Pc1 pulsations cover the 0.2–5 Hz frequency range with electromagnetic ion cyclotron (EMIC) waves of magnetospheric origin being generally accepted as their most important source. In the ionosphere, the initially transverse EMIC waves can couple to the compressional mode and propagate long distances in the ionospheric waveguide. By studying the Pc1 frequency range in the topside ionosphere, we can obtain information on the spatial distribution of both the transverse (incident EMIC) waves and the compressional waves. In the present paper, we make use of a new Swarm L2 product developed for characterizing ultra low frequency Pc1 waves to explore the spatial distribution of these waves relative to the midlatitude ionospheric trough (MIT), which corresponds to the ionospheric footprint of the plasmapause (PP) at night. It is shown that the vast majority of Pc1 events are located inside the plasmasphere and that the spatial distributions clearly follow changes in the MIT/PP position at all levels of geomagnetic activity. In the topside ionosphere, the number of transverse Pc1 (incident EMIC) waves rapidly decreases outside the PP, while their occurrence peak is located considerably equatorward (|ΔMlat| = -5o – -15o) of the PP. On the other hand, the compressional Pc1 waves can propagate in the ionosphere outside the PP towards the poles, while in the equatorial direction there is a secondary maximum in their spatial distribution at low magnetic latitudes. Our results suggest that mode conversion taking place at the plasmapause plays a crucial role in the formation of the presented spatial distributions.

IntroductionMethadone, a synthetic opioid agonist, has been widely used for the treatment of opioid dependence and chronic pain management for several decades (1). Methadone maintenance treatment (MMT) has proven to be an effective strategy in reducing illicit opioid use, improving patient outcomes, and decreasing the risk of HIV transmission among individuals with opioid use disorders (2). However, despite its therapeutic benefits, methadone can cause significant adverse effects, particularly in opioid-naive individuals or those who consume it recreationally (3).Methadone overdose can lead to severe respiratory depression, cardiac arrhythmias, and death (4). The risk of overdose is particularly high in individuals who are not tolerant to opioids, as they may be more sensitive to the drug’s effects (5). In addition to its well-known respiratory and cardiovascular side effects, methadone can also cause rare neurological and ocular manifestations, such as strabismus (6).Strabismus, a misalignment of the eyes, can occur due to various etiologies, including congenital factors, nerve palsies, and pharmacological agents (7). Opioid-induced strabismus is an uncommon but documented consequence of opioid toxicity, with most cases reported in the context of heroin use (8). The exact mechanism underlying opioid-induced strabismus remains unclear, but it is thought to involve the drug’s effects on the central nervous system and the oculomotor nerve (9).In this case report, we present a rare instance of methadone-induced loss of consciousness and subsequent strabismus in a patient who consumed methadone for the first time. This case underscores the importance of recognizing the potential dangers associated with methadone use, especially in opioid-naive individuals, and the need for healthcare providers to be aware of the uncommon ocular manifestations of opioid toxicity. Prompt identification and appropriate management of methadone overdose and its associated complications can prevent long-term morbidity and mortality (10).

This paper aims to establish a method to accurately describe transpiration by employing appropriate physical equations. Although some simplifications are made, including use of a simplified treatment of turbulence and neglecting of the thermal capacity of transpiring leaves, it is argued that the chosen scheme has general validity in identifying the primary mechanisms governing transpiration. To achieve this objective, a traditional treatment involving five equations, including the mass budget, is used. Initially, a simplified approach that does not consider the water budget is introduced to outline the general procedure to explicitly address canopies. Subsequently, the water budget is incorporated to appropriately account for water stress in transpiration. In this context, a novel linearization of the extended Clausius-Clapeyron equation, incorporating the Kelvin effect, is employed. It is demonstrated that the well-known Penman formula emerges as one of the solutions within a system of equations, providing estimates for temperature (T), vapor content in air (e), and the thermal transport of heat (H). The method, initially conceived for homogeneous canopies, is expanded to encompass sun-shade canopy layers. By employing the water mass balance, the trade-off between atmospheric evaporation demand and the water delivery capacity of the soil and stem is elucidated. Notably, it is revealed that the pressure potential within leaves is not solely determined by capillarity, but rather represents the dynamic outcome of the intricate interactions within the soil-plant-atmosphere continuum. These findings highlight differences from more simplistic approaches commonly employed, particularly concerning canopies. Overall, this study presents a methodological framework to accurately describe transpiration, incorporating key equations and addressing the complex dynamics involved in the soil-plant-atmosphere continuum, and suggests various directions of research in the field.

Predictive maintenance has been a key component of the aerospace sector in the United Kingdom, helping to guarantee the dependability and security of aircraft. Predictive maintenance systems have reportedly cut maintenance costs by 15% and unscheduled downtime by 20% in major airlines, according to a Johnson (2017) study. The findings of this study are divided into conceptual and contextual research gap categories. This paper further describes the machine learning approach for predictive maintenance in manufacturing companies, furthermore, highlights on the potential opportunities have been suggested at the end of this paper.

Blockchain technology has the potential to transform the education system in various ways, including the verification of certificates and digital identity management. The use of blockchain-based systems can eliminate the need for manual verification of certificates, which is time-consuming and labour-intensive. Instead, blockchain-based systems can enable instant and secure verification of certificates, making the process more efficient and reliable.This proposed work focuses on implementing the Plonk system in the education sector to enhance the security, privacy, and efficiency of blockchain-based verifiable certificate (VC) digital identity verification and management systems (DIVMS). The methodology involves designing and developing the Plonk system, integrating it with existing DIVMS, testing and evaluating its functionality and performance, deploying and implementing it in the education sector, and monitoring and maintaining its effectiveness. The Plonk system is based on zero-knowledge proof technology, identity provider verification, and digital signature verification. Simulation data is used to compare the transaction throughput of the Plonk system with Ethereum, a well-known blockchain platform. The results demonstrate that the Plonk system outperforms Ethereum in terms of transaction processing speed, security, privacy, scalability, verification speed, and cost-effectiveness. Additionally, a response time dataset is created for both the Plonk system and Ethereum under different user load scenarios, and statistical measures such as average response times and standard deviations are calculated. The analysis shows that the Plonk system exhibits lower response times and variability compared to Ethereum, indicating faster and more consistent transaction processing. Overall, the Plonk system shows the promising potential for enhancing the security, privacy, and performance of blockchain-based DIVMS in the education sector.

A document by Muhammad Nadeem Ahmad. Click on the document to view its contents.

Aim: Research has shown that people from ethnic minority backgrounds living with psychosis are less likely to seek support from healthcare professionals (e.g. GP), but more likely to seek support from non-healthcare professionals (e.g. faith leaders). This systematic review assessed the impact of community-level interventions aimed at improving help seeking and access to support for psychosis in non-secondary care settings among ethnic minority populations. Methods: The EMBASE, PsychINFO, Medline Ultimate, CINAHL Ultimate and Scopus databases were searched in December 2023. Studies were included if published in English, conducted in high-income countries, reported on psychosis and minority ethnic groups aged 18-65 years, and interventions targeted at people from minority ethnic groups with or at risk of psychosis, caregivers, or the general public. Outcomes of interest were changes in help-seeking behaviours, pathways to care characteristics and barriers and facilitators of intervention implementation. Results: Five studies reporting two interventions met the inclusion criteria. All studies were conducted in the United States. Narrative synthesis revealed mixed results about the effectiveness of interventions on help seeking and duration of untreated psychosis. However, the results show promise for professional help-seeking recommendations post-intervention across the studies. Barriers and facilitators were identified for intervention implementation. Conclusions: Community-level interventions have some success in promoting help-seeking for psychosis in ethnic minority populations. However, research in this area was limited. Future research could include studies across different countries, ethnicities, genders and socioeconomic status to ensure generalisable results.

Background Currently, there is no reliable endpoint for the conclusion of atrial fibrillation (AF) ablation. Atrial burst pacing and/or isoproterenol challenge are poor diagnostic tools. A newly proposed Vagal AF Induction Test(VAFIT) uses effective atrial refractory period measurement, simultaneously with extra-cardiac vagal stimulation(ECVS) to study AF inducibility pre and post-ablation. Objective Prospective study in patients submitted to radiofrequency catheter pulmonary vein isolation(PVI) plus cardioneuroablation(CNA) evaluating the VAFIT result before and at the end of the procedure with AF recurrence. Methods Prospective study of 142 patients, 57.5[48.9-70.2] years-old, 71% males, with symptomatic AF (79.6% paroxysmal/20.4% persistent), left atrium diameter of 38.0[35.0-41.2] mm, and left ventricular ejection fraction of 63.0 [62.0-68.2]. VAFIT was considered positive or negative depending on whether AF induction occurred. It was performed at baseline and after PVI+CNA, with a single atrial extra stimulus during ECVS (5s/50Hz/1V/kg up to 70V/Pulse Width=50 µs). Patients were followed for a median of 15.0[7.0-20.0] months. The association of VAFIT-positive status at the end of the procedure with AF recurrence was investigated by univariate and multivariate Cox regression analysis. Results Pre-ablation VAFIT was positive in all cases and became negative in 62.9% of patients. AF recurrence: 18.7% in VAFIT-positive and 5.6% in VAFIT-negative patients(p=0.012). VAFIT-positivity was associated with AF recurrence (HR: 4.56(1.37-15.23,p=0.014). Conclusion A VAFIT-positive status following PVI+CNA was strongly and independently associated with AF recurrence. It remains to be investigated in randomized studies whether achieving VAFIT-negativity at the end of the procedure, as demonstrated in this study, would lead to better clinical outcomes.

This paper proposes novel fractional cosine and sine matrix functions derived from a determining matrix. It introduces a new controllability criterion based on the controllability Gramian for achieving exact controllability in oscillating fractional linear systems with damping term. We present Cayley-Hamilton type theorem for determining matrix. Finally, we prove a novel Kalman type rank criterion for controllability in oscillating fractional linear systems with damping, which is a novel contribution even in systems without damping. Numerical examples are provided to validate these findings.

The accelerated ageing of the population is leading to an increase in the incidence and disability of nervous system diseases globally, which is seriously affecting the quality of patient survival. Propofol is a widely used intravenous anaesthetic drug for the induction and maintenance of anaesthesia. A large number of studies have demonstrated that propofol has a protective effect against nervous system diseases; however, the underlying mechanisms remain unclear. This review examines the various signaling pathways through which propofol exerts its modulatory effects on the inflammatory response, as evidenced by in vitro and in vivo experiments. It also presents an overview of the current evidence on the role of inflammation in nervous system diseases associated with isoproterenol treatment. The findings suggest that the pro-inflammatory and anti-inflammatory effects of propofol are contingent upon the dosage of the drug, the duration of exposure, and the stage of the patient (neonatal and geriatric). It is recommended that care be taken to differentiate between drug dosage and exposure time in order to avoid exacerbating disease progression. Further extensive and in-depth studies will facilitate a fuller understanding of the mechanisms of propofol-mediated inflammation, which will in turn provide new ideas for treating nervous system diseases and drug selection.

The integration of optogenetic techniques with traditional behavioral paradigms has provided novel insights into the neural mechanisms underlying olfactory-based fear conditioning. Olfactory cues are potent triggers for fear responses, and understanding the intricate neural dynamics involved in olfactory fear learning is crucial for unraveling the complexities of aversive memory formation. In this study, a robust method is presented that combines optogenetic stimulation of olfactory bulb glomeruli with foot shock fear conditioning to investigate olfactory-based fear learning in mice. By merging optogenetic manipulation with behavioral assays, a comprehensive framework for studying the mechanisms of olfactory fear conditioning is provided. This method offers new avenues for exploring the neural dynamics of adaptive responses to olfactory threats and may have implications for understanding fear-related disorders.

Non-classical crystallization (NCC) mechanisms in organic small molecules, characterized by nanoparticle attachment during early crystallization stages, is not completely understood. Herein we report the formation of prenucleation clusters of ibuprofen and their evaluation from amorphous to crystalline to further understand the progression of NCC mechanisms of organic small molecules. Key to differentiating NCC from classical pathways is the formation of an intermediate dense liquid phase. We employ liquid phase transmission electron microscopy (LPTEM), to understand the real-time monitoring of nanoscale nucleation events with high temporo-spatial resolution. LPTEM studies have confirmed NCC pathways showing a dense liquid phase in the range of 100 nm and their cluster orderliness in selected area electron diffraction (SAED). This study also provides insight into the antisolvent influences of prenucleation formation in NCC pathways.

Ransomware continues to pose one of the most severe cybersecurity challenges, particularly in enterprise environments that rely on Active Directory Domain Services (AD DS) for managing network resources and permissions. The detection of ransomware in AD environments has remained complex due to the sophisticated tactics used by modern ransomware, such as privilege escalation and lateral movement, making traditional security measures insufficient. A novel machine learning-based detection model is proposed, designed to autonomously identify ransomware activity in real time through the analysis of process creation, file access patterns, and network traffic behaviors. The model, leveraging Random Forests, demonstrates high accuracy in detecting ransomware at early stages by learning from features that reflect typical ransomware behaviors without relying solely on signature-based methods. Results show that the model performs effectively across various ransomware families, enabling rapid detection before substantial damage is done, while minimizing false positives from legitimate administrative tasks. The study also demonstrates the importance of feature engineering in enhancing detection accuracy, as well as the potential for future integration of the model into real-time monitoring systems in enterprise environments. The controlled experimental setup provided consistency in testing, but future work could explore the model's scalability and adaptability to more dynamic network conditions.

A document by Emrah Konuralp. Click on the document to view its contents.

Objective: To evaluate whether there is a difference in lower limb muscle strength in elderly fallers and non-fallers after chronic stroke. Methods: Cross-sectional observational study. Individuals after chronic stroke had their knee extensor and plantar flexor muscle strength assessed by the modified sphygmomanometer test, and the residual deficit was obtained using the formula: 100 - [(strength on paretic side/ strength on non-paretic side)] X 100. The incidence of falls was obtained through a direct question to the individuals, in which they were asked whether they had had falls in the last 6 months. The Independent Student t test was used to compare the lower limb muscle strength of the fallers and non-fallers. Results: 53 individuals, 43% male and a mean age of 71.1 (7.5) years were included. The mean of falls in the last 6 months was 4.1. Twenty-two (41%) individuals were fallers. Non-fallers individuals presented higher knee extensors muscle strength of the paretic side (MD=0.52mmHg; 95%CI 9.50 to 78.47; p=0.01). Non-fallers individuals presented lower residual deficit of knee extensors (MD=0.17mmHg; 95%CI -30.21 to -2.28; p=0.02). Conclusions: Elderly individuals after chronic stroke that are fallers have less strength of knee extensors at the paretic side and present more RD of knee extensors.

Cardiovascular magnetic resonance imaging (MRI) in patients with cardiac implants, such as pacemakers and defibrillators, has gained importance in recent years with the development of modern cardiac implantable electronic devices. The increasing clinical need to perform MRI examinations in patients with cardiac implants has driven the development of new advanced MRI sequences to mitigate image artifacts associated with cardiac implants. More specifically, advances in imaging techniques, such as wideband late gadolinium enhancement imaging, wideband T1 mapping, and wideband perfusion, have been designed to improve image quality and examinations in patients with cardiac implants, enabling a comprehensive and more reliable diagnosis, which was previously unattainable in these patients. This review article explores recent developments and applications of wideband techniques in the field of cardiovascular MRI, offering insights into their transformative potential. Clinical applications of wideband cardiovascular MRI are highlighted, particularly in assessing myocardial viability, guiding ventricular tachycardia ablation, and characterizing myocardial tissue.

Cardiovascular diseases caused 20.5 million deaths in 2021, accounting for nearly one-third of global mortality 1. This underscores the importance of identifying practical prognostic markers for effective patient stratification and treatment, particularly in ischemic heart disease (IHD). Transthoracic echocardiography (TTE) is a fundamental and accessible, non-invasive imaging tool widely used in clinical cardiology for diagnosis and management concerning patients with a broad spectrum of cardiovascular diseases. It is the first level non-invasive imaging method and the most widely used in clinical practice for the diagnosis and follow-up of patients with acute coronary syndrome (ACS). Alongside established echocardiographic prognostic parameters, new measurements have shown their predictive relevance for adverse events in IHD patients, including three-dimensional (3D) imaging, tissue Doppler (TDI) and speckle tracking technology. The aim of this review is to identify the current diagnostics echocardiographic tools in the literature that may provide new prognostic parameters applicable in the acute phase and at follow-up in patients following an acute myocardial infarction. We focused on the latest imaging methods such as TDI, Myocardial Work Index, Speckle-Tracking Strain and 3D technologies evaluated using TTE, given its ease of use, and widespread accessibility at all stages of coronary artery disease.

This study investigated the formation and characterization of aerogels from soy protein isolate (SPI) at different pHs (6-10) using supercritical carbon dioxide (SC-CO2) drying. The extraction yield of proteins was 33% with 92% purity. The formed SPI hydrogels and aerogels were characterized for their rheological properties, textural properties, morphology, crystallinity, chemical structure, thermal stability, and solubility. The gels formed at pH values of 7.0-10.0 were able to form strong gels, where all the gels showed a shear-thinning behavior. The hardness of the gels increased with the increase in pH from 7.0 to 10.0. The resulting SPI aerogels upon SC-CO2 drying showed outstanding properties, such as surface areas of 222-278 m²/g, pore sizes of 8-11 nm, and pore volumes of 1.88-3.13 cm3/g. Their densities were ~0.21 g/cm3 with high porosities of ~83%. Further, SEM images of aerogels showed three-dimensional open porous structures, where the structures were more heterogeneous at higher pH values, leading to a higher surface area. XRD and FTIR data provided information about the crystallinity and changes in the chemical structure of proteins, respectively. The main thermal degradation peak for aerogels was ~295 °C, which was slightly lower than that for SPI powder at 303 °C. The water solubility of SPI powder increased with the formation of aerogels from 17-19% to 30-36%. Overall, this study provides one of the first reports on generating SPI aerogels with high surface areas for their potential use in food, pharmaceutical, and biomaterial industries.

The emergence of infectious diseases is often associated with changes to host-pathogen ecology, and wildfires are known to profoundly modify the ecology of terrestrial and freshwater ecosystems. Nevertheless, few studies have employed manipulative experiments to quantify effects of fire on infections across parasite species. In a mark-recapture study, prescribed burns did not affect densities of Cuban treefrog (CTF; Osteopilus septentrionalis) definitive hosts. However, a prescribed burn field experiment and before-after-control-impact mesocosm study revealed that fire decreased a skin-penetrating nematode in CTFs by killing the parasite’s soil- dwelling, free-living stage. Additionally, prescribed burns were associated with increases in a terrestrial acuariid nematode and several aquatic trematode metacercariae in CTFs, likely by increasing intermediate host densities. We found no evidence of recovery in the trajectories of these parasites seven years after burns. These results suggest that fire can have predictable and long-term direct and indirect positive and negative effects on parasite transmission.

Objective: To evaluate the relationship between ciliary ultrastructure/genotype and prevalence of neonatal respiratory distress in primary ciliary dyskinesia (PCD). Study Design: This was a retrospective analysis from a multicenter, prospective study of children and adults with PCD. Participants were classified by ultrastructural defect associated with their diagnostic genetic variants: 1) outer dynein arm defect alone (ODA), 2) outer plus inner dynein arm defect (ODA/IDA), 3) inner dynein arm defect with microtubular disorganization (IDA/MTD), 4) DNAH11 (encodes ODA protein but has normal ultrastructure), and 5) normal/near normal/other. The likelihood of neonatal respiratory distress between ultrastructure groups or genotypes was evaluated by multivariate analysis using logistic regression, controlled for age, gender, race, and variant type. Similar analysis was performed within individual genotypes to assess association of neonatal respiratory distress with the presence of 2 loss-of-function variants. Results: Of the 455 participants analyzed, 305 (67.0%) reported neonatal respiratory distress. The odds ratio for neonatal respiratory distress in the DNAH11 group was significantly lower (OR 0.35, 95% CI 0.16-0.76) compared to neonatal respiratory distress in the ODA group. Within the DNAH5 group, those with 2 loss-of-function variants were more likely to have neonatal respiratory distress compared to those with possible residual function variants (OR 3.06, 95% CI 1.33-7). Conclusion: Neonatal respiratory distress is less common in those with DNAH11 variants, thus a high index of suspicion should remain for PCD in the absence of neonatal respiratory distress.

The utilization of distributed arithmetic (DA) in AF algorithms has gained significant attention in recent years due to its potential to enhance computational efficiency and reduce resource requirements. This paper presents an exploration of the application of DA to adaptive filtering (AF) algorithms, analyzing trends, discussing challenges, and outlining future prospects. It begins by providing an overview of both DA and AF algorithms, highlighting their individual merits and established applications. Subsequently, the integration of DA into AF algorithms is explored, showcasing its ability to optimize multiply-accumulate operations and mitigate the computational burden associated with AF algorithms. Throughout the paper, the critical trends observed in the field are discussed, including advancements in DA-based hardware architectures. Moreover, the challenges encountered in implementing DA-based AF is also discussed. The continued evolution of DA techniques to cater to the demands of modern AF applications, including realtime processing, resource-constrained environments, and highdimensional data streams is anticipated. In conclusion, this paper consolidates the current state of applying DA to AF algorithms, offering insights into prevailing trends, discussing challenges, and presenting future research and development in the field. The fusion of these two domains holds promise for achieving improved computational efficiency, reduced hardware complexity, and enhanced performance in various signal processing applications.

Next generation sequencing technology has revolutionized all fields of living systems and its applications almost reinvented some research areas including metagenomics. The microbiotas in our body, including those of the oral, nasal, ocular, alveolar, skin regions and particularly gut microbiota, have close relations with our health status. Metagenomics enables medical research to uncover those links. Maturation of experimental techniques for metagenomics has been followed by the others for meta-transcriptomics, meta-proteomics and all the possible counterparts of multi-omics studies. Now we are on the eve of a meta-multiomics era for the analysis of human holobiome in medical researches. This era will help to illuminate the relationship between human holobiome and all medical conditions including not only cancer, infectious diseases, auto-immune diseases, obesity, aging and genetic disorders, but also psychiatric conditions.

The interactions between nanoparticles and biological systems hold significant promise for medical applications, but understanding these complex interactions remains a major challenge. This study presents a computational biology approach utilizing GPU-accelerated simulations to investigate nanoparticle interactions in biological systems. By leveraging graphics processing units (GPUs), we developed highly scalable and efficient molecular dynamics simulations to model nanoparticle-biomolecule interactions. Our results provide novel insights into the dynamics of nanoparticle-cell membrane interactions, proteinnanoparticle binding, and nanoparticle-mediated drug delivery. The GPU-accelerated simulations demonstrate significant performance enhancements (up to 10x) compared to traditional CPU-based methods. This work has important implications for optimizing nanoparticle design, predicting toxicity, and enhancing therapeutic efficacy. Our computational framework offers a valuable tool for researchers in nanomedicine, enabling rapid exploration of nanoparticle properties and behavior in biological environments.

A document by Xavier Pardell Peña. Click on the document to view its contents.