Improving the Quality and Use of Malaria Surveillance Data: Results from Evaluating an Integrated Malaria Information Storage System at the Health Facility Level in Selected Districts in Mozambique

Mozambique addressed critical malaria surveillance system challenges by rolling out an integrated malaria information storage system (iMISS) at the district level in February 2021. The iMISS integrates malaria data from existing systems across thematic program areas to improve data availability and use. In seven districts, the platform was extended to health facilities (HFs), allowing HFs to access iMISS and use tablets to submit monthly malaria reports to a central database, eliminating the need for paper-based reporting to districts. A structured evaluation of the iMISS rollout to HFs was carried out in February-July 2021. The four evaluation areas were data quality (reporting rate, timeliness, and fidelity) of monthly malaria reports electronically submitted to the iMISS, adoption of the iMISS for data-informed decision-making, system maintenance, and acceptability of the iMISS among target users. All 94 HFs in the seven targeted districts were assessed. Over the 6-month period, 86.1% of reported cases on the iMISS were consistent with cases recorded in paper-based reports, allowing for up to 10% discrepancy. In addition, 69.0% of expected monthly district meetings were held, and information from iMISS was discussed during 58.6% of these meetings. Maintenance issues, mostly related to tablet access and internet connectivity, were experienced by 74.5% of HFs; 33.7% of issues were resolved within 1 month. The iMISS and electronic submission of malaria reports were well accepted by HF- and district-level users. Continued political commitment and timely execution of issue management workflows are crucial to ensure trust in the new platform and facilitate higher levels of data use.

Procedimentos para vinculação de dados da saúde: aplicações na vigilância em saúde / Procedimientos para la vinculación de datos sanitarios: aplicaciones en la vigilancia en la salud / Procedures for health data linkage: applications in health surveillance

Objetivo: Apresentar metodologia padronizada para vinculação de diferentes bancos de dados em saúde pública. Métodos: Artigo de revisão metodológica, com descrição específica de processos de tratamento de dados para vinculação (linkage) determinística entre bancos de dados estruturados. Instruiu-se como tratar os dados, selecionar chaves de vinculação e vincular os bancos, utilizando-se dois bancos de dados simulados no software R. Resultados: Foram apresentados os comandos utilizados para a vinculação determinística, do tipo inner_join. O processo de vinculação resultou em um banco de dados com 40.108 pares ao se utilizar apenas a chave "Nome". Com a adição da segunda chave, "Nome da mãe", o resultado caiu para 112 pares. Ao adicionar a terceira chave, "Data de nascimento", apenas dois pares foram identificados. Conclusão: A vinculação de bancos de dados e suas análises são ferramentas válidas e úteis para os serviços de saúde, no apoio a ações de vigilância em saúde.

Objetivo: Presentar metodología estandarizada para vincular diferentes bases de datos de salud pública. Métodos: Artículo de revisión metodológica y descripción de los procesos de tratamiento de datos para la vinculación determinista entre bases de datos. Se dieron instrucciones sobre como manejar los datos, seleccionar claves de vinculación y vincular las bases de datos empleando dos bases de datos simuladas en el software R. Resultados: Se presentaron los comandos utilizados para la vinculación determinista, del tipo inner-join. El proceso resultó en una base de datos con 40.108 pares utilizando únicamente la clave "Nombre". Con la adición de la segunda clave, "Nombre de la madre", el resultado se redujo a 112 pares. Al agregar la tercera clave, "Fecha de nacimiento", solo se identificaron dos pares. Conclusión: La vinculación de bases de datos y sus análisis son herramientas válidas y útiles para que los servicios de salud las utilicen para apoyar las acciones de vigilancia en la salud.

Objective: To present a standardized methodology for linking different public health databases. Methods: This was a methodological review article specifically describing data processing procedures for deterministic linkage between structured databases. It instructs on how to: treat data, select linkage keys, and link databases using two databases simulated in R software. Results: The commands used for the deterministic linkage of the inner_join type were presented. The linkage process resulted in a database with 40,108 pairs using only the "Name" key. Adding the second key, "Name of mother", the resulted dropped to 112 pairs. By adding the third key, "Date of birth", only two pairs were identified. Conclusion: Database linkage and its analysis are valid and valuable tools for health services in supporting health surveillance actions.

Error-Free Synthetic DNA by Molecular Dictation.

Synthetic DNA is the linchpin of the rapidly accelerating biotechnological era and is perhaps the most promising candidate for long-term digital data storage. Despite huge advances, manufacturing error-free DNA at low cost and high throughput remains challenging. Borrowing from well-established sequencing-by-synthesis technologies, we describe a new solution for DNA error correction.

Google search volume trends for cancer screening terms during the COVID-19 pandemic.

The COVID-19 pandemic has led to delays in cancer diagnosis, in part due to postponement of cancer screening. We used Google Trends data to assess public attention to cancer screening during the first peak of the COVID-19 pandemic. Search volume for terms related to established cancer screening tests ("colonoscopy," "mammogram," "lung cancer screening," and "pap smear") showed a marked decrease of up to 76% compared to the pre-pandemic period, a significantly greater drop than for search volume for terms denoting common chronic diseases. Maintaining awareness of cancer screening during future public health crises may decrease delays in cancer diagnosis.

What every researcher should know about searching - clarified concepts, search advice, and an agenda to improve finding in academia.

We researchers have taken searching for information for granted for far too long. The COVID-19 pandemic shows us the boundaries of academic searching capabilities, both in terms of our know-how and of the systems we have. With hundreds of studies published daily on COVID-19, for example, we struggle to find, stay up-to-date, and synthesize information-all hampering evidence-informed decision making. This COVID-19 information crisis is indicative of the broader problem of information overloaded academic research. To improve our finding capabilities, we urgently need to improve how we search and the systems we use. We respond to Klopfenstein and Dampier (Res Syn Meth. 2020) who commented on our 2020 paper and proposed a way of improving PubMed's and Google Scholar's search functionalities. Our response puts their commentary in a larger frame and suggests how we can improve academic searching altogether. We urge that researchers need to understand that search skills require dedicated education and training. Better and more efficient searching requires an initial understanding of the different goals that define the way searching needs to be conducted. We explain the main types of searching that we academics routinely engage in; distinguishing lookup, exploratory, and systematic searching. These three types must be conducted using different search methods (heuristics) and using search systems with specific capabilities. To improve academic searching, we introduce the "Search Triangle" model emphasizing the importance of matching goals, heuristics, and systems. Further, we suggest an urgently needed agenda toward search literacy as the norm in academic research and fit-for-purpose search systems.

Bridging multimedia heterogeneity gap via Graph Representation Learning for cross-modal retrieval.

Information retrieval among different modalities becomes a significant issue with many promising applications. However, inconsistent feature representation of various multimedia data causes the "heterogeneity gap" among various modalities, which is a challenge in cross-modal retrieval. For bridging the "heterogeneity gap," the popular methods attempt to project the original data into a common representation space, which needs great fitting ability of the model. To address the above issue, we propose a novel Graph Representation Learning (GRL) method for bridging the heterogeneity gap, which does not project the original feature into an aligned representation space but adopts a cross-modal graph to link different modalities. The GRL approach consists of two subnetworks, Feature Transfer Learning Network (FTLN) and Graph Representation Learning Network (GRLN). Firstly, FTLN model finds a latent space for each modality, where the cosine similarity is suitable to describe their similarity. Then, we build a cross-modal graph to reconstruct the original data and their relationships. Finally, we abandon the features in the latent space and turn into embedding the graph vertexes into a common representation space directly. During the process, the proposed Graph Representation Learning method bypasses the most challenging issue by utilizing a cross-modal graph as a bridge to link the "heterogeneity gap" among different modalities. This attempt utilizes a cross-modal graph as an intermediary agent to bridge the "heterogeneity gap" in cross-modal retrieval, which is simple but effective. Extensive experiment results on six widely-used datasets indicate that the proposed GRL outperforms other state-of-the-art cross-modal retrieval methods.

Cluster equilibrium scheduling method based on backpressure flow control in railway power supply systems.

The purpose of the study is to solve problems, i.e., increasingly significant processing delay of massive monitoring data and imbalanced tasks in the scheduling and monitoring center for a railway network. To tackle these problems, a method by using a smooth weighted round-robin scheduling based on backpressure flow control (BF-SWRR) is proposed. The method is developed based on a model for message queues and real-time streaming computing. By using telemetry data flow as input data sources, the fields of data sources are segmented into different sets by using a distributed model of stream computing parallel processing. Moreover, the round-robin (RR) scheduling method for the distributed server is improved. The parallelism, memory occupancy, and system delay are tested by taking a high-speed train section of a certain line as an example. The result showed that the BF-SWRR method for clusters can control the delay to within 1 s. When the parallelism of distributed clusters is set to 8, occupancy rates of the CPU and memory can be decreased by about 15%. In this way, the overall load of the cluster during stream computing is more balanced.

Treating medical data as a durable asset.

Access to medical data is central for conducting research on genomics. However, to tap these metadata (observable traits and phenotypes, diagnoses and medication, and labels), researchers must grapple with the complex and sensitive nature of the information. In this Perspective, we argue that, at this exciting time for genomics and artificial intelligence, several critical aspects of data generation, infrastructure and management are pillars of a modern data ecosystem. Many risks to privacy and many obstacles to medical research can be eliminated or mitigated by new secure data analytics. Finally, we discuss the potential consequences of medical data exiting the institutions and being managed by individuals. These shifts in data ownership have the potential for profound disruption and opportunity across many fields.

Finding useful cancer information may reduce cancer information overload for Internet users.

BACKGROUND: An excessive overload of information causes an ineffective management of information, stress and indefiniteness. Furthermore, this situation can prevent persons from learning and making conscious decisions. OBJECTIVE: This study aims to determine the cancer information overload (CIO) and the factors related to it in adults who are Internet users. METHODS: A cross-sectional study with 482 Internet users was conducted. The data were collected by using an Introductory Information Form and the Cancer Information Overload Scale. RESULTS: It was found that the Internet was the most used information source (62.2%). The CIO of those with a university level education was found to be high (P = 0.012). It was found that the CIO of individuals who used the Internet (P = 0.031) and newspapers/magazines (P = 0.004) as sources of information was high compared with those who did not use these sources. It was determined from the information obtained that those who found the information to be beneficial and enough had a low CIO (P = 0.004, P = 0.00). CONCLUSION: Health literacy around cancer information is challenging for frequent Internet users. Health professionals, information specialists and librarians should orient people to reliable sources.

A mixed culture of bacterial cells enables an economic DNA storage on a large scale.

DNA emerged as a novel potential material for mass data storage, offering the possibility to cheaply solve a great data storage problem. Large oligonucleotide pools demonstrated high potential of large-scale data storage in test tube, meanwhile, living cell with high fidelity in information replication. Here we show a mixed culture of bacterial cells carrying a large oligo pool that was assembled in a high-copy-number plasmid was presented as a stable material for large-scale data storage. The underlying principle was explored by deep bioinformatic analysis. Although homology assembly showed sequence context dependent bias, the large oligonucleotide pools in the mixed culture were constant over multiple successive passages. Finally, over ten thousand distinct oligos encompassing 2304 Kbps encoding 445 KB digital data, were stored in cells, the largest storage in living cells reported so far and present a previously unreported approach for bridging the gap between in vitro and in vivo systems.

La divulgación científica en los tiempos de Twitter / Scientific dissemination in the Twitter era

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Choosing Among Common Data Models for Real-World Data Analyses Fit for Making Decisions About the Effectiveness of Medical Products.

Many real-world data analyses use common data models (CDMs) to standardize terminologies for medication use, medical events and procedures, data structures, and interpretations of data to facilitate analyses across data sources. For decision makers, key aspects that influence the choice of a CDM may include (i) adaptability to a specific question; (ii) transparency to reproduce findings, assess validity, and instill confidence in findings; and (iii) ease and speed of use. Organizing CDMs preserve the original information from a data source and have maximum adaptability. Full mapping data models, or preconfigured rules systems, are easy to use, since all raw codes are mapped to medical constructs. Adaptive rule systems grow libraries of reusable measures that can easily adjust to preserve adaptability, expedite analyses, and ensure study-specific transparency.

Siamese Neural Networks for User Identity Linkage Through Web Browsing.

Linking online identities of users among countless heterogeneous network services on the Internet can provide an explicit digital representation of users, which can benefit both research and industry. In recent years, user identity linkage (UIL) through the Internet has become an emerging task with great potential and many challenges. Existing works mainly focus on online social networks that consider inconsistent profiles, content, and networks as features or use sparse location-based data sets to link the online behaviors of a real person. To extend the UIL problem to a general scenario, we try to link the web-browsing behaviors of users, which can help to distinguish specific users from others, such as children or malicious users. More specifically, we propose a Siamese neural network (NN) architecture-based UIL (SAUIL) model that learns and compares the highest-level feature representation of input web-browsing behaviors with deep NNs. Although the number of matching and nonmatching pairs for the UIL problem is highly imbalanced, previous studies have not considered imbalanced UIL data sets. Therefore, we further address the imbalanced learning issue by proposing cost-sensitive SAUIL (C-SAUIL) model, which assumes higher costs for misclassifying the minority class. In the experiments, the proposed model is robust and exhibits a good performance on very large, real-world data sets collected from different regions with distinct characteristics.

How can big data science transform the psychological sciences?

Big data and related technologies are radically altering our society. In a similar way, these approaches can transform the psychological sciences. The goal of this commentary is to motivate psychologists to embrace big data science for the betterment of the field. Big data sources, algorithmic methods, and a culture that embraces prediction has the potential to advance our science, improve the robustness and replicability of our research, and allow us to focus more centrally on actual behaviors. We highlight these key transformations, acknowledge criticisms of big data approaches, and emphasize specific ways psychologists can contribute to the big data science revolution

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A Parser to Support the Definition of Access Control Policies and Rules Using Natural Languages.

As a consequence of the epidemiological transition towards non-communicable diseases, integrated care approaches are required, not solely focused on medical purposes, but also on a range of essential activities for the maintenance of the individuals' quality of life. In order to allow the exchange of information, these integrated approaches might be supported by digital platforms, which need to provide trustful environments and to guarantee the integrity of the information exchanged. Therefore, together with mechanisms such as authentication, logging or auditing, the definition of access control policies assumes a paramount importance. This article focuses on the development of a parser as a component of a platform to support the care of community-dwelling older adults, the SOCIAL platform, to allow the definition of access control policies and rules using natural languages.

Current status of use of big data and artificial intelligence in RMDs: a systematic literature review informing EULAR recommendations.

Objective: To assess the current use of big data and artificial intelligence (AI) in the field of rheumatic and musculoskeletal diseases (RMDs). Methods: A systematic literature review was performed in PubMed MEDLINE in November 2018, with key words referring to big data, AI and RMDs. All original reports published in English were analysed. A mirror literature review was also performed outside of RMDs on the same number of articles. The number of data analysed, data sources and statistical methods used (traditional statistics, AI or both) were collected. The analysis compared findings within and beyond the field of RMDs. Results: Of 567 articles relating to RMDs, 55 met the inclusion criteria and were analysed, as well as 55 articles in other medical fields. The mean number of data points was 746 million (range 2000-5 billion) in RMDs, and 9.1 billion (range 100 000-200 billion) outside of RMDs. Data sources were varied: in RMDs, 26 (47%) were clinical, 8 (15%) biological and 16 (29%) radiological. Both traditional and AI methods were used to analyse big data (respectively, 10 (18%) and 45 (82%) in RMDs and 8 (15%) and 47 (85%) out of RMDs). Machine learning represented 97% of AI methods in RMDs and among these methods, the most represented was artificial neural network (20/44 articles in RMDs). Conclusions: Big data sources and types are varied within the field of RMDs, and methods used to analyse big data were heterogeneous. These findings will inform a European League Against Rheumatism taskforce on big data in RMDs.

Implementation and Evaluation of the Value of Improved and Sustained Information Access by Library Expertise (VISIBLE) Program.

OBJECTIVE: To increase knowledge of National Library of Medicine resources by using a train-the-trainer approach. METHODS: Workshops were held in spring 2016 to increase knowledge of 4 National Library of Medicine tools. Data were collected before the workshop and immediately, 3 months, and 1 year after the workshop. Knowledge questions were scored as 1 point per question; an aggregated knowledge score could range from 0 to 16 points. A paired t test assessed the change in knowledge from before to after the workshop. RESULTS: Four workshops were hosted, with a total of 74 attendees. The response rate for the surveys ranged from 50% to 100%. Knowledge scores changed significantly from 7.2 to 11.9 (t = 15, P < .001). One year after the workshop, more of the participants reported having informally trained others (56.8%) than reported providing 1 or more formal training session (8.1%)(P < .001). CONCLUSION: Objective measures of knowledge and information dissemination showed that the National Library of Medicine workshop was successful and resulted in both short- and long-term gains. This workshop could be repeated with other populations to further disseminate information regarding the National Library of Medicine tools, which could help improve disaster response.