Social robots in research on social and cognitive development in infants and toddlers: A scoping review.

There is currently no systematic review of the growing body of literature on using social robots in early developmental research. Designing appropriate methods for early childhood research is crucial for broadening our understanding of young children's social and cognitive development. This scoping review systematically examines the existing literature on using social robots to study social and cognitive development in infants and toddlers aged between 2 and 35 months. Moreover, it aims to identify the research focus, findings, and reported gaps and challenges when using robots in research. We included empirical studies published between 1990 and May 29, 2023. We searched for literature in PsychINFO, ERIC, Web of Science, and PsyArXiv. Twenty-nine studies met the inclusion criteria and were mapped using the scoping review method. Our findings reveal that most studies were quantitative, with experimental designs conducted in a laboratory setting where children were exposed to physically present or virtual robots in a one-to-one situation. We found that robots were used to investigate four main concepts: animacy concept, action understanding, imitation, and early conversational skills. Many studies focused on whether young children regard robots as agents or social partners. The studies demonstrated that young children could learn from and understand social robots in some situations but not always. For instance, children's understanding of social robots was often facilitated by robots that behaved interactively and contingently. This scoping review highlights the need to design social robots that can engage in interactive and contingent social behaviors for early developmental research.

An individually adjusted approach for communicating epidemiological results on health and lifestyle to patients.

If scientific research on modifiable risk factors was more accessible to the general population there is a potential to prevent disease and promote health. Mobile applications can automatically combine individual characteristics and statistical models of health to present scientific information as individually tailored visuals, and thus there is untapped potential in incorporating scientific research into apps aimed at promoting healthier lifestyles. As a proof-of-concept, we develop a statistical model of the relationship between Self-rated-health (SRH) and lifestyle-related factors, and a simple app for conveying its effects through a visualisation that sets the individual as the frame of reference. Using data from the 6th (n = 12 981, 53.4% women and 46.6% men) and 7th (n = 21 083, 52.5% women and 47.5% men) iteration of the Tromsø population survey, we fitted a mixed effects linear regression model that models mean SRH as a function of self-reported intensity and frequency of physical activity (PA), BMI, mental health symptoms (HSCL-10), smoking, support from friends, and HbA1c ≥ 6.5%. We adjusted for socioeconomic and demographic factors and comorbidity. We designed a simple proof-of-concept app to register relevant user information, and use the SRH-model to translate the present status of the user into suggestions for lifestyle changes along with predicted health effects. SRH was strongly related to modifiable health factors. The strongest modifiable predictors of SRH were mental health symptoms and PA. The mean adjusted difference in SRH between those with 10-HSCL index = 1.85 (threshold for mental distress) and HSCL-10 = 1 was 0.59 (CI 0.61-0.57). Vigorous physical activity (exercising to exhaustion ≥ 4 days/week relative to sedentary) was associated with an increase on the SRH scale of 0.64 (CI 0.56-0.73). Physical activity intensity and frequency interacted positively, with large PA-volume (frequency ⨯ intensity) being particularly predictive of high SRH. Incorporating statistical models of health into lifestyle apps have great potential for effectively communicating complex health research to a general audience. Such an approach could improve lifestyle apps by helping to make the recommendations more scientifically rigorous and personalised, and offer a more comprehensive overview of lifestyle factors and their importance.

Publicly available datasets of breast histopathology H&E whole-slide images: A scoping review.

Advancements in digital pathology and computing resources have made a significant impact in the field of computational pathology for breast cancer diagnosis and treatment. However, access to high-quality labeled histopathological images of breast cancer is a big challenge that limits the development of accurate and robust deep learning models. In this scoping review, we identified the publicly available datasets of breast H&E-stained whole-slide images (WSIs) that can be used to develop deep learning algorithms. We systematically searched 9 scientific literature databases and 9 research data repositories and found 17 publicly available datasets containing 10 385 H&E WSIs of breast cancer. Moreover, we reported image metadata and characteristics for each dataset to assist researchers in selecting proper datasets for specific tasks in breast cancer computational pathology. In addition, we compiled 2 lists of breast H&E patches and private datasets as supplementary resources for researchers. Notably, only 28% of the included articles utilized multiple datasets, and only 14% used an external validation set, suggesting that the performance of other developed models may be susceptible to overestimation. The TCGA-BRCA was used in 52% of the selected studies. This dataset has a considerable selection bias that can impact the robustness and generalizability of the trained algorithms. There is also a lack of consistent metadata reporting of breast WSI datasets that can be an issue in developing accurate deep learning models, indicating the necessity of establishing explicit guidelines for documenting breast WSI dataset characteristics and metadata.

Algorithm for predicting valvular heart disease from heart sounds in an unselected cohort.

Objective: This study aims to assess the ability of state-of-the-art machine learning algorithms to detect valvular heart disease (VHD) from digital heart sound recordings in a general population that includes asymptomatic cases and intermediate stages of disease progression. Methods: We trained a recurrent neural network to predict murmurs from heart sound audio using annotated recordings collected with digital stethoscopes from four auscultation positions in 2,124 participants from the Tromsø7 study. The predicted murmurs were used to predict VHD as determined by echocardiography. Results: The presence of aortic stenosis (AS) was detected with a sensitivity of 90.9%, a specificity of 94.5%, and an area under the curve (AUC) of 0.979 (CI: 0.963-0.995). At least moderate AS was detected with an AUC of 0.993 (CI: 0.989-0.997). Moderate or greater aortic and mitral regurgitation (AR and MR) were predicted with AUC values of 0.634 (CI: 0.565-703) and 0.549 (CI: 0.506-0.593), respectively, which increased to 0.766 and 0.677 when clinical variables were added as predictors. The AUC for predicting symptomatic cases was higher for AR and MR, 0.756 and 0.711, respectively. Screening jointly for symptomatic regurgitation or presence of stenosis resulted in an AUC of 0.86, with 97.7% of AS cases (n = 44) and all 12 MS cases detected. Conclusions: The algorithm demonstrated excellent performance in detecting AS in a general cohort, surpassing observations from similar studies on selected cohorts. The detection of AR and MR based on HS audio was poor, but accuracy was considerably higher for symptomatic cases, and the inclusion of clinical variables improved the performance of the model significantly.

Social network analysis of Staphylococcus aureus carriage in a general youth population.

OBJECTIVES: Staphylococcus aureus carriage increases the risk of infection. We used social network analysis to evaluate whether contacts have the same S. aureus genotype indicating direct transmission or whether contagiousness is an indirect effect of contacts sharing the same lifestyle or characteristics. METHODS: The Fit Futures 1 study collected data on social contact among 1038 high school students. S. aureus carriage was determined from two nasal swab cultures and the genotype was determined by spa-typing of positive throat swabs. RESULTS: S. aureus carriage and spa-type were transmitted in the social network (P < 0.001). The probability of carriage increased by 5% for each S. aureus positive contact. Male sex was associated with a 15% lower risk of transmission compared to the female sex, although the carriage prevalence was higher for men (36% vs 24%). Students with medium physical activity levels, medium/high alcohol use, or normal weight had a higher number of contacts and an increased risk of transmission (P < 0.002). CONCLUSION: We demonstrated the direct social transmission of S. aureus. Lifestyle factors are associated with the risk of transmission, suggesting the effects of indirect social groups on S. aureus carriage, such as friends having more similar environmental exposures. The male predominance in the carriage is determined by sex-specific predisposing host characteristics as the social transmission is less frequent in males than females. Information on social networks may add to a better understanding of S. aureus epidemiology.

A Pragmatic Machine Learning Approach to Quantify Tumor-Infiltrating Lymphocytes in Whole Slide Images.

Increased levels of tumor-infiltrating lymphocytes (TILs) indicate favorable outcomes in many types of cancer. The manual quantification of immune cells is inaccurate and time-consuming for pathologists. Our aim is to leverage a computational solution to automatically quantify TILs in standard diagnostic hematoxylin and eosin-stained sections (H&E slides) from lung cancer patients. Our approach is to transfer an open-source machine learning method for the segmentation and classification of nuclei in H&E slides trained on public data to TIL quantification without manual labeling of the data. Our results show that the resulting TIL quantification correlates to the patient prognosis and compares favorably to the current state-of-the-art method for immune cell detection in non-small cell lung cancer (current standard CD8 cells in DAB-stained TMAs HR 0.34, 95% CI 0.17-0.68 vs. TILs in HE WSIs: HoVer-Net PanNuke Aug Model HR 0.30, 95% CI 0.15-0.60 and HoVer-Net MoNuSAC Aug model HR 0.27, 95% CI 0.14-0.53). Our approach bridges the gap between machine learning research, translational clinical research and clinical implementation. However, further validation is warranted before implementation in a clinical setting.

Predicting breast cancer metastasis from whole-blood transcriptomic measurements.

OBJECTIVE: In this exploratory work we investigate whether blood gene expression measurements predict breast cancer metastasis. Early detection of increased metastatic risk could potentially be life-saving. Our data comes from the Norwegian Women and Cancer epidemiological cohort study. The women who contributed to these data provided a blood sample up to a year before receiving a breast cancer diagnosis. We estimate a penalized maximum likelihood logistic regression. We evaluate this in terms of calibration, concordance probability, and stability, all of which we estimate by the bootstrap. RESULTS: We identify a set of 108 candidate predictor genes that exhibit a fold change in average metastasized observation where there is none for the average non-metastasized observation.

Reproduction study using public data of: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs.

We have attempted to reproduce the results in Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs, published in JAMA 2016; 316(22), using publicly available data sets. We re-implemented the main method in the original study since the source code is not available. The original study used non-public fundus images from EyePACS and three hospitals in India for training. We used a different EyePACS data set from Kaggle. The original study used the benchmark data set Messidor-2 to evaluate the algorithm's performance. We used another distribution of the Messidor-2 data set, since the original data set is no longer available. In the original study, ophthalmologists re-graded all images for diabetic retinopathy, macular edema, and image gradability. We have one diabetic retinopathy grade per image for our data sets, and we assessed image gradability ourselves. We were not able to reproduce the original study's results with publicly available data. Our algorithm's area under the receiver operating characteristic curve (AUC) of 0.951 (95% CI, 0.947-0.956) on the Kaggle EyePACS test set and 0.853 (95% CI, 0.835-0.871) on Messidor-2 did not come close to the reported AUC of 0.99 on both test sets in the original study. This may be caused by the use of a single grade per image, or different data. This study shows the challenges of reproducing deep learning method results, and the need for more replication and reproduction studies to validate deep learning methods, especially for medical image analysis. Our source code and instructions are available at: https://github.com/mikevoets/jama16-retina-replication.

Norwegian e-Infrastructure for Life Sciences (NeLS).

The Norwegian e-Infrastructure for Life Sciences (NeLS) has been developed by ELIXIR Norway to provide its users with a system enabling data storage, sharing, and analysis in a project-oriented fashion. The system is available through easy-to-use web interfaces, including the Galaxy workbench for data analysis and workflow execution. Users confident with a command-line interface and programming may also access it through Secure Shell (SSH) and application programming interfaces (APIs).  NeLS has been in production since 2015, with training and support provided by the help desk of ELIXIR Norway. Through collaboration with NorSeq, the national consortium for high-throughput sequencing, an integrated service is offered so that sequencing data generated in a research project is provided to the involved researchers through NeLS. Sensitive data, such as individual genomic sequencing data, are handled using the TSD (Services for Sensitive Data) platform provided by Sigma2 and the University of Oslo. NeLS integrates national e-infrastructure storage and computing resources, and is also integrated with the SEEK platform in order to store large data files produced by experiments described in SEEK.   In this article, we outline the architecture of NeLS and discuss possible directions for further development.

META-pipe Authorization service.

We describe the design, implementation, and use of the META-pipe Authorization service. META-pipe is a complete workflow for the analysis of marine metagenomics data. We will provide META-pipe as a web based data analysis service for ELIXIR users. We have integrated our Authorization service with the ELIXIR Authorization and Authentication Infrastructure (AAI) that allows single sign-on to services across the ELIXIR infrastructure. We use the Authorization service to authorize access to data on the META-pipe storage system and jobs in the META-pipe job queue. Our Authorization server was among the first SAML2 service providers  that integrated with ELIXIR AAI. The code is open source at: https://gitlab.com/uit-sfb/AuthService2.

Interactions between the tumor and the blood systemic response of breast cancer patients.

Although systemic immunity is critical to the process of tumor rejection, cancer research has largely focused on immune cells in the tumor microenvironment. To understand molecular changes in the patient systemic response (SR) to the presence of BC, we profiled RNA in blood and matched tumor from 173 patients. We designed a system (MIxT, Matched Interactions Across Tissues) to systematically explore and link molecular processes expressed in each tissue. MIxT confirmed that processes active in the patient SR are especially relevant to BC immunogenicity. The nature of interactions across tissues (i.e. which biological processes are associated and their patterns of expression) varies highly with tumor subtype. For example, aspects of the immune SR are underexpressed proportionally to the level of expression of defined molecular processes specific to basal tumors. The catalog of subtype-specific interactions across tissues from BC patients provides promising new ways to tackle or monitor the disease by exploiting the patient SR.

The metagenomic data life-cycle: standards and best practices.

Metagenomics data analyses from independent studies can only be compared if the analysis workflows are described in a harmonized way. In this overview, we have mapped the landscape of data standards available for the description of essential steps in metagenomics: (i) material sampling, (ii) material sequencing, (iii) data analysis, and (iv) data archiving and publishing. Taking examples from marine research, we summarize essential variables used to describe material sampling processes and sequencing procedures in a metagenomics experiment. These aspects of metagenomics dataset generation have been to some extent addressed by the scientific community, but greater awareness and adoption is still needed. We emphasize the lack of standards relating to reporting how metagenomics datasets are analysed and how the metagenomics data analysis outputs should be archived and published. We propose best practice as a foundation for a community standard to enable reproducibility and better sharing of metagenomics datasets, leading ultimately to greater metagenomics data reuse and repurposing.

ELIXIR pilot action: Marine metagenomics - towards a domain specific set of sustainable services.

Metagenomics, the study of genetic material recovered directly from environmental samples, has the potential to provide insight into the structure and function of heterogeneous microbial communities.  There has been an increased use of metagenomics to discover and understand the diverse biosynthetic capacities of marine microbes, thereby allowing them to be exploited for industrial, food, and health care products. This ELIXIR pilot action was motivated by the need to establish dedicated data resources and harmonized metagenomics pipelines for the marine domain, in order to enhance the exploration and exploitation of marine genetic resources. In this paper, we summarize some of the results from the ELIXIR pilot action "Marine metagenomics - towards user centric services".

META-pipe cloud setup and execution.

META-pipe is a complete service for the analysis of marine metagenomic data. It provides assembly of high-throughput sequence data, functional annotation of predicted genes, and taxonomic profiling. The functional annotation is computationally demanding and is therefore currently run on a high-performance computing cluster in Norway. However, additional compute resources are necessary to open the service to all ELIXIR users. We describe our approach for setting up and executing the functional analysis of META-pipe on additional academic and commercial clouds. Our goal is to provide a powerful analysis service that is easy to use and to maintain. Our design therefore uses a distributed architecture where we combine central servers with multiple distributed backends that execute the computationally intensive jobs. We believe our experiences developing and operating META-pipe provides a useful model for others that plan to provide a portal based data analysis service in ELIXIR and other organizations with geographically distributed compute and storage resources.

Kvik: three-tier data exploration tools for flexible analysis of genomic data in epidemiological studies.

Kvik is an open-source framework that we developed for explorative analysis of functional genomics data from large epidemiological studies. Creating such studies requires a significant amount of time and resources. It is therefore usual to reuse the data from one study for several research projects. Often each project requires implementing new analysis code, integration with specific knowledge bases, and specific visualizations. Although existing data exploration tools are available for single study data exploration, no tool provides all the required functionality for multistudy data exploration. We have therefore used the Kvik framework to develop Kvik Pathways, an application for exploring gene expression data in the context of biological pathways. We have used Kvik Pathways to explore data from both a cross-sectional study design and a case-control study within the Norwegian Women and Cancer (NOWAC) cohort. Kvik Pathways follows the three-tier architecture in web applications using a powerful back-end for statistical analyses and retrieval of metadata.In this note, we describe how we used the Kvik framework to develop the Kvik Pathways application. Kvik Pathways was used by our team of epidemiologists toexplore gene expression data from healthy women with high and low plasma ratios of essential fatty acids.