Background and Objectives The choroid plexus has been shown to play a crucial role in CNS inflammation. Previous studies found larger choroid plexus in multiple sclerosis (MS) compared with healthy controls. However, it is not clear whether the choroid plexus is similarly involved in MS and in neuromyelitis optica spectrum disorder (NMOSD). Thus, the aim of this study was to compare the choroid plexus volume in MS and NMOSD. Methods In this retrospective, cross-sectional study, patients were included by convenience sampling from 4 international MS centers. The choroid plexus of the lateral ventricles was segmented fully automatically on T1-weighted MRI sequences using a deep learning algorithm (Multi-Dimensional Gated Recurrent Units). Uni- and multivariable linear models were applied to investigate associations between the choroid plexus volume, clinically meaningful disease characteristics, and MRI parameters. Results We studied 180 patients with MS and 98 patients with NMOSD. In total, 94 healthy individuals and 47 patients with migraine served as controls. The choroid plexus volume was larger in MS (median 1,690 µL, interquartile range [IQR] 648 µL) than in NMOSD (median 1,403 µL, IQR 510 µL), healthy individuals (median 1,533 µL, IQR 570 µL), and patients with migraine (median 1,404 µL, IQR 524 µL; all p < 0.001), whereas there was no difference between NMOSD, migraine, and healthy controls. This was also true when adjusted for age, sex, and the intracranial volume. In contrast to NMOSD, the choroid plexus volume in MS was associated with the number of T2-weighted lesions in a linear model adjusted for age, sex, total intracranial volume, disease duration, relapses in the year before MRI, disease course, Expanded Disability Status Scale score, disease-modifying treatment, and treatment duration (beta 4.4; 95% CI 0.78–8.1; p = 0.018). Discussion This study supports an involvement of the choroid plexus in MS in contrast to NMOSD and provides clues to better understand the respective pathogenesis.

Background: Hypertension remains a major risk factor for cardiovascular diseases, but the underlying mechanisms are not well understood. We hypothesize that appropriate mechanotransduction and contractile function in vascular smooth muscle cells are crucial to maintain vascular wall integrity. The Hippo pathway effectors YAP (yes-associated protein 1) and TAZ (WW domain containing transcription regulator 1) have been identified as mechanosensitive transcriptional coactivators. However, their role in vascular smooth muscle cell mechanotransduction has not been investigated in vivo. Methods: We performed physiological and molecular analyses utilizing an inducible smooth muscle–specific YAP/TAZ knockout mouse model. Results: Arteries lacking YAP/TAZ have reduced agonist-mediated contraction, decreased myogenic response, and attenuated stretch-induced transcriptional regulation of smooth muscle markers. Moreover, in established hypertension, YAP/TAZ knockout results in severe vascular lesions in small mesenteric arteries characterized by neointimal hyperplasia, elastin degradation, and adventitial thickening. Conclusions: This study demonstrates a protective role of YAP/TAZ against hypertensive vasculopathy.

Shear wave elastography (SWE) is an ultrasound‐based stiffness quantification technology that is used for noninvasive liver fibrosis assessment. However, despite widescale clinical adoption, SWE is largely unused by preclinical researchers and drug developers for studies of liver disease progression in small animal models due to significant experimental, technical, and reproducibility challenges. Therefore, the aim of this work was to develop a tool designed specifically for assessing liver stiffness and echogenicity in small animals to better enable longitudinal preclinical studies. A high‐frequency linear array transducer (12‐24 MHz) was integrated into a robotic small animal ultrasound system (Vega; SonoVol, Inc., Durham, NC) to perform liver stiffness and echogenicity measurements in three dimensions. The instrument was validated with tissue‐mimicking phantoms and a mouse model of nonalcoholic steatohepatitis. Female C57BL/6J mice (n = 40) were placed on choline‐deficient, L‐amino acid‐defined, high‐fat diet and imaged longitudinally for 15 weeks. A subset was sacrificed after each imaging timepoint (n = 5) for histological validation, and analyses of receiver operating characteristic (ROC) curves were performed. Results demonstrated that robotic measurements of echogenicity and stiffness were most strongly correlated with macrovesicular steatosis (R2 = 0.891) and fibrosis (R2 = 0.839), respectively. For diagnostic classification of fibrosis (Ishak score), areas under ROC (AUROCs) curves were 0.969 for ≥Ishak1, 0.984 for ≥Ishak2, 0.980 for ≥Ishak3, and 0.969 for ≥Ishak4. For classification of macrovesicular steatosis (S‐score), AUROCs were 1.00 for ≥S2 and 0.997 for ≥S3. Average scanning and analysis time was <5 minutes/liver. Conclusion: Robotic SWE in small animals is feasible and sensitive to small changes in liver disease state, facilitating in vivo staging of rodent liver disease with minimal sonographic expertise.

Isotopically purified semiconductors potentially dissipate heat better than their natural, isotopically mixed counterparts as they have higher thermal conductivity (κ). But the benefit is low for Si at room temperature, amounting to only ∼10% higher κ for bulk ^{28}Si than for bulk natural Si (^{nat}Si). We show that in stark contrast to this bulk behavior, ^{28}Si (99.92% enriched) nanowires have up to 150% higher κ than ^{nat}Si nanowires with similar diameters and surface morphology. Using a first-principles phonon dispersion model, this giant isotope effect is attributed to a mutual enhancement of isotope scattering and surface scattering of phonons in ^{nat}Si nanowires, correlated via transmission of phonons to the native amorphous SiO_{2} shell. The Letter discovers the strongest isotope effect of κ at room temperature among all materials reported to date and inspires potential applications of isotopically enriched semiconductors in microelectronics.

Background: Patients with suspected non-ST-elevation acute coronary syndrome (NSTE-ACS) assigned to the “observe” zone of the European Society of Cardiology (ESC) 0/1-h algorithm form a heterogeneous group known to have an unfavourable prognosis. We aim to elucidate the clinical characteristics and management of these patients and generate a model that is predictive of a coronary diagnosis at index visit to the emergency department (ED). Methods: A retrospective observational cohort study, including adult patients presenting to the ED with suspected NSTE-ACS assigned to the “observe” zone of the ESC 0/1-h algorithm. Multivariable logistic regression analysis was performed for the prediction of a coronary diagnosis. Internal validation was performed using bootstrap resampling. Results: A total of 750 patients were included; mean age 66 ± 13 years, 35% women, 50% with prior history of coronary artery disease (CAD). In 372 (50%) patients a diagnosis was established within 30 days of index presentation, of whom 169 (45%) patients had a coronary-related event. Multivariable logistic regression analysis generated a 12-point risk score incorporating 5 variables for the prediction of such event, including type of angina, chest pain occurring during inspiration, prior history of CAD, ST-segment deviation on electrocardiogram, and estimated glomerular filtration rate <60. The final model had an optimism-corrected c-statistic of 0.78 (95% confidence interval [CI]: 0.74–0.82). A score <6 ruled out a coronary event in 276 (37%) patients, with a sensitivity and negative predictive value of 90% (95% CI: 84–94) and 94% (91–96), respectively. Conclusion: A score <6 identifies patients at low risk of a coronary diagnosis and can guide clinical decision-making in choosing the appropriate diagnostic test.

This paper describes the process of designing models and tools for an automated way of creating 3D city model based on a raw point cloud.Also, making and forming 3D models of buildings. Models and tools for creating tools made in the model builder application within the ArcGIS Pro software. An unclassified point cloud obtained by the LiDAR system was used for the model input data. The point cloud, collected by the airborne laser scanning system (ALS), is classified into several classes: ground, high and low noise, and buildings. Based on the created DEMs, points classified as buildings and formed prints of buildings, realistic 3D city models were created. Created 3D models of cities can be used as a basis for monitoring the infrastructure of settlements and other analyzes that are important for further development and architecture of cities.

Extreme terahertz (THz) pulses can be generated via interaction of strong infrared ultrashort laser pulses with a suitable target. Inverting this scheme, we propose to use such THz pulses to control strong-laser-field-driven processes. In particular, we show that for THz-pulse-assisted strong-laser-field ionization the electron yield can be increased by one order of magnitude for some energies, and that the maximal emitted photoelectron energy can be a few times higher than that realized with the laser field alone. This can be achieved with the THz field intensity many orders of magnitude lower than that of the ionizing laser field. The only requirement is that the vector potential amplitude of the THz field, which governs the electron dynamics after the ionization by the laser field, be comparable with that of the used laser field. An important control parameter is the time delay between the THz pulse and the laser pulse. Strong-field ionization of Cs atoms is used for an illustration. The numerical results are obtained applying the improved strong-field approximation. For a physical explanation, we use quantum-orbit theory supported by the modified saddle-point method, as well as a classical model.

OBJECTIVES To estimate the incidence and describe the spectrum of inflammatory and autoimmune diseases linked to SARS-CoV-2 infection and COVID-19 vaccination in children from two neighbouring south central European countries. METHODS We performed a multi-centre prospective cohort study of children under 18 years diagnosed with inflammatory/autoimmune diseases linked to SARS-CoV-2 infection or COVID-19 vaccination, who were admitted to the paediatric tertiary care hospitals in Slovenia and Friuli Venezia Giulia, Italy, from January 1, 2020, to December 31, 2021. Disease incidence was calculated based on laboratory-confirmed cases only. RESULTS Inflammatory and autoimmune diseases linked to SARS-CoV-2 were diagnosed in 192 children (127 laboratory-confirmed), of whom 112 had multisystem inflammatory syndrome (MIS-C), followed by vasculitis, neurological and cardiac diseases. Calculated risk of MIS-C was 1 in 860 children after SARS-CoV-2 infection and cumulative incidence of MIS-C was 18.3/100,000 of all children. Fifteen children had severe COVID-19. Two patients with MIS-C and a patient with myositis presented after COVID-19 vaccination. All 3 had at presentation also a serologically proven recent SARS-CoV-2 infection. After MIS-C, nine patients were vaccinated against COVID-19 and 25 patients had a SARS-CoV-2 reinfection, without recurrence of MIS-C. CONCLUSIONS Autoimmune diseases following SARS-CoV-2 infection in children were 8.5 times as common as severe COVID-19. MIS-C was the most common manifestation and its incidence in this predominantly white population was higher than previously reported. MIS-C does not seem to recur after SARS-CoV-2 reinfection or COVID-19 vaccination. Autoimmune diseases were much more common after SARS-CoV-2 infection than after COVID-19 vaccination.

Abstract Given recent environmental reforms and the focus on the problem of climate change, it is necessary to evaluate whether green growth and environmental taxes can reduce CO2 emissions for countries. Even though a number of studies have analysed the ways to reduce environmental pollution, the literature lacks enough evidence for the role of green growth and environmental taxes in determining the level of carbon emissions. Therefore, the objective of the empirical analysis is to estimate the impacts on CO2 emissions of green growth and environmental taxes by including sustainable indicators for a group of 25 environmentally friendly countries from 1994 to 2018 by applying advanced panel data analysis models. By applying the novel quantile regressions on the largest amount of available data from 1994 to 2018, this article shows that the coefficients of green growth, environmental taxes, renewable energy and energy efficiency are negative at lower, medium and higher quantiles. According to the results of the quantile regression, environmental taxes, renewable energy and energy efficiency are key factors in decreasing CO2 emissions. Overall, renewable energy should be given greater priority through research supports, subsidies and government incentives while environmental taxes should be more implemented to discourage activities that promote pollution.

In the paper by Obradovic et al., the first name and surname of the authors have been interchanged. The correct names of the author are listed below: Slobodan Obradovic, Boris Dzudovic, Bojana Subotic, Jovan Matijasevic, Zorica Mladenovic, Aleksandar Bokan, Jadranka Trobok, Sandra Pekovic, Sonja Salinger-Martinovic, Ljiljana Jovanovic, Ljiljana Kos, Tamara Kovacevic-Preradovic, Maja Nikolic, Vladimir Miloradovic, Ana Kovacevic-Kuzmanovic, Zec Nenad, Natasa Markovic-Nikolic, Ilija Srdanovic, Zoran Gluvic, Srdjan Kafedzic, Sasa Pancevacki, Aleksandar Neskovic and Stavros Konstantinides.

There are several issues and problems that are inevitably produced by the phenomenon and the emergence of social exclusion. These are, first of all: unemployment, inadequate education, extremely poor housing and an environment characterized by high rates of corruption and crime, insufficient and inadequate care and nursing of the elderly, low level of general health, cases of family breakdown, addiction problems, and discrimination on various grounds. Therefore, the fight against poverty and social exclusion, through the promotion of integration and inclusion in the labor market and social integration, supports marginalized groups and helps their active inclusion in social flows. The inclusion in the labor market of socially excluded groups, accompanied by projects to create a supportive environment, is particularly important and represents the best approach in their social reaffirmation and fight against all forms of social exclusion. In this sense, the development and implementation of social inclusion programs that are regulated by positive legal regulations by the state are crucial. It is the state that should, on the basis of legal regulation and through financial support, encourage social inclusion since this is the only way and basis to ensure the prevention of all forms of discrimination against socially excluded groups of citizens. In this context, the development and implementation of disability education programs are key. Support for such projects increases opportunities for education and subsequent employment and reduces the risk of social exclusion and poverty of socially excluded categories. The presented case study shows that the development and implementation of targeted inclusive education programs will give excellent results in terms of strengthening this marginalized category by improving the conditions of their education without physical and mental barriers, as well as by creating conditions for improving their later employment and labor market integration.

This convergent mixed methods study aims to compare effectiveness of using three online consultation resources entailing deductive vs. data-driven learning on learning and retention of 18 verb-noun collocations by EFL learners. The participants ( N =45) randomly assigned to treatment conditions consulted three different online resources in different orders to complete the same online error correction tasks and gap-fill exercises in three sessions. The participants were given the Vocabulary Size Test (VST), and a productive collocation translation test (CTT) as the pretest, the posttest and the retention test. A sub-set of the participants also performed think aloud protocols during the treatment. After the treatment, the participants were given the CTT and responded a rating scale and an open-ended question. The results indicated that all of the resources led to significantly higher learning and retention rates with no significant differences among the resources. It was also found that the participants rate the resources differently and go through different cognitive processes when consulting resources. The VST scores positively correlated with the posttest scores, suggesting that participants’ vocabulary sizes can be a moderating variable. The findings are discussed based on previous research and within the framework of data-driven learning