An Information-theoretical Approach to Semi-supervised Learning under Covariate-shift

A common assumption in semi-supervised learning is that the labeled, unlabeled, and test data are drawn from the same distribution. However, this assumption is not satisfied in many applications. In many scenarios, the data is collected sequentially (e.g., healthcare) and the distribution of the data may change over time often exhibiting so-called covariate shifts. In this paper, we propose an approach for semi-supervised learning algorithms that is capable of addressing this issue. Our framework also recovers some popular methods, including entropy minimization and pseudo-labeling. We provide new information-theoretical based generalization error upper bounds inspired by our novel framework. Our bounds are applicable to both general semi-supervised learning and the covariate-shift scenario. Finally, we show numerically that our method outperforms previous approaches proposed for semi-supervised learning under the covariate shift.Comment: Accepted at AISTATS 202

Characterizing and Understanding the Generalization Error of Transfer Learning with Gibbs Algorithm

We provide an information-theoretic analysis of the generalization ability of Gibbs-based transfer learning algorithms by focusing on two popular transfer learning approaches, $\alpha$-weighted-ERM and two-stage-ERM. Our key result is an exact characterization of the generalization behaviour using the conditional symmetrized KL information between the output hypothesis and the target training samples given the source samples. Our results can also be applied to provide novel distribution-free generalization error upper bounds on these two aforementioned Gibbs algorithms. Our approach is versatile, as it also characterizes the generalization errors and excess risks of these two Gibbs algorithms in the asymptotic regime, where they converge to the $\alpha$-weighted-ERM and two-stage-ERM, respectively. Based on our theoretical results, we show that the benefits of transfer learning can be viewed as a bias-variance trade-off, with the bias induced by the source distribution and the variance induced by the lack of target samples. We believe this viewpoint can guide the choice of transfer learning algorithms in practice

Information-Theoretic Characterizations of Generalization Error for the Gibbs Algorithm

Various approaches have been developed to upper bound the generalization error of a supervised learning algorithm. However, existing bounds are often loose and even vacuous when evaluated in practice. As a result, they may fail to characterize the exact generalization ability of a learning algorithm. Our main contributions are exact characterizations of the expected generalization error of the well-known Gibbs algorithm (a.k.a. Gibbs posterior) using different information measures, in particular, the symmetrized KL information between the input training samples and the output hypothesis. Our result can be applied to tighten existing expected generalization errors and PAC-Bayesian bounds. Our information-theoretic approach is versatile, as it also characterizes the generalization error of the Gibbs algorithm with a data-dependent regularizer and that of the Gibbs algorithm in the asymptotic regime, where it converges to the standard empirical risk minimization algorithm. Of particular relevance, our results highlight the role the symmetrized KL information plays in controlling the generalization error of the Gibbs algorithm

Key considerations when involving children in health intervention design: reflections on working in partnership with South Asian children in the UK on a tailored Management and Intervention for Asthma (MIA) study

Participatory research is an empowering process through which individuals can increase control over their lives, and allows researchers/clinicians to gain a clearer understanding of a child’s needs. However, involving children in participatory research is still relatively novel, despite national and international mandates to engage children in decision making. This paper draws on the learnings from designing the Management and Intervention for Asthma (MIA) study, which used a collaborative participatory method to develop an intervention-planning framework for South Asian children with asthma. There are currently 1 million children in the UK receiving treatment for asthma, making it one of the most prevalent chronic childhood illnesses. Symptoms of asthma are often underrecognized in children from South Asian communities in the UK, contributing to increased disease severity and increased attendance at the emergency department compared to White British children. Despite this, ethnic minorities are often excluded from research and thus absent from the ‘evidence base’, making it essential to hear their perspectives if health inequalities are to be successfully addressed. We worked alongside healthcare professionals, community facilitators, parents, and children to identify the key concerns and priorities they had and then designed the framework around their needs. Reflecting on the process, we identified several key considerations that need to be addressed when co-developing interventions with children. These include the power dynamics between the parent/researcher and child; navigating the consent/assent process; how parental involvement might affect the research; establishing a convenient time and location; how to keep children engaged throughout the process; tailoring activities to different levels of ability; and accounting for cultural differences. These factors were considered by the researchers when designing the study, however, implementing them was not without its challenges and highlighted the need for researchers to develop expertise in this field. Tailoring existing research methods allowed us to explore children’s perceptions, priorities, and experiences of illness more effectively. However, involving children in participatory research is a complex undertaking, and researchers need to ensure that they have the expertise, time, and resources necessary to be able to fully support the needs of child participants before deciding to commit to this approach

Mtss1 promotes cell-cell junction assembly and stability through the small GTPase Rac1

Cell-cell junctions are an integral part of epithelia and are often disrupted in cancer cells during epithelial-to-mesenchymal transition (EMT), which is a main driver of metastatic spread. We show here that Metastasis suppressor-1 (Mtss1; Missing in Metastasis, MIM), a member of the IMD-family of proteins, inhibits cell-cell junction disassembly in wound healing or HGF-induced scatter assays by enhancing cell-cell junction strength. Mtss1 not only makes cells more resistant to cell-cell junction disassembly, but also accelerates the kinetics of adherens junction assembly. Mtss1 drives enhanced junction formation specifically by elevating Rac-GTP. Lastly, we show that Mtss1 depletion reduces recruitment of F-actin at cell-cell junctions. We thus propose that Mtss1 promotes Rac1 activation and actin recruitment driving junction maintenance. We suggest that the observed loss of Mtss1 in cancers may compromise junction stability and thus promote EMT and metastasis

Binary orbits as the driver of γ-ray emission and mass ejection in classical novae

Classical novae are the most common astrophysical thermonuclear explosions, occurring on the surfaces of white dwarf stars accreting gas from companions in binary star systems. Novae typically expel �10,000 solar masses of material at velocities exceeding 1,000 km/s. However, the mechanism of mass ejection in novae is poorly understood, and could be dominated by the impulsive flash of the thermonuclear runaway, prolonged optically thick winds, or binary interaction with the nova envelope. Classical novae are now routinely detected in GeV gamma-rays, suggesting that relativistic particles are accelerated by strong shocks in nova ejecta. Here we present high-resolution imaging of the gamma-ray-emitting nova V959 Mon at radio wavelengths, showing that its ejecta were shaped by binary motion: some gas was expelled rapidly along the poles as a wind from the white dwarf, while denser material drifted out along the equatorial plane, propelled by orbital motion. At the interface between the equatorial and polar regions, we observe synchrotron emission indicative of shocks and relativistic particle acceleration, thereby pinpointing the location of gamma-ray production. Binary shaping of the nova ejecta and associated internal shocks are expected to be widespread among novae, explaining why many novae are gamma-ray emitters

Activation of Type 1 Cannabinoid Receptor (CB1R) promotes neurogenesis in murine subventricular zone cell cultures

The endocannabinoid system has been implicated in the modulation of adult neurogenesis. Here, we describe the effect of type 1 cannabinoid receptor (CB1R) activation on self-renewal, proliferation and neuronal differentiation in mouse neonatal subventricular zone (SVZ) stem/progenitor cell cultures. Expression of CB1R was detected in SVZ-derived immature cells (Nestin-positive), neurons and astrocytes. Stimulation of the CB1R by (R)-(+)-Methanandamide (R-m-AEA) increased self-renewal of SVZ cells, as assessed by counting the number of secondary neurospheres and the number of Sox2+/+ cell pairs, an effect blocked by Notch pathway inhibition. Moreover, R-m-AEA treatment for 48 h, increased proliferation as assessed by BrdU incorporation assay, an effect mediated by activation of MAPK-ERK and AKT pathways. Surprisingly, stimulation of CB1R by R-m-AEA also promoted neuronal differentiation (without affecting glial differentiation), at 7 days, as shown by counting the number of NeuN-positive neurons in the cultures. Moreover, by monitoring intracellular calcium concentrations ([Ca2+](i)) in single cells following KCl and histamine stimuli, a method that allows the functional evaluation of neuronal differentiation, we observed an increase in neuronal-like cells. This proneurogenic effect was blocked when SVZ cells were co-incubated with R-m-AEA and the CB1R antagonist AM 251, for 7 days, thus indicating that this effect involves CB1R activation. In accordance with an effect on neuronal differentiation and maturation, R-m-AEA also increased neurite growth, as evaluated by quantifying and measuring the number of MAP2-positive processes. Taken together, these results demonstrate that CB1R activation induces proliferation, self-renewal and neuronal differentiation from mouse neonatal SVZ cell cultures.Fundacao para a Ciencia e a Tecnologia - Portugal [POCTI/SAU-NEU/68465/2006, PTDC/SAU-NEU/104415/2008, PTDC/SAU-NEU/101783/2008, POCTI/SAU-NEU/110838/2009]; Fundacao Calouste Gulbenkian [96542]; Fundacao para a Ciencia e Tecnologiainfo:eu-repo/semantics/publishedVersio

Ovine pedomics : the first study of the ovine foot 16S rRNA-based microbiome

We report the first study of the bacterial microbiome of ovine interdigital skin based on 16S rRNA by pyrosequencing and conventional cloning with Sanger-sequencing. Three flocks were selected, one a flock with no signs of footrot or interdigital dermatitis, a second flock with interdigital dermatitis alone and a third flock with both interdigital dermatitis and footrot. The sheep were classified as having either healthy interdigital skin (H), interdigital dermatitis (ID) or virulent footrot (VFR). The ovine interdigital skin bacterial community varied significantly by flock and clinical condition. The diversity and richness of operational taxonomic units was greater in tissue from sheep with ID than H or VFR affected sheep. Actinobacteria, Bacteriodetes, Firmicutes and Proteobacteria were the most abundant phyla comprising 25 genera. Peptostreptococcus, Corynebacterium and Staphylococcus were associated with H, ID and VFR respectively. Sequences of Dichelobacter nodosus, the causal agent of ovine footrot, were not amplified due to mismatches in the 16S rRNA universal forward primer (27F). A specific real time PCR assay was used to demonstrate the presence of D. nodosus which was detected in all samples including the flock with no signs of ID or VFR. Sheep with ID had significantly higher numbers of D. nodosus (104-109 cells/g tissue) than those with H or VFR feet

Fostering implementation of health services research findings into practice: a consolidated framework for advancing implementation science

Abstract Background Many interventions found to be effective in health services research studies fail to translate into meaningful patient care outcomes across multiple contexts. Health services researchers recognize the need to evaluate not only summative outcomes but also formative outcomes to assess the extent to which implementation is effective in a specific setting, prolongs sustainability, and promotes dissemination into other settings. Many implementation theories have been published to help promote effective implementation. However, they overlap considerably in the constructs included in individual theories, and a comparison of theories reveals that each is missing important constructs included in other theories. In addition, terminology and definitions are not consistent across theories. We describe the Consolidated Framework For Implementation Research (CFIR) that offers an overarching typology to promote implementation theory development and verification about what works where and why across multiple contexts. Methods We used a snowball sampling approach to identify published theories that were evaluated to identify constructs based on strength of conceptual or empirical support for influence on implementation, consistency in definitions, alignment with our own findings, and potential for measurement. We combined constructs across published theories that had different labels but were redundant or overlapping in definition, and we parsed apart constructs that conflated underlying concepts. Results The CFIR is composed of five major domains: intervention characteristics, outer setting, inner setting, characteristics of the individuals involved, and the process of implementation. Eight constructs were identified related to the intervention (e.g., evidence strength and quality), four constructs were identified related to outer setting (e.g., patient needs and resources), 12 constructs were identified related to inner setting (e.g., culture, leadership engagement), five constructs were identified related to individual characteristics, and eight constructs were identified related to process (e.g., plan, evaluate, and reflect). We present explicit definitions for each construct. Conclusion The CFIR provides a pragmatic structure for approaching complex, interacting, multi-level, and transient states of constructs in the real world by embracing, consolidating, and unifying key constructs from published implementation theories. It can be used to guide formative evaluations and build the implementation knowledge base across multiple studies and settings.http://deepblue.lib.umich.edu/bitstream/2027.42/78272/1/1748-5908-4-50.xmlhttp://deepblue.lib.umich.edu/bitstream/2027.42/78272/2/1748-5908-4-50-S1.PDFhttp://deepblue.lib.umich.edu/bitstream/2027.42/78272/3/1748-5908-4-50-S3.PDFhttp://deepblue.lib.umich.edu/bitstream/2027.42/78272/4/1748-5908-4-50-S4.PDFhttp://deepblue.lib.umich.edu/bitstream/2027.42/78272/5/1748-5908-4-50.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/78272/6/1748-5908-4-50-S2.PDFPeer Reviewe