bluuebunny/crossref_metadata_2025_split huggingface dataset:
Eutrophication Resistance and Microbial Nitrogen Removal in South San Francisco Bay
Authors: Czapla Kenneth , Owens Michael S , Cornwell Jeffrey C , Senn David B , Francis Christopher A , Chelsky Ariella 
Published: August 2024

Abstract

The ecosystem response to anthropogenic nitrogen (N) loading in estuarine systems is determined by hydrodynamics, biogeochemical transformation rates, and other system-specific characteristics. Historically, San Francisco (SF) Bay has been an outlier from other estuaries with an unusual resistance to eutrophication, despite having extremely high rates of nitrogen loading. Recent increases in phytoplankton biomass and an unprecedented harmful algal bloom, however, have increased the urgency to understand rates and drivers of nitrogen removal in the system. To assess benthic N cycling rates, we conducted seasonal measurements across nine sites in South and Lower South SF Bay, the two sub-embayments with the highest rates of areanormalized N loading, to determine the rates and potential drivers of denitrification and dissimilatory nitrate reduction to ammonium (DNRA). Denitrification rates averaged 60.6 ± 8.1 µmol m-2 hr-1 and were primarily coupled to nitrification. Denitrification rates were positively correlated with DNRA rates and % clay. DNRA rates ranged from 0 to 20 µmol m-2 hr-1 and on an annual basis averaged ~10% of total benthic nitrate reduction, with a negative correlation to % clay content in the surface sediment. The measured denitrification rates account for the removal of, on average, 14% of N loaded annually to South SF Bay, leaving a sizeable portion for alternate fates (e.g, recycling, export, or burial) and potential for substantial temporal and spatial variability (1-79%). This identifies the relative importance of sediment denitrification in ecosystems characterized by high nutrients and low productivity.
Genomic insights into the cold adaptation of human populations in the Amur River Basin
Authors: Xu Shuhua , Chen Siqin , Chen Hao , Yang Zhaoqing , Gao Yang , Tao Kuiting , Chu Jiayou 
Published: August 2024

Abstract

Chinese ethnic minorities, including the Daur, Evenki, and Oroqen, living in the Amur River Basin, may have biologically adapted to extreme cold, but the genetic basis of such adaptations remains underinvestigated. Here, we conducted the first whole-genome sequencing study of 90 individuals representing the three groups to gain insights into their genetic structure, population history, and potential cold adaptation. We observed both shared and differentiated natural selection signatures associated with cold adaptation among these populations. Notably, we identified several novel cold adaptation genes, including FGGY, ADH1A, ADH1B, and NELL1, and noted an independent selection event on alcohol dehydrogenase genes, distinguishing these populations from East Asians south of the Amur River. Furthermore, we modeled the evolutionary scenarios of adaptive alleles for each population. Our findings advance the understanding of human adaptation to cold stress and the demographic history of populations in ultracold regions.
Mutagenesis combined with 2-deoxyglucose is not a suitable tool to select strains of Papiliotrema laurentii less sensitive to glucose catabolite repression
Authors: de Almeida Eduardo Luís Menezes , Lobato Pâmela Carvalho , Ventorim Rafaela Zandonade , Silveira Wendel Batista da 
Published: August 2024

Abstract

Assimilation of major sugars from lignocellulosic biomasses is pivotal for achieving a feasible oil production by oleaginous yeasts in biorefineries. Papiliotrema laurentii UFV-1 is an oleaginous yeast capable of converting lignocellulosic sugars such as glucose and xylose into lipids; however, glucose is assimilated before xylose, impairing high volumetric lipid productivity. To circumvent this drawback, we hypothesized that random mutagenesis combined with 2-deoxyglucose (2DG) selection would be a suitable strategy for selecting strains of P. laurentii UFV-1 less sensitive to glucose repression. First, we determined the growth kinetics parameters of the wild strain in minimum medium with glucose and/or xylose. Then, the yeast was subjected to mutagenesis by ultraviolet irradiation, and mutants were selected in a culture medium containing 2DG. Among the 24 selected mutants, the M17 strain stood out due to its capacity to achieve a higher cell density at the 2DG inhibitory concentration. Surprisingly, both M17 and wildtype strains presented the same xylose and glucose consumption profile. Although M17 grew faster in xylose and preserved the oleaginous phenotype, it could not co-assimilate glucose and xylose. Interestingly, the tolerant strain grew assimilating 2DG and xylose simultaneously, likely incorporating 2DG into its biomass. Otherwise, the wild strain presented arrested growth and only grew after exporting 2DG back to the media. Since carbon catabolite repression and 2DG response mechanisms are poorly studied and remains elusive in Basidiomycota yeasts, we provided cues to guide future studies that will allow a better understanding of the mechanisms involved with 2DG resistance in these yeasts.
Predicting physical performance after training: insights from machine learning using small samples
Authors: Vos Luuk , Vergeer Renske , Goulding Richie , Weide Guido , de Koning Jos , Jaspers Richard , Zwaard Stephan van der 
Published: August 2024

Abstract

Background
Performance optimization is a major goal in sports science. However, this remains difficult due to the small samples and large individual variation in physiology and training adaptations. Machine learning (ML) solutions seem promising, but have not been tested for their capability to predict performance in this setting. The aim of this study was to predict 4-km cycling performance following a 12-week training intervention based on ML models with predictors from physiological profiling, individual training load and well-being, and to retrieve the most important predictors. Specific techniques were applied to reduce the risk of overfitting.
Results
Twenty-seven recreational cyclists completed the 4-km time trial with a mean power output of 4.1 ± 0.7 W/kg. Changes in time-trial performance after training were not different between moderate-intensity endurance training (n = 6), polarised endurance training (n = 8), concurrent polarised with concentric strength training (n = 7) and concurrent polarised with eccentric strength training (n = 6) groups (P > 0.05), but included substantial inter-individual differences. ML models predicted cycling performance with excellent model performance on unseen data before (R2 = 0.923, mean absolute error (MAE) = 0.183 W/kg using a generalized linear model) and after training (R2 = 0.758, MAE = 0.338 W/kg using a generalized linear model). Absolute changes in performance were more difficult to predict (R2 = 0.483, MAE = 0.191 W/kg using a random forest model). Important predictors included power at V̇O2max, performance V̇O2, ventilatory thresholds and efficiency, but also parameters related to body composition, training impulse, sleep, sickness and well-being. Conclusion
ML models allow accurate predictions of cycling performance based on physiological profiling, individual training load and well-being during a 12-week training intervention, even using small sample sizes, although changes in cycling performance were more difficult to predict.
Exploring User Adoption and Experience of AutoML Platforms: Learning Curves, Usability, and Design Considerations
Authors: Sakpere Aderonke Busayo , Aworinde Halleluyah Oluwatobi , Afe Oluwaseyi Funmi , Adebayo Segun , Adeniyi Abidemi Emmanuel 
Published: August 2024

Abstract

Human daily activities and businesses generate a high volume of data which are expected to be transformed for the benefit of businesses and mankind. Organizations make use of machine learning platforms to make informed decisions from well gleaned insights of their real-time data. The process of learning machine learning is seemingly not an easy one, making it tedious for employees to learn easily and quickly. Meanwhile, the introduction of automated machine learning (AutoML) has made this easier. However, it is essential to understand how users embrace and implement the AutoML platform for their real-world problems. To achieve this, we conducted a quantitative study with 38 users focusing on understanding firstly, the learning curve (i.e. the alignment of users’ performance proportionately with the time required to complete a given machine learning task at any given time) and experience of people in the process of learning machine learning. Secondly, the strengths and weaknesses in the design and usability of ML and AutoML. Thirdly, the gaps in the usage or user experience of a newbie - an inexperienced or fresh entrant in the machine learning domain- an inexperienced and fresh entrant in the machine learning domain and professionals, and fourthly, the design factors needed to improve the user experience. Our findings revealed that users have high expectations in the usability of AutoML. In this study, we were able to carry out an assessment of awareness rate of AutoML among the respondents, users’ learning curve in AutoML environment, usability assessment as it relates to variations in users of AutoML, human computer interactivity in relation to in terms of user centeredness and experience of the environment. The study revealed the level of awareness, reasons for apathy and some usability concerns begging for improvement to attract a high rate of usefulness and adoption. In the near future, we hope to take this work further by engaging frequent users of various AutoML environments to ascertain the level of satisfaction using such platforms and identify areas of concern.
Nonlinear Dynamics of Three-Ring Reducer with Dynamic Tooth-side Clearance
Authors: Luo Lan , Cui Kangkang , Lu Jiamin , Jiao Haofeng , Wei Yongqiao , Zhao Fei 
Published: August 2024

Abstract

Reduction gears, especially those with fewer gears similar to the three-ring reducer, can cause changes in the gap between gear teeth due to wear of the tooth surfaces during the application process. The dynamic variation of the gear tooth gap adds nonlinear factors to the dynamic behavior of the engagement process, thus complicating its dynamic behavior and leading to increased vibration and wear, which affects the expansion of the application of fewer gears in the aerospace field. To solve this problem, the aeronautical three-ring gear reducer with fewer gears is taken as the research object, the tooth surface state at different wear stages is considered, the Weierstrass-Mandelbor function is introduced to describe the tooth surface morphology and the time-varying tooth flank gap is calculated, a bending-torsion coupled Six-degree-of-freedom nonlinear vibration model is established. Using the fractal dimension of the Weierstrass-Mandelbor function to describe the state of the tooth surface at different wear stages and the dynamics of aviation three-ring reducer with small tooth difference is analyzed, with the increase of fractal dimension under the same initial conditions, the gear transmission system changes from quasi-periodic motion state to chaotic motion state. By considering the influencing factors of torque and rotational speed, the nonlinear dynamic behaviors at different wear stages are analyzed. The results show that as the rotational speed increases, the system response cycle exhibits a chaotic-proposed cycle-chaotic law of motion. As the torque increases, the system responds to periodic processes now chaotic-hypothetical periodic laws of motion. The findings of the study can effectively support the vibration and noise reduction of the three-ring reducer and provide a theoretical basis for the expansion of its application in the aerospace field.
Tax reform when commuting has demerit properties
Authors: Defloor Bart , de gaer Dirk Van 
Published: August 2024

Abstract

People spend a lot of their time commuting. Research in health economics indicates that spending time in traffic has long run adverse mental and physical health consequences. Literature suggests that, when making decisions about commuting, the individual might underestimate these long run consequences and take them insufficiently into consideration. For this reason, we argue that commuting has demerit properties: an internality is involved. From a social welfare perspective, apart from the demerit aspect, commuting is also associated with an externality. If commuters underestimate the impact on their welfare of commuting, this aggravates also the externality. We develop a formal model incorporating both the externality and the internality aspects of commuting, and its interplay. From the model we derive clearly two types of externalities: direct and indirect ones, both of which need internality correction. Then we incorporate both these considerations into marginal costs of funds (MCF) formulae. MCF are calculated for all U.S. states using the American Time Use Survey. We show that the demerit considerations cause rank switches in over half of the States and that an increase in the taxes on commuting accompanied by a decrease in other taxes benefits social welfare.
HiCMC: High-Efficiency Contact Matrix Compressor
Authors: Adhisantoso Yeremia Gunawan , ̈orner Tim K , ̈untefering Fabian M , Ostermann J ̈orn , Voges Jan 
Published: August 2024

Abstract

Background: Chromosome organization plays an important role in biologicalprocesses such as replication, regulation, and transcription. One way to study therelationship between chromosome structure and its biological functions is through Hi-C studies, a genome-wide method for capturing chromosome conformation.Such studies generate vast amounts of data. The problem is exacerbated by thefact that chromosome organization is dynamic, requiring snapshots at differentpoints in time, further increasing the amount of data to be stored. We present anovel approach called the High-Efficiency Contact Matrix Compressor (HiCMC)for efficient compression of Hi-C data.
Results: By modeling the underlying structures found in the contact matrix,such as compartments and domains, HiCMC outperforms the state-of-the-artmethod CMC by approximately 8% and the other state-of-the-art methods cooler,LZMA, and bzip2 by over 50% across multiple cell lines and contact matrixresolutions. In addition, HiCMC integrates domain-specific information into thecompressed bitstreams that it generates, and this information can be used tospeed up downstream analyses.
Conclusion: The HiCMC is a novel compression approach that utilizes intrinsicproperties of contact matrix, such as compartments and domains. It allows fora better compression in comparison to the state-of-the-art methods. HiCMC isavailable at https://github.com/sXperfect/hicmc.
Brain Cancer, MRI, Deep Learning, Generalization, Overfitting, Image Classification
Authors: Ali Mohamad Abou , Dornaika Fadi , Arganda-Carreras Ignacio 
Published: August 2024

Abstract

Purpose:: Brain cancer represents a significant global health challenge with increasing incidence and mortality rates. Magnetic Resonance Imaging (MRI) plays a pivotal role in early detection and treatment planning.
Methods:: This study adopts a systematic approach across four phases: (1) Optimal Model Selection using the Adam optimizer, emphasizing accuracy metrics, weight computation, early stopping, and ReduceLROnPlateau techniques. (2) Real-world Scenario Simulation through synthetic perturbed datasets created by applying noise, blur (to simulate various magnetic field strengths: 1T, 1.5T, 3T), and patient motion artifacts (mimicking MRI scanning motion effects) to the testing data from the BT-MRI dataset, an online published brain tumor MRI dataset. (3) Optimization involving a range of optimizers (Adam, Adagrad, Nadam, RMSprop, SGD) and online augmentation techniques (AutoMix, CutMix, LGCOAMix, PatchUp). (4) Solution Exploration integrating Gaussian Noise and Blur as augmentation strategies during training to enhance model generalization under diverse conditions.
Results:: Initial evaluations achieved strong performance, consistently reaching 99.45\% accuracy on the BT-MRI dataset. However, testing against synthetic perturbed datasets mimicking real-world conditions revealed challenges in maintaining robust model performance.
Conclusion:: Despite employing diverse optimization methods and advanced augmentation techniques, this study identifies persistent challenges in ensuring model robustness with synthetic perturbed datasets. Notably, the integration of Gaussian Noise and Blur during training significantly improved model resilience. This research underscores the critical role of methodological rigor and innovative augmentation strategies in advancing deep learning applications for precise brain cancer diagnosis using MRI.
Buckling Cluster-based H-Bonded Icosahedral Capsules and Their Propagation to a Robust Zeolite-like Supramolecular Framework
Authors: Sun Di , Zhao Zhan-Hua , Han Bao-Liang , Su Haifeng , Guo Qi-Lin , Wang Wen-Xin , Zhuo Jing-Qiu , Guo Yong-Nan , Liu Jia-Long , Luo Geng-Geng , Cui Ping 
Published: August 2024

Abstract

Hydrogen-bonded assembly of multiple components into well-defined icosahedral capsules akin to virus capsids, has been elusive. In parallel, constructing robust zeolitic-like cluster-based supramolecular frameworks (CSFs) without any coordination bond linkages is challenging. Here, we report the first cluster-based pseudoicosahedral H-bonded capsule Cu60, which is buckled by the self-organization of judiciously designed constituent clusters and anions. The formation of the icosahedron in the solid state takes advantage of 48 charge-assisted CH···F hydrogen bonds between cationic clusters and anions (PF6-), and is highly sensitive to the surface ligands on the clusters with minor structural modification inhibiting its formation. Most excitingly, an extended three-periodic robust zeolitic-like CSF, is inaugurally constructed by edge-sharing the resultant icosahedrons. The perpendicular channels of the CSF feature unusual 3D orthogonal double-helical patterns. The CSF not only keeps its single-crystal character in the desolvated phase, but also exhibits excellent chemical and thermal stabilities as well as long-lived phosphorescence emission.
Structure-guided disulfide engineering restricts antibody conformation and flexibility to elicit TNFR agonism
Authors: Tews Ivo , Elliott Isabel , Essex Jonathan , Herniman Julie , Fisher Hayden , Chan H.T. Claude , Inzhelevskaya Tatyana , Mockridge C. Ian , Penfold Christine , Duriez Patrick , Müller Kri , Cragg Mark , Orr Christian 
Published: August 2024

Abstract

A promising strategy in cancer immunotherapy is activation of immune signalling pathways through antibodies that target co-stimulatory receptors. hIgG2, one of four human antibody isotypes, is known to deliver strong agonistic activity, and modification of hIgG2 hinge disulfides can influence immune-stimulating activity. This was shown for antibodies directed against the hCD40 receptor, where cysteine-to-serine exchange mutations caused changes in antibody conformational dynamics and flexibility. Here we demonstrate that the principles of increasing agonism by restricting antibody conformation through hinge disulfide modification can be translated to the co-stimulatory receptor h4-1BB, another member of the tumour necrosis factor receptor superfamily. Furthermore, we explore structure-guided design of the anti-hCD40 antibody ChiLob7/4 and show that engineering additional disulfides can elicit even greater conformational restriction, concomitant with enhanced agonism. These results reveal the impact of modulating conformational flexibility to tune immunostimulatory activity and provide strategies for the rational design of more powerful antibody therapeutics.
Soil-erosion events on arable land are nowcast by machine learning
Authors: Batista ‪Pedro , Möller Markus , Schmidt Karsten , Waldau Timm , Seufferheld Kay , Htitiou Abdelaziz , Golla Burkhard , Ebertseder Florian , Auerswald Karl , Fiener Peter 
Published: August 2024

Abstract

Accurate estimates of the location, timing, and severity of soil-erosion events on arable land have eluded erosion-prediction technology for decades. Here, for the first time, we demonstrate how a machine learning model can nowcast the occurrence and relatively rank the severity of erosion events on arable field parcels at the regional scale with high accuracy and interpretable outputs. Our findings pave the way for dynamic, large-scale erosion-monitoring systems to achieve healthy soils and improve food security.
Early Diagnosis of Hereditary Angioedema in Japan Based on a US Medical Dataset: Algorithm Development and Validation
Authors: Yamashita Kouhei , Nomoto Yuji , Hirose Tomoya , Yutani Akira , Okada Akira , Watanabe Nayu , Suzuki Ken , Senzaki Munenori , Kuroda Tomohiro 
Published: August 2024

Background
Hereditary angioedema (HAE), a rare genetic disease, induces acute attacks of swelling in various regions of the body. Its prevalence is estimated to be 1 in 50,000 people, with no reported bias among different ethnic groups. However, considering the estimated prevalence, the number of patients in Japan diagnosed with HAE remains approximately 1 in 250,000, which means that only 20% of potential HAE cases are identified.

Objective
This study aimed to develop an artificial intelligence (AI) model that can detect patients with suspected HAE using medical history data (medical claims, prescriptions, and electronic medical records [EMRs]) in the United States. We also aimed to validate the detection performance of the model for HAE cases using the Japanese dataset.

Methods
The HAE patient and control groups were identified using the US claims and EMR datasets. We analyzed the characteristics of the diagnostic history of patients with HAE and developed an AI model to predict the probability of HAE based on a generalized linear model and bootstrap method. The model was then applied to the EMR data of the Kyoto University Hospital to verify its applicability to the Japanese dataset.

Results
Precision and sensitivity were measured to validate the model performance. Using the comprehensive US dataset, the precision score was 2% in the initial model development step. Our model can screen out suspected patients, where 1 in 50 of these patients have HAE. In addition, in the validation step with Japanese EMR data, the precision score was 23.6%, which exceeded our expectations. We achieved a sensitivity score of 61.5% for the US dataset and 37.6% for the validation exercise using data from a single Japanese hospital. Overall, our model could predict patients with typical HAE symptoms.

Conclusions
This study indicates that our AI model can detect HAE in patients with typical symptoms and is effective in Japanese data. However, further prospective clinical studies are required to investigate whether this model can be used to diagnose HAE.
Contestation and Polemic of Islamic Thought in Indonesia
Authors: Pomalingo Samsi , Idrus Nurul Ilmi , Mohammad Basir  , Mashadi  
Published: August 2024

This study seeks to uncover the intricacies of Islamic thought and the issues that lead to religious polemics in Indonesia. Utilizing a narrative ethnographic approach, this study examines the polemic of religious thought among the “New Islam” groups, namely Jama'ah Tabligh, Wahdah Islamiyah, and former Hizbut Tahrir, as well as “old Islam” (PMII and Ansor) by taking the Gorontalo context. which was carried out in July-September 2021. within the context of Gorontalo. Data were collected from July to September 2021. The findings indicate that the polemics among Muslims primarily revolve around the topic of khilafiyah, such as practices like tahlilan and the issue of wishing Merry Christmas, and extend to ideological matters. This polemic occurs among Muslims affiliated with groups or organizations that possess an epistemological basis of the extreme right (fundamental) in contrast to those with a left (liberal) epistemological basis.