bluuebunny/crossref_metadata_2025_split huggingface dataset:
Insulin resistance, clinical presentation and resistance to selective serotonin and noradrenaline reuptake inhibitors in major depressive disorder
Authors: Krupa Anna J. , Chrobak Adrian A. , Sołtys Zbigniew , Dudek Dominika , Szewczyk Bernadeta , Siwek Marcin 
Published: July 2024

Abstract
Background
The understanding of mechanisms underlying non-response to antidepressants is limited. The latest data highlights the role of insulin resistance (IR) in major depressive disorder (MDD) pathophysiology, presentation, and treatment efficacy. This work aimed to assess IR in MDD and explore the relationships between IR, MDD presentation and non-response to selective serotonin and noradrenaline reuptake inhibitors (SNRI).

Methods
67 MDD individuals: 36 responsive (MDD T[+]), 31 non-responsive (MDD T[−]) to SNRI and 30 healthy controls were recruited. The treatment response criteria were: Clinical Global Impression Scale-Improvement score of 1 or 2 after ≥ 8 weeks of treatment. Participants were assessed by physician and self-report tools measuring depression, anhedonia, anxiety, bipolarity, sleep quality. Blood samples were collected to assess fasting glucose and insulin levels and calculate HOMA-IR (homeostasis model assessment of insulin resistance).

Results
MDD T[-] vs. MDD T[+] had significantly higher body mass index, insulin levels, and HOMA-IR. MDD T[-] presented higher levels of depressed mood, appetite/weight changes, loss of interest, energy, overall depressive symptoms, and sleep impairment; some evaluations suggested higher anhedonia and anxiety in MDD T[-] vs. MDD T[+]. Insulin and IR were weakly but significantly correlated with the severity of psychomotor symptoms, energy level, thoughts of death/suicide, self-criticism, appetite/weight, depressed mood symptoms, sleep problems. IR was weakly but significantly correlated with anhedonia.

Conclusion
IR appears to be linked to depressive symptoms characteristic of the “metabolic” MDD subtype, such as psychomotor changes, energy level, anhedonia, sleep problems, appetite/weight changes, state and trait anxiety, sleep quality, and non-response to SNRI.
Extraction of polyphenols and antioxidant compounds from SCOBY, as a by-product of Kombucha, using different types of extraction
Authors: das Chagas Eduardo Galvão Leite , Zamarian Felipe Coelho , de Souza Handray Fernandes , Kamimura Eliana Setsuko , Boffo Elisangela Fabiana , Ambrozin Alessandra Regina Pepe 
Published: July 2024

AbstractKombucha is obtained through a symbiosis fermentation of bacteria and yeast, generating a floating film called SCOBY (Symbiotic Culture of Bacteria and Yeast). After a period of use, SCOBY becomes a production waste. Thus, alternative uses of SCOBY can add value to Kombucha production. In this way, this work aimed to produce and evaluate antioxidant activity and total phenolic contents of SCOBY ethanolic extracts, which were obtained by two different solid–liquid extraction with three different waste weight/extractor solvent volume ratios. The SCOBY evaluated had a high concentration of cellulose (9.42 ± 1.00%) and low concentrations of proteins (0.84 ± 0.04%) and lipids (0.29 ± 0.05%). All evaluated extracts showed a high concentration of total phenolics (40.7–64.3 mg GAE/100 g of SCOBY) and high antioxidant activity (ABTS•+, FRAP and ORAC assays). The most efficient extraction of bioactive compounds was obtained by decoction using the highest solvent volume (ratio 1:20). The PCA showed clusters of the samples both by the type of extraction and by the different ratios of the weight of SCOBY to the solvent volume. Of our knowledge, our work is the first in the literature that describes such data. Therefore, the present work showed the possibility of using this industrial waste mainly to obtain active compounds and antioxidants.
Training Neural Mapping Schemes for Satellite Altimetry With Simulation Data
Authors: Febvre Q. , Le Sommer J. , Ubelmann C. , Fablet R. 
Published: July 2024

AbstractSatellite altimetry combined with data assimilation and optimal interpolation schemes have deeply renewed our ability to monitor sea surface dynamics. Recently, deep learning schemes have emerged as appealing solutions to address space‐time interpolation problems. However, the training of state‐of‐the‐art neural schemes on real‐world case‐studies is hindered by the sparse space‐time coverage of the sea surface of real altimetry data set. Here, we introduce an innovative approach that leverages state‐of‐the‐art ocean models to train simulation‐based neural schemes for the mapping of sea surface height and demonstrate their performance on real altimetry data sets. We analyze further how the ocean simulation data set used during the training phase impacts this performance. This experimental analysis covers both the resolution from eddy‐present configurations to eddy‐rich ones, forced simulations versus reanalyzes using data assimilation and tide‐free versus tide‐resolving simulations. Our benchmarking framework focuses on a Gulf Stream region for a realistic 5‐altimeter constellation using NEMO ocean simulations and 4DVarNet mapping schemes. All simulation‐based 4DVarNets outperform the operational observation‐driven and reanalysis products, namely DUACS and GLORYS. The more realistic the ocean simulation data set used during the training phase, the better the mapping. The best 4DVarNet mapping was trained from an eddy‐rich and tide‐free simulation data sets. It improves the resolved longitudinal scale from 151 km for DUACS and 241 km for GLORYS to 98 km and reduces the root mean square error by 23% and 61%. These results open research avenues for new synergies between ocean modeling and ocean observation using learning‐based approaches.
Physics‐Informed Convolutional Decoder (PICD): A Novel Approach for Direct Inversion of Heterogeneous Subsurface Flow
Authors: Wang Nanzhe , Kong Xiang‐Zhao , Zhang Dongxiao 
Published: July 2024

AbstractWe propose a physics‐informed convolutional decoder (PICD) framework for inverse modeling of heterogenous groundwater flow. PICD stands out as a direct inversion method, eliminating the need for repeated forward model simulations. The framework combines data‐driven and physics‐driven approaches by integrating monitoring data and domain knowledge into the inversion process. PICD utilizes a convolutional decoder to effectively approximate the spatial distribution of hydraulic heads, while Karhunen–Loève expansion (KLE) is employed to parameterize hydraulic conductivities. During the training process, the stochastic vector in KLE and the parameters of the convolutional decoder are adjusted simultaneously to minimize the data‐mismatch and the physical violation. The final optimized stochastic vectors correspond to the estimation of hydraulic conductivities, and the trained convolutional decoder can predict the evolution and distribution of hydraulic heads. Various scenarios of groundwater flow are examined and results demonstrate the framework's capability to accurately estimate heterogeneous hydraulic conductivities and to deliver satisfactory predictions of hydraulic heads, even with sparse measurements.
Revisiting Aerosol–Cloud Interactions From Weekly Cycles
Authors: Jia H. , Hasekamp O. , Quaas J. 
Published: July 2024

AbstractWeekly cycles (WCs) in cloud properties have been reported and linked to aerosol effects. Yet the extent to which human activities contribute to their occurrence remains unclear. Here, we revisit aerosol–cloud interactions from the WCs over central Europe using long‐term satellite and reanalysis data. Significant WCs in aerosol and cloud droplet number concentration (Nd) are detected with minima/maxima on Monday/Friday, indicating a clear signal of the Twomey effect. Notably, Nd–to–aerosol sensitivity from WCs is found to decrease at larger aerosol concentrations, confirming the nonlinear behavior of the aerosol–Nd relation (in log–log space) reported previously, but from a distinct perspective. Nevertheless, no discernible WCs in liquid water path are found. The pronounced WCs in cloud cover are demonstrated to be driven by natural variability. Our results indicate that the WCs offer a useful pathway for investigating the Twomey effect, but are not as effective for detecting cloud adjustments.
Realistic Precipitation Diurnal Cycle in Global Convection‐Permitting Models by Resolving Mesoscale Convective Systems
Authors: Song Jinyan , Song Fengfei , Feng Zhe , Leung L. Ruby , Li Chao , Wu Lixin 
Published: July 2024

AbstractAccurately representing the precipitation diurnal cycle has long been a challenge for global climate models (GCMs). Here we evaluate the precipitation diurnal cycle in the DYAMOND global convection‐permitting models (CPMs) and CMIP6 HighResMIP models. Comparison of the high‐ (25–50 km) and low‐resolution (100–250 km) models with parameterized convection in HighResMIP shows that simply increasing model resolution does not noticeably improve the precipitation diurnal cycle. In contrast, CPMs can better capture the observed amplitude and timing of precipitation diurnal cycle. However, the simulated spatial variation of timing in CPMs is smaller than observed, leading to an exaggeration of the spatially averaged diurnal amplitude. The better‐simulated precipitation diurnal cycle in the CPMs is tied to mesoscale convective systems (MCSs), which contribute about half of the total precipitation. The observed life cycle of MCSs, including initiation and mature stages, is well captured in the CPMs, leading to a more realistic precipitation diurnal cycle.
Intensified Currents Associated With Benthic Storms Underneath an Eddying Jet
Authors: Chen Si‐Yuan Sean , Marchal Olivier , Gardner Wilford , Andres Magdalena 
Published: July 2024

AbstractBenthic storms are episodes of intensified near‐bottom currents capable of sediment resuspension in the deep ocean. They typically occur under regions of high surface eddy kinetic energy (EKE), such as the Gulf Stream. Although they have long been observed, the mechanism(s) responsible for their formation and their relationships with salient features of the deep ocean, such as bottom mixed layers (BMLs) and benthic nepheloid layers (BNLs), remain poorly understood. Here we conduct idealized experiments with a primitive‐equation model to explore the impacts of the unforced instability of a surface‐intensified jet on near‐bottom flows of a deep zonal channel. Vertical resolution is increased near the bottom to represent the bottom boundary layer. We find that the unstable near‐surface jet develops meanders and evolves into alternating, deep‐reaching cyclones and anticyclones. Simultaneously, EKE increases near the bottom due to the convergence of vertical eddy pressure fluxes, leading to near‐bottom currents comparable to those observed during benthic storms. These currents in turn form BMLs with thickness of O(100 m) from enhanced velocity shears and turbulence production near the bottom. The deep cyclonic eddies transport fluid particles both laterally and vertically, from near the bottom through the entire BML and may contribute to the formation of the lower part of BNLs. A sloping bottom reduces both the intensity of the near‐bottom currents and the extent of vertical transport. Overall, our study highlights a significant response of the abyssal environment to near‐surface current instability, with potential implications for sediment transport in the deep ocean.
Riverine Influences and Seasonal Dynamics: Exploring Carbonate System Variations and Air‐Sea CO2 Fluxes in the Southern Yellow Sea and East China Sea
Authors: Li Bing‐Han , Gong Jiang‐Chen , Liu Chun‐Ying , Hu Jing‐Wen , Yang Gui‐Peng 
Published: July 2024

AbstractIn this work, the seasonal variations of the carbonate system and air‐sea CO2 fluxes were investigated by two cruises in the southern Yellow Sea (SYS) and the East China Sea (ECS), which were significantly influenced by the Changjiang riverine inputs across seasons. Biological‐mediated change of dissolved inorganic carbon (DIC) was first quantitated through a three end‐member mixing model from winter to spring. Our modeling results suggested that DIC addition through biological remineralization persisted in the SYS during winter and spring, while DIC removal was evident in the Changjiang River Plume and the offshore ECS triggered by river inputs in spring. Horizontal and vertical mixing together constituted the largest contribution to the interseasonal variability of partial pressure of CO2 (pCO2) in the SYS (−69.5%), the river plume (−59.3%), and the ECS offshore (−43.8%), followed by temperature effects in the plume area (29.4%), and biological processes in the ECS offshore (24.1%) and the SYS (−7.7%), with air‐sea CO2 exchange contributing the least in both three subregions. The SYS, the river plume, and the ECS offshore all acted as atmospheric CO2 sinks in both winter and spring. Furthermore, their ability to absorb atmospheric CO2 increased from winter to spring, as reflected in a ∼1.8‐fold increase in the overall spring air‐sea CO2 flux compared to winter estimation.
The Role and Lifetime of Dissociative Heterogeneous Processes in Improving Simulated Ozone on Mars
Authors: Brown M. A. J. , Patel M. R. , Lewis S. R. , Holmes J. A. , Lefèvre F. , Mason J. P. , Crismani M. 
Published: July 2024

AbstractOzone simulated in Mars Global Climate Models (MGCMs) is used to assess the underlying chemistry occurring in the atmosphere. Currently, ozone total column abundance (TCA) is under‐predicted in MGCMs by up to 120%, implying missing or inaccurate chemistry in models. Heterogeneous reactions of hydroxyl radicals (HOX) have been offered as an explanation for some of this bias, because they cause ozone to increase at locations where it's currently under‐predicted. We use four simulations to compare modeled ozone TCA with observations from the UVIS spectrometer aboard the ExoMars Trace Gas Orbiter to improve the representation of heterogeneous processes and their impact on ozone. We use a gas‐phase only run, a dissociative scheme, an adsorbed HOX retention scheme, and a hybrid scheme that combines the dissociative mechanism with the retention of HOX on water ice. We find retention of HOX is dependent on water ice sublimation, and ozone abundance increases when water ice persists for longer periods (1–20 sols). Over time, the loss of HOX causes a depletion in H2O2 concentration (HOX reservoir), and thus allows ozone concentration to increase. When adsorbed HOX are desorbed and dissociate into other by‐products, HOX are not immediately available to destroy ozone. This results in larger ozone concentrations than if desorbed HOX are released directly back into their gaseous states. When using the hybrid scheme, ozone TCA is increased up to 50% where the ozone deficit is greatest, demonstrating the best agreement with observations, and implying that HOX radicals are both retained when adsorbed and dissociate.
Robust Reconstruction of Historical Climate Change From Permafrost Boreholes
Authors: Groenke Brian , Langer Moritz , Miesner Frederieke , Westermann Sebastian , Gallego Guillermo , Boike Julia 
Published: July 2024

AbstractReconstructing historical climate change from deep ground temperature measurements in cold regions is often complicated by the presence of permafrost. Existing methods are typically unable to account for latent heat effects due to the freezing and thawing of the active layer. In this work, we propose a novel method for reconstructing historical ground surface temperature (GST) from borehole temperature measurements that accounts for seasonal thawing and refreezing of the active layer. Our method couples a recently developed fast numerical modeling scheme for two‐phase heat transport in permafrost soils with an ensemble‐based method for approximate Bayesian inference. We evaluate our method on two synthetic test cases covering both cold and warm permafrost conditions as well as using real data from a 100 m deep borehole on Sardakh Island in northeastern Siberia. Our analysis of the Sardakh Island borehole data confirms previous findings that GST in the region have likely risen by 5–9°C between the pre‐industrial period of 1750–1855 and 2012. We also show that latent heat effects due to seasonal freeze‐thaw have a substantial impact on the resulting reconstructed surface temperatures. We find that neglecting the thermal dynamics of the active layer can result in biases of roughly −1°C in cold conditions (i.e., mean annual ground temperature below −5°C) and as much as −2.6°C in warmer conditions where substantial active layer thickening (>200 cm) has occurred. Our results highlight the importance of considering seasonal freeze‐thaw in GST reconstructions from permafrost boreholes.
Constraining Displacement Magnitude on Crustal‐Scale Extensional Faults Using Thermochronology Combined With Flexural‐Kinematic and Thermal‐Kinematic Modeling: An Example From the Teton Fault, Wyoming, USA
Authors: Helfrich Autumn L. , Thigpen J. Ryan , Buford‐Parks Victoria M. , McQuarrie Nadine , Brown Summer J. , Goldsby Ryan C. 
Published: July 2024

AbstractConstraining the geometry and displacement of crustal‐scale normal faults has historically been challenging, owing to difficulties with geophysical imaging and inability to identify precise cut‐offs at depth. Using a modified workflow previously applied to contractional systems, flexural‐kinematic (Move) and thermal‐kinematic (Pecube) models are integrated with apatite (U‐Th)/He (AHe) and apatite fission track (AFT) data from Teton footwall transects to constrain total Teton fault displacement (Dmax). Models with slip onset at ∼10 Ma and flexure parameters that best match the observed Teton flexural profile require Dmax > 8 km to produce young (<10 Ma) AHe ages observed at low elevation footwall positions in the Tetons. For the same slip onset, models with Dmax of 11–13 km provide the best match to observed AHe data, but displacements ≥16 km are required to produce observed AFT ages (13.6–12.0 Ma) at low elevations. A more complex model with slow slip onset at ∼25 Ma followed by faster slip at ∼10 Ma yields a good match between modeled and observed AHe ages at a Dmax of 13–15 km. However, this model predicts low elevation AFT ages 6–8 Ma older than observed ages, even at Dmax values of 16–17 km. Based on this analysis and integration with previous studies, we propose a unified evolution wherein the Teton fault likely experienced 11–13 km of Miocene‐recent displacement, with AFT data likely indicating a pre‐to early Miocene cooling history. Importantly, this study highlights the utility of using integrated flexural‐ and thermal‐kinematic models to resolve displacement histories in extensional systems.
Ultrasound sensing with optical microcavities
Authors: Cao Xuening , Yang Hao , Wu Zu-Lei , Li Bei-Bei 
Published: July 2024

AbstractUltrasound sensors play an important role in biomedical imaging, industrial nondestructive inspection, etc. Traditional ultrasound sensors that use piezoelectric transducers face limitations in sensitivity and spatial resolution when miniaturized, with typical sizes at the millimeter to centimeter scale. To overcome these challenges, optical ultrasound sensors have emerged as a promising alternative, offering both high sensitivity and spatial resolution. In particular, ultrasound sensors utilizing high-quality factor (Q) optical microcavities have achieved unprecedented performance in terms of sensitivity and bandwidth, while also enabling mass production on silicon chips. In this review, we focus on recent advances in ultrasound sensing applications using three types of optical microcavities: Fabry-Perot cavities, π-phase-shifted Bragg gratings, and whispering gallery mode microcavities. We provide an overview of the ultrasound sensing mechanisms employed by these microcavities and discuss the key parameters for optimizing ultrasound sensors. Furthermore, we survey recent advances in ultrasound sensing using these microcavity-based approaches, highlighting their applications in diverse detection scenarios, such as photoacoustic imaging, ranging, and particle detection. The goal of this review is to provide a comprehensive understanding of the latest advances in ultrasound sensing with optical microcavities and their potential for future development in high-performance ultrasound imaging and sensing technologies.
Photocascade chemoselective controlling of ambident thio(seleno)cyanates with alkenes via catalyst modulation
Authors: Hoque Injamam Ul , Samanta Apurba , Pramanik Shyamal , Chowdhury Soumyadeep Roy , Lo Rabindranath , Maity Soumitra 
Published: July 2024

AbstractControlling the ambident reactivity of thiocyanates in reaction manifolds has been a long-standing and formidable challenge. We report herein a photoredox strategy for installing thiocyanates and isothiocyanates in a controlled chemoselective fashion by manipulating the ambident-SCN through catalyst modulation. The methodology allows redox-, and pot-economical ‘on-demand’ direct access to both hydrothiophene and pyrrolidine heterocycles from the same feedstock alkenes and bifunctional thiocyanomalonates in a photocascade sequence. Its excellent chemoselectivity profile was further expanded to access Se- and N-heterocycles by harnessing selenonitriles. Redox capability of the catalysts, which dictates the substrates to participate in a single or cascade catalytic cycle, was proposed as the key to the present chemodivergency of this process. In addition, detailed mechanistic insights are provided by a conjugation of extensive control experiments and dispersion-corrected density functional theory (DFT) calculations.
Application of machine learning models for property prediction to targeted protein degraders
Authors: Peteani Giulia , Huynh Minh Tam Davide , Gerebtzoff Grégori , Rodríguez-Pérez Raquel 
Published: July 2024

AbstractMachine learning (ML) systems can model quantitative structure-property relationships (QSPR) using existing experimental data and make property predictions for new molecules. With the advent of modalities such as targeted protein degraders (TPD), the applicability of QSPR models is questioned and ML usage in TPD-centric projects remains limited. Herein, ML models are developed and evaluated for TPDs’ property predictions, including passive permeability, metabolic clearance, cytochrome P450 inhibition, plasma protein binding, and lipophilicity. Interestingly, performance on TPDs is comparable to that of other modalities. Predictions for glues and heterobifunctionals often yield lower and higher errors, respectively. For permeability, CYP3A4 inhibition, and human and rat microsomal clearance, misclassification errors into high and low risk categories are lower than 4% for glues and 15% for heterobifunctionals. For all modalities, misclassification errors range from 0.8% to 8.1%. Investigated transfer learning strategies improve predictions for heterobifunctionals. This is the first comprehensive evaluation of ML for the prediction of absorption, distribution, metabolism, and excretion (ADME) and physicochemical properties of TPD molecules, including heterobifunctional and molecular glue sub-modalities. Taken together, our investigations show that ML-based QSPR models are applicable to TPDs and support ML usage for TPDs’ design, to potentially accelerate drug discovery.