bluuebunny/crossref_metadata_2025_split huggingface dataset:
Immobilized Lipases - a Versatile Industrial Tool for Catalyzing Transesterification of Phytosterols Solubilized in Plant Oils to Produce their Fatty Acid Esters
Authors: Basheer Sobhi Awad , Masri Ramez Mohammed 
Published: January 2024

The conjugation of phytosterols (PSs) with fatty acids results in producing phytosterol esters (PSEs) characterized with enhanced lipophilicity and improved functional properties of major interest in food and nutraceutical applications. The use of immobilized lipases to catalyze direct transesterification reactions between PSs and plant oils to form PSEs as a green alternative to conventional chemical production methods has attracted interest during the last two decades. The low solubility of PSs in common plant oil triglycerides, typically below 3% at ambient temperatures, remains the main challenge for bringing lipase-catalyzed direct transesterification reactions of PSs and oil triglycerides to commercial scales.
This study focuses on the enzymatic synthesis of PSEs starting from solubilized PSs at concentrations of up to 30% wt./wt. of oil mixtures comprising fatty acid ethyl esters (FAEEs), monoglycerides (MGs), diglycerides (DGs) and triglycerides (TGs) as a homogeneous medium for the direct transesterification reaction. The results of this study show for the first time that the addition of FAEEs into the reaction medium results in altering of the substrate preference of the enzyme making MGs as the favorite fatty acyl group donors for PSs amongst all other fatty acyl donors present in the reaction system. The proposed new enzymatic route allows starting with high concentrations of solubilized PSs making the direct transesterification of oil glycerides attractive for production of PSEs at industrials scales.
A mechanism to explain soil liquefaction environmental effects during an earthquake
Authors: Teixeira Fernando 
Published: January 2024

Abstract
Soil liquefaction has potentially devastating consequences for infrastructures and endangers human lives. The mechanisms used to explain the above-ground environmental effects of soil liquefaction during and after an earthquake stops require a set of conditions seldom observed in situ(e.g. in the water degassing mechanism). The nature of the high-pressure heads observed remains unknown. Herein, a conceptual model is presented based on the interplay between the pressure heads of the liquefied soil and water (or of a slurry). Water or a slurry of low density, for example, from water springs flowing out of bedrock fissures, is a requirement of the model. A simulation of a simplified system shows that a pressure head of the slurry well over the required to reach the soil surface is obtained in most of the function's domain. The flow velocity at the surface will depend on the soil and water mixture ratio, which is expected to be correlated with the soil characteristics through the profile- the potential for the walls of the cracks to collapse. The inexistence of active water springs prevents the ejection of water mixtures at the soil surface.
Direct Electrolytic Extraction of Lithium Metal from Brines Based on Sandwich-structured Garnet Electrolyte
Authors: Duan Huanan , Zhang Nan , Zheng Hongpeng , Li Guoyao , Ouyang Cheng , Yang Yu , Zhu Hong , Zhang Rongzi , Sun Haidong , Lin Yuhan , Liu Hezhou 
Published: January 2024

Abstract

Lithium metal as an important strategic resource has diverse industrial applications such as glass, ceramics, and alloys. With the increasing demand for lithium, lithium extraction from brines has become the focus of attention. However, due to the similarity of the radii of Li+ and Mg2+, conventional lithium extraction methods are inefficient for lithium extraction from brines with a high Mg/Li ratio. Herein we propose a direct electrolytic extraction of lithium (DEEL) system based on a sandwich-structured LLZTO (Li6.5La3Zr1.5Ta0.5O12) electrolyte with excellent water stability. The lanthanum zirconate (La2Zr2O7) on the surface of this sandwich-structured LLZTO can achieve high stability to water and air while still maintaining high ionic conductivity (7.5 × 10− 4 S cm− 1) and low lithium interface resistance (40 Ω cm2). This sandwich-structured LLZTO has been successfully demonstrated to stably extract lithium from simulated brines and natural brines. The current density for lithium extraction can reach 0.5 mA cm− 2, corresponding to a lithium extraction rate of 126 µg cm− 2 h− 1; the electrochemical extraction devices can stably work for 48 hours; the current efficiency can reach 97.71%. The extracted lithium metal exhibits a purity of 99.475 wt%. This work provides a highly efficient approach for extracting lithium from brines by electrolysis.
Residual Diffusion Modeling for Km-scale Atmospheric Downscaling
Authors: Mardani Morteza , Brenowitz Noah , Cohen Yair , Pathak Jaideep , Chen Chieh-Yu , Liu Cheng-Chin , Vahdat Arash , Kashinath Karthik , Kautz Jan , Pritchard Mike 
Published: January 2024

Abstract

Predictions of weather hazard require expensive km-scale simulations driven by coarser global inputs. Here, a cost-effective stochastic downscaling model is trained from a high-resolution 2-km weather model over Taiwan conditioned on 25-km ERA5 reanalysis. To address the multi-scale machine learning challenges of weather data, we employ a two-step approach Corrector Diffusion (CorrDiff), where a UNet prediction of the mean is corrected by a diffusion step. Akin to Reynolds decomposition in fluid dynamics, this isolates generative learning to the stochastic scales. CorrDiff exhibits skillful RMSE and CRPS and faithfully recovers spectra and distributions even for extremes. Case studies of coherent weather phenomena reveal appropriate multivariate relationships reminiscent of learnt physics: the collocation of intense rainfall and sharp gradients in fronts and extreme winds and rainfall bands near the eyewall of typhoons. Downscaling global forecasts successfully retains many of these benefits, foreshadowing the potential of end-to-end, global-to- km-scales machine learning weather predictions.
Deep generative modeling of the human proteome reveals over a hundred novel genes involved in rare genetic disorders
Authors: Marks Debora , Orenbuch Rose , Kollasch Aaron , Spinner Hansen , Shearer Courtney , Hopf Thomas , Franceschi Dinko , Dias Mafalda , Frazer Jonathan 
Published: January 2024

Abstract

Identifying causal mutations accelerates genetic disease diagnosis, and therapeutic development. Missense variants present a bottleneck in genetic diagnoses as their effects are less straightforward than truncations or nonsense mutations. While computational prediction methods are increasingly successful at prediction for variants in known disease genes, they do not generalize well to other genes as the scores are not calibrated across the proteome1-6. To address this, we developed a deep generative model, popEVE, that combines evolutionary information with population sequence data7 and achieves state-of-the-art performance at ranking variants by severity to distinguish patients with severe developmental disorders8 from potentially healthy individuals9. popEVE identifies 442 genes in patients this developmental disorder cohort, including evidence of 123 novel genetic disorders, many without the need for gene-level enrichment and without overestimating the prevalence of pathogenic variants in the population. A majority of these variants are close to interacting partners in 3D complexes. Preliminary analyses on child exomes indicate that popEVE can identify candidate variants without the need for inheritance labels. By placing variants on a unified scale, our model offers a comprehensive perspective on the distribution of fitness effects across the entire proteome and the broader human population. popEVE provides compelling evidence for genetic diagnoses even in exceptionally rare single-patient disorders where conventional techniques relying on repeated observations may not be applicable.
Impact of hydrological connectivity on rainfed annual crops in Mediterranean hilly landscapes: a numerical exploration
Authors: Dhouib Mariem , Molénat Jérôme , Prévot Laurent , Mekki Insaf , Zitouna-Chebbi Rim , Jacob Frédéric 
Published: January 2024

Abstract
Within hilly agricultural landscapes, topography induces lateral transfers of runoff water, so-called interplot hydrological connectivity. Runoff water generated in upstream plots can infiltrate downstream plots, thus influencing the water content in the root zone that drives crop functioning. As the impact of runoff on crop functioning in the context of Mediterranean rainfed annual crops has been understudied thus far, the current study aims to comprehensively investigate this effect. To quantify how the downstream infiltration of upstream runoff affects crop functioning, we conduct a numerical experiment using the AquaCrop crop model and consider two hydrologically connected plots. The experiment explores a range of upstream and downstream agro-pedo-climatic conditions: crop type, soil texture and depth, climate forcing, and the area of the upstream plot. The experiment relies on data collected over the last 25 years within the Kamech catchment, an environment research observatory in northeastern Tunisia. Overall, the results show that the downstream infiltration of upstream runoff has a positive impact on crop functioning in a moderate number of situations, ranging from 16% (wheat) to 33% (faba bean) as the average across aboveground biomass and yield. This positive impact is mostly found for soils with higher available water capacity and under semiarid and dry subhumid climate conditions, with a significant impact of rainfall intra-annual distribution in relation to crop phenology. These results need to be deepened by considering both a wider range of crops and future climate conditions, for which larger occurrences of semiarid climate conditions are expected.
Structure and mechanism of the Zorya anti-phage defense system
Authors: Taylor Nicholas , Hu Haidai , Hughes Thomas , Popp Philipp , Roa-Eguiara Aritz , Martin Freddie , Rutbeek Nicole , Hendriks Ivo , Payne Leighton , Yan Yumeng , Sousa Victor , Wang Yong , Nielsen Michael , Berry Richard , Erhardt Marc , Jackson Simon 
Published: January 2024

Abstract

Zorya is a recently identified and widely distributed bacterial immune system, which protects against phage invasion. It consists of a predicted membrane-embedded complex (ZorAB) and soluble components that differ among Zorya subtypes, notably ZorC and ZorD, in type I Zorya systems. Here, we reveal the molecular basis of the Zorya defense system using cryo-electron microscopy, mutagenesis, fluorescence microscopy, proteomics, and functional studies. We demonstrate that ZorAB shares the stoichiometry of other 5:2 inner membrane ion-driven rotary motors. Additionally, ZorA5B2 features a dimeric ZorB peptidoglycan binding domain and a pentameric α-helical coiled-coil tail made of ZorA that projects approximately 700 Å into the cytoplasm. We further characterize the structure and function of the soluble Zorya components, ZorC and ZorD, and find that they harbour DNA binding and nuclease activity, respectively. Comprehensive functional and mutational analyses demonstrates that all Zorya components work in concert to protect bacterial cells against invading phages. We present evidence that ZorAB operates as an ion-driven motor that becomes activated and anchors to the cell wall upon sensing of cell envelope perturbations during phage invasion. Subsequently, ZorAB transfers the phage invasion signal through the ZorA cytoplasmic tail to the soluble effectors, which function to prevent phage propagation. In summary, our study elucidates the foundational mechanisms of Zorya function and reveals a novel triggering signal for the rapid activation of an anti-phage defense system.
Socioeconomic Inequalities in Avoidable Mortality in Italy: Results From a Nationwide Longitudinal Cohort
Authors: Petrelli Alessio , Ventura Martina , Napoli Anteo , Pappagallo Marilena , Simeoni Silvia , Frova Luisa 
Published: January 2024

Abstract
BACKGROUND
Disparities in avoidable mortality have never been evaluated in Italy at the national level.
The present study aimed to assess the association between socioeconomic status, measured by individual education level, and avoidable mortality.
METHODS
The nationwide closed cohort of the 2011 Census of Population and Housing was followed up for 2012-2019 mortality. Outcomes of preventable and of treatable mortality were separately evaluated among people aged 30-74. Education level (elementary school or less, middle school, high school diploma, university degree or more) and residence macro area (North-West, North-East, Center, South-Islands) were the exposures, for which adjusted mortality rate ratios (MRRs) were calculated through multivariate quasi-Poisson regression models, adjusted for age at death. Relative index of inequalities was estimated for preventable, treatable, and non-avoidable mortality and for some specific causes.
RESULTS
The cohort consisted of 35,708,459 residents (48.8% men, 17.5% aged 65-74), 34% with a high school diploma, 33.5% living in the South-Islands; 1,127,760 deaths were observed, of which 65.2% for avoidable causes (40.4% preventable and 24.9% treatable).
Inverse trends between education level and mortality were observed for all causes; comparing the least with the most educated groups, a strong association was observed for preventable (males MRR=2.39; females MRR=1.65) and for treatable causes of death (males MRR=1.93; females MRR=1.45).
The greatest inequalities were observed for HIV/AIDS and alcohol-related diseases (both sexes), drug-related diseases and tuberculosis (males), and diabetes mellitus, cardiovascular diseases, and renal failure (females). Excess risk of preventable and of treatable mortality were observed for the South-Islands.
CONCLUSIONS
Socioeconomic inequalities in mortality persist in Italy, with an extremely varied response to policies at the regional level, representing a possible missed gain in health and suggesting a reassessment of priorities and definition of health targets.
Stress-Testing USB Accelerators for Efficient Edge Inference
Authors: Fischer Raphael , Staay Alexander van der , Buschjäger Sebastian 
Published: January 2024

Abstract
Several manufacturers sell specialized USB devices for accelerating machine learning (ML) on the edge. While being generally promoted as a versatile solution for more efficient edge inference with deep learning models, extensive practical insights on their usability and performance are hard to find. In order to make ML deployment on the edge more sustainable, our work investigates how resource efficient these USB accelerators really are. For that, we first introduce a novel and theoretically sound methodology. It allows for comparing intricate model performance in terms of quality and resource consumption across different execution environments. We then put it into practice by studying the usability and efficiency of Google's Coral edge tensor processing unit (TPU) and Intel's neural compute stick 2 (NCS). In total, we benchmark over 30 models across nine hardware configurations, which reveals intricate trade-offs. Our work demonstrates that USB accelerators are indeed capable of reducing the energy consumption by a factor up to ten, however this improvement cannot be observed for all configurations - more than 50% of the investigated models cannot be run on accelerator hardware, and in several other cases, the power draw is only marginally improved. Our experiments show that the NCS improves efficiency in a more stable way, while the TPU shows further benefits in specific cases but performs less predictable. We hope that our paper provides valuable insights for practitioners that want to deploy ML on the edge in the most efficient and sustainable way.
The 2022 Menyuan Mw6.7 Earthquake Triggered by 2016 M6.4 Earthquake and Stress Transfer on Surrounding Fault Systems in NE margin of Tibetan Plateau, China
Authors: lei dongning , Qiao Yueqiang , Yang Gang , Kong Yuyang 
Published: January 2024

Abstract
The Tuolaishan-Lenglongling fault, situated on the northeastern margin of Tibetan plateau, is characterized by highly seismic activity. Over the past four decades, three moderate earthquakes with a magnitude exceeding M6.0 have occurred around this fault. The most recent event, the 2022 Menyuan Mw6.7 earthquake has inspired the interest to its seismic hazard since the 2016 M6.4 and the 1984 M6.4 earthquake. Based source fault models we conducted the calculation of static Coulomb stress change for the recent two earthquake events. Our studies show that the 2022 M6.7 earthquake including its aftershocks has been triggered by the previous event of the 2016 M6.4 earthquake. Utilizing source fault models, we conducted calculations of static Coulomb stress change for the two recent earthquake events. Our analysis indicates that the 2022 M6.7 earthquake, along with its aftershocks, was triggered by the preceding 2016 M6.4 earthquake. Additionally, we performed calculations to assess the stress transfer resulting from the 2022 Mw6.7 earthquake on the adjacent fault systems, identifying the fault segments experiencing increased stress. Furthermore, the Tuolashan-Lenglongling fault exhibits the highest expected seismicity rate among the calculated fault systems. Consequently, increased attention should be directed towards these fault segments experiencing heightened stress to evaluate potential seismic hazard.
WITHDRAWN: Doxorubicin Involves in Cardiomyocyte Ferroptosis by Inhibiting STAT3 Phosphorylation and Reducing GPX4 Expression
Authors: Gao Ge , Shen Cheng , Wang Manman , Ji Cuiling , Fang Fang , Liu Zhichao , Chen Wenqiang , Zhang Jinguo 
Published: January 2024

Abstract
Doxorubicin (DOX) is a commonly employed cancer-fighting medication in medical settings, and its adverse effects on the heart pose a significant challenge in the treatment of tumors. The programmed cell death, encompassing cell death linked with apoptosis, pyroptosis, necroptosis, ferroptosis and autophagy/mitophagy, can be witnessed in the advancement of doxorubicin-induced cardiotoxicity, yet the precise mechanism remains uncertain. Recent studies have tended to ferroptosis as the predominant form of DOX-induced cell death. To identify differentially expressed genes (DEGs), the GSE193861 dataset obtained from the GEO database through high throughput sequencing was downloaded. GO and KEGG pathway enrichment analysis was performed on the ferroptosis-related differentially expressed genes (FRDEGs) obtained by screening the ferroptosis-related genes (FRGs) downloaded from the FerrDb database. Among the screened hub genes, transcription factor STAT3 was significantly associated with ferroptosis. Through our prediction, we discovered that STAT3 attaches to the promoter area of glutathione peroxidase 4 (GPX4), an essential controller of ferroptosis. In order to investigate the impact of DOX on ferroptosis in cardiomyocytes through the regulation of STAT3 and GPX4 expression, we created both in vivo and in vitro cardiotoxicity models utilizing DOX. The results demonstrated that DOX inhibited the phosphorylation and nuclear translocation of STAT3 and the expression of its downstream target gene GPX4, but had no effect on STAT3 expression. Notably, when the STAT3 was overexpressed, DOX lost its ability to inactivate GPX4. Consequently, activation of STAT3 ameliorated DOX-induced ferroptosis in cardiomyocytes. In summary, DOX induces ferroptosis in cardio- myocyte by inhibiting the phosphorylation of STAT3 and decreasing the expression of GPX4.
Autonomous Driving Comfort Prediction: Improve Comfort through Global Path Planning
Authors: Huang Chaosheng , Chen Zhengxian , Liu Yuqi , Xu Boyang , Ling Zihan , Li Zheyu , Yu Wenhao , Shen Yang , Wang Huanan , Li Jun , Yao Feiyu 
Published: January 2024

Abstract

Comfort is a pivotal consideration in the realm of autonomous driving, prompting researchers to address methods of enhancing driving comfort for autonomous driving vehicles. A central focus of current research revolves around the judicious planning of driving trajectories with a primary emphasis on comfort. Given the transient nature of comfort, prevailing path planning strategies predominantly center on local considerations. Nevertheless, it is imperative to recognize that macroscopic factors, including traffic flow and road conditions, wield a substantial influence on comfort. For instance, complex traffic scenarios elevate the likelihood of emergency braking, thereby affecting comfort. Consequently, investigating the intricate interplay between comfort and global path planning becomes essential. In the context of autonomous driving, this paper introduces a methodology and framework for predicting driving comfort by leveraging road information. The study establishes a comprehensive road information-comfort dataset and devises predictive models for driving comfort, employing a multi-head attention approach. The ensuing discussion elucidates the practical application of the model in path selection and planning through illustrative examples. Finally, real-road vehicle tests were conducted. Following the utilization of the path optimized by the model, the autonomous driving car exhibited an approximate 17.3% reduction in the mean longitudinal jerk and a 9.7% reduction in the mean lateral jerk. These results serve to validate the effectiveness and significance of the proposed model. Notably, this research marks the pioneering integration of driving comfort with global path planning, which is of immense importance for autonomous driving navigation systems, offering an insightful and valuable reference for related study.
Phytochemical Profiling and Molecular Docking Investigation of Avocado (Persea Americana Mill. Cultivar Hass) Leaves and Seeds: Implications for Antioxidant Activity and Health Benefits
Authors: Olufemi Ademoyegun , Rabiat Ahmed Shola , Raphael David Olamide , Mustapha Balikis Oluwakemi , Salau Shina 
Published: January 2024

Abstract
Despite the rising interest in avocado leaves and seeds' health benefits, little or no research has been conducted on both their phytochemical profiles in conjunction with molecular docking investigations, particularly in relation to its antioxidant activity. Utilizing phytochemical screening, molecular docking, and ADMET predictions, this study, investigates the antioxidant properties of avocado leaves (AVL) and seeds (AVS). Results show that AVS has a high presence of flavonoids (+++), terpenoids (+++), but a low presence of phenols (+), while AVL has a high presence of tannins (+++) and phenols (++). Molecular docking studies validate two AVL (L01 and L02) and two AVS (S02 and S03) compounds based on binding affinity and interactions with 2rgu.pdb, 3mng.pdb, and 2vwi.pdb protein targets. ADMET studies indicate that AVL and AVS extracts have favourable bioavailability and health safety characteristics. Additionally, DPPH, ABTS and FRAP radical inhibition (%) results show that AVL (46.36%/DPPH, 6.67%/ABTS, 11.07%/FRAP) has lesser antioxidant activity than AVS (70.97%/DPPH, 47.35%/ABTS, 3.66%/FRAP).