IDS) over adult-directed speech (ADS), and exposure to IDS has been correlated with language outcome measures such as vocabulary. The present multi-laboratory study explores this issue by investigating whether there is a link between early preference for IDS and later vocabulary size. Infants’ preference for IDS was tested as part of the ManyBabies 1 project, and follow-up CDI data were collected from a subsample of this dataset at 18 and 24 months. A total of 341 (18 months) and 327 (24 months) infants were tested across 21 laboratories. In neither preregistered analyses with North American and UK English, nor exploratory analyses with a larger sample did we find evidence for a relation between IDS preference and later vocabulary. We discuss implications of this finding in light of recent work suggesting that IDS preference measured in the laboratory has low test-retest reliability.
Design education prepares novice designers to solve complex and challenging problems requiring diverse skill sets and an interdisciplinary approach. Hackathons, for example, offer a hands-on, collaborative learning approach in a limited time frame to gain practical experience and develop problem-solving skills quickly. They enable collaboration, prototyping and testing among interdisciplinary teams. Typically, hackathons strongly focus on the solution, assuming that this will support learning. However, building the best product and achieving a strong learning effect may not be related. This paper presents the results of an empirical study that examines the relationship between product quality, learning effect and effort spent in an academic 2-week hackathon. Thirty teams identified user problems in this course and developed hardware and mechatronic products. This study collected the following data: (1) effort spent during the hackathon through task tracking, (2) learning effect through self-assessment by the participants and (3) product quality after the hackathon by an external jury. The study found that the team effort spent has a statistically significant but moderate correlation with product quality. The correlation between product quality and learning effect is statistically insignificant, suggesting that for this setting, there is no relevant association.
If people with episodic mental-health conditions lose their job due to an episode of their mental illness, they often experience personal negative consequences. Therefore, reintegration after sick leave is critical to avoid unfavorable courses of disease, longer inability to work, long payment of sickness benefits, and unemployment. Existing return-to-work (RTW) programs have mainly focused on “common mental disorders” and often used very elaborate and costly interventions without yielding convincing effects. It was the aim of the RETURN study to evaluate an easy-to-implement RTW intervention specifically addressing persons with mental illnesses being so severe that they require inpatient treatment.
Methods
The RETURN study was a multi-center, cluster-randomized controlled trial in acute psychiatric wards addressing inpatients suffering from a psychiatric disorder. In intervention wards, case managers (RTW experts) were introduced who supported patients in their RTW process, while in control wards treatment, as usual, was continued.
Results
A total of 268 patients were recruited for the trial. Patients in the intervention group had more often returned to their workplace at 6 and 12 months, which was also mirrored in more days at work. These group differences were statistically significant at 6 months. However, for the main outcome (days at work at 12 months), differences were no longer statistically significant (p = 0.14). Intervention patients returned to their workplace earlier than patients in the control group (p = 0.040).
Conclusions
The RETURN intervention has shown the potential of case-management interventions when addressing RTW. Further analyses, especially the qualitative ones, may help to better understand limitations and potential areas for improvement.
The chapter discusses how to process data from irregular discrete domains, an emerging area called graph signal processing (GSP). Basically, the type of graph we deal with consists of a network with distributed vertices and weighted edges defining the neighborhood and the connections among the nodes. As such, the graph signals are collected in a vector whose entries represent the values of the signal nodes at a given time. A common issue related to GSP is the sampling problem, given the irregular structure of the data, where some sort of interpolation is possible whenever the graph signals are bandlimited or nearly bandlimited. These interpolations can be performed through the extension of the conventional adaptive filtering to signals distributed on graphs where there is no traditional data structure. The chapter presents the LMS, NLMS, and RLS algorithms for GSP along with their analyses and application to estimate bandlimited signals defined on graphs. In addition, the chapter presents a general framework for data-selective adaptive algorithms for GSP.
The chapter briefly introduces the main concepts of array signal processing, emphasizing those related to adaptive beamforming, and discusses how to impose linear constraints to adaptive filtering algorithms to achieve the beamforming effect. Adaptive beamforming, emphasizing the incoming signal impinging from a known direction by means of an adaptive filter, is the primary objective of the array signal processing addressed in this chapter. We start this study with the narrowband beamformer. The constrained LMS, RLS, conjugate gradient, and SMAP algorithms are introduced along with the generalized sidelobe canceller, and the Householder constrained structures; sparse promoting adaptive beamforming algorithms are also addressed in this chapter. In the following, it introduces the concepts of frequency-domain and time-domain broadband adaptive beamforming and shows their equivalence. The chapter wraps up with brief discussions and reference suggestions on essential topics related to adaptive beamforming, including the numerical robustness of adaptive beamforming algorithms.
This chapter explains the basic concepts of kernel-based methods, a widely used tool in machine learning. The idea is to present online parameter estimation of nonlinear models using kernel-based tools. The chapters aim is to introduce the kernel version of classical algorithms such as least mean square (LMS), recursive least squares (RLS), affine projection (AP), and set membership affine projection (SM-AP). In particular, we will discuss how to keep the dictionary of the kernel finite through a series of model reduction strategies. This way, all discussed kernel algorithms are tailored for online implementation.
It provides a brief description of the classical adaptive filtering algorithms, starting with defining the actual objective function each algorithm minimizes. It also includes a summary of the expected performance according to available results from the literature.
The chapter shows how the classical adaptive filtering algorithms can be adapted to distributed learning. In distributed learning, there is a set of adaptive filtering placed at nodes utilizing a local input and desired signals. These distributed networks of sensor nodes are located at distinct positions, which might improve the reliability and robustness of the parameter estimation in comparison to stand-alone adaptive filters. In distributed adaptive networks, parameter estimation might be obtained in a centralized form or a decentralized form. The centralized case processes the signals from all nodes of the network in a single fusion center, whereas in the decentralized case, processing is performed locally followed by a proper combination of partial estimates to result in a consensus parameter estimate. The main drawbacks of the centralized configuration are its data communication and computational costs, particularly in networks with a large number of nodes. On the other hand, the decentralized estimators require fewer data to feed the estimators and improve on robustness. The provides a discussion on equilibrium and consensus using arguments drawn from the pari-mutuel betting system. The expert opinion pool is the concept to induce improved estimation and data modeling, utilizing De-Groot’s algorithm and Markov chains as tools to probate equilibrium at consensus. It also introduces the distributed versions of the LMS and RLS adaptive filtering algorithms with emphasis on the decentralized parameter estimation case. This chapter also addresses how data broadcasting can be confined to a subset of nodes so that the overall network reduces the power consumption and bandwidth usage. Then, the chapter discusses a strategy to incorporate a data selection based on the SM adaptive filtering.
Chapter 2 presents several strategies to exploit sparsity in the parameters being estimated in order to obtain better estimates and accelerate convergence, two advantages of paramount importance when dealing with real problems requiring the estimation of many parameters. In these cases, the classical adaptive filtering algorithms exhibit a slow and often unacceptable convergence rate. In this chapter, many algorithms capable of exploiting sparse models are presented. Also, the two most widely used approaches to exploit sparsity are presented, and their pros and cons are discussed. The first approach explicitly models sparsity by relying on sparsity-promoting regularization functions. The second approach utilizes updates proportional to the magnitude of the coefficient being updated, thus accelerating the convergence of large magnitude coefficients. After reading this chapter, the reader will not only obtain a deeper understanding of the subject but also be able to adapt or develop algorithms based on his own needs.
Learn to solve the unprecedented challenges facing Online Learning and Adaptive Signal Processing in this concise, intuitive text. The ever-increasing amount of data generated every day requires new strategies to tackle issues such as: combining data from a large number of sensors; improving spectral usage, utilizing multiple-antennas with adaptive capabilities; or learning from signals placed on graphs, generating unstructured data. Solutions to all of these and more are described in a condensed and unified way, enabling you to expose valuable information from data and signals in a fast and economical way. The up-to-date techniques explained here can be implemented in simple electronic hardware, or as part of multi-purpose systems. Also featuring alternative explanations for online learning, including newly developed methods and data selection, and several easily implemented algorithms, this one-of-a-kind book is an ideal resource for graduate students, researchers, and professionals in online learning and adaptive filtering.
Horseshoe crabs as a group are renowned for their morphological conservatism punctuated by marked shifts in morphology associated with the occupation of non-marine environments and have been suggested to exhibit a consistent developmental trajectory throughout their evolutionary history. Here, we report a new species of horseshoe crab from the Ordovician (Late Sandbian) of Kingston, Ontario, Canada, from juvenile and adult material. This new species provides critical insight into the ontogeny and morphology of the earliest horseshoe crabs, indicating that at least some Palaeozoic forms had freely articulating tergites anterior to the fused thoracetron and an opisthosoma comprising 13 segments.
Tree-ring chronologies encode interannual variability in forest growth rates over long time periods from decades to centuries or even millennia. However, each chronology is a highly localized measurement describing conditions at specific sites where wood samples have been collected. The question whether these local growth variabilites are representative for large geographical regions remains an open issue. To overcome the limitations of interpreting a sparse network of sites, we propose an upscaling approach for annual tree-ring indices that approximate forest growth variability and compute gridded data products that generalize the available information for multiple tree genera. Using regression approaches from machine learning, we predict tree-ring indices in space and time based on climate variables, but considering also species range maps as constraints for the upscaling. We compare various prediction strategies in cross-validation experiments to identify the best performing setup. Our estimated maps of tree-ring indices are the first data products that provide a dense view on forest growth variability at the continental level with 0.5° and 0.0083° spatial resolution covering the years 1902–2013. Furthermore, we find that different genera show very variable spatial patterns of anomalies. We have selected Europe as study region and focused on the six most prominent tree genera, but our approach is very generic and can easily be applied elsewhere. Overall, the study shows perspectives but also limitations for reconstructing spatiotemporal dynamics of complex biological processes. The data products are available at https://www.doi.org/10.17871/BACI.248.
Predation has strongly shaped past and modern marine ecosystems, but the scale dependency of patterns in drilling predation, the most widely used proxy for predator–prey interactions in the fossil record, is a matter of debate. To assess the effects of spatial and taxonomic scale on temporal trends in the drilling frequencies (DFs), we analyzed Holocene molluscan assemblages of different benthic habitats and nutrient regimes from the northern Adriatic shelf in a sequence-stratigraphic context. Although it has been postulated that low predation pressures facilitated the development of high-biomass epifaunal communities in the eastern, relatively oligotrophic portion of the northern Adriatic shelf, DFs reaching up to 30%–40% in the studied assemblage show that drilling predation levels are comparable to those typical of late Cenozoic ecosystems. DFs tend to increase from the transgressive systems tract (TST) into the highstand systems tract (HST) at the local scale, reflecting an increase in water depth by 20–40 m and a shift from infralittoral to circalittoral habitats over the past 10,000 years. As transgressive deposits are thicker at shallower locations and highstand deposits are thicker at deeper locations, a regional increase in DFs from TST to HST is evident only when these differences are accounted for. The increase in DF toward the HST can be recognized at the level of total assemblages, classes, and few abundant and widespread families, but it disappears at the level of genera and species because of their specific environmental requirements, leading to uneven or patchy distribution in space and time.