KANPHOS: Kinase-associated neural phospho-signaling database for data-driven research.

Protein phosphorylation, a key regulator of cellular processes, plays a central role in brain function and is implicated in neurological disorders. Information on protein phosphorylation is expected to be a clue for understanding various neuropsychiatric disorders and developing therapeutic strategies. Nonetheless, existing databases lack a specific focus on phosphorylation events in the brain, which are crucial for investigating the downstream pathway regulated by neurotransmitters. To overcome the gap, we have developed a web-based database named "Kinase-Associated Neural PHOspho-Signaling (KANPHOS)." This paper presents the design concept, detailed features, and a series of improvements for KANPHOS. KANPHOS is designed to support data-driven research by fulfilling three key objectives: (1) enabling the search for protein kinases and their substrates related to extracellular signals or diseases; (2) facilitating a consolidated search for information encompassing phosphorylated substrate genes, proteins, mutant mice, diseases, and more; and (3) offering integrated functionalities to support pathway and network analysis. KANPHOS is also equipped with API functionality to interact with external databases and analysis tools, enhancing its utility in data-driven investigations. Those key features represent a critical step toward unraveling the complex landscape of protein phosphorylation in the brain, with implications for elucidating the molecular mechanisms underlying neurological disorders. KANPHOS is freely accessible to all researchers at https://kanphos.jp.

Declaration of Helsinki: ethical norm in pursuit of common global goals.

The World Medical Association's Declaration of Helsinki is in the process of being revised. The following amendments are recommended to be incorporated in pursuit of the common goal of promoting health for all. 1. Data-driven research that facilitates broad informed consent and dynamic consent, assuring participant's rights, and the sharing of individual participant data (IPD) and research results to promote open science and generate social value. 2. Risk minimisation in a placebo-controlled study and post-trial access to the best-proven interventions for all who need them. 3. A future-oriented research framework for co-creation with all the relevant stakeholders.

Magnetic State Control of Non-van der Waals 2D Materials by Hydrogenation.

Controlling the magnetic state of two-dimensional (2D) materials is crucial for spintronics. By employing data-mining and autonomous density functional theory calculations, we demonstrate the switching of magnetic properties of 2D non-van der Waals materials upon hydrogen passivation. The magnetic configurations are tuned to states with flipped and enhanced moments. For 2D CdTiO3─a diamagnetic compound in the pristine case─we observe an onset of ferromagnetism upon hydrogenation. Further investigation of the magnetization density of the pristine and passivated systems provides a detailed analysis of modified local spin symmetries and the emergence of ferromagnetism. Our results indicate that selective surface passivation is a powerful tool for tailoring magnetic properties of nanomaterials, such as non-vdW 2D compounds.

Leveraging Comprehensive Cancer Registry Data to Enable a Broad Range of Research, Audit and Patient Support Activities.

Traditional cancer registries have often been siloed efforts, established by single groups with limited objectives. There is the potential for registry data to support a broad range of research, audit and education initiatives. Here, we describe the establishment of a series of comprehensive cancer registries across the spectrum of common solid cancers. The experience and learnings of each registry team as they develop, implement and then use collected data for a range of purposes, that informs the conduct and output of other registries in a virtuous cycle. Each registry is multi-site, multi-disciplinary and aims to collect data of maximal interest and value to a broad range of enquiry, which would be accessible to any researcher with a high-quality proposal. Lessons learnt include the need for careful and continuous curation of data fields, with regular database updates, and the need for a continued focus on data quality. The registry data as a standalone resource has supported numerous projects, but linkage with external datasets with patients in common has enhanced the audit and research potential. Multiple projects have linked registry data with matched tissue specimens to support prognostic and predictive biomarker studies, both validation and discovery. Registry-based biomarker trials have been successfully supported, generating novel and practice-changing data. Registry-based clinical trials, particularly randomised studies exploring the optimal use of available therapy options are now complementing the research conducted in traditional clinical trials. More recent projects supported by the registries include health economic studies, personalised patient education material, and increased consumer engagement, including consumer entered data.

Data-Driven Materials Innovation and Applications.

Owing to the rapid developments to improve the accuracy and efficiency of both experimental and computational investigative methodologies, the massive amounts of data generated have led the field of materials science into the fourth paradigm of data-driven scientific research. This transition requires the development of authoritative and up-to-date frameworks for data-driven approaches for material innovation. A critical discussion on the current advances in the data-driven discovery of materials with a focus on frameworks, machine-learning algorithms, material-specific databases, descriptors, and targeted applications in the field of inorganic materials is presented. Frameworks for rationalizing data-driven material innovation are described, and a critical review of essential subdisciplines is presented, including: i) advanced data-intensive strategies and machine-learning algorithms; ii) material databases and related tools and platforms for data generation and management; iii) commonly used molecular descriptors used in data-driven processes. Furthermore, an in-depth discussion on the broad applications of material innovation, such as energy conversion and storage, environmental decontamination, flexible electronics, optoelectronics, superconductors, metallic glasses, and magnetic materials, is provided. Finally, how these subdisciplines (with insights into the synergy of materials science, computational tools, and mathematics) support data-driven paradigms is outlined, and the opportunities and challenges in data-driven material innovation are highlighted.

Data-Driven Quest for Two-Dimensional Non-van der Waals Materials.

Two-dimensional (2D) materials are frequently associated with the sheets forming bulk layered compounds bonded by van der Waals (vdW) forces. The anisotropy and weak interaction between the sheets have also been the main criteria in the computational search for new 2D systems, predicting ∼2000 exfoliable compounds. However, some representatives of a new type of non-vdW 2D systems, without layered 3D analogues, were recently manufactured. For this novel materials class, data-driven design principles are still missing. Here, we outline a set of 8 binary and 20 ternary candidates by filtering the AFLOW-ICSD database according to structural prototypes. The oxidation state of the surface cations regulates the exfoliation energy with low oxidation numbers leading to weak bonding─a useful descriptor to obtain novel 2D materials also providing clear guidelines for experiments. A vast range of appealing electronic, optical, and magnetic properties make the candidates attractive for various applications and particularly spintronics.

Sex-specific analysis of traumatic brain injury events: applying computational and data visualization techniques to inform prevention and management.

BACKGROUND: The interplay of host, agent, and environment implicated in traumatic brain injury (TBI) events is difficult to account for in hypothesis-driven research. Data-driven analysis of injury data can enable insight into injury events in novel ways. This research dissected complex and multidimensional data at the time of the TBI event by exploiting data mining and information visualization methods. METHODS: We drew upon population-based decade-long health administrative data collected through the routine operation of the publicly funded health system in Ontario, Canada. We applied a computational approach to categorize health records of 235,003 patients with TBI versus the same number of reference patients without TBI, individually matched based on sex, age, place of residence, and neighbourhood income quantile. We adopted the basic concepts of the Haddon Matrix (host, agent, environment) to organize emerging factors significantly related to TBI versus non-TBI events. To explore sex differences, the data of male and female patients with TBI were plotted on heatmaps and clustered using hierarchical clustering algorithms. RESULTS: Based on detected similarities, the computational technique yielded 34 factors on which individual TBI-event codes were loaded, allowing observation of a set of definable patterns within the host, the agent, and the environment. Differences in the patterns of host, agent and environment were found between male and female patients with TBI, which are currently not identified based on data from injury surveillance databases. The results were internally validated. CONCLUSIONS: The study outlines novel areas for research relevant to TBI and offers insight into how computational and visual techniques can be applied to advance the understanding of TBI event. Results highlight unique aspects of sex differences of the host and agent at the injury event, as well as differences in exposure to adverse social and environmental circumstances, which can be a function of gender, aiding in future studies of injury prevention and gender-transformative care.

Exploring Abnormal Behavior Patterns of Online Users With Emotional Eating Behavior: Topic Modeling Study.

BACKGROUND: Emotional eating (EE) is one of the most significant symptoms of various eating disorders. It has been difficult to collect a large amount of behavioral data on EE; therefore, only partial studies of this symptom have been conducted. To provide adequate support for online social media users with symptoms of EE, we must understand their behavior patterns to design a sophisticated personalized support system (PSS). OBJECTIVE: This study aimed to analyze the behavior patterns of emotional eaters as the first step to designing a personalized intervention system. METHODS: The machine learning (ML) framework and Latent Dirichlet Allocation (LDA) topic modeling tool were used to collect and analyze behavioral data on EE. Data from a subcommunity of Reddit, /r/loseit, were analyzed. This dataset included all posts and feedback from July 2014 to May 2018, comprising 185,950 posts and 3,528,107 comments. In addition, deleted and improperly collected data were eliminated. Stochastic gradient descent-based ML classifier with an accuracy of 90.64% was developed to collect refined behavioral data of online users with EE behaviors. The expert group that labeled the dataset to train the ML classifiers included a medical doctor specializing in EE diagnosis and a nutritionist with profound knowledge of EE behavior. The experts labeled 5126 posts as EE (coded as 1) or others (coded as 0). Finally, the topic modeling process was conducted with LDA. RESULTS: The following 4 macroperspective topics of online EE behaviors were identified through linguistic evidence regarding each topic: addressing feelings, sharing physical changes, sharing and asking for dietary information, and sharing dietary strategies. The 5 main topics of feedback were dietary information, compliments, consolation, automatic bot feedback, and health information. The feedback topic distribution significantly differed depending on the type of EE behavior (overall P<.001). CONCLUSIONS: This study introduces a data-driven approach for analyzing behavior patterns of social website users with EE behaviors. We discovered the possibility of the LDA topic model as an exploratory user study method for abnormal behaviors in medical research. We also investigated the possibilities of ML- and topic modeling-based classifiers to automatically categorize text-based behavioral data, which could be applied to personalized medicine in future research.

Machine Learning in Nanoscience: Big Data at Small Scales.

Recent advances in machine learning (ML) offer new tools to extract new insights from large data sets and to acquire small data sets more effectively. Researchers in nanoscience are experimenting with these tools to tackle challenges in many fields. In addition to ML's advancement of nanoscience, nanoscience provides the foundation for neuromorphic computing hardware to expand the implementation of ML algorithms. In this Mini Review, we highlight some recent efforts to connect the ML and nanoscience communities by focusing on three types of interaction: (1) using ML to analyze and extract new insights from large nanoscience data sets, (2) applying ML to accelerate material discovery, including the use of active learning to guide experimental design, and (3) the nanoscience of memristive devices to realize hardware tailored for ML. We conclude with a discussion of challenges and opportunities for future interactions between nanoscience and ML researchers.

Discovering Associations Among Older Adults' Characteristics and Planned Nursing Interventions Using Electronic Health Record Data.

BACKGROUND AND PURPOSE: Little is known about how nursing assessments of strengths and signs/symptoms inform intervention planning in assisted living communities. The purpose of this study was to discover associations among older adults' characteristics and their planned nursing interventions. METHODS: This study employed a data-driven method, latent class analysis, using existing electronic health record data from a senior living community in the Midwest. A convenience sample comprised de-identified data of well-being assessments and care plans for 243 residents. Latent class analysis, descriptive, and inferential statistics were used to group the sample, summarize strengths and problems attributes, nursing interventions, and Knowledge, Behavior, and Status scores, and detect differences. RESULTS: Three groups presented based on patterns of strengths and signs/symptoms combined with problem concepts: Living Well (n = 95) had more strengths and fewer signs/symptoms; Lower Strengths (n = 99) had fewer strengths and more signs/symptoms; and Resilient Survivors (n = 49) had more strengths and more signs/symptoms. Some associations were found among group characteristics and planned interventions. Living Well had the lowest average number of planned interventions per resident (Mean = 2.7; standard deviation [SD] = 1.7) followed by Lower Strengths (Mean = 3.8; SD = 2.6) and Resilient Survivors (Mean = 4.1; SD = 3.4). IMPLICATIONS FOR PRACTICE: This study offers new knowledge in the use of a strengths-based ontology to facilitate a nursing discourse that leverages use of older adults' strengths to address their problems and support their living a healthier life. It also offers the potential to complement the problem-based infrastructure in clinical practice and documentation.

Power of the Past, Celebrate the Present, Force of the Future Part 2: Our Story-NASN's Second 25 Years.

The July 2018 issue of the NASN School Nurse, featured the first in a series of articles exploring the history, examining the present, and visioning the future of our organization in celebration of NASN's 50th anniversary. Part 2 of our historical account reflects on the leadership of a new generation of clinicians, reviewing the major emphases and accomplishments of NASN presidents serving from 1993 to today.

Computational functional genomics-based approaches in analgesic drug discovery and repurposing.

Persistent pain is a major healthcare problem affecting a fifth of adults worldwide with still limited treatment options. The search for new analgesics increasingly includes the novel research area of functional genomics, which combines data derived from various processes related to DNA sequence, gene expression or protein function and uses advanced methods of data mining and knowledge discovery with the goal of understanding the relationship between the genome and the phenotype. Its use in drug discovery and repurposing for analgesic indications has so far been performed using knowledge discovery in gene function and drug target-related databases; next-generation sequencing; and functional proteomics-based approaches. Here, we discuss recent efforts in functional genomics-based approaches to analgesic drug discovery and repurposing and highlight the potential of computational functional genomics in this field including a demonstration of the workflow using a novel R library 'dbtORA'.

Data-Driven Design of Intelligent Wireless Networks: An Overview and Tutorial.

Data science or "data-driven research" is a research approach that uses real-life data to gain insight about the behavior of systems. It enables the analysis of small, simple as well as large and more complex systems in order to assess whether they function according to the intended design and as seen in simulation. Data science approaches have been successfully applied to analyze networked interactions in several research areas such as large-scale social networks, advanced business and healthcare processes. Wireless networks can exhibit unpredictable interactions between algorithms from multiple protocol layers, interactions between multiple devices, and hardware specific influences. These interactions can lead to a difference between real-world functioning and design time functioning. Data science methods can help to detect the actual behavior and possibly help to correct it. Data science is increasingly used in wireless research. To support data-driven research in wireless networks, this paper illustrates the step-by-step methodology that has to be applied to extract knowledge from raw data traces. To this end, the paper (i) clarifies when, why and how to use data science in wireless network research; (ii) provides a generic framework for applying data science in wireless networks; (iii) gives an overview of existing research papers that utilized data science approaches in wireless networks; (iv) illustrates the overall knowledge discovery process through an extensive example in which device types are identified based on their traffic patterns; (v) provides the reader the necessary datasets and scripts to go through the tutorial steps themselves.