bluuebunny/crossref_metadata_2025_split huggingface dataset:
Field Mapping and Modeling of Terrestrial Lava Tube Magnetic Anomalies as an Analog for Lunar Lava Tube Exploration and Prospecting
Authors: Bell Ernest , Schmerr Nicholas , Young Kelsey , Esmaeili Sanaz , Garry W. Brent , Jazayeri Sajad , Kruse Sarah , Richardson Jacob , Whelley Patrick 
Published: May 2022

AbstractLava tubes are a commonplace feature on the terrestrial planets, and knowledge of tube size and location informs lava flow processes. Future exploration of lava tubes on the Moon can provide access to geologic environments that likely remain unaltered from their emplacement billions of years ago. Lunar lava tubes may also provide astronauts protection from thermal extremes, meteoroid impacts, and radiation. High‐resolution magnetic identification and characterization of lava tubes can be used to help inform future scientific investigations of lava tubes for human exploration and utilization. We demonstrate how magnetometry is useful for determining the geometry and extent of lava tubes on the Earth and, by proxy, the Moon, by relating the magnetic anomalies produced by lava tubes to their location and geomorphology. Using a proton‐precession total field magnetometer, we surveyed an area of more than 100,000 m2, with cross‐tube linear traverses spaced at 3–5 m, perpendicular to an approximately 1,000 m length of the Modoc Crater lava tube complex, within the Lava Beds National Monument (California, USA). The observed magnetic anomalies of the sections known as Incline, Skull, and Ship Caves are compared against synthetic predictions, and the sensitivity of the magnetic anomalies to the tube geometry used to derive a basic relationship between the two. We use our model of terrestrial lava tube magnetic anomalies and adjust for the lunar magnetic environment to predict the signature of anomalies resulting from tubes on the Moon.
Sharp Increase of Extreme Turbidity Events Due To Deglaciation in the Subtropical Andes
Authors: Vergara Iván , Garreaud René , Ayala Álvaro 
Published: May 2022

AbstractClimate change may affect sediment fluvial export from high mountain regions, leading to downstream environmental disruptions and direct impacts on human activities. In this paper, three decades (1990–2020) of turbidity measurements, along with climate and hydro‐glaciological variables, were used to investigate the interannual and interdecadal variability in the number of extreme turbidity events (ETE) in the glacierized Maipo River basin, located in the western subtropical Andes. ETE are defined as a sequence of days (most often 1 or 2) during which the daily maximum turbidity was in the 99% quantile of the entire study period. Some of these events compromised the drinking water provision for the city of Santiago, with more than 6 million inhabitants. ETE are more frequent during summer and are mostly associated with melt‐favourable conditions. The number of ETE tends to increase in summers with large glacier ice melt and low snowmelt (outside or over glaciers). Most notable, the mean annual number of ETE exhibits a 6‐fold increase in the last decade compared with the 1990–2010 period. After 2010, ETE also shifted their seasonal maximum from late spring to mid‐summer and their occurrence became strongly coupled with large ice melt rates. We hypothesize that such regime change was caused by an enhanced hydrological connectivity of subglacial sediment pools that increased the sensitivity of the sediment system to glacier melt. The latter is in line with recent research and is consistent with the ongoing glacier retreat due to strong regional warming and drying.
Internal Lee Waves Generated by Shear Flow Over Small‐Scale Topography
Authors: Sun Hui , Yang Qingxuan , Zheng Kaiwen , Zhao Wei , Huang Xiaodong , Tian Jiwei 
Published: May 2022

AbstractWhen the bottom current flows over a small‐scale topographic feature (with characteristic horizontal scale of 0.1–10.0 km), it generates internal lee waves. These lee waves furnish the energy cascade from mesoscale to dissipative scale, resulting in enhanced turbulent mixing. The existing lee wave theory and parameterization are mostly based on uniform background flow. We improve the linear lee wave theory by considering shear background flow and viscous and diffusive effects, which are ubiquitous in the ocean. In particular, the introduction of vertical viscous and diffusive terms effectively improves the problem of critical layer (where the wave frequency is close to the Coriolis frequency) for linear lee waves under weakly dissipative limit. Then, the influences of different types of shear flow on the frequency, vertical wavelength, group velocity, energy flux, energy density, energy dissipation and irreversible mixing of lee waves are explored through both theory and numerical simulation. A bottom‐up decreasing background flow can enhance energy dissipation and irreversible mixing below the critical layer. However, in the real ocean, the dissipation and mixing caused by a decreasing background flow may be weaker than that caused by an increasing background flow in a depth integrated sense, especially when considering a bounded domain. Additionally, the theory of wave action conservation in the absence of dissipation, which is commonly used as a criterion in research of internal waves propagating in varying background fields, is extended to a “modified” wave action equation considering dissipation, which may potentially improve the lee wave parameterization in the future.
A Sensitivity Study of the Thermal Tides in the Venusian Atmosphere: Structures and Dynamical Effects on the Superrotation
Authors: Suzuki Anna , Takagi Masahiro , Ando Hiroki , Imai Masataka , Sugimoto Norihiko , Matsuda Yoshihisa 
Published: May 2022

AbstractIn order to resolve discrepancy between recent observational and numerical studies on the thermal tides in the Venusian atmosphere, we investigated by means of a general circulation model how the thermal tides are affected by the static stability in and above the upper cloud layer by using three different distributions of the static stability. The results show that the vertical structure of the semidiurnal tide, which propagates vertically, is strongly affected by the static stability. The diurnal tide, which has an equivalent barotropic structure in 62–73 km altitudes, becomes weaker with the higher static stability although its vertical structure is almost unchanged. The horizontal distribution of the thermal tides with the realistic static stability distribution, which is consistent with radio occultation measurements, agrees with the observations at the cloud top. The meridional angular momentum flux associated with the thermal tides is equatorward in low latitudes near the altitude where the equatorial zonal‐mean wind takes its maximum. This result is consistent with the recent Akatsuki UVI observations, suggesting that the thermal tides could contribute to the maintenance of the superrotation in the equatorial region near the cloud top. In the most realistic case, the zonal‐mean zonal wind is effectively accelerated at rates of 0.2–0.5 m s−1 day−1 in low latitudes at altitudes of 52–76 km by both the meridional and vertical angular momentum transports. The thermal tides also induce significant meridional heat flux, which cannot be ignored in the dynamical effect on the zonal‐mean zonal wind.
Anxiolytic and antidepressants’ effect of Crataegus pinnatifida (Shan Zha): biochemical mechanisms
Authors: Nitzan Keren , David Dekel , Franko Motty , Toledano Roni , Fidelman Sharon , Tenenbaum Yaarit Simchon , Blonder Maya , Armoza-Eilat Shir , Shamir Alon , Rehavi Moshe , Ben-chaim Yair , Doron Ravid 
Published: May 2022

AbstractDepression and anxiety disorders are highly prevalent. Selective serotonin reuptake inhibitors (SSRIs) are the current first-line treatment for depression, but they have pronounced limitations. Traditional Chinese medicine can serve as a safe and effective alternative to conventional drugs, particularly since many herbal remedies have already been approved for human use as food additives, making the transition from bench to bedside more efficient. We previously demonstrated that a novel herbal treatment (NHT) induces anxiolytic- and antidepressant-like effects. NHT consists of four herbs: Crataegus pinnatifida (Shan Zha), Triticum aestivum (Fu Xiao Mai), Lilium brownii (Baihe), and the fruit of Ziziphus jujuba (Da Zao). In the current study, we examined the antidepressant-like and anxiolytic-like activities of each individual herb on stressed mice and compared those to the effects of NHT and escitalopram. We show here that Shan Zha is sufficient to produce an anxiolytic and antidepressant-like effect similar to NHT or the escitalopram through activation of 5-HT1A receptor and an elevation in BDNF levels in the hippocampus and Pre-frontal cortex (PFC). Chronic treatment with Shan Zha did not alter serotonin transporter levels in the PFC, as opposed to escitalopram treatment. These results were confirmed in vitro, as none of the herbs blocked SERT activity in Xenopus oocytes. Notably, Shan Zha is sold as a nutritional supplement; thus, its transition to clinical trials can be easier. Once its efficacy and safety are substantiated, Shan Zha may serve as an alternative to conventional antidepressants.
Rationale for integration of services for diabetes mellitus and diabetic retinopathy in Kenya
Authors: Mwangi Nyawira , Bascaran Covadonga , Gichuhi Stephen , Kipturgo Mathew , Manyara Lucy , Macleod David , Moorman Consuela , Foster Allen 
Published: May 2022

Abstract
Background
Good diabetes mellitus (diabetes) and diabetic retinopathy (DR) management depends on the strength of the health system, prompting us to conduct a health system assessment for diabetes and DR in Kenya. We used diabetes and DR as tracer conditions to assess the strengths and weaknesses in the health system, and potential interventions to strengthen the health system. In this paper, we report on the need and relevance of integration to strengthen diabetes and DR care. This theme emerged from the health system assessment.

Methods
Using a mixed methods study design, we collected data from service providers in diabetes clinics and eye clinics in three counties, from key informants at national and county level, and from documents review.

Results
There is interest in integration of diabetes and DR services to address discontinuity of care. We report the findings describing the context of integration, why integration is a goal and how these services can be integrated. We use the results to develop a conceptual framework for implementation.

Conclusions
The principal rationale for integrated service provision is to address service gaps and to prevent complications of diabetes and DR. The stakeholder interest and the existing infrastructure can be leveraged to improve these health outcomes.
Ribosome inhibition by C9ORF72-ALS/FTD-associated poly-PR and poly-GR proteins revealed by cryo-EM
Authors: Loveland Anna B. , Svidritskiy Egor , Susorov Denis , Lee Soojin , Park Alexander , Zvornicanin Sarah , Demo Gabriel , Gao Fen-Biao , Korostelev Andrei A. 
Published: May 2022

AbstractToxic dipeptide-repeat (DPR) proteins are produced from expanded G4C2 repeats in the C9ORF72 gene, the most common genetic cause of amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). Two DPR proteins, poly-PR and poly-GR, repress cellular translation but the molecular mechanism remains unknown. Here we show that poly-PR and poly-GR of ≥20 repeats inhibit the ribosome’s peptidyl-transferase activity at nanomolar concentrations, comparable to specific translation inhibitors. High-resolution cryogenic electron microscopy (cryo-EM) reveals that poly-PR and poly-GR block the polypeptide tunnel of the ribosome, extending into the peptidyl-transferase center (PTC). Consistent with these findings, the macrolide erythromycin, which binds in the tunnel, competes with poly-PR and restores peptidyl-transferase activity. Our results demonstrate that strong and specific binding of poly-PR and poly-GR in the ribosomal tunnel blocks translation, revealing the structural basis of their toxicity in C9ORF72-ALS/FTD.
Evidence of partner similarity for autistic traits, systemizing, and theory of mind via facial expressions
Authors: Richards Gareth , Baron-Cohen Simon , Warrier Varun , Mellor Ben , Davies Jessica , Gee Laura , Galvin John 
Published: May 2022

AbstractIt has been hypothesised that romantic partners are more similar than chance in relation to autistic traits. To test this theory, we recruited n = 105 heterosexual couples and examined within-couple correlations for autistic traits [measured using the Autism Spectrum Quotient (AQ)], empathizing [measured using the Empathy Quotient (EQ)], and systemizing [measured using the Systemizing Quotient-Revised (SQ-R)]. For a subsample that attended the lab (n = 58 couples), we also investigated theory of mind via facial expressions using the Reading the Mind in the Eyes Test (RMET) and attention to detail, a component within systemizing, using the Embedded Figures Task (EFT). Variable-centred analyses revealed positive within-couple correlations for all measures except EQ, although these effects were only statistically significant for unmarried couples and not for married/engaged couples. Follow-up analyses indicated that the observed couple similarity effects are likely consistent with people pairing with those more similar than chance (initial assortment) rather than becoming alike over time (convergence), and to seeking out self-resembling partners (active assortment) rather than pairing in this manner via social stratification processes (social homogamy). Additionally, a significant within-couple correlation for autistic traits was observed at the meta-analytic level. However, it should be noted that the meta-analytic effect size estimate was small (r = 0.153) and indicates that only ~ 2% of variance in a person’s score on a phenotypic measure of autistic traits can be predicted by that of their partner.
Modeling of charged-particle multiplicity and transverse-momentum distributions in pp collisions using a DNN
Authors: Shokr E. , De Roeck A. , Mahmoud M. A. 
Published: May 2022

AbstractA machine learning technique is used to fit multiplicity distributions in high energy proton-proton collisions and applied to make predictions for collisions at higher energies. The method is tested with Monte Carlo event generators. Charged-particle multiplicity and transverse-momentum distributions within different pseudorapidity intervals in proton-proton collisions were simulated using the PYTHIA event generator for center of mass energies $$\sqrt{s}$$

s

= 0.9, 2.36, 2.76, 5, 7, 8, 13 TeV for model training and validation and at 10, 20, 27, 50, 100 and 150 TeV for model predictions. Comparisons are made in order to ensure the model reproduces the relation between input variables and output distributions for the charged particle multiplicity and transverse-momentum. The multiplicity and transverse-momentum distributions are described and predicted very well, not only in the case of the trained but also in the case of untrained energy values. The study proposes a way to predict multiplicity distributions at a new energy by extrapolating the information inherent in the lower energy data. Using real data instead of Monte Carlo, as measured at the LHC, the technique has the potential to project the multiplicity distributions for different intervals at very high collision energies, e.g. 27 TeV or 100 TeV for the upgraded HE-LHC and FCC-hh respectively, using only data collected at the LHC, i.e. at center of mass energies from 0.9 up to 13 TeV.
The hospital environment versus carriage: transmission pathways for third-generation cephalosporin-resistant bacteria in blood in neonates in a low-resource country healthcare setting
Authors: Kovacs Dory , Silago Vitus , Msanga Delfina R. , Mshana Stephen E. , Seni Jeremiah , Oravcova Katarina , Matthews Louise 
Published: May 2022

AbstractNeonatal bloodstream infections (BSI) can lead to sepsis, with high morbidity and mortality, particularly in low-income settings. The high prevalence of third-generation cephalosporin-resistant organisms (3GC-RO) complicates the management of BSI. Whether BSI is linked to carriage of 3GC-RO, or to acquisition from the hospital environment is important for infection prevention and control, but the relationship remains unclear, especially in low-income settings. At a tertiary hospital in Mwanza, Tanzania, we screened neonatal blood and rectal samples from 200 neonates, and 400 (hospital) environmental samples. We used logistic regression to identify risk factors, and Kolmogorov–Smirnov tests and randomisation analyses to compare distributions of species and resistance patterns to assess potential routes of transmission. We found that BSIs caused by 3GC-RO were frequent (of 59 cases of BSI, 55 were caused by 3GC-RO), as was carriage of 3GC-RO, particularly Escherichia coli, Klebsiella pneumoniae, and Acinetobacter species. In the 28 infants with both a carriage and blood isolate, there were more (4 of 28) isolate pairs of the same species and susceptibility profile than expected by chance (p < 0.05), but most pairs were discordant (24 of 28). Logistic regression models found no association between BSI and carriage with either 3GC-RO or only 3GC-R K. pneumoniae. These analyses suggest that carriage of 3GC-RO is not a major driver of BSI caused by 3GC-RO in this setting. Comparison with environmental isolates showed very similar distributions of species and resistance patterns in the carriage, BSI, and the environment. These similar distributions, a high frequency of Acinetobacter spp. isolations, the lack of strong association between carriage and BSI, together with the high proportion of 3GC-RO in BSI all suggest that these neonates acquire multidrug-resistant carriage and blood isolates directly from the hospital environment.
Targeted metabolomics analysis of amino acids and acylcarnitines as risk markers for diabetes by LC–MS/MS technique
Authors: Hosseinkhani Shaghayegh , Arjmand Babak , Dilmaghani-Marand Arezou , Mohammadi Fateh Sahar , Dehghanbanadaki Hojat , Najjar Niloufar , Alavi-Moghadam Sepideh , Ghodssi-Ghassemabadi Robabeh , Nasli-Esfahani Ensieh , Farzadfar Farshad , Larijani Bagher , Razi Farideh 
Published: May 2022

AbstractDiabetes is a common chronic disease affecting millions of people worldwide. It underlies various complications and imposes many costs on individuals and society. Discovering early diagnostic biomarkers takes excellent insight into preventive plans and the best use of interventions. Therefore, in the present study, we aimed to evaluate the association between the level of amino acids and acylcarnitines and diabetes to develop diabetes predictive models. Using the targeted LC–MS/MS technique, we analyzed fasting plasma samples of 206 cases and 206 controls that were matched by age, sex, and BMI. The association between metabolites and diabetes was evaluated using univariate and multivariate regression analysis with adjustment for systolic and diastolic blood pressure and lipid profile. To deal with multiple comparisons, factor analysis was used. Participants' average age and BMI were 61.6 years, 28.9 kg/m2, and 55% were female. After adjustment, Factor 3 (tyrosine, valine, leucine, methionine, tryptophan, phenylalanine), 5 (C3DC, C5, C5OH, C5:1), 6 (C14OH, C16OH, C18OH, C18:1OH), 8 (C2, C4OH, C8:1), 10 (alanine, proline) and 11 (glutamic acid, C18:2OH) were positively associated with diabetes. Inline, factor 9 (C4DC, serine, glycine, threonine) and 12 (citrulline, ornithine) showed a reverse trend. Some amino acids and acylcarnitines were found as potential risk markers for diabetes incidents that reflected the disturbances in the several metabolic pathways among the diabetic population and could be targeted to prevent, diagnose, and treat diabetes.
Microfluidic-assisted synthesis and modeling of stimuli-responsive monodispersed chitosan microgels for drug delivery applications
Authors: Sartipzadeh Omid , Naghib Seyed Morteza , Haghiralsadat Fatemeh , Shokati Farhad , Rahmanian Mehdi 
Published: May 2022

AbstractDroplet microfluidic has been established to synthesize and functionalize micro/nanoparticles for drug delivery and screening, biosensing, cell/tissue engineering, lab-on-a-chip, and organ-on-a-chip have attracted much attention in chemical and biomedical engineering. Chitosan (CS) has been suggested for different biomedical applications due to its unique characteristics, such as antibacterial bioactivities, immune-enhancing influences, and anticancer bioactivities. The simulation results exhibited an alternative for attaining visions in this complex method. In this regard, the role of the flow rate ratio on the CS droplet features, including the generation rate and droplet size, were thoroughly described. Based on the results, an appropriate protocol was advanced for controlling the CS droplet properties for comparing their properties, such as the rate and size of the CS droplets in the microchip. Also, a level set (LS) laminar two-phase flow system was utilized to study the CS droplet-breaking process in the Flow Focused-based microchip. The outcomes demonstrated that different sizes and geometries of CS droplets could be established via varying the several parameters that validated addressing the different challenges for several purposes like drug delivery (the droplets with smaller sizes), tissue engineering, and cell encapsulation (the droplets with larger sizes), lab-on-a-chip, organ-on-a-chip, biosensing and bioimaging (the droplets with different sizes). An experimental study was added to confirm the simulation results. A drug delivery application was established to verify the claim.
Predicting target–ligand interactions with graph convolutional networks for interpretable pharmaceutical discovery
Authors: Ruiz Puentes Paola , Rueda-Gensini Laura , Valderrama Natalia , Hernández Isabela , González Cristina , Daza Laura , Muñoz-Camargo Carolina , Cruz Juan C. , Arbeláez Pablo 
Published: May 2022

AbstractDrug Discovery is an active research area that demands great investments and generates low returns due to its inherent complexity and great costs. To identify potential therapeutic candidates more effectively, we propose protein–ligand with adversarial augmentations network (PLA-Net), a deep learning-based approach to predict target–ligand interactions. PLA-Net consists of a two-module deep graph convolutional network that considers ligands’ and targets’ most relevant chemical information, successfully combining them to find their binding capability. Moreover, we generate adversarial data augmentations that preserve relevant biological backgrounds and improve the interpretability of our model, highlighting the relevant substructures of the ligands reported to interact with the protein targets. Our experiments demonstrate that the joint ligand–target information and the adversarial augmentations significantly increase the interaction prediction performance. PLA-Net achieves 86.52% in mean average precision for 102 target proteins with perfect performance for 30 of them, in a curated version of actives as decoys dataset. Lastly, we accurately predict pharmacologically-relevant molecules when screening the ligands of ChEMBL and drug repurposing Hub datasets with the perfect-scoring targets.