Research Challenges Research Articles

Body mass index and all-cause mortality in HUNT and UK biobank studies: revised non-linear Mendelian randomisation analyses

ObjectivesTo estimate the shape of the causal relationship between body mass index (BMI) and mortality risk in a Mendelian randomisation framework.DesignMendelian randomisation analyses of two prospective population-based cohorts.SettingIndividuals of European...

Winter Conference 2023 Editorial.

Multimorbidity, the existence of two or more concurrent chronic conditions in a single individual, represents a major global health challenge. The Nutrition Society's 2023 Winter Conference at the Royal Society, London focused on the topic of 'Diet and lifestyle strategies for prevention and management of multimorbidity', with symposia designed to explore pathways for prevention of multimorbidity across the lifecourse, the role of ageing, the gut-brain-heart connection and lifestyle strategies for prevention and management of multimorbidity. It also considered machine learning and precision nutrition approaches for addressing research challenges in multimorbidity. The opening plenary lecture discussed advancing diet and lifestyle research to address the increasing burden and complexity of multimorbidity. The two-day programme concluded with a plenary which addressed the key dietary risk factors and policies in multimorbidity prevention.

Network representation of multicellular activity in pancreatic islets: Technical considerations for functional connectivity analysis.

Within the islets of Langerhans, beta cells orchestrate synchronized insulin secretion, a pivotal aspect of metabolic homeostasis. Despite the inherent heterogeneity and multimodal activity of individual cells, intercellular coupling acts as a homogenizing force, enabling coordinated responses through the propagation of intercellular waves. Disruptions in this coordination are implicated in irregular insulin secretion, a hallmark of diabetes. Recently, innovative approaches, such as integrating multicellular calcium imaging with network analysis, have emerged for a quantitative assessment of the cellular activity in islets. However, different groups use distinct experimental preparations, microscopic techniques, apply different methods to process the measured signals and use various methods to derive functional connectivity patterns. This makes comparisons between findings and their integration into a bigger picture difficult and has led to disputes in functional connectivity interpretations. To address these issues, we present here a systematic analysis of how different approaches influence the network representation of islet activity. Our findings show that the choice of methods used to construct networks is not crucial, although care is needed when combining data from different islets. Conversely, the conclusions drawn from network analysis can be heavily affected by the pre-processing of the time series, the type of the oscillatory component in the signals, and by the experimental preparation. Our tutorial-like investigation aims to resolve interpretational issues, reconcile conflicting views, advance functional implications, and encourage researchers to adopt connectivity analysis. As we conclude, we outline challenges for future research, emphasizing the broader applicability of our conclusions to other tissues exhibiting complex multicellular dynamics.

Distance plus attention for binding affinity prediction

Protein-ligand binding affinity plays a pivotal role in drug development, particularly in identifying potential ligands for target disease-related proteins. Accurate affinity predictions can significantly reduce both the time and cost involved in drug development. However, highly precise affinity prediction remains a research challenge. A key to improve affinity prediction is to capture interactions between proteins and ligands effectively. Existing deep-learning-based computational approaches use 3D grids, 4D tensors, molecular graphs, or proximity-based adjacency matrices, which are either resource-intensive or do not directly represent potential interactions. In this paper, we propose atomic-level distance features and attention mechanisms to capture better specific protein-ligand interactions based on donor-acceptor relations, hydrophobicity, and π\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\pi $$\\end{document}-stacking atoms. We argue that distances encompass both short-range direct and long-range indirect interaction effects while attention mechanisms capture levels of interaction effects. On the very well-known CASF-2016 dataset, our proposed method, named Distance plus Attention for Affinity Prediction (DAAP), significantly outperforms existing methods by achieving Correlation Coefficient (R) 0.909, Root Mean Squared Error (RMSE) 0.987, Mean Absolute Error (MAE) 0.745, Standard Deviation (SD) 0.988, and Concordance Index (CI) 0.876. The proposed method also shows substantial improvement, around 2% to 37%, on five other benchmark datasets. The program and data are publicly available on the website https://gitlab.com/mahnewton/daap.Scientific Contribution StatementThis study innovatively introducesdistance-based features to predict protein-ligand binding affinity, capitalizing onunique molecular interactions. Furthermore, the incorporation of protein sequencefeatures of specific residues enhances the model’s proficiency in capturing intricatebinding patterns. The predictive capabilities are further strengthened through theuse of a deep learning architecture with attention mechanisms, and an ensembleapproach, averaging the outputs of five models, is implemented to ensure robustand reliable predictions.

Comparative Analysis of Classification Methods and Suitable Datasets for Protocol Recognition in Operational Technologies

The interconnection of Operational Technology (OT) and Information Technology (IT) has created new opportunities for remote management, data storage in the cloud, real-time data transfer over long distances, or integration between different OT and IT networks. OT networks require increased attention due to the convergence of IT and OT, mainly due to the increased risk of cyber-attacks targeting these networks. This paper focuses on the analysis of different methods and data processing for protocol recognition and traffic classification in the context of OT specifics. Therefore, this paper summarizes the methods used to classify network traffic, analyzes the methods used to recognize and identify the protocol used in the industrial network, and describes machine learning methods to recognize industrial protocols. The output of this work is a comparative analysis of approaches specifically for protocol recognition and traffic classification in OT networks. In addition, publicly available datasets are compared in relation to their applicability for industrial protocol recognition. Research challenges are also identified, highlighting the lack of relevant datasets and defining directions for further research in the area of protocol recognition and classification in OT environments.

Creativity and Machine Learning: A Survey

There is a growing interest in the area of machine learning and creativity. This survey presents an overview of the history and the state of the art of computational creativity theories, key machine learning techniques (including generative deep learning), and corresponding automatic evaluation methods. After presenting a critical discussion of the key contributions in this area, we outline the current research challenges and emerging opportunities in this field.

Genetic Underpinnings of Cuticular Hydrocarbon Biosynthesis in the German Cockroach, Blattella germanica (L.): Progress and Perspectives.

Insect cuticular hydrocarbons (CHCs) serve as important waterproofing barriers and as signals and cues in chemical communication. Over the past 30 years, numerous studies on CHCs have been conducted in the German cockroach, Blattella germanica, leading to substantial progress in the field. However, there has not been a systematic review of CHC studies in this species in recent years. This review aims to provide a concise overview of the chemical composition, storage, transport, and physical properties of different CHCs in B. germanica. Additionally, we focus on the biosynthetic pathway and the genetic regulation of HC biosynthesis in this species. A considerable amount of biochemical evidence regarding the biosynthetic pathway of insect CHCs has been gathered from studies conducted in B. germanica. In recent years, there has also been an improved understanding of the molecular mechanisms that underlie CHC production in this insect. In this article, we summarize the biosynthesis of different classes of CHCs in B. germanica. Then, we review CHCs reaction to various environmental conditions and stressors and internal physiological states. Additionally, we review a body of work showing that in B. germanica, CHC profiles exhibit significant sexual dimorphism, specific CHCs act as essential precursors for female contact sex pheromone components, and we summarize the molecular regulatory mechanisms that underlie sexual dimorphism of CHC profiles. Finally, we highlight future directions and challenges in research on the biosynthesis and regulatory mechanisms of CHCs in B. germanica, and also identify potential applications of CHC studies in the pest control.

An improved positioning method based on compensation and optimization of ultra-wideband ranging results

Achieving precision in positioning under conditions of significant interference remains an unresolved challenge in research. This study introduces a low-cost ultra-wideband (UWB) distance compensation model that addresses electromagnetic wave loss in practical indoor settings. This paper employs kurtosis to detect non-line-of-sight environments, which are frequently induced by pedestrian movement. The Generalized Extreme Studentized Deviate algorithm is utilized to discern and eliminate outliers in ranging values and the Piecewise Cubic Hermite Interpolating Polynomial algorithm compensates for the eliminated data points. Finally, Kalman filtering is used to improve UWB ranging results, allowing for better error elimination and compensation. Experimental results demonstrate that our proposed algorithm has higher accuracy and the mean square error improvement ratio can reach more than 20% in dynamic positioning tests.

Emerging Trends and Challenges in Pink Hydrogen Research

Pink hydrogen is the name given to the technological variant of hydrogen generation from nuclear energy. This technology aims to address the environmental challenges associated with conventional hydrogen production, positioning itself as a more sustainable and eco-efficient alternative, while offering a viable alternative to nuclear power as a source of electricity generation. The present research analyzes the landscape of pink hydrogen research, an innovative strand of renewable energy research. The methodology included a comprehensive search of scientific databases, which revealed a steady increase in the number of publications in recent years. This increase suggests a growing interest in and recognition of the importance of pink hydrogen in the transition to cleaner and more sustainable energy sources. The results reflect the immaturity of this technology, where there is no single international strategy and where there is some diversity of research topic areas, as well as a small number of relevant topics. It is estimated that the future development of Gen IV nuclear reactors, as well as Small Modular Reactor (SMR) designs, will also favor the implementation of pink hydrogen.

Viscoelastic Dampers for Vibration Control of Building Structures: A State-of-Art Review

ABSTRACT Due to its high effectiveness and low cost, viscoelastic damper (VED) is a commonly used type of passive energy dissipation device to reduce structural vibrations and responses against earthquakes and strong winds. Over the past decades, scholars have developed new types of VEDs to be installed at different structural locations. These VEDs offer better post-disaster recoverability and smarter behaviors for structures. Nonetheless, existing efforts of various VEDs and the technologies supporting VEDs were seldomly summarized. This article presents a critical state-of-art review of the existing research on VEDs, hybrid VED devices, and the design methods for structures installed with VEDs. First, the VEDs are classified based on the design locations in building structures, including VEDs used as coupling beams and damping walls, installed in braces and beam-column joints, and used to connect parallel structures. In addition to these classic VEDs, the study presents the high-performance VEDs and the corresponding techniques, such as the combined usage with other materials and/or devices. Furthermore, as an important contribution to the presented work, various design methods for structures enhanced by VEDs were systematically summarized. These methods considered different evaluation parameters aiming at different design targets. Finally, this article identifies and highlights research challenges in the existing studies. Possible improvements that could be made in the future were also provided.

Hot‐spot aware multicost‐based energy‐efficient routing protocol for WBANs

SummaryWireless body area networks (WBANs) are becoming widely considered in remote healthcare monitoring applications. However, WBAN nodes have limited resources; therefore, effective and reliable routing protocols are pivotal research challenges. Furthermore, balancing the traffic load among sensor nodes is also highly required to increase network stability. In recent years, many interesting routing solutions have been proposed for WBANs; however, the significant feature in terms of stability and energy in these solutions has not been sufficiently addressed. Therefore, in this context, the Hot‐Sspot Aaware Multicost‐based Energy‐efficient Routing (H‐SAMER) protocol is proposed in this paper. The suggested protocol used multieffective cost function for next‐hop node selection based on the data type, where the patient's data are categorized into three classes: normal data, on‐demand data, and emergency data. Furthermore, the H‐SAMER protocol adds awareness to the transmission of control packets by adding the cost value to the RREQ packets. Thus, the simulation scenario shows that the H‐SAMER saves energy 8.57%, 88%, 98%, 97%, and 100% greater than E‐HARP, EH‐RCP, CO‐LAEEBA, EECBSR, and ELR‐W, respectively. Moreover, the first node death of the proposed H‐SAMER is delayed to round number 7500, which proves H‐SAMER to be a stable and reliable effective solution for WBANs.

A mini review of mycotoxin’s occurrence in food in South America in the last 5 years: research gaps and challenges in a climate change era

Mycotoxins are natural metabolites produced by species of filamentous fungi belonging mainly to the genera Aspergillus, Fusarium, Penicillium, and Alternaria, which can grow in various crops and foodstuffs. The South American climate is diverse, varying from tropical, temperate, and arid to cold, ideal for the growth of different types of fungi and mycotoxin production. This mini review aimed to describe the natural occurrence of mycotoxin in food in South America from 2018 to 2023, identifying research gaps and challenges in an era of climate change. We analyzed 53 studies, 21 from Brazil. Most of the mycotoxins analyzed in South America were the traditional and regulated mycotoxins, with variable occurrences depending on the region, climatic conditions, and methodology used. Emerging and modified mycotoxins have only been studied in Argentina and Brazil, where some studies have shown high occurrences. Given this, it is essential to strengthen food safety laboratories and surveillance capabilities and establish early warning systems. It is also essential to continue working to raise awareness of mycotoxins as a public health issue and to study and prevent the impact of climate change on soil microbial population, the new prevalence of fungi, and the profile of toxigenic species. An effective connection and collaboration between disciplines and sectors in different countries is needed to meet this research challenge.

Adaptation in Edge Computing: A review on design principles and research challenges

Edge Computing places the computational services and resources closer to the user proximity, to reduce latency, and ensure the quality of service and experience. Low latency, context awareness, and mobility support are the major contributors to edge-enabled smart systems. Such systems require handling new situations and change on the fly and ensuring the quality of service while only having access to constrained computation and communication resources and operating in mobile, dynamic, and ever-changing environments. Hence, adaptation and self-organisation are crucial for such systems to maintain their performance, and operability while accommodating new changes in their environment. This paper reviews the current literature in the field of adaptive Edge Computing systems. We use a widely accepted taxonomy, which describes the important aspects of adaptive behaviour implementation in computing systems. This taxonomy discusses aspects such as adaptation reasons, the various levels an adaptation strategy can be implemented, the time of reaction to a change, categories of adaptation technique, and control of the adaptive behaviour. In this paper, we discuss how these aspects are addressed in the literature, and identify the open research challenges and future direction in adaptive Edge Computing systems. The results of our analysis show that most of the identified approaches target adaptation at the application level, and only a few focus on middleware, communication infrastructure, and context. Adaptations that are required to address the changes in the context, changes caused by users or in the system itself are also less explored. Furthermore, most of the literature has opted for reactive adaptation, although proactive adaptation is essential to maintain the edge computing systems’ performance and interoperability by anticipating the required adaptations on the fly. Additionally, most approaches apply a centralised adaptation control, which does not perfectly fit the mostly decentralised/distributed Edge Computing settings.

PlantMine: A Machine-Learning Framework to Detect Core SNPs in Rice Genomics

As a fundamental global staple crop, rice plays a pivotal role in human nutrition and agricultural production systems. However, its complex genetic architecture and extensive trait variability pose challenges for breeders and researchers in optimizing yield and quality. Particularly to expedite breeding methods like genomic selection, isolating core SNPs related to target traits from genome-wide data reduces irrelevant mutation noise, enhancing computational precision and efficiency. Thus, exploring efficient computational approaches to mine core SNPs is of great importance. This study introduces PlantMine, an innovative computational framework that integrates feature selection and machine learning techniques to effectively identify core SNPs critical for the improvement of rice traits. Utilizing the dataset from the 3000 Rice Genomes Project, we applied different algorithms for analysis. The findings underscore the effectiveness of combining feature selection with machine learning in accurately identifying core SNPs, offering a promising avenue to expedite rice breeding efforts and improve crop productivity and resilience to stress.

Navigating the uncommon: challenges in applying evidence-based medicine to rare diseases and the prospects of artificial intelligence solutions.

The study of rare diseases has long been an area of challenge for medical researchers, with agonizingly slow movement towards improved understanding of pathophysiology and treatments compared with more common illnesses. The push towards evidence-based medicine (EBM), which prioritizes certain types of evidence over others, poses a particular issue when mapped onto rare diseases, which may not be feasibly investigated using the methodologies endorsed by EBM, due to a number of constraints. While other trial designs have been suggested to overcome these limitations (with varying success), perhaps the most recent and enthusiastically adopted is the application of artificial intelligence to rare disease data. This paper critically examines the pitfalls of EBM (and its trial design offshoots) as it pertains to rare diseases, exploring the current landscape of AI as a potential solution to these challenges. This discussion is also taken a step further, providing philosophical commentary on the weaknesses and dangers of AI algorithms applied to rare disease research. While not proposing a singular solution, this article does provide a thoughtful reminder that no 'one-size-fits-all' approach exists in the complex world of rare diseases. We must balance cautious optimism with critical evaluation of new research paradigms and technology, while at the same time not neglecting the ever-important aspect of patient values and preferences, which may be challenging to incorporate into computer-driven models.

Understanding service ecosystem dynamics: a typology

PurposeThe service environment is becoming increasingly turbulent, leading to calls for a systemic understanding of it as a set of dynamic service ecosystems. This paper advances this understanding by developing a typology of service ecosystem dynamics that explains the varying interplay between change and stability within the service environment through distinct behavioral patterns exhibited by service ecosystems over time.Design/methodology/approachThis study builds upon a systematic literature review of service ecosystems literature and uses system dynamics as a method theory to abductively analyze extant literature and develop a typology of service ecosystem dynamics.FindingsThe paper identifies three types of service ecosystem dynamics—behavioral patterns of service ecosystems—and explains how they unfold through self-adjustment processes and changes within different systemic leverage points. The typology of service ecosystem dynamics consists of (1) reproduction (i.e. stable behavioral pattern), (2) reconfiguration (i.e. unstable behavioral pattern) and (3) transition (i.e. disrupting, shifting behavioral pattern).Practical implicationsThe typology enables practitioners to gain a deeper understanding of their service environment by discerning the behavioral patterns exhibited by the constituent service ecosystems. This, in turn, supports them in devising more effective strategies for navigating through it.Originality/valueThe paper provides a precise definition of service ecosystem dynamics and shows how the identified three types of dynamics can be used as a lens to empirically examine change and stability in the service environment. It also offers a set of research directions for tackling service research challenges.

Unveiling the Dark Side of Datura in Pediatric Poisoning With Insights From Jordanian Experience: A Retrospective Clinical Study.

Datura stramonium, jimsonweed, is a toxic plant with hallucinogenic properties. Although there are many studies on Datura poisoning, none reported cases in Jordan. This study offers a comprehensive review on D. stramonium ingestion, covering its epidemiology, clinical presentation, and treatment. We aimed to provide better understanding of the factors for Datura ingestion, identify prevention and management strategies, and address research challenges. This study adopted a retrospective review design to evaluate the cases of Datura poisoning in Al Karak, province of Jordan during the spring of 2022. Data collected from medical records, toxicology databases, and consultation records were analyzed using descriptive statistics. The common symptoms of Datura poisoning included agitation, mydriasis, and tachycardia. The management approaches comprised supportive care, administration of Diazepam for agitation, and, in some cases, neostigmine to counteract anticholinergic effects. Understanding the risks associated with D. stramonium poisoning and implementing effective prevention and management strategies are crucial. This study highlights the importance of recognizing Datura poisoning as a potential diagnosis in children presenting with unexplained anticholinergic symptoms or agitation to the emergency room.

Generating Multi-Depth 3D Holograms Using a Fully Convolutional Neural Network.

Efficiently generating 3D holograms is one of the most challenging research topics in the field of holography. This work introduces a method for generating multi-depth phase-only holograms using a fully convolutional neural network (FCN). The method primarily involves a forward-backward-diffraction framework to compute multi-depth diffraction fields, along with a layer-by-layer replacement method (L2RM) to handle occlusion relationships. The diffraction fields computed by the former are fed into the carefully designed FCN, which leverages its powerful non-linear fitting capability to generate multi-depth holograms of 3D scenes. The latter can smooth the boundaries of different layers in scene reconstruction by complementing information of occluded objects, thus enhancing the reconstruction quality of holograms. The proposed method can generate a multi-depth 3D hologram with a PSNR of 31.8dB in just 90ms for a resolution of 2160×3840 on the NVIDIA Tesla A100 40G tensor core GPU. Additionally, numerical and experimental results indicate that the generated holograms accurately reconstruct clear 3D scenes with correct occlusion relationships and provide excellent depth focusing.

Metabolomics analysis using matrix-assisted diffusion-ordered spectroscopy (DOSY) and its application in acrylamide-induced cardiovascular toxicity

Untargeted NMR-based metabolomics strategies are increasingly applied for metabolite screening in a wide variety of medical conditions. However, the identification of metabolites remains a great challenge in NMR metabolomics research. Here, we used matrix-assisted diffusion-ordered spectroscopy (DOSY) to identify new biomarkers in body fluids. DOSY-based metabolomics can exploit the unique strengths of NMR and, thanks to improvements in its resolution, can be successfully used to address a significantly wider range of biological issues. In this study, a metabolite model consisting of ten common metabolites was employed to optimize the matrix-assisted DOSY experimental parameters. Polyvinylpyrrolidone (PVP), a high-molecular-weight polymer, was found to be the most appropriate matrix with which to accomplish the separation of the mixture components. 1H NMR and PVP-assisted DOSY analyses were performed to characterize the metabolic profile of serum samples from acrylamide (ACR)-treated rats and study the mechanism of ACR-induced cardiovascular toxicity. Biomarker identification by 1H NMR and PVP-assisted DOSY yielded similar results. However, multivariate statistical analysis showed that, compared with 1H NMR, PVP-assisted DOSY achieved a better discrimination of the serum samples obtained from rats treated with different doses of ACR. A total of 11 differential metabolites were identified. An enrichment analysis of the metabolic pathways showed that, in rats treated with a high dose of ACR, the glutamate and glutathione metabolic pathways and the energy metabolism pathway of the tricarboxylic acid cycle were mainly affected.

Deep learning-based semantic segmentation for morphological fractography

Fractographic analysis poses a significant challenge for field researchers without specialized training in fractography. To address this issue, this study introduces a comprehensive integrated workflow that encapsulates the entire process from dataset preparation and data enhancement to leveraging the SegFormer model for deep learning-driven semantic segmentation. An extensive collection of fractography images is formulated and augmented to train the SegFormer model, enabling precise semantic segmentation of morphological fracture regions including cleavage, ductile, dimple, fatigue striations, and others. To accommodate the demanding SEM imaging conditions which frequently include distortions, noise, and aberrations, we developed a two-stage method with diverse data augmentation strategies. This method resulted in a robust model demonstrating exceptional performance, as evidenced by a high mean Intersection over Union (mIOU) score of 59.7 and other metrics. The findings validate the potential of deep learning techniques, particularly the SegFormer model's efficacy in morphological fractography image segmentation for the first time. Our work offers a cost-effective, and efficient alternative deep learning approach to traditional experimental fracture analysis, thereby expanding opportunities for a broader range of professionals in the engineering domain.