The role of ideation on the effect of an SBC intervention on consistent bed net use among caregivers of children under 5 years in Nigeria: a multilevel mediation analysis

Background Malaria remains a significant public health challenge in Nigeria. Consistent bed net use (sleeping under a treated net every night) has been identified as a key malaria prevention behavior. This paper explores the relationship between mass media social and behavior change interventions, psychosocial factors, and consistent bed net use. Methods Data is from the endline survey of a USAID-funded social and behavior change communication campaign conducted from 2012 to 2017 across five states in Nigeria. The outcome measure was consistent bed net use, and the mediator variable was a composite measure called ideation from a set of psychosocial factors believed to influence bed net use. The independent variable was recall of malaria specific media messages. Multilevel mediation analysis explored if recall of malaria specific media messages had any effect on bed net related ideation and if this ideation had any effect on consistent net use. Results Respondents included in this study were on average aged 31 years, mostly married or cohabiting (97.5%) and female 75%. Four in 10 (39.7%) respondents were able to recall malaria specific messages. Respondents with low, moderate and high recall were 23, 32 and 80% more likely to have a higher ideational score in the emotional domain compared to those not able to recall. Respondents were more likely to have higher ideational scores in the cognitive domain if they had low (AOR = 1.26, 95% CI 1.15–1.38), moderate (AOR = 1.16, 95% CI 1.00–1.34) or high recall (AOR = 1.55, 95% CI 1.16–2.06), respectively compared to those with no recall. Similarly, respondents with low (AOR = 1.03, 95% CI .99–1.08), moderate (AOR = 1.15, 95% CI 1.08–1.23) and high (AOR = 1.15, 95% CI 1.01–1.30) recall were more likely to have a higher ideational score in the social domain compared to those with no recall. After adjusting for recall of media messages and other potential covariates, all three ideational domains also had a significant positive effect on consistent bed net use. For every unit increase in ideational score, the likelihood of reporting consistent bed net use increased by 5 to 10%. There was a significant indirect effect of recalling malaria specific messages on consistent bed net use through each of the ideational domains. Conclusion Access to a bed net is a critical first step in the process of bed net utilization. However, psychosocial factors e.g., emotional, cognitive, and social domains of ideation also play a major role in bed net use. Mass media SBC interventions could potentially influence bed net related ideation and consequently improve net use behavior. Future Social and behavior change interventions should employ approaches that improve these domains of ideation within their audiences in order to increase bed net utilization.

Multistage BiCross encoder for multilingual access to COVID-19 health information

The Coronavirus (COVID-19) pandemic has led to a rapidly growing ‘infodemic’ of health information online. This has motivated the need for accurate semantic search and retrieval of reliable COVID-19 information across millions of documents, in multiple languages. To address this challenge, this paper proposes a novel high precision and high recall neural Multistage BiCross encoder approach. It is a sequential three-stage ranking pipeline which uses the Okapi BM25 retrieval algorithm and transformer-based bi-encoder and cross-encoder to effectively rank the documents with respect to the given query. We present experimental results from our participation in the Multilingual Information Access (MLIA) shared task on COVID-19 multilingual semantic search. The independently evaluated MLIA results validate our approach and demonstrate that it outperforms other state-of-the-art approaches according to nearly all evaluation metrics in cases of both monolingual and bilingual runs.

AMULET: a novel read count-based method for effective multiplet detection from single nucleus ATAC-seq data

Detecting multiplets in single nucleus (sn)ATAC-seq data is challenging due to data sparsity and limited dynamic range. AMULET (ATAC-seq MULtiplet Estimation Tool) enumerates regions with greater than two uniquely aligned reads across the genome to effectively detect multiplets. We evaluate the method by generating snATAC-seq data in the human blood and pancreatic islet samples. AMULET has high precision, estimated via donor-based multiplexing, and high recall, estimated via simulated multiplets, compared to alternatives and identifies multiplets most effectively when a certain read depth of 25K median valid reads per nucleus is achieved.

Comparison of Mammography and Ultrasonography for Early Detection of Breast Cancer

Aim: This systematic review is specifically aimed to compare mammography and ultrasonography in early detection of breast cancer. For this systematic review, major purpose is to compare both screening methods and also analyze the performance of supplemental ultrasonography for early detection of breast cancer. Methodology: For this systematic review, total 23 studies are included which follow the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. Electronic articles from year 2007 to from year 2017 on PUB Med, online Willey library, and Science Direct site were searched by using keywords related to sonographic and mammography imaging for breast cancer. Results: Out of 23 studies, 12 studies are conducted on women with dense breasts. Twenty studies performed their imaging with hand held ultrasound (HHUS). Out of twenty-three studies, sixteen studies followed BI-RADS procedures. In eleven studies that used joint methods, it was observed that mammography (MAM) has 65% whereas ultrasound (US) has 68% efficiency for early detection of breast cancer. 88% area under a cover (AUCs) among MAM and 98% among US imaging was observed. No major difference was found in sensitivity and specificity of both techniques. Conclusion: Study concludes that Ultrasound is more efficient to diagnose factors suggestive of breast cancer that cannot be detected on mammography. It also has the potential to evaluate cancer among dense breast women but unfortunately in some cases, it may cause a high recall rate. Keywords: Breast, Cancer, Mammography, Ultrasonography, Screening.

Using 3D Convolutional Neural Networks for Real-time Detection of Soccer Events

Developing systems for the automatic detection of events in video is a task which has gained attention in many areas including sports. More specifically, event detection for soccer videos has been studied widely in the literature. However, there are still a number of shortcomings in the state-of-the-art such as high latency, making it challenging to operate at the live edge. In this paper, we present an algorithm to detect events in soccer videos in real time, using 3D convolutional neural networks. We test our algorithm on three different datasets from SoccerNet, the Swedish Allsvenskan, and the Norwegian Eliteserien. Overall, the results show that we can detect events with high recall, low latency, and accurate time estimation. The trade-off is a slightly lower precision compared to the current state-of-the-art, which has higher latency and performs better when a less accurate time estimation can be accepted. In addition to the presented algorithm, we perform an extensive ablation study on how the different parts of the training pipeline affect the final results.

Need of Sentiments Analysis with CF for Quality Recommendations

Recommendation system (RS) help user for purchasing the right product of their interest within the affordable right price. Presently many RS make use of only filtering methods to recommend products to the user which is not taking care of the quality of products. Quality of products can be found from textual reviews available on various e-commerce websites and hence this RS performs Sentiment Analysis (SA)of extracted relevant textual reviews along with Collaborative Filtering (CF) to give accurate and good quality recommendations to the user. Reviews are analyzed using optimized Artificial Neural Network (ANN) which shows notified improvement than traditional ANN on real-time extracted data of reviews.CF performance is proved by using the standard dataset of movilense used in many research papers. Results show high recall and accuracy of CF for the recommendation of products to the target user.

ORCID-linked labeled data for evaluating author name disambiguation at scale

How can we evaluate the performance of a disambiguation method implemented on big bibliographic data? This study suggests that the open researcher profile system, ORCID, can be used as an authority source to label name instances at scale. This study demonstrates the potential by evaluating the disambiguation performances of Author-ity2009 (which algorithmically disambiguates author names in MEDLINE) using 3 million name instances that are automatically labeled through linkage to 5 million ORCID researcher profiles. Results show that although ORCID-linked labeled data do not effectively represent the population of name instances in Author-ity2009, they do effectively capture the ‘high precision over high recall’ performances of Author-ity2009. In addition, ORCID-linked labeled data can provide nuanced details about the Author-ity2009’s performance when name instances are evaluated within and across ethnicity categories. As ORCID continues to be expanded to include more researchers, labeled data via ORCID-linkage can be improved in representing the population of a whole disambiguated data and updated on a regular basis. This can benefit author name disambiguation researchers and practitioners who need large-scale labeled data but lack resources for manual labeling or access to other authority sources for linkage-based labeling. The ORCID-linked labeled data for Author-ity2009 are publicly available for validation and reuse.