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Although traditional methods of data collection in naturalistic settings can shed light on constructs of interest to researchers, advances in sensor-based technology allow researchers to capture continuous physiological and behavioral data to provide a more comprehensive understanding of the constructs that are examined in a dynamic health care setting. This study gives examples for implementing technology-facilitated approaches and provides the following recommendations for conducting such longitudinal, sensor-based research, with both environmental and wearable sensors in a health care setting: pilot test sensors and software early and often; build trust with key stakeholders and with potential participants who may be wary of sensor-based data collection and concerned about privacy; generate excitement for novel, new technology during recruitment; monitor incoming sensor data to troubleshoot sensor issues; and consider the logistical constraints of sensor-based research. The study describes how these recommendations were successfully implemented by providing examples from a large-scale, longitudinal, sensor-based study of hospital employees at a large hospital in California. The knowledge gained from this study may be helpful to researchers interested in obtaining dynamic, longitudinal sensor data from both wearable and environmental sensors in a health care setting (eg, a hospital) to obtain a more comprehensive understanding of constructs of interest in an ecologically valid, secure, and efficient way.

Objective: Epicardial adipose tissue (EAT) is implicated in the pathogenesis and progression of coronary artery disease (CAD). Limited data exists on the interplay between EAT and atherosclerosis in young individuals. Our study aims to explore the relationship between EAT and CAD in a young cohort. Methods: All young (18–45 years) patients without prior CAD, referred for coronary computed tomography angiography (CCTA) from 2016 to 2022 were included. EAT volume and coronary artery calcium (CAC) were calculated from dedicated non-contrast scans. Coronary plaque presence, extent, and volume were quantified from CCTA. Multivariable logistic regression models for the presence of CAD, defined as any coronary atherosclerosis, were performed. Results: Overall, 712 patients (39±4.8 years, 54 % female) with 45 % Hispanic, and 21 % non-Hispanic Black were included. Patients with CAD had higher EAT volume than those without (80.80 mL ± 36.00 vs 55.16 mL ± 27.92; P < 0.001). In those with CAC=0, higher EAT was associated with the presence of CAD compared to lower EAT volume (P < 0.001). An EAT volume >76 mL was associated with higher CAC (P < 0.001), segment involvement score (P < 0.001), and quantitative total, non-calcified, and low-attenuation plaque volumes (P < 0.002). At multivariable analysis, EAT volume (per 10 mL, OR: 1.21; 95 %CI: 1.12–1.30; P < 0.0001) was independently associated with the presence of CAD. Conclusion: In a diverse cohort of young adults without history of CAD and undergoing a clinically indicated CCTA, EAT volume was independently associated with the presence of CAD. Our findings highlight EAT potential as a novel marker for CAD risk-assessment and a potential therapeutic target in young patients.

This study aims to investigate the role of hydrogen peroxide (HP) continuous dosage in removing Paracetamol (PCT) from different water matrices using the solar photo-Fenton process. Different parameters in the HP dosage strategies (initial HP pulse, dosing time, and HP concentration) were systematically analysed to assess their impacts on pollutant removal (XPCT), oxidant specific consumption (YHP/PCTt), and toxicity levels (I(%)). The analysis involved various water matrices (ultrapure water UW, groundwater GW, anion matrix AW, and synthetic pharmaceutical wastewater IW0.01 or IW0.1), which were firstly treated in a laboratory reactor and subsequently scaled up to a solar prototype. After laboratory testing, the most effective reaction configuration (maximum XPCT and YHP/PCTt close to the stoichiometric one) was chosen as the starting point for scaling up the reaction system. Using the solar reactor setup, complete PCT conversion was achieved within just 60 min of reaction time (UW matrix). However, under IW0.1 condition and employing the same HP dosing strategy, a XPCT of 95.4 % was attained but at 180 min of reaction, highlighting the significant influence of the real matrix. Additionally, the I(%) remained high towards the end of the reaction (close to 60 %), attributed to the presence of hydroquinone in the system, demanding longer reaction times to completely reduce the toxicity when working with industrial wastewater. This comprehensive approach aims to close the gap between lab results and practical applications, offering crucial insights to address pharmaceutical wastewater pollution.

The rapid growth in index funds and significant consolidation in the asset- management industry over the past few decades has led to higher levels of common ownership and increased attention on the topic by academic researchers. A consensus has yet to emerge from the literature regarding the consequences of increased common ownership on firm behavior and market outcomes. Given the potential implications for firms and investors alike, it is perhaps not surprising that policymakers, legal scholars, finance and accounting academics, and practitioners have all taken a keen interest in the subject. This paper provides an overview of the theoretical underpinnings of common ownership and critically reviews the empirical literature. Measurement issues and identification challenges are detailed, and a discussion of plausible causal mechanisms is provided. Across the newest papers employing the most credible identification techniques, there is relatively little evidence that common ownership causes lower competition. However, further research is necessary before broad conclusions can be reached. JEL classification: G23, G32, G34, L22 Key words: common ownership, institutional investors, corporate governance, 1 Introduction Common ownership refers to situations in which investors own shares in multiple firms that compete in the same product market. Over the past few decades, substantial growth in [...]

Abstract Population-based association studies have identified many genetic risk loci for coronary artery disease (CAD), but it is often unclear how genes within these loci are linked to CAD. Here, we perform interaction proteomics for 11 CAD-risk genes to map their protein-protein interactions (PPIs) in human vascular cells and elucidate their roles in CAD. The resulting PPI networks contain interactions that are outside of known biology in the vasculature and are enriched for genes involved in immunity-related and arterial-wall-specific mechanisms. Several PPI networks derived from smooth muscle cells are significantly enriched for genetic variants associated with CAD and related vascular phenotypes. Furthermore, the networks identify 61 genes that are found in genetic loci associated with risk of CAD, prioritizing them as the causal candidates within these loci. These findings indicate that the PPI networks we have generated are a rich resource for guiding future research into the molecular pathogenesis of CAD.

Abstract Biallelic mutations of the chromatin regulator SMARCAL1 cause Schimke Immunoosseous Dysplasia (SIOD), characterized by severe growth defects and premature mortality. Atherosclerosis and hyperlipidemia are common among SIOD patients, yet their onset and progression are poorly understood. Using an integrative approach involving proteomics, mouse models, and population genetics, we investigated SMARCAL1’s role. We found that SmarcAL1 interacts with angiopoietin-like 3 (Angptl3), a key regulator of lipoprotein metabolism. In vitro and in vivo analyses demonstrate SmarcAL1’s vital role in maintaining cellular lipid homeostasis. The observed translocation of SmarcAL1 to cytoplasmic peroxisomes suggests a potential regulatory role in lipid metabolism through gene expression. SmarcAL1 gene inactivation reduces the expression of key genes in cellular lipid catabolism. Population genetics investigations highlight significant associations between SMARCAL1 genetic variations and body mass index, along with lipid-related traits. This study underscores SMARCAL1’s pivotal role in cellular lipid metabolism, likely contributing to the observed lipid phenotypes in SIOD patients.

Sleep-disordered breathing has been reported in Charcot–Marie–Tooth disease (CMT) type 1A in association with diaphragmatic weakness and sleep apnea syndrome, mainly of the obstructive type (OSA). Improvement has been observed not only in sleep quality but also in neuropathy symptoms in CMT1A patients with OSA following the initiation of continuous positive airway pressure. We report the cases of two siblings affected by CMT1A associated with hemidiaphragm relaxatio necessitating nocturnal non-invasive ventilation (NIV). Two twins, now 42 years old, with a family history of CMT1A, received a genetic diagnosis of CMT1A at the age of 16. Over the years, they developed a slowly worsening gait disorder and a decline in fine motor hand movements, currently presenting with moderate disability (CMTES:13). At the age of 40, they both started complaining of daytime sleepiness, orthopnea, and exertional dyspnea. They received a diagnosis of relaxatio of the right hemidiaphragm associated with impairment of nocturnal ventilation and they both have benefited from nocturnal NIV. Disorders of breathing during sleep may be underestimated in CMT1A since routine investigations of sleep quality are rarely performed. Our two clinical cases and a literature review suggest the importance of inquiring about symptoms of excessive daytime sleepiness and respiratory disturbances in individuals with CMT1A, even in the absence of severe neuropathy. In the presence of compatible symptoms, a pneumological assessment, along with an overnight polysomnogram and lung function tests, should be performed. Recognizing sleep-related symptoms is essential for providing accurate treatment and improving the quality of life for patients with CMT1A.

External motivational stimuli have been shown to improve athletic performance. However, the neurophysiological mechanisms underlying this improvement remain poorly understood. This randomized crossover study investigated the effects of music and verbal encouragement on measures of muscle excitation and myoelectric manifestations of fatigue in the biceps brachii and brachioradialis muscles during an endurance task. Fifteen untrained (mean age 29.57 ± 2.77 years) and 13 trained individuals (mean age 32.92 ± 2.90 years) were included. The endurance task, performed to exhaustion, consisted of keeping the dominant arm flexed to 90 degrees while holding a dumbbell loaded to 80% of 1RM with a supine grip in three randomized conditions: standard, with self-selected music, and with verbal encouragement. The untrained subjects showed an increase in task duration of 15.26% (p < 0.003) with music and 15.85% (p < 0.002) with verbal encouragement compared to the condition without external stimuli. There were no significant differences in the myoelectric manifestations of fatigue between the different conditions. Regarding the muscle excitation metrics, although the mean amplitude, peak value, and area under the curve remained unchanged across conditions, a significant reduction in the trend coefficient, indicating motor unit recruitment over time, was observed with both music (biceps brachii: −10.39%, p < 0.001; brachioradialis: −9.40%, p < 0.001) and verbal encouragement (biceps brachii: −7.61%, p < 0.001; brachioradialis: −6.51%, p < 0.001) compared to the standard condition. For the trained participants, no significant differences were observed between conditions in terms of task duration and outcome measures related to muscle excitation and myoelectric manifestations of fatigue, suggesting the possible presence of a ceiling effect on motivation. These results highlight the important role of external motivational stimuli, such as music and verbal encouragement, in improving task performance in untrained subjects, probably through more effective and efficient recruitment of motor units.

The limited range of available flu treatments due to virus mutations and drug resistance have prompted the search for new therapies. RNA-dependent RNA polymerase (RdRp) is a heterotrimeric complex of three subunits, i.e., polymerase acidic protein (PA) and polymerase basic proteins 1 and 2 (PB1 and PB2). It is widely recognized as one of the most promising anti-flu targets because of its critical role in influenza infection and high amino acid conservation. In particular, the disruption of RdRp complex assembly through protein–protein interaction (PPI) inhibition has emerged as a valuable strategy for discovering a new therapy. Our group previously identified the 3-cyano-4,6-diphenyl-pyridine core as a privileged scaffold for developing PA–PB1 PPI inhibitors. Encouraged by these findings, we synthesized a small library of pyridine and pyrimidine derivatives decorated with a thio-N-(m-tolyl)acetamide side chain (compounds 2a–n) or several amino acid groups (compounds 3a–n) at the C2 position. Interestingly, derivative 2d, characterized by a pyrimidine core and a phenyl and 4-chloro phenyl ring at the C4 and C6 positions, respectively, showed an IC50 value of 90.1 μM in PA–PB1 ELISA, an EC50 value of 2.8 μM in PRA, and a favorable cytotoxic profile, emerging as a significant breakthrough in the pursuit of new PPI inhibitors. A molecular modeling study was also completed as part of this project, allowing us to clarify the biological profile of these compounds.

Quantitative inversion of GPR data opens the door to precise characterization of underground environments. However, in order to make the inverse scattering problem solution easier from a computational viewpoint, simplifying assumptions are often applied, i.e., two-dimensional approximations or the consideration of idealized field probes and electromagnetic sources. These assumptions usually produce modeling errors, which can degrade the dielectric reconstruction results considerably. In this article, a processing step based on long short-term memory cells is proposed for the first time to correct the modeling error in a multiantenna GPR setting. In particular, time-domain GPR data are fed into a neural network trained with couples of finite-difference time-domain simulations, where a set of sample targets are simulated in both realistic and idealized configurations. Once trained, the neural network outputs an approximation of multiantenna GPR data as they are collected by an ideal two-dimensional measurement setup. The inversion of the processed data is then accomplished by means of a regularizing Newton-based nonlinear scheme with variable exponent Lebesgue space formulation. A numerical study has been conducted to assess the capabilities of the proposed inversion methodology. The results indicate the possibility of effectively compensating for modeling error in the considered test cases.