Class reconstruction driven adversarial domain adaptation for hyperspectral image classification

We address the problem of cross-domain classification of hyperspectral image (HSI) pairs under the notion of unsupervised domain adaptation (UDA). The UDA problem aims at classifying the test samples of a target domain by exploiting the labeled training samples from a related but different source domain. In this respect, the use of adversarial training driven domain classifiers is popular which seeks to learn a shared feature space for both the domains. However, such a formalism apparently fails to ensure the (i) discriminativeness, and (ii) non-redundancy of the learned space. In general, the feature space learned by domain classifier does not convey any meaningful insight regarding the data. On the other hand, we are interested in constraining the space which is deemed to be simultaneously discriminative and reconstructive at the class-scale. In particular, the reconstructive constraint enables the learning of category-specific meaningful feature abstractions and UDA in such a latent space is expected to better associate the domains. On the other hand, we consider an orthogonality constraint to ensure non-redundancy of the learned space. Experimental results obtained on benchmark HSI datasets (Botswana and Pavia) confirm the efficacy of the proposal approach

Interventions to prevent spontaneous preterm birth in high-risk women with singleton pregnancy: A systematic review and network meta-analysis

© 2019 The Cochrane Collaboration. This is a protocol for a Cochrane Review (Intervention). The objectives are as follows: To compare the efficacy of current, relevant interventions to prevent preterm birth in women with singleton pregnancy and high individual risk of spontaneous preterm birth. We will consider interventions for women with a history of spontaneous preterm birth or short cervical length and women with asymptomatic vaginal infections

The role of viral genomics in understanding COVID-19 outbreaks in long-term care facilities

We reviewed all genomic epidemiology studies on COVID-19 in long-term care facilities (LTCFs) that had been published to date. We found that staff and residents were usually infected with identical, or near identical, SARS-CoV-2 genomes. Outbreaks usually involved one predominant cluster, and the same lineages persisted in LTCFs despite infection control measures. Outbreaks were most commonly due to single or few introductions followed by a spread rather than a series of seeding events from the community into LTCFs. The sequencing of samples taken consecutively from the same individuals at the same facilities showed the persistence of the same genome sequence, indicating that the sequencing technique was robust over time. When combined with local epidemiology, genomics allowed probable transmission sources to be better characterised. The transmission between LTCFs was detected in multiple studies. The mortality rate among residents was high in all facilities, regardless of the lineage. Bioinformatics methods were inadequate in a third of the studies reviewed, and reproducing the analyses was difficult because sequencing data were not available in many facilities

Exploring the (missed) connections between digital scholarship and faculty development: a conceptual analysis

Abstract The aim of this paper is to explore the relationship between two research topics: digital scholarship and faculty development. The former topic drives attention on academics' new practices in digital, open and networked contexts; the second is focused on the requirements and strategies to promote academics' professional learning and career advancement. The research question addressing this study is: are faculty development strategies hindered by the lack of a cohesive view in the research on digital scholarship? The main assumption guiding this research question is that clear conceptual frameworks and models of professional practice lead to effective faculty development strategies. Through a wide overview of the evolution of both digital scholarship and faculty development, followed by a conceptual analysis of the intersections between fields, the paper attempts to show the extent on which the situation in one area (digital scholarship) might encompass criticalities for the other (faculty development) in terms of research and practices. Furthermore, three scenarios based on the several perspectives of digital scholarship are built in order to explore the research question in depth. We conclude that at the current state of art the relationship between these two topics is weak. Moreover, the dialogue between digital scholarship and faculty development could put the basis to forge effective professional learning contexts and instruments, with the ultimate goal of supporting academics to become digital scholars towards a more open and democratic vision of scholarship

Data science

Even though it has only entered public perception relatively recently, the term "data science" already means many things to many people. This chapter explores both top-down and bottom-up views on the field, on the basis of which we define data science as "a unique blend of principles and methods from analytics, engineering, entrepreneurship and communication that aim at generating value from the data itself". The chapter then discusses the disciplines that contribute to this "blend", briefly outlining their contributions and giving pointers for readers interested in exploring their backgrounds further

Adherence to treatment in children and adolescents with cystic fibrosis:a cross-sectional, multi-method study investigating the influence of beliefs about treatment and parental depressive symptoms

Background: Adherence to treatment is often reported to be low in children with cystic fibrosis. Adherence in cystic fibrosis is an important research area and more research is needed to better understand family barriers to adherence in order for clinicians to provide appropriate intervention. The aim of this study was to evaluate adherence to enzyme supplements, vitamins and chest physiotherapy in children with cystic fibrosis and to determine if any modifiable risk factors are associated with adherence. Methods: A sample of 100 children (≤18 years) with cystic fibrosis (44 male; median [range] 10.1 [0.2-18.6] years) and their parents were recruited to the study from the Northern Ireland Paediatric Cystic Fibrosis Centre. Adherence to enzyme supplements, vitamins and chest physiotherapy was assessed using a multi-method approach including; Medication Adherence Report Scale, pharmacy prescription refill data and general practitioner prescription issue data. Beliefs about treatments were assessed using refined versions of the Beliefs about Medicines Questionnaire-specific. Parental depressive symptoms were assessed using the Center for Epidemiologic Studies Depression Scale. Results: Using the multi-method approach 72% of children were classified as low-adherers to enzyme supplements, 59% low-adherers to vitamins and 49% low-adherers to chest physiotherapy. Variations in adherence were observed between measurement methods, treatments and respondents. Parental necessity beliefs and child age were significant independent predictors of child adherence to enzyme supplements and chest physiotherapy, but parental depressive symptoms were not found to be predictive of adherence. Conclusions: Child age and parental beliefs about treatments should be taken into account by clinicians when addressing adherence at routine clinic appointments. Low adherence is more likely to occur in older children, whereas, better adherence to cystic fibrosis therapies is more likely in children whose parents strongly believe the treatments are necessary. The necessity of treatments should be reinforced regularly to both parents and children

How sharing can contribute to more sustainable cities

\ua9 2017 by the authors. Recently, much of the literature on sharing in cities has focused on the sharing economy, in which people use online platforms to share underutilized assets in the marketplace. This view of sharing is too narrow for cities, as it neglects the myriad of ways, reasons, and scales in which citizens share in urban environments. Research presented here by the Liveable Cities team in the form of participant workshops in Lancaster and Birmingham, UK, suggests that a broader approach to understanding sharing in cities is essential. The research also highlighted tools and methods that may be used to help to identify sharing in communities. The paper ends with advice to city stakeholders, such as policymakers, urban planners, and urban designers, who are considering how to enhance sustainability in cities through sharing

Transparency and Trust in Human-AI-Interaction: The Role of Model-Agnostic Explanations in Computer Vision-Based Decision Support

Computer Vision, and hence Artificial Intelligence-based extraction of information from images, has increasingly received attention over the last years, for instance in medical diagnostics. While the algorithms' complexity is a reason for their increased performance, it also leads to the "black box" problem, consequently decreasing trust towards AI. In this regard, "Explainable Artificial Intelligence" (XAI) allows to open that black box and to improve the degree of AI transparency. In this paper, we first discuss the theoretical impact of explainability on trust towards AI, followed by showcasing how the usage of XAI in a health-related setting can look like. More specifically, we show how XAI can be applied to understand why Computer Vision, based on deep learning, did or did not detect a disease (malaria) on image data (thin blood smear slide images). Furthermore, we investigate, how XAI can be used to compare the detection strategy of two different deep learning models often used for Computer Vision: Convolutional Neural Network and Multi-Layer Perceptron. Our empirical results show that i) the AI sometimes used questionable or irrelevant data features of an image to detect malaria (even if correctly predicted), and ii) that there may be significant discrepancies in how different deep learning models explain the same prediction. Our theoretical discussion highlights that XAI can support trust in Computer Vision systems, and AI systems in general, especially through an increased understandability and predictability

Hypertension and type 2 diabetes: What family physicians can do to improve control of blood pressure - an observational study

Background: The prevalence of type 2 diabetes is rising, and most of these patients also have hypertension, substantially increasing the risk of cardiovascular morbidity and mortality. The majority of these patients do not reach target blood pressure levels for a wide variety of reasons. When a literature review provided no clear focus for action when patients are not at target, we initiated a study to identify characteristics of patients and providers associated with achieving target BP levels in community-based practice. Methods: We conducted a practice- based, cross-sectional observational and mailed survey study. The setting was the practices of 27 family physicians and nurse practitioners in 3 eastern provinces in Canada. The participants were all patients with type 2 diabetes who could understand English, were able to give consent, and would be available for follow-up for more than one year. Data were collected from each patient’s medical record and from each patient and physician/nurse practitioner by mailed survey. Our main outcome measures were overall blood pressure at target (< 130/80), systolic blood pressure at target, and diastolic blood pressure at target. Analysis included initial descriptive statistics, logistic regression models, and multivariate regression using hierarchical nonlinear modeling (HNLM). Results: Fifty-four percent were at target for both systolic and diastolic pressures. Sixty-two percent were at systolic target, and 79% were at diastolic target. Patients who reported eating food low in salt had higher odds of reaching target blood pressure. Similarly, patients reporting low adherence to their medication regimen had lower odds of reaching target blood pressure. Conclusions: When primary care health professionals are dealing with blood pressures above target in a patient with type 2 diabetes, they should pay particular attention to two factors. They should inquire about dietary salt intake, strongly emphasize the importance of reduction, and refer for detailed counseling if necessary. Similarly, they should inquire about adherence to the medication regimen, and employ a variety of patient-oriented strategies to improve adherence