bluuebunny/crossref_metadata_2025_split huggingface dataset:
A multi-scale digital twin for adiposity-driven insulin resistance in humans: diet and drug effects
Authors: Herrgardh Tilda , Simonsson Christian , Ekstedt Mattias , Lundberg Peter , Stenkula Karin G. , Nyman Elin , Gennemark Peter , Cedersund Gunnar 
Published: August 2023

Abstract
Background
The increased prevalence of insulin resistance is one of the major health risks in society today. Insulin resistance involves both short-term dynamics, such as altered meal responses, and long-term dynamics, such as the development of type 2 diabetes. Insulin resistance also occurs on different physiological levels, ranging from disease phenotypes to organ-organ communication and intracellular signaling. To better understand the progression of insulin resistance, an analysis method is needed that can combine different timescales and physiological levels. One such method is digital twins, consisting of combined mechanistic mathematical models. We have previously developed a model for short-term glucose homeostasis and intracellular insulin signaling, and there exist long-term weight regulation models. Herein, we combine these models into a first interconnected digital twin for the progression of insulin resistance in humans.
Methods
The model is based on ordinary differential equations representing biochemical and physiological processes, in which unknown parameters were fitted to data using a MATLAB toolbox.
Results
The interconnected twin correctly predicts independent data from a weight increase study, both for weight-changes, fasting plasma insulin and glucose levels, and intracellular insulin signaling. Similarly, the model can predict independent weight-change data in a weight loss study with the weight loss drug topiramate. The model can also predict non-measured variables.
Conclusions
The model presented herein constitutes the basis for a new digital twin technology, which in the future could be used to aid medical pedagogy and increase motivation and compliance and thus aid in the prevention and treatment of insulin resistance.
Available but inaccessible: Patient experiences during the first two years of a primary care-based medical cannabis program at an academic medical center
Authors: Freitag Eloise , Zolotov Yuval , Annam Jayabhargav , Labins Jaquelin , Yamada Jaclyn , Jillani Syeda Masharab , Arnsten Julia , Slawek Deepika 
Published: August 2023

Abstract
Background
Medical cannabis use and public acceptance in the United States have increased over the past 25 years. However, access to medical cannabis remains limited, particularly for underserved populations. To understand how patients experience medical cannabis accessibility, we measured medical cannabis use and barriers to use after medical cannabis certification in an urban safety-net academic medical center.
Methods
We conducted a retrospective cohort study among patients seen in Montefiore’s Medical Cannabis Program (MMCP) from 2017 to 2019. Patient demographic and clinical characteristics, as well purchase history of medical cannabis, were extracted from electronic medical records. We also administered a phone questionnaire to a subset of patients to assess usage patterns, effectiveness, and barriers to medical cannabis use.
Results
Among 562 patients who were newly certified for medical cannabis between 2017 and 2019, 45% purchased medical cannabis, while 55% did not. Patients who purchased medical cannabis were more likely to be white and have private insurance or Medicare. Unregulated cannabis use and current tobacco use were less common among those who purchased medical cannabis. In multivariable logistic regression analysis, unregulated cannabis use remained negatively associated with purchasing medical cannabis. Patients reported that affordability and dispensary accessibility were their main barriers to purchasing medical cannabis.
Conclusion
Among patients certified for medical cannabis use, fewer than half purchased medical cannabis after certification. Improving access to medical cannabis is crucial for ensuring equitable access to regulated cannabis, and to reducing unregulated cannabis use.
Potential distribution modeling based on machine learning of Sechium edule (Jacq.) Sw. in Japan
Authors: Cadena-Iñiguez Jorge , Barrera-Guzmán Luis Ángel , Cadena-Zamudio Daniel Alejandro , Galarza Gustavo Antonio Arévalo , Medina Juan Francisco Aguirre 
Published: August 2023

Abstract

Species distribution models identify regions with ideal environmental characteristics for the establishment and proliferation of species. The chayote is a crop that originated and domesticated in Mexico; however, it is cultivated in different parts of the world due to its nutritional and pharmaceutical importance. The objective of this research was to locate the potential distribution of S. edule in Japan supported on seven machine learning models, to also determine which bioclimatic variables influence its distribution, and which are the most suitable regions for its establishment. Thirty-one occurrence points, elevation, and the bioclimatic variables bio1, bio3, bio4, bio7, bio8, bio12, bio14, bio15, and bio17 were used to infer the models. Hundred percent of the occurrence points coincided with the Cfa climate distributed in Acrisol (60.9%), Andosol (17.4%), Cambisol (13%), Fluvisol (4.35%), and Gleysol (4.35%) soil. The Maxent model reported the highest AUC value (0.93), while the GLM obtained the best TSS value (0.84); the SVM model reported the largest suitability area ≥ 0.5 with 100,394.4 km2. Temperature-related variables were the major contributors to the models and the ones explaining the distribution limits of S. edule in Japan. The coastal eastern prefectures of Kantō, Chūbu, Kinki, Chūgoku, Kyūshū, and Shikoku regions showed a suitability ≥ 0.5.
The complete mitochondrial genome of Chilo infuscatellus (Lepidoptera: Pyralidae), and related phylogenetic analysis
Authors: Yi Jiequn , Liu Jianbai , Mao Yongkai , Cheng Yinjie , Lin Minjiang , Xu Hanliang , An Yuxing , Li Jihu , Wu Han 
Published: August 2023

Abstract
The Chilo infuscatellus (Lepidoptera: Pyralidae) is a significant pest of sugarcane in China. The genome-level characteristics of this pest are important genetic resources for identification, phylogenetic analysis, and even management. In the present study, the complete mitogenome of C. infuscatellus was sequenced and characterized. The complete mitogenome of C. infuscatellus is 15,252 bp in length and comprises 13 protein-coding genes (PCGs), 22 transfer RNA genes (tRNAs), 2 ribosomal RNA genes (rRNAs), and an A + T-rich region. Except for the CGA codon for the cox1 gene, the PCGs initiated with ATN codons (ATG, ATT, and ATA). These PCGs terminated with TAA or an incomplete T. Except for the loss of the “DHU” arm for trnS1, the tRNA genes were folded into the typical cloverleaf structure. The A + T-rich region has a high AT content of 96.19% and contains the motifs “ATAGA” and “ATTTA”, as well as a 19 bp poly-T stretch and microsatellite regions. The C. infuscatellus mitogenome exhibits a conserved gene order among lepidopteran insects, with a rearrangement of the trnM gene compared to the ancestral mitogenome. Phylogenetic analysis based on the 13 PCGs using Bayesian inference (BI) and maximum likelihood (ML) methods confirmed the monophyly of Pyralidae and Crambidae within Pyraloidea. The relationships between subfamilies in Pyralidae can be described as (Galleriinae + (Phycitinae + (Pyralinae + Epipaschiinae))). The “PS clade” and “non-PS clade” were formed in the family Crambidae. Our results enrich the genetic resources of sugarcane borers and provide insights into the phylogeny of Pyraloidea insects.
A Markovian approach for dynamic transit assignment on multimodal public transport networks
Authors: Paz-Guala Ricardo de la , Munizaga Marcela , Arriagada Jacqueline 
Published: August 2023

Abstract
We develop the Markovian dynamic transit assignment (MDTrA) modeling framework for large multi-modal networks to incorporate into the transit assignment analysis the dynamic aspects that come from the time-dependent relationship between demand and supply and the stochasticity that comes from differences in passenger's costs perception, measurement errors, and other sources of uncertainty. The intuition is that passengers choose their routes by a recursive arc-choice process, according to the expected minimum costs from their current node to their destinations. Our approach presents an important opportunity to use smartcard data, as demand profiles and users' choices over time are obtained from it.
Clearance and Nutrition in Neonatal Continuous Renal Replacement Therapy (CRRT) Using the CARPEDIEM™ System
Authors: Vuong Kim T , Vega Molly R. , Casey Lauren , Swartz Sarah J. , Sriva Poyyapakkam , Osborne Scott W. , Rhee Christopher J. , Arikan Ayse Akcan , Joseph Catherine 
Published: August 2023

Abstract
Background: Infants with end stage kidney disease (ESKD) demonstrate poor growth partly due to obligate fluid and protein restrictions. Delivery of liberalized nutrition on continuous renal replacement therapy (CRRT) is impacted by clinical instability, technical dialysis challenges, solute clearance, and nitrogen balance. We analyzed delivered nutrition and growth in infants receiving CRRT with the Cardio-Renal, Pediatric Dialysis Emergency Machine (CARPEDIEM™).
Methods: Single center observational study of infants receiving CRRT with the CARPEDIEM™ between June 1 and December 31, 2021. We collected prospective circuit characteristics, delivered nutrition, anthropometric measurements, and illness severity Score for Neonatal Acute Physiology-II. As a surrogate to normalized protein catabolic rate in maintenance hemodialysis, we calculated normalized protein nitrogen appearance (nPNA) using the Randerson II continuous dialysis model. Descriptive statistics, Spearman correlation coefficient, Mann Whitney, Wilcoxon signed rank, Receiver-Operating Characteristic curves, and Kruskal-Wallis analysis were performed using SAS version 9.4.
Results: Eight infants received 31.9 [22.0, 49.7] days of CRRT using mostly (90%) regional citrate anticoagulation. Delivered nutritional volume, protein, calories, enteral calories, nPNA, and nitrogen balance increased on CRRT. Using parenteral nutrition, 90 ml/kg/day should meet caloric and protein needs. Following initial weight loss of likely fluid overload, weight gain occurred after 14 days of CRRT. Despite adequate nutritional delivery, goal weight (z-score=0) and growth velocity were not achieved. Most (62.5%) survived and transitioned to peritoneal dialysis (PD).
Conclusions: CARPEDIEM™ is a safe and efficacious bridge to PD in neonatal ESKD. Growth velocity of infants on CRRT appears delayed despite delivery of adequate calories and protein.
Stabilization of Semi-Markovian Jumping Uncertain Complex-valued Networks with Time-varying Delay: A Sliding-Mode Control Approach
Authors: Li Qiang , Wei Hanqing , Hua Dingli 
Published: August 2023

Abstract
This paper pays close attention to the stabilization issue for delayed uncertain semi-Markovian jumping complex-valued networks via sliding mode control. The concerned corresponding transition rates depend on a positive constant, i.e., sojourn-time, which is not required to obey the general exponential distribution. Combine the generalized Dynkin’s formula with Lyapunov stability theory as well as the characteristics of cumulative distribution functions, a few sufficient criteria are proposed to ascertain the stochastic stability of the obtained sliding mode dynamical system. In addition, design a novel sliding mode controller to ensure all state trajectories of the potential closed-loop system can reach the synthesized sliding mode switching surface in a finite time and maintain there in the subsequent time. In the end of paper, one simple example is presented to verify superiority and feasibility of the provided controller design scheme.
Nationwide genomic surveillance revealed the presence of viruses, bacteria, and antimicrobial resistance genes associated with animal and human health carried by domestic cats in China
Authors: Li Qiuyan , Zhou Dengyuan , Wang Xugang , Cao Longlong , Wang Wenyuan , Zhang Chenxi , Hu Siyun , Qian Chaonan , Chen Huanchun , Cao Shengbo , Peng Zhong , Ye Jing 
Published: August 2023

Abstract
Background:
As a highly popular choice for a household pet worldwide, domestic cats may carry pathogens and antimicrobial resistance genes (ARGs) that pose severe risks to human health. However, our knowledge about pathogens and ARGs carried by cats remains limited. This large-scale genomic surveillance study is designed to identify viruses and bacteria as well as ARGs that may pose risks to animal and human health in the upper respiratory tracts of domestic cats in China.
Results:
Meta-transcriptome sequencing of 1454 oropharyngeal-nasal swabs collected from domestic cats in different living conditions in 22 Chinese provinces identified 371 viral species that can affect the health of animals and humans. A total of 24 viral species that had been known to cause infections in cats were detected. Among them, felis catus gammaherpesvirus 1, feline dependoparvovirus, feline foamy virus, fesavirus 4, puma lentivirus, and puma lentivirus-14 were detected in Chinese cats for the first time. Our study also identified the presence of nucleic acids from viruses associated with other animal species commonly found in human daily lives, including poultry, birds, pigs, cattle and other ruminants, minks, rabbits, and dogs. Notably, we identified many well-known viruses associated with public health concerns, including SARS-related coronavirus, monkeypox virus, and Zika virus (ZIKV). For the first time we found cats could be experimentally infected with and transmit ZIKV via mosquito bites, indicating a potential role of domestic cats in the maintenance and spread of the virus. We also identified a substantial number of bacterial species associated with public health, including the ESKAPE pathogens (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter spp.) which showed a broad detection range. Additionally, a plentiful amount of ARGs, including those conferring resistance to the last resort antibiotics (carbapenems [blaNDM, blaOXA, blaVIM], colistin [mcr], and high-level tigecycline [tetX3, tetX4, tetX5, tetX6]), were also detected broadly.
Conclusions:
This study indicate that domestic cats may act as a role in the worldwide emergence and increasing of zoonotic diseases and antimicrobial resistance. It is important to monitoring domestic cats within the One Health approach to control these critical global public health issues.
Traveling wave solutions of the generalized Burgers-Huxley Equation
Authors: Habiba Umme , Salam Md. Abdus 
Published: August 2023

Abstract
In the field of mathematical sciences and engineering, traveling wave solutions serve as crucial for illustrating the wave nature of nonlinear problems. On the other hand, the fractional order nonlinear partial differential equations demonstrate the nonlinear physical phenomenon more accurately. As a result, investigations on such equations are of common interest to the researchers. In our article, we investigate the generalized Burgers-Huxley equation by means of the improved Bernoulli sub-equation function method (IBSEFM). Additionally, we study the bifurcation, and stability of the equilibria, and perform phase plane analysis of the model. The solutions and numerical analysis are drawn with the aid of the mathematical software Maple and the figures are obtained using MATLAB. It is expected that the investigation will be helpful in describing the nonlinear physical phenomena.
Disaggregating IMERG Satellite Precipitation Over Czech Republic: An Innovative Approach using Hybrid Extreme Gradient Boosting based on Fuzzy Spatial-Temporal Multivariate Clustering
Authors: Singh Ujjwal , Nasreen Sadaf , Tripathi Gaurav , Mehrishi Pragya , Pradhan Rajni Kumar , Bestakova Zuzana , Singh Vivek Vikram , Gouda K C , Sharma Laxmi Kant , Jalem Kiran , Maca Petr , Nidamanuri Rama Rao , Raghubanshi Akhilesh Singh , Markonis Yannis , Oldˇrich Rakovec , Hanel Martin 
Published: August 2023

Abstract
Achieving accurate precipitation estimation at high spatial and temporal resolution is critical in hydrology and meteorology, particularly in regions experiencing water resource degradation. Integrating original products of generally coarse-resolution hydrological parameters from multiple satellites is a promising technology for producing massive repositories of space-time varying datasets such as precipitation. A methodological modelling framework commonly known as downscaling or disaggregation is evolving as a viable approach for generating hydrological datasets with spatio-temporal scales suitable for operational usage. In this study, we propose a non-parametric method for generating a high-resolution dataset from a coarse-resolution integrated multiple satellite-based precipitation dataset. The disaggregation involves using a hybrid Extreme Gradient Boosting (XGBoost) approach combined with multivariate spatial-temporal Fuzzy clustering. This clustering relies on Integrated Multi-satellite Retrievals for GPM (IMERG) precipitation and Shuttle Radar Topography Mission (SRTM) Digital Elevation Data to establish eight distinct clusters.The proposed method is experimentally demonstrated implementing to downscale 255 months (June 2000 to September 2021) of IMERG satellite data from 11km to 1km spatial resolution over the Czech Republic. We utilized eight stations, one per cluster, for training and validation purposes, with the remaining 19 stations used solely for validation. Our findings demonstrate a strong agreement between the disaggregated monthly precipitation over the 20 years and ground-observed precipitation, suggesting that our proposed methodology substantially enhances the accuracy of IMERG precipitation data. This method holds promise for applications in other regions with remotely sensed data, especially where ground-measured station data is sparse, facilitating the generation of high-resolution, accurate precipitation data.
Top-Down or Bottom-up? Growth direction of gas flow-assisted perovskite layers
Authors: Mathies Florian , Nandayapa Edgar , Rehermann Carolin , Shargaeiva Oleksandra , List-Kratochvil Emil , Unger Eva 
Published: August 2023

Abstract
Photovoltaic devices based on metal halide perovskites have been established as a leading technology for future energetic applications, making scalabledeposition methods, compatible with high-throughput yields, more relevant than ever before. However, the key to producing highly efficient devices is to control the film formation process to create homogeneous pinhole-free films. This process becomes more challenging as the active area of the device increases. Our work provides insights into the crystallization dynamics of metal halide perovskite thin films by in-situ monitoring. By utilizing photoluminescence and X-ray diffraction techniques, we identified that the film quality strongly depends on the dynamics of the gas flowing in the drying chamber. Optical and structural in-situ measurements revealed the growth of the perovskite films from bottom to top using a gas-assisted vacuum drying method. These results emphasize the importance of controlling the quenching stream of gases used to crystallize the films after deposition. Furthermore, it establishes a clear development path for future large area perovskite solar cells.
Quasi-Opposition-Based Learning in a Shuffled Particle Swarm Optimization for Solving Frequency Modulation Sounds Parameter Identification Problem
Authors: Ahandani Morteza Alinia 
Published: August 2023

Abstract
This paper proposes some novel versions of the shuffled particle swarm optimization (SPSO) for solving the frequency modulation sound parameter identification (FMSPI) problem. In the SPSO, a population is divided into several parallel groups and then each group is independently evolved in an evolutionary process using a particle swarm optimization (PSO). This paper employs two different strategies to prevent a premature convergence and providing a better balance between the exploration and exploitation abilities of the SPSO algorithm. Firstly, it proposes that we can use a separate strategy for the inertia weight factor parameter of each group in each iteration of the SPSO algorithm. For the second strategy to provide a deep search of promising areas, a quasi-opposition-based strategy is inserted in the SPSO. Experimental results on FMSPI problems show that new employed strategies reduction lead to achieving a more effective and robust algorithm so as it can considerably improve the performance of the SPSO.
Identifying Neotectonic Motions in Germany Using Discontinuity-corrected GNSS Data
Authors: Le Nhung , Männel Benjamin , Bui Luyen K. , Schuh Harald 
Published: August 2023

Abstract
The crustal motions throughout Germany have not yet been fully understood because the research scope of previous studies mainly focuses on some active grabens. Thus, we investigate it in detail to identify the neotectonic motion characteristics and specific deformation-ongoing regions. High accuracy for monitoring and data analyses is required because the expected crustal deformation in Germany is small. We use high-precision GNSS time series processing techniques and interdisciplinary data to reflect actual motions and determine the causes of deformation. Also, an advanced technique of discontinuity correction is introduced to unify the fragments of the GNSS coordinate time series for better velocity field reliability. Our findings show that the Earth’s crust in Germany tends to increase at a maximum speed of +1.0 mm/yr. Meanwhile, local subsidence of around -0.8 mm/yr is concentrated in the river basins (e.g., the Rhine, Ems, Elbe, Northern Oder, and Danube) and extensive mining regions. The Earth’s crust here also behaves with noticeable compressions. The intra-plate motion in Germany is ∼0.8 mm/yr. A special region with an extension rate of +4.3 nstrain/yr is observed along the North-South trending Regensburg-Leipzig-Rostock shear zone. Machine Learning clusters the 3D plate velocity field into three distinct regions with increasing speeds: Northwest, East, and Southwest. Significant surface deformations are detected mainly in the Upper Rhine Graben, Eifel volcanic field, and Thuringian-Vogtland slate mountains. The harmonic motions of the Earth’s crust in Germany have an amplitude of ∼4.7 mm, in which the surface loads contribute half to this type of motion. The findings will contribute to the overall picture of neotectonics here.
Microstructural Analysis &amp; Tribology Characteristics of Magnesium Alloy AZ31B and Its Composites Using Machine Learning Modeling
Authors: Prakash Chinnam Dilip shyam , Ramanaiah N. , Subbaiah K.Venkata 
Published: August 2023

Abstract
Examining the effects of wear factors and the wear rate (WR) of magnesium (AZ31) composites is the main objective of this work. Silicon carbide (SiC) and graphene are used as reinforcing materials in the stir casting technique used to create the composite materials. In the current study, three tribological factors sliding distance, velocity, and load as well as one material factor, material type were chosen to investigate their effects on wear rate. The Taguchi technique is used to design the tests, and it has been found that load (L), followed by MT, D, and V, has the greatest impact on WR. The following are the ideal values for the influencing parameters for WR: MT = T3, L = 10 N, V = 3m/s and D = 500 m. SEM micrographs of the wear pin's surface and its by-products were used to study the wear mechanisms under the highest and lowest WR conditions. According to the SEM study, the worn surface displayed signs of oxidation, adhesion, delamination, and abrasion mechanisms. Decision trees (DT) is examples of machine learning (ML) model that were used to create an efficient prediction model that accurately predicted the output responses to the subsequent input variables. Confirmation tests were run under ideal circumstances, and the same was checked against the outcomes of DT.