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Introduction Selective serotonin reuptake inhibitors (SSRI group antidepressant drugs) are not significantly different from tricyclic antidepressants and other antidepressants in terms of efficacy, but provide significant advantages in terms of side effects and toxicity. One of the most important side effects of antidepressant drugs is weight gain. However, there is not yet enough study on weight gain mechanism. Nutrition and hunger-satiety circle are occured under the control of neuropeptids and hormones that are synthesized and secreted from the hypothalamic arcuate nucleus (ARC), adipose tissue and the pancreas. In this study, we examined how escitalopram affects the body weight, the body mass index, the serum lipid profile, the liver function tests, the underlying molecular mechanisms of weight change, the relationships these mechanizms and the hypotalamic nutrition regulatory neuropeptides such as POMC, NPY, leptin, CCK and insülin that is a pancreatic hormone. Objectives In order to understand the relationship between antidepressants and metabolic risk factors such as diabetes and obesity and to understand the underlying mechanisms, body weight, waist and hip circumference, POMC and NPY levels from hypothalamic nutrition regulating neuropeptides, CCK from peripheral neuropeptides, a pancreatic hormone insulin, and the effects of escitalopram use on these parameters were investigated. Methods In this prospective study, 30 patients, who were decided to have escitalopram treatment and who met the inclusion criteria and continued the treatment for 12 weeks, were included in the study. Results Weight, waist circumference increase and waist-hip ratio decreased significantly after 12 weeks. The decrease in neuropeptide level in POMC was significant. Conclusions In our study, according to the insignificant change in lipid parameters it was thought that the use of escitalopram does not cause a metabolic change that would increase the risk in terms of metabolic syndrome and cardiovascular disease, despite the short study period. The decrease in POMC levels due to escitalopram use; It was thought that it may lead to weight gain by modulating eating behavior modulation. Disclosure of Interest None Declared

Complex Land Surface Models (LSMs) rely on a plethora of parameters. These parameters and the associated process formulations are often poorly constrained, which hampers reliable predictions of ecosystem dynamics and climate feedbacks. Robust and uncertainty‐aware parameter estimation with observations is complicated by, for example, the high dimensionality of the model parameter space and the computational cost of LSM simulations. Herein, we adapt a novel Bayesian data assimilation (DA) and machine learning framework termed “calibrate, emulate, sample” (CES) to infer parameters in a widely‐used LSM coupled with a demographic vegetation model (CLM‐FATES). First, an iterative ensemble Kalman smoother provides an initial estimate of the posterior distribution (“calibrate”). Subsequently, a machine‐learning‐based emulator is trained on the resulting model‐observation mismatches to predict outcomes for unseen parameter combinations (“emulate”). Finally, this emulator replaces CLM‐FATES simulations in an adaptive Markov Chain Monte Carlo approach enabling computationally feasible posterior sampling with enhanced uncertainty quantification (“sample”). We test our implementation with synthetic and real observations representing a boreal forest site in southern Finland. We estimate a total of six plant‐functional‐type‐specific photosynthetic parameters by assimilating evapotranspiration (ET) and gross primary production (GPP) flux data. CES provided the best estimates of the synthetic truth parameters when compared to data‐blind emulator sampling designs while all approaches reduced model‐observation errors compared to a default parameter simulation (GPP: −10${-}10$ % to −30${-}30$ %, ET: −4${-}4$ % to −6${-}6$ %). Although errors were also consistently reduced with real data, comparing the emulator designs was less conclusive, which we mainly attribute to equifinality, structural uncertainty within CLM‐FATES, and/or unknown errors in the data that are not accounted for. Plain Language Summary Land surface models (LSMs) are important tools for simulating interactions between climate and terrestrial systems, and we need to improve them for more reliable climate and ecological predictions. LSM equations include adjustable model parameters that can be tuned to better match predictions with available environmental observations. As modeling complex processes involves many uncertainties and gathering sufficient data is challenging, the default parameter values are often poorly constrained. Data assimilation provides a statistical toolbox for estimating model parameters and their uncertainties from available observations while considering measurement inaccuracies. A recently presented approach termed “calibrate, emulate, sample” (CES) combines powerful data assimilation techniques and machine learning, which allows us to tackle some of the main challenges we traditionally face in LSM parameter estimation. For example, CES only requires running the computationally expensive models a few hundred rather than hundreds of thousands of times. Herein, we estimate important photosynthesis parameters to better reproduce land surface carbon and water exchange observations from a boreal forest in southern Finland. Our results with artificial data suggest that the method is suitable for estimating the complex LSM's parameters. However, experiments with real observations were less conclusive. Follow‐up work thus needs to better account for real‐world complexities. Key Points We adapt the Bayesian inference approach “calibrate, emulate, sample” to estimate parameters in the land surface model CLM‐FATES The framework leverages computationally efficient and uncertainty‐aware inference in a multidimensional parameter space We jointly assimilate water and carbon flux data and improve model agreement with synthetic and real observations at a boreal forest site

Vital aspects of transportation networks, such as the extraction of road information and analysis of road conditions, have become increasingly important research topics as they outline the foundation of many applications such as high-precision mapping, infrastructure planning and maintenance, intelligent transportation, or road design analysis. Therefore, regularly obtaining accurate high-density point cloud data of infrastructures supports many transportation-based applications and provides up-to-date information for smart cities or digital twins. Low-cost smartphone platforms equipped with a variety of sensors provide new and powerful data acquisition capabilities that can be exploited in the geospatial field. For example, mobile phones are now capable of collecting valuable data to generate accurate models to support digital reconstruction of infrastructures. These platforms can provide simple and effective data acquisition, while offering useful geospatial data that can be an alternative to traditional measurement techniques. However, the sensor performance with respect to spatial accuracy of point clouds generated in different applications have not yet been fully investigated. Thus, this paper evaluates the feasibility of using the point clouds generated by the built-in camera and LiDAR sensors integrated into iPhone 14 Pro for extracting road-related information. Additionally, the use of the viDoc RTK Rover on the iPhone 14 Pro increases the platform positioning accuracy, consequently improving the georeferencing accuracy of the point clouds. To validate the performance of the point clouds obtained by the iPhone 14 Pro, a reference dataset of the road features was obtained by measuring with a single-point RTK-GNSS receiver, receiving corrections from the Turkish CORS network (TUSAGA-Aktif) which provides two to three centimetres of accuracy. In addition, reference point cloud data over the same area was obtained from different platforms such as Mobile LiDAR and UAS, and the road features were extracted from these dataset and performance validated. The data acquired by the iPhone 14 Pro was processed and evaluated with respect to the reference datasets. The advantages and disadvantages of using iPhone 14 Pro are analysed in detail and the findings are reported.

Neotectonics involves the study of the motions and deformations of the Earth's crust that are current or recent in geologic time. The Mediterranean region is one of the most important regions for neotectonics and related natural hazards. This volume focuses on the neotectonics of the Eastern Mediterranean region, which has experienced many major extensive earthquakes, including the devastating Izmit, Turkey earthquake on August 17, 1999. The event lasted for 37 seconds, killing around 17,000 people, injuring 44,000 people, and leaving approximately half a million people homeless. Since then, several North American, European, and Turkish research groups have studied the neotectonics and earthquake potential of the region using different geological and geophysical methods, including GPS studies, geodesy, and passive source seismology. Some results from their studies were presented in major North American and European geological meetings. This volume highlights the work involving the Eastern Mediterranean region, which has one of the world's longest and best studied active strike-slip (horizontal motion) faults: the east-west trending North Anatolian fault zone, which is very similar to the San Andreas fault in California. This volume features discussions of: Widespread applications in measuring plate motion that have strong implications in predicting natural disasters like earthquakes, both on a regional and a global scale Recent motions, particularly those produced by earthquakes, that provide insights on the physics of earthquake recurrence, the growth of mountains, orogenic movements, and seismic hazards Unique methodical approaches in collecting tectonophysical data, including field, seismic, experimental, computer-based, and theoretical approaches. Active Global Seismology is a valuable resource for geoscientists, particularly in the field of tectonophysics, geophysics, geodynamics, seismology, structural geology, environmental geology, and geoengineering. Read an interview with the editors to find out more:https://eos.org/editors-vox/neotectonics-and-earthquake-forecasting

Allicin is an organosulfur compound formed in garlics, and it is slightly yellow in colour and gives unique odour to garlic. Allicin has been known to have an antioxidant and antimicrobial activity, and it can react with thiol groups containing proteins. Allicin potential (AP) in Taskopru garlic slices and its loss were monitored during drying in a cabinet drier at three temperatures (50, 60 and 70 °C). Initial AP of fresh garlic samples was 10.91±0.15 mg/g on the basis of dry matter (dm), and it reduced significantly during drying (P<0.05). APs of garlic samples dried at 50, 60 and 70 °C for up to 480 minutes were 5.35±0.029, 4.32±0.13 and 3.95±0.26 mg/g dm, respectively (P<0.05). Loss of AP values determined during drying followed a second-order reaction. Drying temperature had a significant influence on the loss of AP in garlic slices. Activation energy for AP loss was 25.48 kJ/mol. Q10 value was 4.18 for the drying temperature increase from 50 to 60 °C, and it reduced to 3.07 for the temperature increase from 60 to 70 °C. Therefore, the effect of the first temperature rise on AP loss was bigger than the second temperature rise.

In this study, poly( N -isopropylacrylamide) hydrogel is used as a matrix for synthesizing zinc oxide (ZnO) nanoparticles in the porous regions of the gel. When the gelation is completed,  OH - ions of potassium hydroxide were diffused into the gel in which zinc acetate molecules were trapped previously during the gelation. The size of the nanoparticles formed in the gel changes with changing the pore size of the slab gels or with changing the concentration of  OH - ions along the axis of a long cylindrical gel. The slices from different parts of the cylindrical gel or the gels prepared in slab forms include different size distribution of the nanoparticles. The synthesis of nanocrystals in a gel by these methods presents two innovations: (i) depending on the position of the slice along the cylindrical gel or cross-linker content of the slab gels, the average size can be tuned; and (ii) the visible emission from the surface states of ZnO nanoparticles can be controlled via the swelling degree of the gel and the type of the solvent. Absorbance, XRD, SEM, and HRTEM measurements were performed to characterize the crystal structure and to estimate the size of the nanoparticles. The steady state fluorescence technique is used to compare qualitatively the change in the size of the nanoparticles and to carry out the interactions of ZnO nanoparticles with the polymer matrix both in collapsed and swollen states.

Anemia of chronic kidney disease is a multifactorial condition secondary to various etiologies, including nutritional deficiencies, chronic inflammation, erythropoietin deficiency or resistance, bone marrow suppression, iron deficiency and adverse drug effects. The major therapeutic intervention for anemia among chronic kidney disease patients is erythropoiesis-stimulating agents. However, a limitation of erythropoiesis-stimulating agents is the risk for thromboembolic events, hypertension, seizures, solid organ malignancies and hyporesponsiveness. A novel interleukin-6 monoclonal antibody, ziltivekimab, has been evaluated for managing anemia in chronic kidney disease patients in pilot clinical trials with promising outcomes, including an improvement in hemoglobin levels and reduction of inflammatory parameters. These trials have shown that ziltivekimab does not increase the risk for cytopenia or infectious complications as has been described for other interleukin-6-targeting monoclonal antibodies, like tocilizumab. Furthermore, potentially beneficial effects on serum lipid profile have been reported, leading to the hypothesis of a favorable impact of the drug on atherosclerotic complications. In addition, ziltivekimab has shown efficacy in improving anemia parameters, including hemoglobin levels and iron studies. Ziltivekimab deserves full scale clinical development, and to this aim, large-scale clinical trials are under way. Graphical abstract

Information capacity enhancement through the coherent control of channels has attracted much attention of late, with work exploring the effect of coherent control of channel causal orders, channel superpositions, and information encoding. Coherently controlling channels necessitates a non-trivial expansion of the channel description, which for superposing qubit channels, is equivalent to expanding the channel to act on qutrits. Here we explore the nature of this capacity enhancement for the superposition of channels by comparing the maximum coherent information through depolarizing qubit channels and relevant superposed and qutrit channels. We show that the expanded qutrit channel description in itself is sufficient to explain the capacity enhancement without any use of superposition.

In this article, global asymptotic stability of solutions of non-homogeneous differential-operator equations of the third order is studied. It is proved that every solution of the equations decays exponentially under the Routh–Hurwitz criterion for the third order equations.