bluuebunny/crossref_metadata_2025 huggingface dataset:
Density estimation via measure transport: Outlook for applications in the biological sciences
Authors: López‐Marrero Vanessa , Johnstone Patrick R. , Park Gilchan , Luo Xihaier
Published: May 2024

One among several advantages of measure transport methods is that they allow or a unified framework for processing and analysis of data distributed according to a wide class of probability measures. Within this context, we present results from computational studies aimed at assessing the potential of measure transport techniques, specifically, the use of triangular transport maps, as part of a workflow intended to support research in the biological sciences. Scenarios characterized by the availability of limited amount of sample data, which are common in domains such as radiation biology, are of particular interest. We find that when estimating a distribution density function given limited amount of sample data, adaptive transport maps are advantageous. In particular, statistics gathered from computing series of adaptive transport maps, trained on a series of randomly chosen subsets of the set of available data samples, leads to uncovering information hidden in the data. As a result, in the radiation biology application considered here, this approach provides a tool for generating hypotheses about gene relationships and their dynamics under radiation exposure.
The relationship between social capital and inflammatory biomarkers in early adulthood: A longitudinal study
Authors: Teixeira Paula , Leão Teresa , Severo Milton , Ramos Elisabete , Fraga Sílvia
Published: May 2024

Higher levels of social capital (SC) are associated with better health status. However, there is little evidence of the impact of SC on biological health outcomes in the early ages. To identify the association between SC in adolescence and inflammation levels in early adulthood. Prospective study using data from 2435 participants from the Epidemiological Health Investigation of Teenagers in Porto cohort. SC was assessed at age 17 through a self‐administered questionnaire, and high‐sensitivity C‐reactive protein (hs‐CRP) and leucocytes were measured in a fasting blood sample at 17 and 21 years‐old. A principal components analysis was performed to identify the domains of SC. Simple linear regressions were performed to assess the association between SC components and inflammatory status at 17 and 21 years old. Pathway analysis was performed to assess the direct, indirect, and total effects of SC on hs‐CRP and leucocyte levels. We did not find a significant total effect between SC at 17 years‐old and hs‐CRP at 21 years‐old. However, the Trust/Reciprocity dimension showed a significant direct effect between SC and hs‐CRP levels at 21 (β = −0.065, 95% CI: −0.129; −0.001), as well as a significant total effect (β = −0.075, 95% CI: −0.139; −0.011). Regarding leucocyte levels, total SC at 17 years‐old was associated with leucocytes levels at 21 (β = −0.115, 95% CI: −0.205; −0.024). Significant direct (β = −0.104, 95% CI: −0.194; −0.014) and total effect (β = −0.107, 95% CI: −0.199; −0.015) of Trust/Reciprocity on leucocyte levels were observed. Adolescents with higher SC have a low inflammatory level in early adulthood, especially those with greater levels of trust/reciprocity.
Giving up learning from failures? An examination of learning from one's own failures in the context of heart surgeons
Authors: Lee Sunkee , Park Jisoo
Published: May 2024

Research Summary

We reassess existing theories on individual failure learning and propose an inverted‐U‐shaped relationship between an individual's accumulated failures and learning, based on a theoretical framework that jointly considers the opportunity, motivation, and perceived ability to learn. Using data on 307 California‐based cardiothoracic surgeons who performed coronary artery bypass graft surgeries in 133 hospitals between 2003 and 2018, we find compelling evidence that individuals reach a threshold at which they discontinue learning from their own failures. We also find that this threshold is higher for surgeons who had higher perceived ability to learn. This article aims to shed new light on the relationship between individuals' failure experience and their learning, and advance our understanding of the microfoundations of organizational learning, an important basis of firm performance.

Managerial Summary

This article explores how individuals learn from their own failures. Contrary to prior theories, we propose a non‐monotonic relationship between accumulated failures and learning: as a function of failures, an individual's performance will initially increase, then taper off, and finally decrease. Analyzing data on 307 cardiothoracic surgeons operating coronary artery bypass graft surgeries, we find such an inverted‐U‐shaped pattern. Notably, surgeons with higher perceived ability to learn—those with elite training, certified expertise, and specialization in patient care—reached the tipping point later than their counterparts. Our findings imply that repeated failures can have both beneficial and harmful impacts on individuals' learning processes, and therefore, both impacts must be simultaneously considered for understanding and improving individuals' performance.
Discovering the Order‐Disorder Transition in Quinoline Intercalated Vanadium Oxide with Superior Calcium Storage via Polyhedral Distortion
Authors: Zhao Xu , Li Linyuan , Zhang Guobin , Yi Yong , Yang Tao , Han Cuiping , Li Baohua
Published: May 2024

Calcium‐ion batteries (CIBs) are considered as potential next‐generation energy storage systems due to their abundant reserves and relatively low cost. However, irreversible structural changes and weak conductivity still hinder in current CIBs cathode materials. Herein, an organic molecular intercalation strategy is proposed, in which V2O5 regulated with quinoline, pyridine, and water molecules are studied as cathode material to provide fast ion diffusion channels, large storage host, and high conductivity for Ca ions. Among them, V2O5‐quinoline (QVO) owns the largest interplanar spacing of 1.25 nm and the V‐O chains are connected with organic molecular by hydrogen bond, which stabilizes the crystal structure. As a result, QVO exhibits a specific capacity of 168 mAh g−1 at 1 A g−1 and capacity retention of 80% after 500 cycles at 5 A g−1 than the other materials. Furthermore, X‐Ray diffraction and X‐ray absorption spectroscopy results reveal a reversible order‐disorder transformation mechanism of Ca2+ for QVO, which can make full use of the abundant active sites for high capacity and simultaneously achieve fast reaction kinetics for excellent rate performance. These results demonstrate that QVO is a promising cathode material for CIBs, providing more choices for the development of high‐performance CIBs.
Engineered shapes using electrohydrodynamic atomization for an improved drug delivery
Authors: Yu Deng‐Guang , Gong Wenjian , Zhou Jianfeng , Liu Yanan , Zhu Yunajie , Lu Xuhua
Published: May 2024

The shapes of micro‐ and nano‐products have profound influences on their functional performances, which has not received sufficient attention during the past several decades. Electrohydrodynamic atomization (EHDA) techniques, mainly include electrospinning and electrospraying, are facile in manipulate their products' shapes. In this review, the shapes generated using EHDA for modifying drug release profiles are reviewed. These shapes include linear nanofibers, round micro‐/nano‐particles, and beads‐on‐a‐string hybrids. They can be further divided into different kinds of sub‐shapes, and can be explored for providing the desired pulsatile release, sustained release, biphasic release, delayed release, and pH‐sensitive release. Additionally, the shapes resulted from the organizations of electrospun nanofibers are discussed for drug delivery, and the shapes and inner structures can be considered together for developing novel drug delivery systems. In future, the shapes and the related shape–performance relationships at nanoscale, besides the size, inner structure and the related structure–performance relationships, would further play their important roles in promoting the further developments of drug delivery field.

This article is categorized under:

Therapeutic Approaches and Drug Discovery > Emerging Technologies
Expert Perspectives on Future 6G-Enabled Hospital Metaverse
Authors: Wang Fan , Jurva Risto , Ahokangas Petri , Yrjölä Seppo , Matinmikko-Blue Marja
Published: May 2024

This paper aims to understand the value-added services that the future 6G-enabled metaverse can and will bring to hospitals. This is important since most studies on 6G and the metaverse are heavily driven by technological solutions. Adopting a qualitative research approach, this paper collects experts’ opinions on the usage scenarios of the 6G-enabled metaverse in hospitals. Six use cases within hospital contexts have been identified from open-ended interviews. The analysis of each case reveals that 6G, as a general-purpose technology, offers the necessary capabilities to support the development of the metaverse in hospitals. The metaverse-enabled services are expected to design future smart hospitals and improve work processes and resource allocation in hospitals, while also promoting preventive healthcare and training and enhancing the quality of care in emergency, treatment, and rehabilitation. Consequently, the development of both metaverse and 6G will progress in tandem, hand in hand, offering local services in hospitals. From a value perspective, this paper contributes to the development of the 6G and metaverse in the hospital vertical by understanding the needs, capabilities, and key values of the future 6G-enabled hospital metaverse.
The Adoption of MyData-Based Health Applications Among Elderly Citizens in Nordic Countries and the UK
Authors: Jayathilake Chathurangani , Keikhosrokiani Pantea , Isomursu Minna
Published: May 2024

This study addresses a crucial gap in current literature by examining the use of MyData-based health apps among individuals aged 50 and above in the UK and Nordic nations. With the advancement of personalized health technologies, understanding the factors influencing adoption among the elderly is essential. The research provides insights tailored to this demographic within the broader framework of digital health adoption. The primary scientific objective was to identify technological and health-related factors influencing the willingness of senior adults (50 and above) to use MyData-based preventive healthcare applications. The conditions for adoption, technological considerations, health-related variables, willingness to share MyData, and demographic variations were explored. Grounded in the Universal Theory of Acceptance and Use of Technology (UTAUT2) and Health Protection Motivation components, the research employed a quantitative approach, integrating a new concept called sharing personal data into the framework. Data collection occurred through an online survey in the UK and the Nordic region, yielding 374 responses from the Nordic sample and 1165 from the UK sample, resulting in a cleaned dataset of 1016. Findings revealed the significance of willingness to share MyData for both the UK and Nordic regions, with performance expectancy emerging as an outstanding technological factor for the Nordic population, but not for the UK. Across nations and genders, self-efficacy is portrayed as a strong driver in health-related aspects. These contribute to academic knowledge and have societal value by guiding the development of digital health solutions for the elderly, ultimately improving their quality of life and health outcomes.
Digital Twins for Development of Microwave-Based Brain Tumor Detection
Authors: Särestöniemi Mariella , Singh Daljeet , Heredia Charline , Nikkinen Juha , von und zu Fraunberg Mikael , Myllylä Teemu
Published: May 2024

Digital twins for different healthcare applications are currently being studied actively since they could revolutionize research on customized and personalized healthcare and enable realistic evaluations of new medical devices and applications in early phase. This paper presents a study on the development of digital twins aiming to be utilized for the development of microwave technique-based brain tumor detection. Realistic anatomical models of the digital twins were designed based on magnetic resonance images (MRI) scanned from the brain with brain tumor. These twins aim to correspond to the human brain and brain tumor in terms of size, shape, and tissue dielectric properties. Furthermore, developed digital twins include both phantom models for measurement emulation as well as corresponding simulation models designed using electromagnetic simulation software. By using the developed digital twins, our aim is to evaluate microwave-based sensing technique for brain tumor detection. Evaluations were carried out using flexible ultrawideband (UWB) antennas which would be beneficial for practical solutions. Our simulation and emulation results show that microwave technique with flexible antennas has high potential for brain tumor detection.
Enhancing Independent Auditory and Speechreading Training – Two Finnish Free Mobile Applications Constructed for Deaf and Hard of Hearing Children and Adults
Authors: Huttunen Kerttu , Kauramäki Jaakko , Pajo Kati , Saalasti Satu
Published: May 2024

The users of hearing technology often need auditory training for getting used to their hearing devices and maximally benefiting from them. Because auditory training given by professionals is only sparsely available, there is a great need for materials and applications with which self-training is possible. Moreover, deaf and hard-of-hearing persons need to improve their speechreading skills to help in speech reception and children to strengthen their reading skills. We describe the background, contents, construction and features of two Finnish free applications: Auditory Track for auditory training and Optic Track for speechreading (lip reading) training. Both can be used by children and adults, even though the Auditory Track is mainly aimed at adults and the Optic Track at primary school age children. The features of both applications include exercises carefully selected based on extensive knowledge of the acoustic and visual characteristics of speech. In addition, during the implementation of both applications, careful attention has been paid to the usability, accessibility, gamification and construction of feedback systems. The applications developed can be used in independent training, clinical use and research.
Social and Health Care Teachers’ Experiences of Implementing Multidisciplinary Specialisation Studies in a Digital Learning Environment
Authors: Naakka Hanna , Heinonen Jarmo , Männistö Merja , Perälä Sami , Rauha Anna , Paldanius Mika , Ahonen Outi , Sanerma Päivi
Published: May 2024

The aim of the study was to find out how teachers and project actors experienced the implementation of the specialisation education in a digital learning environment. The methods were triangulation with paired t-test, ANOVA and content analysis of qualitative data. The results showed a statistical difference between the responses after the first and second implementation of specialisation education. The overall results show that teachers and project actors demonstrate a strong commitment to producing and developing student-centred and work-life-centred online specialisation education.
Supporting Sense of Meaningful Life and Human Dignity in Digitally Assisted Physiotherapy Environment – Qualitative Secondary Research with Thematic Analyses and Inductive Synthesis
Authors: Sjögren Tuulikki , Korpi Hilkka
Published: May 2024

The goal of this secondary research was to create a model that aims at increasing the sense of meaningful life and human dignity in physiotherapy by utilizing remote and digital rehabilitation technologies, focusing on people with cerebrovascular accident (CVA) and multiple sclerosis (MS). The results of this qualitative research effort have been obtained using thematic analyses and inductive synthesis. Our primary research, which provides materials for the secondary analysis, is based on qualitative systematic literature reviews and a meta-synthesis of the perceived meaningfulness of physiotherapy. The outcome of the secondary analysis is a research-based model, which combines rehabilitees´ experiences and expectations towards physiotherapy, professionals’ competence needs, and the technological prerequisites for using digital rehabilitation technologies with CVA and MS rehabilitees. Maintenance of human dignity and promotion of the sense of meaningful life, and various issues related to them, were most meaningful for the rehabilitees. The essential prerequisites of meaningful use of re- mote and digital rehabilitation technologies in physiotherapy were enabling identification of the rehabilitees’ own current functioning, needs and goals, supporting their motivation and commitment, choosing relevant and meaningful activities, creating safe and variable environments, and enabling social interaction and relationships. As far as we know, this is the first model which combines neuro- logical rehabilitees’ experiences of physiotherapy with remote and digital technology’s potential. In the future, the usefulness of our novel research-based holistic and biopsychosocial physiotherapy model should be tested in real life situations.
Preliminary Study on Wellbeing and Healthcare Services Needs in Japan and Finland for Telehealth Solutions Based on Dwelling
Authors: Hyry Jaakko , Karppinen Pasi , Kobayashi Takumi , Anzai Daisuke
Published: May 2024

The Japanese and Finnish healthcare systems have several longstanding challenges from the scattered data in storing databases due to location sensitivity and sometimes unequal services for their users. In addition to the data itself, location plays another role for the citizens living in urban or rural areas. They suffer from different well-being outcomes as stress and sedentary lifestyles have presented negative impacts on the urban dwellers. As remote work and technological solutions have become more common, in this conceptual research, we explore the general healthcare and living area challenges and how to make services more equal to everyone. We also discuss the possible telehealth solutions and how, for example, wearable body sensors’ use could offer improvements to the availability and accessibility of healthcare services.
Closing the Loop for Controlled Substances Surveillance: A Field Study of the Usability and User Experience of an Integrated Electronic Narcotic Consumption
Authors: Häkkinen Annika , Viitanen Johanna , Savolainen Kaisa , Mäkinen Ville-Matti , Siven Mia , Lääveri Tinja , Tolonen Hanna M.
Published: May 2024

The distribution and handling of controlled substances (CSs), i.e., narcotics, is strictly regulated to decrease the risk of abuse and drug diversion. In Finland, hospital pharmacies are mandated to keep records of CS distribution and consumption in healthcare through a labor-intensive paper-based process. After implementing a new electronic health record (EHR) system, a large university hospital started to streamline the process by transferring the CS documentation process from paper to digital format. Although the benefits of digital archiving, surveillance, and consumption monitoring are self-evident from the hospital pharmacy’s perspective the advantages at wards remain less explored. Therefore, our goal was to explore the usability and user experience (UX) of the recently implemented electronic narcotic consumption card (eNCC) solution built into the EHR system, and the related workflows of nurses, pharmacists, and physicians. The field study consisted of two parts and was conducted using observation, interviews, and survey methods in two wards. Our findings suggest that the digitalized process enables reliable real-time documentation of CSs and improves process efficiency, particularly for oral tablets and capsules. Considering diverse end-users’ perspectives is crucial when assessing the practical benefits of newly implemented digital solutions targeted at several healthcare professional groups. This approach enables a broader understanding of UX; supports development efforts, including usability improvements; and facilitates broader implementation. More research is needed to analyze the long-term impacts of the digital CSs’ consumption documentation workflow and surveillance at different healthcare units.
A User-Centric Exploration of a Digital Health Experience
Authors: Haanpää Milka , Saraniemi Saila
Published: May 2024

This paper explores digital health experience through focus on the experiences of diabetics who utilize digital health technology in their daily diabetes management. Theoretically, the paper draws from research on digital experience and from theoretical discussions concerned with digital health. Empirically, it analyzes three multimodal datasets using reflective thematic analysis. Three interlinked themes – always on, co-creation through interaction, and it makes things so much easier – are revealed. These themes reveal the key characteristics of users’ digital health experiences and highlight how users shape their experiences in their daily lives. The study contributes theoretically to digital experience literature within which user-centric, longitudinal studies are scarce. Practically, it demonstrates to health professionals and developers how digital health technology becomes integrated into their users’ lives.
Using Machine Learning Methods to Predict the Lactate Trend of Sepsis Patients in the ICU
Authors: Arslantas Mustafa Kemal , Asuroglu Tunc , Arslantas Reyhan , Pashazade Emin , Dincer Pelin Corman , Altun Gulbin Tore , Kararmaz Alper
Published: May 2024

Serum lactate levels are considered a biomarker of tissue hypoxia. In sepsis or septic shock patients, as suggested by The Surviving Sepsis Campaign, early lactate clearance-directed therapy is associated with decreased mortality; thus, serum lactate levels should be assessed. Monitoring a patient’s vital parameters and repetitive blood analysis may have deleterious effects on the patient and also bring an economic burden. Machine learning and trend analysis are gaining importance to overcome these issues. In this context, we aimed to investigate if a machine learning approach can predict lactate trends from non-invasive parameters of patients with sepsis. This retrospective study analyzed adult sepsis patients in the Medical Information Mart for Intensive Care IV (MIMIC-IV) dataset. Inclusion criteria were two or more lactate tests within 6 h of diagnosis, an ICU stay of at least 24 h, and a change of ≥1 mmol/liter in lactate level. Naïve Bayes, J48 Decision Tree, Logistic Regression, Random Forest, and Logistic Model Tree (LMT) classifiers were evaluated for lactate trend prediction. LMT algorithm outperformed other classifiers (AUC = 0.803; AUPRC = 0.921). J48 decision tree performed worse than the other methods when predicting constant trend. LMT algorithm with four features (heart rate, oxygen saturation, initial lactate, and time interval variables) achieved 0.80 in terms of AUC (AUPRC = 0.921). We can say that machine learning models that employ logistic regression architectures, i.e., LMT algorithm achieved good results in lactate trend prediction tasks, and it can be effectively used to assess the state of the patient, whether it is stable or improving.
A Hybrid Images Deep Trained Feature Extraction and Ensemble Learning Models for Classification of Multi Disease in Fundus Images
Authors: Verma Jyoti , Kansal Isha , Popli Renu , Khullar Vikas , Singh Daljeet , Snehi Manish , Kumar Rajeev
Published: May 2024

Retinal disorders, including diabetic retinopathy and macular degeneration due to aging, can lead to preventable blindness in diabetics. Vision loss caused by diseases that affect the retinal fundus cannot be reversed if not diagnosed and treated on time. This paper employs deep-learned feature extraction with ensemble learning models to improve the multi-disease classification of fundus images. This research presents a novel approach to the multi-classification of fundus images, utilizing deep-learned feature extraction techniques and ensemble learning to diagnose retinal disorders and diagnosing eye illnesses involving feature extraction, classification, and preprocessing of fundus images. The study involves analysis of deep learning and implementation of image processing. The ensemble learning classifiers have used retinal photos to increase the classification accuracy. The results demonstrate improved accuracy in diagnosing retinal disorders using DL feature extraction and ensemble learning models. The study achieved an overall accuracy of 87.2%, which is a significant improvement over the previous study. The deep learning models utilized in the study, including NASNetMobile, InceptionResNetV4, VGG16, and Xception, were effective in extracting relevant features from the Fundus images. The average F1-score for Extra Tree was 99%, while for Histogram Gradient Boosting and Random Forest, it was 98.8% and 98.4%, respectively. The results show that all three algorithms are suitable for the classification task. The combination of DenseNet feature extraction technique and RF, ET, and HG classifiers outperforms other techniques and classifiers. This indicates that using DenseNet for feature extraction can effectively enhance the performance of classifiers in the task of image classification.
Exploring and Extending Human-Centered Design to Develop AI-Enabled Wellbeing Technology in Healthcare
Authors: Tahvanainen Laura , Tetri Birgitta , Ahonen Outi
Published: May 2024

Digital transformation and digitalisation are rapidly affecting the society. The gradually increasing applications of different types of AI into solutions and services are welcome, but there are associated risks. These include, for example, within human aspects of care undermining fundamental rights, ethical considerations, sustainability, and policies and regulations. This change permeates every societal level, but it is especially evident in the healthcare sector due to the ageing population and shortage of professionals. This situation also places pressure on the development of competencies among healthcare professionals. A human-centered approach in design and design methods can promote the development of AI-based solutions in transdisciplinary and cross-disciplinary processes encompassing numerous stakeholders, scientific orientations, and perspectives. There is a need for research and evaluation of Human-Centered Design (HCD) processes and design methods to develop and gain more insights for future development.

This study was conducted as research through design. It aimed to elucidate the application and insights gained from the adopted Service design process for AI-enabled services and HCD approach while developing AI-empowered solution, Voima-chatbot. One of this research's main conclusions and realization is the shift from purely HCD towards Life-Centered design of AI-enabled solutions with a human-in-the-loop. In addition, this project increased the understanding of the deep importance of having a transdisciplinary dialogue with developers during the process of developing digital well-being devices and combining different professional competencies to achieve the best working solutions.
Influencing Factors in Digital Health Intervention Uptake: The Interplay of Education, Lifestyle, and Digital Literacy
Authors: Ruotsalainen Ilona , Valtanen Mikko , Kärsämä Riikka , Umer Adil , Parikka Suvi , Lundqvist Annamari , Lindström Jaana
Published: May 2024

Chronic diseases strain global healthcare economically, and integrating digital solutions are proposed to help in meeting the rising demand. Digital health interventions (DHIs) offer promise for personalized, and cost-effective health services, however, factors influencing their uptake remain unclear. We examined whether the probability of lifestyle DHI uptake varies among individuals with different educational levels and lifestyles, based on their attitudes and usage of e-services. We also examined the effect of sex and age, and the association between DHI uptake and both educational attainment and overall lifestyle. A possibility to start using a web-based lifestyle DHI was offered to a subgroup (n = 6978) of Healthy Finland survey participants and adjusted logistic regression models were used to investigate the factors affecting uptake. We found that higher education and healthier lifestyle, as indicated by lifestyle score, were related to higher odds of DHI uptake. However, the effects of age, sex, independence of e-service use, and competence to use online services varied across lifestyle score groups. No significant interactions were observed related to educational attainment. These results imply that lifestyle DHIs are less likely to reach individuals with less-healthy lifestyle habits and lower educational attainment. In addition, some predictors affected the uptake differently across lifestyle score groups, suggesting that implementations of DHIs might attempt strategies to optimize the participation rates in especially targeted subgroups.
Securing Hybrid Wireless Body Area Networks (HyWBAN): Advancements in Semantic Communications and Jamming Techniques
Authors: Soderi Simone , Särestöniemi Mariella , Fuada Syifaul , Hämäläinen Matti , Katz Marcos , Iinatti Jari
Published: May 2024

This paper explores novel strategies to strengthen the security of Hybrid Wireless Body Area Networks (HyWBANs), which are essential in smart healthcare and Internet of Things (IoT) applications. Recognizing the vulnerability of HyWBAN to sophisticated cyber-attacks, we propose an innovative combination of semantic communications and jamming receivers. This dual-layered security mechanism protects against unauthorized access and data breaches, particularly in scenarios involving in-body to on-body communication channels. We conduct comprehensive laboratory measurements to understand hybrid (radio and optical) communication propagation through biological tissues. We utilize these insights to refine a dataset for training a Deep Learning (DL) model. These models, in turn, generate semantic concepts linked to cryptographic keys for enhanced data confidentiality and integrity using a jamming receiver. The proposed model significantly reduces energy consumption compared to traditional cryptographic methods, like Elliptic Curve Diffie-Hellman (ECDH), especially when supplemented with jamming. Our approach addresses the primary security concerns and sets the baseline for future secure biomedical communication systems advancements.
Optical Wireless Power Transmission Through Biological Tissue Using Commercial Photovoltaic Cells Under 810 nm LEDs: Feasibility Study
Authors: Fuada Syifaul , Perera Malalgodage Amila Nilantha , Särestöniemi Mariella , Katz Marcos
Published: May 2024

Ensuring the provision of sustainable and secure electrical power for ingestible/implantable medical devices (IMDs) is crucial for facilitating the multifaceted capabilities of these IMDs and preventing the need for recurrent battery replacements. Using photovoltaic (PV) energy harvesting in conjunction with an external light source can be advantageous for an optical wireless power transfer (OWPT) system to enable energy self-sufficiency in IMDs. This study investigates the performance of OWPT using commercial monocrystalline silicon PV cells exposed to an 810 nm Near-infrared (NIR) LED light. The ethical concerns are addressed by utilizing porcine samples (ex vivo approach), eliminating the need for live animal experimentation. The experimental setup employs porcine meat samples with several compositions, e.g., pure fat, pure muscle, and different layers of fat-muscle. The primary goal of this initial study is to analyze the open-circuit voltage output (VOC) of the PV against received optical power in the presence of biological tissue. Our study demonstrates that PV cells can generate voltage even when exposed to light passing through porcine samples with a thickness of up to 30 mm. Furthermore, the VOC values of PV cells attained in this study meet the required voltage input level for supplying current IMDs, typically ranging from 2V to 3V. The findings of this study provide valuable insights into OWPT systems in the future, where monocrystalline silicon PV cells can be employed as energy harvester devices to supply various IMDs utilizing NIR light.
Inter- and Intra-Day Precision of a Low-Cost and Wearable Bioelectrical Impedance Analysis Device
Authors: Robertz Leon , Rieppo Lassi , Korkala Seppo , Jaako Tommi , Saarakkala Simo
Published: May 2024

Bioimpedance analysis (BIA) is a non-invasive and safe method to measure body composition. Nowadays, due to technological progress, smaller and cheaper devices allow the implementation of BIA into wearable devices. In this pilot study, we analyzed the measurement precision of a cheap BIA solution for wearable devices. Intra-session, intra-day, and inter-day reproducibility of raw impedance values from three subjects at three different body locations (hand-to-hand, hand-to-torso, torso-to-torso), and for three different frequencies (6, 54, and 500 kHz) were analyzed using the coefficient of variation (CV%). Hand-to-hand and hand-to-torso measurements resulted, on average, in high intra-session (CV% = 0.14% and CV% = 0.11%, respectively), intra-day (CV% = 1.67% and CV% = 1.26%, respectively), and inter-day (CV% = 1.53% and CV% = 1.31%) precision. Absolute impedance values for the torso-to-torso measurements showed a larger mean variation (intra-session CV% = 0.68%; intra-day CV% = 5.53%, inter-day CV% = 3.13%). Overall, this cheap BIA solution shows high precision and promising usability for further integration into a wearable measurement environment.
A Distributed Framework for Remote Multimodal Biosignal Acquisition and Analysis
Authors: Álvarez Casado Constantino , Räsänen Pauli , Nguyen Le Ngu , Lämsä Arttu , Peltola Johannes , Bordallo López Miguel
Published: May 2024

In recent times, several studies have presented single-modality systems for non-contact biosignal monitoring. While these systems often yield estimations correlating with clinical-grade devices, their practicality is limited due to constraints in real-time processing, scalability, and interoperability. Moreover, these studies have seldom explored the combined use of multiple modalities or the integration of various sensors. Addressing these gaps, we introduce a distributed computing architecture designed to remotely acquire biosignals from both radars and cameras. This architecture is supported by conceptual blocks that distribute tasks across sensing, computing, data management, analysis, communication, and visualization. Emphasizing interoperability, our system leverages RESTful APIs, efficient video streaming, and standardized health-data protocols. Our framework facilitates the integration of additional sensors and improves signal analysis efficiency. While the architecture is conceptual, its feasibility has been evaluated through simulations targeting specific challenges in networked remote photoplethysmography (rPPG) systems. Additionally, we implemented a prototype to demonstrate the architectural principles in action, with modules and blocks operating in independent threads. This prototype specifically involves the analysis of biosignals using mmWave radars and RGB cameras, illustrating the potential for the architecture to be adapted into a fully distributed system for real-time biosignal processing.
Radiative corrections for factorized jet observables in heavy ion collisions
Authors: Vaidya Varun
Published: May 2024

Abstract

I look at the renormalization of the medium structure function and a medium induced jet function in a factorized cross section for jet substructure observables in Heavy Ion collisions. This is based on the formalism developed in [1], which uses an Open quantum system approach combined with the Effective Field Theory (EFT) for forward scattering to derive a factorization formula for jet observables which work as hard probes of a long lived dilute Quark Gluon Plasma (QGP) medium. I show that the universal medium structure function that captures the observable independent physics of the QGP has both rapidity and UV anomalous dimensions that appear due to medium induced Bremsstrahlung. The resulting Renormalization Group (RG) equations correspond to the BFKL equation and the running of the QCD coupling respectively. I present the first results for the numerical impact of resummation using these RG equations on the mean free path of the jet in the medium. I also briefly discuss the prospects of extending this formalism for a short lived dense medium.
Emergent symmetries at criticality in multi field RFT/DP
Authors: Bartels Jochen , Contreras Carlos , Vacca Gian Paolo
Published: May 2024

Abstract

We study multifield extensions of Reggeon Field Theory (also equivalent to Directed Percolation model) at criticality in the perturbative ϵ-expansion below the upper critical dimension Dc = 4 at one loop, for the special case when all fields have the same scale (anomalous) dimensions. Analyzing all the fixed points of the renormalization group flow for N = 2 flavors and some for N = 3, we find scale invariant solutions which are characterized by specific emergent global symmetries of the interacting potential. We also study two infinite families as a function of N having ℤN and SN+1 symmetries.
Machine Learning Clifford Invariants of ADE Coxeter Elements
Authors: Chen Siqi , Dechant Pierre-Philippe , He Yang-Hui , Heyes Elli , Hirst Edward , Riabchenko Dmitrii
Published: May 2024

There has been recent interest in novel Clifford geometric invariants of linear transformations. This motivates the investigation of such invariants for a certain type of geometric transformation of interest in the context of root systems, reflection groups, Lie groups and Lie algebras: the Coxeter transformations. We perform exhaustive calculations of all Coxeter transformations for$$A_8$$A8,$$D_8$$D8and$$E_8$$E8for a choice of basis of simple roots and compute their invariants, using high-performance computing. This computational algebra paradigm generates a dataset that can then be mined using techniques from data science such as supervised and unsupervised machine learning. In this paper we focus on neural network classification and principal component analysis. Since the output—the invariants—is fully determined by the choice of simple roots and the permutation order of the corresponding reflections in the Coxeter element, we expect huge degeneracy in the mapping. This provides the perfect setup for machine learning, and indeed we see that the datasets can be machine learned to very high accuracy. This paper is a pump-priming study in experimental mathematics using Clifford algebras, showing that such Clifford algebraic datasets are amenable to machine learning, and shedding light on relationships between these novel and other well-known geometric invariants and also giving rise to analytic results.
A note on Ambarzumian’s theorem for quantum graphs
Authors: Bifulco Patrizio , Kerner Joachim
Published: May 2024

Based on the main result presented in Bifulco and Kerner (Proc Am Math Soc 152:295–306, 2024), we derive Ambarzumian–type theorems for Schrödinger operators defined on quantum graphs.
Characterization of arsenic species by liquid sampling-atmospheric pressure glow discharge ionization mass spectrometry
Authors: Goodwin Joseph , Kenneth Marcus R. , McRae Garnet , Sturgeon Ralph E. , Mester Zoltan
Published: May 2024

A liquid sampling-atmospheric pressure glow discharge (LS-APGD) ionization source operating at a nominal power of 30 W and solution flow rate of 30 µL min−1 and supported in a He sheath gas flow rate of 500 mL min−1 was interfaced to an Orbitrap mass spectrometer and assessed for use in rapid identification of inorganic and organic arsenic species, including As(III), As(V), monomethylarsonic acid, dimethylarsinic acid, and arsenobetaine in a 2% (v/v) nitric acid medium. Mass spectral acquisition in low-resolution mode, using only the ion trap analyzer, provided detection of protonated molecular ions for AsBet (m/z 179), DMA (m/z 139), MMA (m/z 141), and As(V) (m/z 143). As(III) is oxidized to As(V), likely due to in-source processes. Typical fragmentation of these compounds resulted in the loss of either water or methyl groups, as appropriate, i.e., introducing DMA also generated ions corresponding to MMA and As(V) as dissociation products. Structure assignments were also confirmed by high-resolution Orbitrap measurements. Spectral fingerprint assignments were based on the introduction of solutions containing 5 µg mL−1 of each arsenic compound.
Diversity and Functionality of Bacteria Associated with Different Tissues of Spider Heteropoda venatoria Revealed through Integration of High-Throughput Sequencing and Culturomics Approaches
Authors: Zhao Likun , Zhang Shanfeng , Xiao Ruoyi , Zhang Chao , Lyu Zhitang , Zhang Feng
Published: May 2024

Spiders host a diverse range of bacteria in their guts and other tissues, which have been found to play a significant role in their fitness. This study aimed to investigate the community diversity and functional characteristics of spider-associated bacteria in four tissues of Heteropoda venatoria using HTS of the 16S rRNA gene and culturomics technologies, as well as the functional verification of the isolated strains. The results of HTS showed that the spider-associated bacteria in different tissues belonged to 34 phyla, 72 classes, 170 orders, 277 families, and 458 genera. Bacillus was found to be the most abundant bacteria in the venom gland, silk gland, and ovary, while Stenotrophomonas, Acinetobacter, and Sphingomonas were dominant in the gut microbiota. Based on the amplicon sequencing results, 21 distinct cultivation conditions were developed using culturomics to isolate bacteria from the ovary, gut, venom gland, and silk gland. A total of 119 bacterial strains, representing 4 phyla and 25 genera, with Bacillus and Serratia as the dominant genera, were isolated. Five strains exhibited high efficiency in degrading pesticides in the in vitro experiments. Out of the 119 isolates, 28 exhibited antibacterial activity against at least one of the tested bacterial strains, including the pathogenic bacteria Staphylococcus aureus, Acinetobacter baumanii, and Enterococcus faecalis. The study also identified three strains, GL312, PL211, and PL316, which exhibited significant cytotoxicity against MGC-803. The crude extract from the fermentation broth of strain PL316 was found to effectively induce apoptosis in MGC-803 cells. Overall, this study offers a comprehensive understanding of the bacterial community structure associated with H. venatoria. It also provides valuable insights into discovering novel antitumor natural products for gastric cancer and xenobiotic-degrading bacteria of spiders.
Timing of seasonal events is correlated with social network position in a wild mammal
Authors: Philson Conner S. , Bruebach Carla , Bastian Taylor , Barr Billy , Blumstein Daniel T.
Published: May 2024

Across animal systems, abiotic environmental features, including timing of seasonal events and weather patterns, affect fitness. An individual’s degree of social integration also has fitness consequences, but we lack an understanding of how abiotic features relate to patterns of individual sociality. A deeper understanding of this relationship could be developed from studying systems where these two links with fitness have already been identified. We explored the relationship between individual social behavior and seasonal timing, seasonal length, and weather patterns. We used social network analysis on a sixteen-year dataset of a wild population of hibernating yellow-bellied marmots (Marmota flaviventer). We fit a series of generalized linear mixed models and found that longer growing seasons before winter hibernation and longer winters were associated with increased individual sociality in the following spring. However, later snowmelt was associated with decreased sociality that spring. We found no relationship between individual sociality and various measures of precipitation and temperature. This suggests that seasonal timing and length may be a more important driver of sociality than weather patterns in this system, both as a lag and contemporary effect. Seasonal timing and length may mediate the opportunity or intensity of social interactions. The entwined relationships between the seasonal schedule and weather, and the seemingly contradictory role of winter length and snowmelt, suggests the timing of seasons and its relationship with sociality is complex and further exploration of environment-sociality relationships is required across taxa.

Significance statement

While the adaptive benefits of social behavior are well studied, less is known about how features of the abiotic environment drive variation in individual social behavior. Given increasing stochasticity in the timing of seasonal events and weather patterns, mapping the environment-sociality relationship will provide important insights to the drivers of sociality in the wild. This is particularly salient for species most vulnerable to climate and environmental change, such as seasonal hibernators, like yellow-bellied marmots (Marmota flaviventer). We found that features of seasonal duration were positively associated with increased sociality, whereas the timing of seasonal onset was negatively associated. This work provides empirical evidence towards an important gap in the behavioral ecology literature.
Long-term effects of an egg-protein hydrolysate on cognitive performance and brain vascular function: a double-blind randomized controlled trial in adults with elevated subjective cognitive failures
Authors: Adams Micah S. , Mensink Ronald P. , Plat Jogchum , Joris Peter J.
Published: May 2024

Purpose

Short-term intake of the egg-protein hydrolysate Newtricious (NWT)-03 improved executive function, but underlying mechanisms and long-term effects, including other cognitive domains, are unknown.

Methods

A 36-week randomized controlled trial involving 44 overweight/obese individuals experiencing elevated Subjective Cognitive Failures (SCF; aged 60–75 years) assessed the impact of daily consumption of 5.7 g of NWT-03 or placebo powders on cognitive performance (psychomotor speed, executive function, memory) and Cerebral Blood Flow (CBF), a marker of brain vascular function. Cognitive performance was evaluated using a neurophysiological test battery (CANTAB) and CBF was measured using magnetic resonance imaging perfusion method Arterial Spin Labeling (ASL). Serum samples were collected to determine brain-derived neurotrophic factor (BDNF) concentrations.

Results

Anthropometrics, and energy and nutrient intakes remained stable throughout the trial. NWT-03 was well tolerated, and compliance was excellent (median: 99%; range: 87–103%). No overall intervention effects were observed on cognitive performance or CBF, but post-hoc analyses revealed significant improvements on executive function in women, but not men. Specifically, a reduction of 74 ms in reaction latency on the multitasking task (95% CI: −134 to −15; p = 0.02), a reduction of 9 between errors (95%CI: −14 to −3; p < 0.001), and a reduction of 9 total errors (95%CI: −15 to −3; p < 0.001) on the spatial working memory task were found in women. No intervention effects were observed on serum BDNF concentrations (p = 0.31).

Conclusion

Long-term consumption of NWT-03 improved multitasking abilities and working memory in women with elevated SCF. Brain vascular function remained unaffected. Sex differences in executive function require additional clarification.