High Volume Research Articles

Computed Tomography Angiography Characteristics of Thin-Cap Fibroatheroma in Patients With Diabetes.

It was recently reported that thin-cap fibroatheroma (TCFA) detected by optical coherence tomography was an independent predictor of future cardiac events in patients with diabetes. However, the clinical usefulness of this finding is limited by the invasive nature of optical coherence tomography. Computed tomography angiography (CTA) characteristics of TCFA have not been systematically studied. The aim of this study was to investigate CTA characteristics of TCFA in patients with diabetes. Patients with diabetes who underwent preintervention CTA and optical coherence tomography were included. Qualitative and quantitative analyses were performed for plaques on CTA. TCFA was assessed by optical coherence tomography. Among 366 plaques in 145 patients with diabetes, 111 plaques had TCFA. The prevalence of positive remodeling (74.8% versus 50.6%, P<0.001), low attenuation plaque (63.1% versus 33.7%, P<0.001), napkin-ring sign (32.4% versus 11.0%, P<0.001), and spotty calcification (55.0% versus 34.9%, P<0.001) was significantly higher in TCFA than in non-TCFA. Low-density noncalcified plaque volume (25.4 versus 15.7 mm3, P<0.001) and remodeling index (1.30 versus 1.20, P=0.002) were higher in TCFA than in non-TCFA. The presence of napkin-ring sign, spotty calcification, high low-density noncalcified plaque volume, and high remodeling index were independent predictors of TCFA. When all 4 predictors were present, the probability of TCFA increased to 82.4%. The combined qualitative and quantitative plaque analysis of CTA may be helpful in identifying TCFA in patients with diabetes. URL: https://www.clinicaltrials.gov; Unique identifier: NCT04523194.

The economic impact of E-commerce on the U.S. airports: Cost savings and productivity growth

The rise of e-commerce, fueled by digitalization and shifting in consumer demand, has revolutionized air cargo. While all e-commerce stakeholders are engaged in fierce competition to increase their market share, airports as integral elements of the air cargo supply chain could not be apathetic to this competition. This study examines the economic implication of e-commerce on high cargo volume airports, focusing on cost savings and productivity growth. Utilizing the Farrell measure of input-oriented technical efficiency, we estimate scope economies for the U.S. airports between 2009 and 2019. The results reveal higher scope economies for high cargo volume airports, indicating greater cost savings resulting from e-commerce activities. In addition, the Malmquist index is employed to assess productivity growth. The index is decomposed into technical change, efficiency change, and scale change. The findings show a 3.3% overall productivity growth in the U.S. airport industry, primarily driven by technical change. Besides, high cargo volume airports exhibit higher productivity growth compared to low cargo volume airports, with 4.1% and 2.9% growth rates, respectively. The gap between high cargo volume and low cargo volume airports widened in 2017, aligning with the pre-pandemic peak in e-commerce activity. Furthermore, high cargo volume airports experience higher efficiency gains and technical advancements. While efficiency gains are attributed to improved performance and increased capacity utilization, the technical advancements can be explained by the digitalization and customized operations associated with e-commerce. Overall, the results highlight the significant contribution of e-commerce to cost savings and productivity growth in high-cargo volume airports.

Differentially expressed genes associated with high metabolic tumor volume served as diagnostic markers and potential therapeutic targets for pancreatic cancer

BackgroundThe lack of distinct biomarkers for pancreatic cancer is a major cause of early-stage detection difficulty. The pancreatic cancer patient group with high metabolic tumor volume (MTV), one of the values measured from positron emission tomography—a confirmatory method and standard care for pancreatic cancer, showed a poorer prognosis than those with low MTV. Therefore, MTV-associated differentially expressed genes (DEGs) may be candidates for distinctive markers for pancreatic cancer. This study aimed to evaluate the possibility of MTV-related DEGs as markers or therapeutic targets for pancreatic cancer.MethodsTumor tissues and their normal counterparts were obtained from patients undergoing preoperative 18F-FDG PET/CT. The tissues were classified into MTV-low and MTV-high groups (7 for each) based on the MTV2.5 value of 4.5 (MTV-low: MTV2.5 < 4.5, MTV-high: MTV2.5 ≥ 4.5). Gene expression fold change was first calculated in cancer tissue compared to its normal counter and then compared between low and high MTV groups to obtain significant DEGs. To assess the suitability of the DEGs for clinical application, the correlation of the DEGs with tumor grades and clinical outcomes was analyzed in TCGA-PAAD, a large dataset without MTV information.ResultsTotal RNA-sequencing (MTV RNA-Seq) revealed that 44 genes were upregulated and 56 were downregulated in the high MTV group. We selected the 29 genes matching MTV RNA-seq patterns in the TCGA-PAAD dataset, a large clinical dataset without MTV information, as MTV-associated genes (MAGs). In the analysis with the TCGA dataset, MAGs were significantly associated with patient survival, treatment outcomes, TCGA-PAAD-suggested markers, and CEACAM family proteins. Some MAGs showed an inverse correlation with miRNAs and were confirmed to be differentially expressed between normal and cancerous pancreatic tissues. Overexpression of KIF11 and RCC1 and underexpression of ADCY1 and SDK1 were detected in ~ 60% of grade 2 pancreatic cancer patients and associated with ~ 60% mortality in stages I and II.ConclusionsMAGs may serve as diagnostic markers and miRNA therapeutic targets for pancreatic cancer. Among the MAGs, KIF11, RCC1, ADCY, and SDK1 may be early diagnostic markers.

On the Model Update Strategies for Supervised Learning in AIOps Solutions

AIOps (Artificial Intelligence for IT Operations) solutions leverage the massive data produced during the operation of large-scale systems and machine learning models to assist software engineers in their system operations. As operation data produced in the field are constantly evolving due to factors such as the changing operational environment and user base, the models in AIOps solutions need to be constantly maintained after deployment. While prior works focus on innovative modeling techniques to improve the performance of AIOps models before releasing them into the field, when and how to update AIOps models remain an under-investigated topic. In this work, we performed a case study on three large-scale public operation data: two trace datasets from the cloud computing platforms of Google and Alibaba and one disk stats dataset from the BackBlaze cloud storage data center. We empirically assessed five different types of model update strategies for supervised learning regarding their performance, updating cost, and stability. We observed that active model update strategies (e.g., periodical retraining, concept drift guided retraining, time-based model ensembles, and online learning) achieve better and more stable performance than a stationary model. Particularly, applying sophisticated model update strategies (e.g., concept drift detection, time-based ensembles, and online learning) could provide better performance, efficiency, and stability than simply retraining AIOps models periodically. In addition, we observed that, although some update strategies (e.g., time-based ensemble and online learning) can save model training time, they significantly sacrifice model testing time, which could hinder their applications in AIOps solutions where the operation data arrive at high pace and volume and where immediate inferences are required. Our findings highlight that practitioners should consider the evolution of operation data and actively maintain AIOps models over time. Our observations can also guide researchers and practitioners in investigating more efficient and effective model update strategies that fit in the context of AIOps.

The application of artificial intelligence in Unmanned Underwater Vehicle communication systems

Artificial Intelligence (AI) has the potential to significantly enhance decision-making techniques in Unmanned Underwater Vehicle (UUV) communication systems by utilizing real-time sensor feedback and environmental data. To address the challenges posed by high data volume and error rates, we propose an advanced methodology known as the Variational Deep Network (VDN). This method integrates Variational Modal Decomposition (VMD) algorithms with the Deep Convolutional Neural Network (DCNN) to create a realizable AI decision-making system. In conjunction with the DCNN and VMD algorithms, our research focuses on accurately identifying received signals under data constraints. Considering the spectral efficiency and robustness, the Filter-Bank Multicarrier (FBMC) technology have been employed in this paper. Our results demonstrate that the VDN significantly reduces the volume of data needed for neural network training and effectively recognizes FBMC signals. These insights confirm the potential of VDN decision-making systems to drive advancements in UUV communication technologies.

Dynamic edge clustering and task scheduling for edge assisted metaverse system in the field of remote work and collaboration

SummaryThe metaverse, a future digital world for living, working, learning, and interacting, is rapidly gaining significance, particularly in the domain of remote work and collaboration. This emerging digital landscape demands high‐performance, low‐latency, and scalable services to provide an immersive user experience. The current technology used in metaverse systems has some limitations, which emphasize the importance of adopting emerging technologies like Edge Computing (EC). However, as the number of users and data volume increases, it can impact both system performance and scalability of the edge‐assisted metaverse system. Additionally, the uneven distribution of edge servers can cause inconsistencies and result in high latency. To overcome these challenges, this paper proposes a dynamic edge clustering and task scheduling approach for edge‐assisted metaverse systems (DTAM) in the field of remote work and collaboration. The proposed approach addresses the challenges of high user volume and uneven resource distribution by incorporating dynamic clustering and edge server assistance to improve clustering performance. Furthermore, a Prioritized Experience Replay‐based Deep Q‐learning algorithm with state augmentation (PERDQSA) for task scheduling is introduced to improve sample efficiency and performance. The performance of the proposed DTAM is evaluated against existing techniques, and experimental results demonstrate its significant superiority in terms of specific metrics such as bandwidth, task response time, energy efficiency, and latency. The experiments demonstrated that DTAM outperforms Transformation‐based Edge Computing Deep Q‐Learning (TransEC‐DQL) in several key metrics. Specifically, DTAM achieves 28.5% reduction in latency, 13.7% reduction in response time, and 6.4% improvement in bandwidth compared to TransEC‐DQL. These results signify that DTAM can deliver a significantly enhanced user experience in the metaverse, particularly in the context of remote work and collaboration.

Surveillance for metachronous cancers after endoscopic resection of esophageal squamous cell carcinoma.

The literature pertaining to surveillance following treatment for esophageal squamous cell carcinoma (SCC) was reviewed and summarized, encompassing the current status and future perspectives. Analysis of the standardized mortality and incidence ratios for these cancers indicates an elevated risk of cancer in the oral cavity, pharynx, larynx, and lungs among patients with esophageal SCC compared to the general population. To enhance the efficacy of surveillance for these metachronous cancers, risk stratification is needed. Various factors, including multiple Lugol-voiding lesions, multiple foci of dilated vascular areas, young age, and high mean corpuscular volume, have been identified as predictors of metachronous SCCs. Current practice involves stratifying the risk of metachronous esophageal and head/neck SCCs based on the presence of multiple Lugol-voiding lesions. Endoscopic surveillance, scheduled 6-12 months post-endoscopic resection, has demonstrated effectiveness, with over 90% of metachronous esophageal SCCs treatable through minimally invasive modalities. Narrow-band imaging emerges as the preferred surveillance method for esophageal and head/neck SCC based on comparative studies of various imaging techniques. Innovative approaches, such as artificial intelligence-assisted detection systems and radiofrequency ablation of high-risk background mucosa, may improve outcomes in patients following endoscopic resection.

Unidirectional Hybrid Three-Phase Rectifier with Boost Converter and Coupled Inductor

The unidirectional hybrid three-phase rectifier (UHTR) with Boost converter consists of two different rectifiers (Rectifier 1 and 2), where Rectifier 2 consists of modules corresponding to the phases. Each rectifier processes different part of the current waveform at the input, so that the sum of the current waveform parts is sinusoidal, or multilevel. Analyzing existing literature, the UHTR with Boost converter and isolation transformer, is proven as a classic solution with high weight, volume and cost. Therefore, in 2019 the UHTR with Boost converter was proposed, without any isolation transformer. To do this, it was necessary to replace the Boost inductor of each Rectifier 2 module with a coupled inductor. However, the proposed UHTR has not described the coupled inductor in details, neither the control circuit and the decoupling of the power circuit with the control circuit were described. Therefore, this main objective this paper is to provide answers to those aspects, and consequently present the UHTR proposal in a more realistic and practical application way, thus favoring the implementation of the prototype. A simulation of the proposed UHTR with coupled inductor was performed in the PSIM version 12.0 software with a power of 20 kW. The results of the proposed UHTR show that there is no current interaction and it works correctly, having a high PF of 99.92% and low total harmonic distortion of 3.96% at full load. In this way, it is proven that it is possible to implement an UHTR with a Boost converter and using coupled inductor.

Spatial mapping of hepatic ER and mitochondria architecture reveals zonated remodeling in fasting and obesity

The hepatocytes within the liver present an immense capacity to adapt to changes in nutrient availability. Here, by using high resolution volume electron microscopy, we map how hepatic subcellular spatial organization is regulated during nutritional fluctuations and as a function of liver zonation. We identify that fasting leads to remodeling of endoplasmic reticulum (ER) architecture in hepatocytes, characterized by the induction of single rough ER sheet around the mitochondria, which becomes larger and flatter. These alterations are enriched in periportal and mid-lobular hepatocytes but not in pericentral hepatocytes. Gain- and loss-of-function in vivo models demonstrate that the Ribosome receptor binding protein1 (RRBP1) is required to enable fasting-induced ER sheet-mitochondria interactions and to regulate hepatic fatty acid oxidation. Endogenous RRBP1 is enriched around periportal and mid-lobular regions of the liver. In obesity, ER-mitochondria interactions are distinct and fasting fails to induce rough ER sheet-mitochondrion interactions. These findings illustrate the importance of a regulated molecular architecture for hepatocyte metabolic flexibility.

The acute effects of kinesio-taping on movement kinematics and muscle co-activation in rowing athletes.

Rowing-related low back disorders may occur from inconsistent technique, high trunk flexion and training volumes, overactivation of paraspinal muscles, and fatigue. To examine if kinesiology tape (KT) affixed to the trunk dorsum affects muscular co-activation and neuromuscular control to limit dangerous rowing movements and associated injuries. Participants (n= 18) completed two 2000m rowing trials under BASELINE and KT conditions. KT was applied to the skin superficial to the paraspinals bilaterally with 60% pre-strain. Participants were instructed to minimize any sensation of tension. Whole body kinematics were obtained using inertial measurement units (IMUs), and surface electromyograms (EMGs) were recorded from trunk and lower extremity. Changes in joint range-of-motion (ROM) and co-activation indices (CAIs) were analyzed for shoulder, lumbar, hip, and knee. Responding participants (n= 5) were identified by reduced maximum lumbar flexion during the KT condition. As expected, significant differences occurred in maximum and minimum lumbar flexion/extension between responders and non-responders to KT. Additionally, there was significant reduction in mean trunk muscle co-activation in both those who did and did not respond to KT through reductions in maximum lumbar flexion. KT can be an effective at reducing mean trunk co-activation during a rowing trial in the flexed catch position. Variable responses suggest that further work is necessary to optimize the efficacy of sensory cues derived from KT during rowing movements.

Strength and Durability Properties of High-Volume Fly Ash (HVFA) Binders: A Systematic Review

South Africa is endowed with a wealth of coal-fired power stations that can produce extremely high volumes of fly ash per year exceeding 34 million tonnes. The use of high-volume fly ash (HVFA) binders in the construction sector has the capacity to significantly reduce greenhouse gas emissions associated with traditional cement production and offset the carbon footprint of Eskom. The excessive production of fly ash by Eskom warrants the need for developing ultra-high-volume fly ash binders (UHVFA, fly ash/binder &gt; 60 wt%). Nonetheless, fly ash (FA) replacement of cement is still largely limited to 35% regardless of more ambitious research indicating the potential to surpass 60%. In view of the urgent need for South Africa to offset and reduce its carbon footprint, this work reviews and summarises the literature on the performance of HVFA binders with a focus on two specific areas: (i) strength and (ii) durability. On HVFA binder strength, the focus is drawn on work that analysed the compressive strength, flexural strength, and split tensile strength. This review focuses on the extant literature analysing the durability of HVFA binders using various tests, including sorptivity, resistivity, permeability, tortuosity, rapid chloride penetration tests, resistance to sulphate attack, and microstructural analysis. As the FA content increases towards optima, i.e., 50–80%, the most indicative composite characteristics of the strength and durability properties are UCS (30–90 MPa) and permeability (low). This review reveals the leading methodologies, instrumentation, findings, challenges, and contradictions.

MINOCA, experience of a single tertiary center with a high volume of ACS

Abstract Funding Acknowledgements None. Introduction Myocardial infarction with non-obstructive coronary arteries (MINOCA) is a clinical entity for which there is limited evidence regarding the clinical data and prognosis. Purpose To evaluate epidemiological and clinical characteristics as well as short-term outcomes of patients with MINOCA presented at a university hospital center with high volume percutaneous coronary intervention (PCI). Methods We evaluated 1035 consecutive patients admitted to our hospital with acute myocardial infarction from June 2022 to January 2023 who underwent invasive evaluation. In-hospital and follow-up data after 30 and 90 days were collected. Primary endpoint was occurrence of a major adverse cardiovascular and cerebrovascular event (composite of cardiovascular death, cerebrovascular accident, re-hospitalization for non-fatal myocardial infarction, re-hospitalization for heart failure, malignant ventricular arrhythmia). Secondary endpoints were improvement in left ventricular ejection fraction and angina status at 30 and 90 days follow-up. Results Among patients presented with acute myocardial infarction, 8.5% (88 patients) were diagnosed with MINOCA. Mean age was 60.9 years and 61.4% were females. Arterial hypertension was present in 86.4%, hypercholesterolemia in 38.6%, diabetes in 12.5% of the patients. Smokers were 29.5% of them. During hospitalization 14.7% had a major adverse cardiovascular event, cardiovascular death occurred in 1.1%, cerebrovascular accident in 1.1%, acute heart failure in 12.5% and malignant ventricular arrhythmia in 1.1%. Primary endpoint at 30 and 90 days follow-up occurred in 10.3% and 13.7% respectively. At 30 days follow-up there was no cardiovascular death, cerebrovascular accident or malignant ventricular arrhythmia; re-hospitalization for MI was 5.7% and re-hospitalization for heart failure was 4.6%. At 90 days follow-up there was no cardiovascular death, cerebrovascular accident or malignant ventricular arrhythmia; re-hospitalization for MI occurred in 8%, re-hospitalization for heart failure in 5.7%. In regard to secondary endpoints, 24.1% experienced angina, while at 30 days and 90 days follow, 66.7% and 72.2% respectively had improvement of left ventricle EF. Conclusions In our study we found a high incidence of MINOCA affecting more frequently female patients. Angina at short term follow up was frequently recorded among out study population.

Early administration of umbilical cord blood cells following brief high tidal volume ventilation in preterm sheep: a cautionary tale

BackgroundUmbilical cord blood (UCB) cells are a promising treatment for preterm brain injury. Access to allogeneic sources of UCB cells offer the potential for early administration to optimise their therapeutic capacities. As preterm infants often require ventilatory support, which can contribute to preterm brain injury, we investigated the efficacy of early UCB cell administration following ventilation to reduce white matter inflammation and injury.MethodsPreterm fetal sheep (0.85 gestation) were randomly allocated to no ventilation (SHAM; n = 5) or 15 min ex utero high tidal volume ventilation. One hour following ventilation, fetuses were randomly allocated to i.v. administration of saline (VENT; n = 7) or allogeneic term-derived UCB cells (24.5 ± 5.0 million cells/kg; VENT + UCB; n = 7). Twenty-four hours after ventilation, lambs were delivered for magnetic resonance imaging and post-mortem brain tissue collected. Arterial plasma was collected throughout the experiment for cytokine analyses. To further investigate the results from the in vivo study, mononuclear cells (MNCs) isolated from human UCB were subjected to in vitro cytokine-spiked culture medium (TNFα and/or IFNγ; 10 ng/mL; n = 3/group) for 16 h then supernatant and cells collected for protein and mRNA assessments respectively.ResultsIn VENT + UCB lambs, systemic IFNγ levels increased and by 24 h, there was white matter neuroglial activation, vascular damage, reduced oligodendrocytes, and increased average, radial and mean diffusivity compared to VENT and SHAM. No evidence of white matter inflammation or injury was present in VENT lambs, except for mRNA downregulation of OCLN and CLDN1 compared to SHAM. In vitro, MNCs subjected to TNFα and/or IFNγ displayed both pro- and anti-inflammatory characteristics indicated by changes in cytokine (IL-18 & IL-10) and growth factor (BDNF & VEGF) gene and protein expression compared to controls.ConclusionsUCB cells administered early after brief high tidal volume ventilation in preterm fetal sheep causes white matter injury, and the mechanisms underlying these changes are likely dysregulated responses of the UCB cells to the degree of injury/inflammation already present. If immunomodulatory therapies such as UCB cells are to become a therapeutic strategy for preterm brain injury, especially after ventilation, our study suggests that the inflammatory state of the preterm infant should be considered when timing UCB cells administration.

What financial topics do people search for? An analysis of search queries using text mining

Billions of web searches are recorded every day; however, little is known about the types of financial information that users search for. While many studies have investigated information exchanges in financial forums, this is the first study to identify the financial information needs of Internet users in Malaysia through an analysis of search queries. We identified financial topics and discovered subtopics of interest using text mining. We found that topics with high search volume were related to financial products and services, and very little was related to concepts and information that would increase financial knowledge. The results of this study can be used to develop more strategic online financial education content that not only meets users’ financial information needs but also increases their financial knowledge, especially when the financial knowledge of the global population has remained low over the years.

Artificial Intelligence Techniques for the Photovoltaic System: A Systematic Review and Analysis for Evaluation and Benchmarking

AbstractNovel algorithms and techniques are being developed for design, forecasting and maintenance in photovoltaic due to high computational costs and volume of data. Machine Learning, artificial intelligence techniques and algorithms provide automated, intelligent and history-based solutions for complex scenarios. This paper aims to identify through a systematic review and analysis the role of artificial intelligence algorithms in photovoltaic systems analysis and control. The main novelty of this work is the exploration of methodological insights in three different ways. The first approach is to investigate the applicability of artificial intelligence techniques in photovoltaic systems. The second approach is the computational study and analysis of data operations, failure predictors, maintenance assessment, safety response, photovoltaic installation issues, intelligent monitoring etc. All these factors are discussed along with the results after applying the artificial intelligence techniques on photovoltaic systems, exploring the challenges and limitations considering a wide variety of latest related manuscripts.

Continuous renal replacement therapy and therapeutic plasma exchange in pediatric liver failure.

Patients with acute liver failure (ALF) and acute on chronic liver failure (ACLF) have significant morbidity and mortality. They require extracorporeal blood purification modalities like continuous renal replacement therapy (CRRT) and therapeutic plasma exchange (TPE) as a bridge to recovery or liver transplantation. Limited data are available on the outcomes of patients treated with these therapies. This is a retrospective single-center study of 23 patients from 2015 to 2022 with ALF/ACLF who underwent CRRT and TPE. We aimed to describe the clinical characteristics and outcomes of these patients.Median (IQR) age was 0.93 years (0.57, 9.88), range 16 days to 20 years. Ten (43%) had ALF and 13 (57%) ACLF. Most (n = 19, 82%) started CRRT for hyperammonemia and/or hepatic encephalopathy and all received TPE for refractory coagulopathy. CRRT was started at a median of 2 days from ICU admission, and TPE started on the same day in most. The liver transplant was done in 17 (74%), and 2 recovered native liver function. Four patients, all with ACLF, died prior to ICU discharge without a liver transplant. The median peak ammonia pre-CRRT was 131 µmol/L for the whole cohort. The mean (SD) drop in ammonia after 48h of CRRT was 95.45 (43.72) µmol/L in those who survived and 69.50 (21.70) µmol/L in those who did not (p 0.26). Those who survived had 0 median co-morbidities compared to 2.5 in non-survivors (aOR (95% CI) for mortality risk of 2.5 (1.1-5.7), p 0.028). Conclusion: In this cohort of 23 pediatric patients with ALF or ACLF who received CRRT and TPE, 83% survived with a liver transplant or recovered with their native liver. Survival was worse in those who had ACLF and those with co-morbid conditions. What is Known: • Pediatric acute liver failure is associated with high mortality. • Patients may require extracorporeal liver assist therapies (like CRRT, TPE, MARS, SPAD) to bridge them over to a transplant or recovery of native liver function. What is New: • Standard volume plasma exhange has not been evaluated against high volume plasma exchange for ALF. • The role, dose, and duration of therapeutic plasma exchange in patients with acute on chronic liver failure is not well described.

Review on Sampling Methods and Health Impacts of Fine (PM2.5, ≤2.5 µm) and Ultrafine (UFP, PM0.1, ≤0.1 µm) Particles

Airborne particulate matter (PM) is of great concern in the modern-day atmosphere owing to its association with a variety of health impacts, such as respiratory and cardiovascular diseases. Of the various size fractions of PM, it is the finer fractions that are most harmful to health, in particular ultrafine particles (PM0.1; UFPs), with an aerodynamic diameter ≤ 100 nm. The smaller size fractions, of ≤2.5 µm (PM2.5; fine particles) and ≤0.1 µm (PM0.1; ultrafine particles), have been shown to have numerous linkages to negative health effects; however, their collection/sampling remains challenging. This review paper employed a comprehensive literature review methodology; 200 studies were evaluated based on the rigor of their methodologies, including the validity of experimental designs, data collection methods, and statistical analyses. Studies with robust methodologies were prioritised for inclusion. This review paper critically assesses the health risks associated with fine and ultrafine particles, highlighting vehicular emissions as the most significant source of particulate-related health effects. While coal combustion, diesel exhaust, household wood combustors’ emissions, and Earth’s crust dust also pose health risks, evidence suggests that exposure to particulates from vehicular emissions has the greatest impact on human health due to their widespread distribution and contribution to air pollution-related diseases. This article comprehensively examines current sampling technologies, specifically focusing on the collection and sampling of ultrafine particles (UFP) from ambient air to facilitate toxicological and physiochemical characterisation efforts. This article discusses diverse approaches to collect fine and ultrafine particulates, along with experimental endeavours to assess ultrafine particle concentrations across various microenvironments. Following meticulous evaluation of sampling techniques, high-volume air samplers such as the Chem Vol Model 2400 High Volume Cascade Impactor and low-volume samplers like the Personal Cascade Impactor Sampler (PCIS) emerge as effective methods. These techniques offer advantages in particle size fractionation, collection efficiency, and adaptability to different sampling environments, positioning them as valuable tools for precise characterisation of particulate matter in air quality research and environmental monitoring.

Erythrocyte Phospholipid Fatty Acid Profile in High-Level Endurance Runners

Fatty acids (FAs) are the major structural component of erythrocyte membranes. Diet and physical exercise directly influence their incorporation and function. Endurance runners engage in high volumes of weekly aerobic training, alternating between low-intensity and high-intensity sessions. The aim of the study was to assess and compare the erythrocyte FA profile in a group of high-level male endurance runners (EG) with a control group of non-athlete subjects (CG). This observational study was conducted on 85 subjects, 63 high-level male endurance runners (23 ± 3 years; height: 1.76 ± 0.05) and 22 subjects who did not engage in regular physical exercise (21 ± 0.5 years; height: 1.68 ± 0.39). Runners had at least five years of training experience, and all of them were participants in national and international tournaments. FAs determination was performed using gas chromatography. Higher percentages of Palmitic Acid (PA), Stearic Acid (SA), Oleic Acid (OA), Calendic Acid (CA), Eicosapentaenoic Acid (EPA) and Docosapentaenoic Acid (DPA), and lower percentages of Docosahexaenoic Acid (DHA) were found in the EG compared to the CG. High-level endurance runners exhibit altered erythrocyte FA profiles with low percentages of omega-3 index (ω-3 index) and DHA, which may affect erythrocyte membrane function as well as their performance.

Seasonal shifts in the habitat selection patterns of male American Marten (Martes americana) at a fine spatial scale

Abstract Old-growth forests harbor a large amount of complex structural features that result in a wide array of wildlife habitats. However, intensive forest management is gradually converting old-growth forest into younger, even-aged stands, reducing structural complexity and threatening the persistence of old-growth-dependent species. Maintaining elements of complex stand structure is critical to the conservation of old-growth forest specialists that use different habitat components at different periods of their annual cycle, and it requires a comprehensive understanding of seasonal variation in the habitat needs of these species. However, difficulties in observing free-ranging animals have sometimes limited our ability to assess such variations in habitat requirements, especially for small, elusive species. To address this, we used GPS telemetry collars to describe fine-scale habitat selection patterns of 6 male American Martens (Martes americana) during 2 contrasting periods of the year (snow-free, from mid-April to mid-November; snow-covered, from mid-November to mid-April), an objective formerly hard to achieve using conventional VHF telemetry. We used resource selection functions conducted at the fourth order of selection to compare habitat characteristics found at the sites used by martens (GPS locations, n = 100) to those found on an equal number of available sites (random points, n = 100) within each individual seasonal home range. We conducted vegetation surveys on these 200 sites to describe habitat and built candidate models representing different concurrent hypotheses. Our results showed that proxies of prey availability, predator avoidance, and thermal constraints were the primary factors influencing marten habitat selection during both periods, although their respective importance differed between periods. Martens selected sites with a high density of large-diameter snags (≥30·ha−1), high conifer canopy closure (≥53%), and a dense lateral cover (≥81%) during the snow-free period, but selected sites with a high volume of coarse woody debris (≥64 m3·ha−1) and high conifer canopy closure (≥48%) during the snow-covered period. Our results highlight the importance of contrasting seasonal changes in habitat selection patterns of small carnivores and may help maintain structural attributes in the landscape that are suitable for male American Martens.

Multiparticle collision dynamics simulations of hydrodynamic interactions in colloidal suspensions: How well does the discrete particle approach do at short range?

The multiparticle collision dynamics (MPCD) simulation method is an attractive technique for studying the effects of hydrodynamic interactions in colloidal suspensions because of its flexibility, computational efficiency, and ease of implementation. Here, we analyze an extension of the basic MPCD method in which colloidal particles are discretized with a surface mesh of sensor nodes/particles that interact with solvent particles (MPCD + Discrete Particle or MPCD + DP). We use several situations that have been described analytically to probe the impact of colloidal particle mesh resolution on the ability of the MPCD + DP method to resolve short-ranged hydrodynamic interactions, which are important in crowded suspensions and especially in self-assembling systems that create high volume fraction phases. Specifically, we consider (A) hard-sphere diffusion near a wall, (B) two-particle diffusion, (C) hard-sphere diffusion in crowded suspensions, and (D) the dynamics of aggregation in an attractive colloidal suspension. We show that in each case, the density of sensor nodes plays a significant role in the accuracy of the simulation and that a surprisingly high number of surface nodes are needed to fully capture hydrodynamic interactions.