Road Map Research Articles

Mapping the Human Cell Surface Interactome: A Key to Decode Cell-to-Cell Communication.

Proteins on the surfaces of cells serve as physical connection points to bridge one cell with another, enabling direct communication between cells and cohesive structure. As biomedical research makes the leap from characterizing individual cells toward understanding the multicellular organization of the human body, the binding interactions between molecules on the surfaces of cells are foundational both for computational models and for clinical efforts to exploit these influential receptor pathways. To achieve this grander vision, we must assemble the full interactome of ways surface proteins can link together. This review investigates how close we are to knowing the human cell surface protein interactome. We summarize the current state of databases and systematic technologies to assemble surface protein interactomes, while highlighting substantial gaps that remain. We aim for this to serve as a road map for eventually building a more robust picture of the human cell surface protein interactome.

Driving Deployment of Bioengineered Products-An Arduous, Sometimes Tedious, Challenging, Rewarding, Most Exciting Journey That Has to Be Made!

More than three decades ago, we embarked on a number of bioengineering explorations using the most advanced materials and fabrication methods. In every area we ventured into, it was our intention to ensure fundamental discoveries were deployed into the clinic to benefit patients. When we embarked on this journey, we did so without a road map, not even a compass, and so the path was arduous, sometimes tedious. Now, we can see the doorway to deployment on the near horizon. We now appreciate that overcoming the challenges has made this a rewarding and exciting journey. However, maybe we could have been here a lot sooner, and so maybe the lessons we have learned could benefit others and accelerate progress in clinical translation. Through a number of case studies, including neural regeneration, cartilage regeneration, skin regeneration, the 3D printing of capsules for islet cell transplantation, and the bioengineered cornea, here, we retrace our steps. We will summarise the journey to date, point out the obstacles encountered, and celebrate the translational impact. Then, we will provide a framework for project design with the clinical deployment of bioengineered products as the goal.

A Road Map of Prompt Engineering for ChatGPT in Healthcare: A Perspective Study.

Generative AI models, such as ChatGPT, have significantly impacted healthcare through the strategic use of prompts to enhance precision, relevance, and ethical standards. This perspective explores the application of prompt engineering to tailor outputs specifically for healthcare stakeholders: patients, providers, policymakers, and researchers. A nine-stage process for prompt engineering in healthcare is proposed, encompassing identifying applications, understanding stakeholder needs, designing tailored prompts, iterative testing and refinement, ethical considerations, collaborative feedback, documentation, training, and continuous updates. A literature review focused on "Generative AI" or "ChatGPT," prompts, and healthcare informed this study, identifying key prompts through qualitative analysis and expert input. This systematic approach ensures that AI-generated prompts align with stakeholder requirements, offering valuable insights into symptoms, treatments, and prevention, thereby supporting informed decision-making among patients.

Monopsony Power in the Labor Market: From Theory to Policy

Labor markets are not perfectly competitive: Monopsony power enables employers to pay workers less than the marginal revenue product of labor. We review three theoretical frameworks explaining monopsony power. Oligopsony models attribute it to strategic interactions among a limited number of firms. Job differentiation models cite imperfect job substitution and heterogeneous worker preferences. Search-and-matching models point to search frictions hindering instantaneous access to all available jobs. We then develop a theory-informed discussion of the empirical evidence on antitrust policies, policies that reduce barriers to job switching, and policies countering monopsony's effects on workers. Preventing mergers and regulating noncompetition agreements can increase wages by preserving competition among employers. Minimum wages can mitigate the effect of monopsony power by increasing wages without reducing employment. The insights garnered from both theoretical models and empirical evidence offer a road map for crafting policies that can enhance competition in the labor market.

Graph Sampling for Map Comparison

Comparing two road maps is a basic operation that arises in a variety of situations. A map comparison method that is commonly used, mainly in the context of comparing reconstructed maps to ground-truth maps, is based on graph sampling . The essential idea is to first compute a set of point samples on each map and then to match pairs of samples—one from each map—in a one-to-one fashion. For deciding whether two samples can be matched, different criteria, e.g., based on distance or orientation, can be used. The total number of matched pairs gives a measure of how similar the maps are. Since the work of Biagioni and Eriksson [ 11 , 12 ], graph sampling methods have become widely used. However, there are different ways to implement each of the steps, which can lead to significant differences in the results. This means that conclusions drawn from different studies that seemingly use the same comparison method cannot necessarily be compared. In this work we present a unified approach to graph sampling for map comparison. We present the method in full generality, discussing the main decisions involved in its implementation. In particular, we point out the importance of the sampling method (GEO vs. TOPO) and that of the matching definition, discussing the main options used, and proposing alternatives for both key steps. We experimentally evaluate the different sampling and matching options considered on map datasets and reconstructed maps. Furthermore, we provide a code base and an interactive visualization tool to set a standard for future evaluations in the field of map construction and map comparison.

IMPLEMENTATION OF THE ROAD MAP POLICY FOR BUREAUCRATIC REFORM AT THE INSTANTIONAL LEVEL (Study at the Regional Secretariat of Konawe Regency, Southeast Sulawesi Province)

<p><span>Bureaucratic reform has become a very important concept in government administration. In Indonesia, bureaucratic reform has been ongoing for two decades and is systematically regulated through Presidential Regulation Number 81 of 2010 concerning the Grand Design of Bureaucratic Reform for 2010-2024 with the aim of achieving a World Class Bureaucracy by 2025. However, in its implementation, the bureaucratic reform policy is ongoing to date. This has not yet shown an impact on improving governance and achieving development targets, especially in regional governments (Hermawan et al, 2020). According to the Ministry of State Apparatus Empowerment and Bureaucratic Reform of the Republic of Indonesia, the number of regional governments that have implemented bureaucratic reform programs is 200 out of 500. This is because bureaucratic reform regulations are too complicated, even though there has been a system approach that has been developed, the implementation of bureaucratic reform policies has not yet been implemented. running as expected (Dwiyanto, 2015). This research focuses on the implementation of the bureaucratic reform road map policy at the Regional Secretariat of Konawe Regency, Southeast Sulawesi Province. The results of the research show that the implementation of the bureaucratic reform road map policy at the Regional Secretariat of Konawe Regency, Southeast Sulawesi Province has not been effective and has not been completed in several areas of change analyzed, although several areas of change have shown relatively good progress, there are still several aspects that need to be implemented. has not been effective and reached the optimal level as expected.</span></p><p class="s37"><span class="s26">Keywords:</span></p><p class="s38"><span class="s26">Bureaucratic Reform, Policy Implementation, Road Map, Regional Government</span></p><p><span><br /></span></p>

Polymorph selection of pharmaceutical cocrystals via bench-top and continuous production techniques

Polymorphism can be a valuable tool as well as an impediment in the development and approval of pharmaceuticals, providing an opportunity to tune active pharmaceutical ingredient (API) physicochemical properties. The control of polymorphism in cocrystalline systems and other multicomponent forms remains underexplored. The study herein aims to investigate the potential of several techniques, liquid-assisted grinding (LAG), solvent evaporation (SE), supercritical enhanced atomization (SEA) and electrospraying, to control the cocrystal polymorphic outcome of three cocrystals: isonicotinamide-citric acid (IsoCa), ethenzamide-saccharin (EthSac) and ethenzamide-gentisic acid (EthGa). Solvent selection employing LAG and SE showed little effect on polymorphic outcome. Electrospraying and SEA primarily produced the α form of IsoCa, with process parameter variations leading to the β form during SEA, and a mixture of α and γ from electrospraying. Electrospraying led to the stable form I of EthSac, while SEA could produce pure form II, and a mixture. Electrospraying produced the form I of EthGa while SEA could produce form II, with an unknown polymorphic impurity. Density functional theory (DFT) computed electron density (ED) maps of cocrystal polymorph binary systems further rationalised the polymorphic predominance observed through the electrospraying. Ultimately this study provides a general road map for polymorph selection via atomization-based methodologies.

Can Turkey Be A Hub For Closed-Loop Systems In the Textile Industry for the EU?

The global textile industry, valued at $414 million, confronts substantial environmental challenges, contributing to waste, pollution, and greenhouse gases. This paper examines Turkey's potential to drive sustainability in textiles, exploring circular systems that minimize waste and promote eco-friendly practices. The paper consists of seven parts. After introduction, in the second part the importance of sustainability and circularity in textile industry is explained. Third part examines the Turkey as a major textile player, and the challenges of textile recycling industry. Overwhelming obstacles like insufficient recycling infrastructure require industry initiatives, government regulations, and technological innovations. Following parts explains the main limitations of the textile recycling in EU and questions if Turkey, with its production capabilities and recycling expertise, could be a key ally for EU to solve the obstacles of current textile recycling challenges. Recommendation and Conclusion parts offer a road map for Turkey to enhance its capabilities to become a key ally for EU, exemplifying a transformative shift towards a sustainable textile industry.

Emerging Nano/Microporous Architectures for Food Hazards: New Strategies for Precise Inspection and New Principles for Controllable Regulation.

The big progress of materials science along with chemical engineering and biotechnology has significantly promoted interdisciplinary development, achieving advanced analytical methodologies, improved inspection performance, as well as promising regulation principles for food safety. The very recent progress on nano/microporous architectures for agri-food science, including new strategies for precise inspection and new principles for controllable regulation of food hazards, are summarized and discussed. Major attention is paid to the newly emerged porous architectures with their derivative nano/microstructures contributing to food safety through their instinctive advantages including special material surface, extraordinary porous structure, ease-of-modification, and excellent diversity and variability. This review clearly and logically displays the research road maps and development trends for current food safety issues and give suggestive directions for future outlook as well as the bottleneck problems to be solved, not only smart inspection and analysis but also elimination and control of ever-emerging food hazards.

Effectiveness and predictors of psychotherapy in eating disorders: state-of-the-art and future directions.

Psychotherapy is the cornerstone of the multidisciplinary treatment approach for eating disorders. This review examines recent evidence regarding effectiveness, predictors, and mechanisms of change of psychotherapy in eating disorders, providing a road map for clinicians and researchers. Family-based treatments (FBT) are effective in adolescents with anorexia nervosa and bulimia nervosa. Evidence-based psychotherapies for anorexia nervosa have no evidence of superiority compared with treatment as usual (TAU) in adults with anorexia nervosa. Cognitive-behavioural therapy (CBT) is the first-choice psychotherapy recommended for adults with bulimia nervosa and binge-eating disorder (BED). Self-help interventions have some evidence of effectiveness in nonunderweight individuals with eating disorders. Early symptom improvement and adolescent age predict more favourable outcomes. Evidence-based psychotherapies can be suggested for eating disorders, although follow-up data are needed. Beyond anorexia nervosa, bulimia nervosa, and BED, there is no evidence of psychotherapy effectiveness in other eating disorders. The effectiveness of novel (e.g. 'third-wave') psychotherapies, treatment delivery modality (e.g. internet-delivered), and adjunctive interventions (e.g. virtual reality) needs to be further explored. A broader definition of recovery is recommended, including behavioural, physical, and psychological criteria. Predictors and mechanisms of changes have not been studied enough: quantitative and qualitative studies are needed to promote more tailored and individualized psychotherapy interventions.

Sociology of Twitter/X: Trends, Challenges, and Future Research Directions

This article provides a sociology of Twitter (now known as X) and charts the development of the study of the platform and its data in sociological venues through an analysis of 1,644 articles published since 2009. This review helps readers understand developments in the field and provides a road map for advancing future Twitter-related sociological research. The works cited in this review advance sociological research on a variety of subfields, including but not limited to race, social movements, segregation, politics, violence, and stratification. After a brief introduction of definitions as well as methodological approaches used to study the platform (computational, qualitative, and mixed), I explore how Twitter has been used in sociological research. Furthermore, using social movements and activism as a case study, I highlight what the platform's usage and communication reveal about the social world. I conclude with a road map for advancing Twitter-related sociological research in the current atmosphere in which Twitter has been renamed X. Ultimately, a sociology of Twitter does not need to be tethered to the platform per se, as it also provides a framework for understanding new platforms if they become home for what people previously posted to Twitter/X.

A review of three-way decision: Triadic understanding, organization, and perspectives

A theory of three-way decision is about thinking, problem-solving, and computing in threes or through triads. In this paper, we review fifteen years of research on three-way decision by using the philosophy-theory-application triad and the who-what-when triad. First, we discuss the philosophy, theory, and application of three-way decision. At the philosophy level, we delve into the philosophical roots and fundamental nature of three-way decision to reveal the underlying philosophical thinking. At the theory level, we provide an insightful analysis of the theory and methodology of three-way decision. At the application level, we examine the integration of three-way decision with other theories and their applications and effectiveness in real-world scenarios. Second, we focus on bibliometrics analytics by using the who-what-when triad, which attempts to answer a fundamental question of “who did what when”. We propose a 3×3 model by applying the 3×3 method of three-way decision. The first 3 is the author-topic-time triad. The second 3 represents a three-level analysis for each of the first three: (1) categorizing authors into the three levels of prolific authors, frequent authors, and occasional authors, (2) classifying topics into the three levels of the core topics, emerging topics, and to-be-explored topics, and (3) dividing articles into the three levels of initial investigations, further developments, and most recent studies. Finally, we perform a bibliometrics analysis of three-way decision articles by using the 3×3 model of three-way decision. The results not only reveal the current status and trend of three-way decision research but also provide a road map for future research.

Doctoral training to support sustainable soil geochemistry research in Africa.

Africa's potential for scientific research is not yet being realized, for various reasons including a lack of researchers in many fields and insufficient funding. Strengthened research capacity through doctoral training programmes in higher education institutes (HEIs) in Africa, to include collaboration with national, regional and international research institutions, can facilitate self-reliant and sustainable research to support socio-economic development. In 2012, the Royal Society and the UK's Department for International Development (now the Foreign, Commonwealth and Development Office) launched the Africa Capacity Building Initiative (ACBI) Doctoral Training Network which aimed to strengthen research capacity and training across sub-Saharan Africa. The ACBI supported 30 core PhD scholarships, all registered/supervised within African HEIs with advisory support from the UK-based institutes. Our 'Soil geochemistry to inform agriculture and health policies' consortium project, which was part of the ACBI doctoral training programme network, was implemented in Malawi, Zambia and Zimbabwe between 2014 and 2020. The aims of our consortium were to explore linkages between soil geochemistry, agriculture and public health for increased crop productivity, nutrition and safety of food systems and support wider training and research activities in soil science. Highlights from our consortium included: (i) the generation of new scientific evidence on linkages between soils, crops and human nutrition; (ii) securing new projects to translate science into policy and practice; and (iii) maintaining sustainable collaborative learning across the consortium. Our consortium delivered high-quality science outputs and secured new research and doctoral training funding from a variety of sources to ensure the continuation of research and training activities. For example, follow-on Global Challenges Research Funded Translation Award provided a strong evidence base on the prevalence of deficiencies in children under 5 years of age and women of reproductive age in Zimbabwe. This new evidence will contribute towards the design and implementation of a nationally representative micronutrient survey as an integral part of the Zimbabwe Demographic and Health Surveys conducted by the Ministry of Health and Child Care. The award also generated new evidence and a road map for creating quality innovative doctorates through a doctoral training landscape activity led by the Zimbabwe Council for Higher Education. Although our project and the wider ACBI has contributed to increasing the self-reliance and sustainability of research within the region, many challenges remain and ongoing investment is required.

Optimizing Camera Exposure Time for Automotive Applications.

Camera-based object detection is integral to advanced driver assistance systems (ADAS) and autonomous vehicle research, and RGB cameras remain indispensable for their spatial resolution and color information. This study investigates exposure time optimization for such cameras, considering image quality in dynamic ADAS scenarios. Exposure time, the period during which the camera sensor is exposed to light, directly influences the amount of information captured. In dynamic scenarios, such as those encountered in typical driving scenarios, optimizing exposure time becomes challenging due to the inherent trade-off between Signal-to-Noise Ratio (SNR) and motion blur, i.e., extending exposure time to maximize information capture increases SNR, but also increases the risk of motion blur and overexposure, particularly in low-light conditions where objects may not be fully illuminated. The study introduces a comprehensive methodology for exposure time optimization under various lighting conditions, examining its impact on image quality and computer vision performance. Traditional image quality metrics show a poor correlation with computer vision performance, highlighting the need for newer metrics that demonstrate improved correlation. The research presented in this paper offers guidance into the enhancement of single-exposure camera-based systems for automotive applications. By addressing the balance between exposure time, image quality, and computer vision performance, the findings provide a road map for optimizing camera settings for ADAS and autonomous driving technologies, contributing to safety and performance advancements in the automotive landscape.

Axiomatic design for safe construction considering lean and ergonomic principles: an application in Turkey

This study proposes a systematic approach based on axiomatic design incorporating lean and ergonomic principles within the proposed design. The contribution to the body of knowledge is to keep construction workers safe by fostering a secure construction environment. Axiomatic design and lean approaches are combined to address human factors and eliminate wastes that hinder a safe construction environment. The application of the proposed system is set in the context of the construction industry. The methodology provides the construction sector with a road map toward reducing the occurrence of accidents and serves as a complementary approach between lean and ergonomic principles. The design solution has been validated and partially applied in a metro construction firm. Results show that the design solution can improve the efficiency of the construction phases by adopting a value-maximization strategy and has the potential to improve the safety and ergonomics of construction projects.

Road map from dirty growth to sustainable green growth in Turkiye: Management of trade and financial processes?

This study examines how business, financial, monetary, and trade freedom influence Turkiye's green growth from 1995 to 2022, utilizing the ARDL approach to cointegration. Our results confirm the long-term cointegration among the variables. Robustness tests, such as Fully Modified Ordinary Least Squares (FMOLS) and Dynamic Ordinary Least Squares (DOLS), consistently show that business and trade freedom hinder Turkiye's green growth. Financial freedom and monetary freedom consistently foster it. Business freedom and heightened trade freedom lead to increased fossil fuel consumption, whereas monetary freedom stabilizes Turkiye's currency, and financial freedom promotes entrepreneurship. Enhancing eco-friendly energy sources and investing in green technologies are crucial for promoting sustainable growth, reducing production costs, fostering entrepreneurship, and encouraging competition. The fact that the impact of these variables on green growth in Turkiye has not been studied before makes the study's findings novel.

Protocol-driven approach to guideline-directed medical therapy optimization for heart failure: A real-world application to recovery

The objective of our study was to evaluate the real-world effects of an aggressive, personalized protocol for guideline-directed medical therapy (GDMT) titration in patients with heart failure (HF) with reduced ejection fraction (HFrEF). We conducted a two-center retrospective cohort study. Patients with HFrEF who presented to a HF clinic from January 2020 to December 2022 were placed on a GDMT protocol. 180 patients were included in the study. Mean GDMT score significantly increased from 4.7 to 5.9 (p < 0.001) between initial and final visits. Mean left ventricular ejection fraction (LVEF) significantly increased from 28 % to 33 % (+5 %, p < 0.001). 27 (15.7 %) of the 172 patients with complete New York Heart Association (NYHA) classification data had improvement by at least 1 class, while 2 (1.2 %) patients had worsening NYHA classification. 140 (77.8 %) patients had no unplanned hospitalizations between visits. 21 (11.7 %) patients had an unplanned hospitalization for acute HF during the study period with a mean time from first clinic visit to hospitalization of 183 days (range: 13–821 days). 2 (1.1 %) patients were hospitalized due to GDMT-associated adverse drug events (i.e. hypotension, hyperkalemia). 7 (3.9 %) patients died during the study period, which was lower than the predicted 1-year death rate for our cohort (12.3 %) using the MAGGIC score. In conclusion, an aggressive, personalized protocol for GDMT titration in patients with HFrEF led to significant improvements in LVEF, NYHA classification, hospitalization, and mortality in a real-world setting. This protocol may help serve as a road map to lessen the gap between clinical knowledge and practice surrounding optimization of GDMT and move HFrEF patients toward a path to recovery.

Implementing AI in Hospitals to Achieve a Learning Health System: Systematic Review of Current Enablers and Barriers.

Efforts are underway to capitalize on the computational power of the data collected in electronic medical records (EMRs) to achieve a learning health system (LHS). Artificial intelligence (AI) in health care has promised to improve clinical outcomes, and many researchers are developing AI algorithms on retrospective data sets. Integrating these algorithms with real-time EMR data is rare. There is a poor understanding of the current enablers and barriers to empower this shift from data set-based use to real-time implementation of AI in health systems. Exploring these factors holds promise for uncovering actionable insights toward the successful integration of AI into clinical workflows. The first objective was to conduct a systematic literature review to identify the evidence of enablers and barriers regarding the real-world implementation of AI in hospital settings. The second objective was to map the identified enablers and barriers to a 3-horizon framework to enable the successful digital health transformation of hospitals to achieve an LHS. The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were adhered to. PubMed, Scopus, Web of Science, and IEEE Xplore were searched for studies published between January 2010 and January 2022. Articles with case studies and guidelines on the implementation of AI analytics in hospital settings using EMR data were included. We excluded studies conducted in primary and community care settings. Quality assessment of the identified papers was conducted using the Mixed Methods Appraisal Tool and ADAPTE frameworks. We coded evidence from the included studies that related to enablers of and barriers to AI implementation. The findings were mapped to the 3-horizon framework to provide a road map for hospitals to integrate AI analytics. Of the 1247 studies screened, 26 (2.09%) met the inclusion criteria. In total, 65% (17/26) of the studies implemented AI analytics for enhancing the care of hospitalized patients, whereas the remaining 35% (9/26) provided implementation guidelines. Of the final 26 papers, the quality of 21 (81%) was assessed as poor. A total of 28 enablers was identified; 8 (29%) were new in this study. A total of 18 barriers was identified; 5 (28%) were newly found. Most of these newly identified factors were related to information and technology. Actionable recommendations for the implementation of AI toward achieving an LHS were provided by mapping the findings to a 3-horizon framework. Significant issues exist in implementing AI in health care. Shifting from validating data sets to working with live data is challenging. This review incorporated the identified enablers and barriers into a 3-horizon framework, offering actionable recommendations for implementing AI analytics to achieve an LHS. The findings of this study can assist hospitals in steering their strategic planning toward successful adoption of AI.

Navigating internal senses: A road map for the vagal interoceptive system.

A road map for the vagal interoceptive system.

The Lifetime Fitness Program: Structured Physical Activity for Older Adults and Meaningful Experiential Learning for Kinesiology Students

Physical activity is associated with improvements in both health and longevity and is highly recommended for older adults. Public health and nonprofit organizations have invested considerable efforts to promote physical activity among this population. The present manuscript describes the specifics of the first university-based adult fitness program (Lifetime Fitness Program) and how this program promotes healthy aging through physical activity while serving as an important venue for experiential learning among students. Thus, the goal of this paper is to provide a “road map” to guide strategies and methods to promote physical activity and healthy aging in an academic setting and provide students with meaningful experiential learning opportunities.