bluuebunny/crossref_metadata_2025_split huggingface dataset:
Prediction of water distribution uniformity of sprinkler irrigation system based on machine learning algorithms
Authors: Elhussiny Khadiga T. , Hassan Ahmed M. , Habssa Ahmed Abu , Mokhtar Ali 
Published: November 2023

AbstractThe coefficients of uniformity Christiansen's uniformity coefficient (CU) and distribution uniformity (DU) are an important parameter for designing irrigation systems, and are an accurate measure for water lose. In this study, three machine learning algorithms Random forest (RF), extreme gradient boosting (XGB) and random forest-extreme gradient boosting (XGB-RF) were developed to predict the water distribution uniformity based on operating pressure, heights of sprinkler, discharge, nozzle diameter, wind speed, humidity, highest and lowest temperature for three different impact sprinklers (KA-4, FOX and 2520) for square and triangular system layout based on four scenarios (input combinations). The main findings were; the highest CU value was 86.7% in the square system of 2520 sprinkler under 200 kPa, 0.5 m height and 0.855 m3/h (Nozzle 2.5 mm). Meanwhile, in the triangular system, it was 87.3% under the same pressure and discharge and 1 m height. For applied machine learning, the highest values of R2 were 0.796, 0.825 and 0.929 in RF, XGB and XGB-RF respectively in the first scenario for CU. Moreover, for the DU, the highest values of R2 were 0.701, 0.479 and 0.826 in RF, XGB and XGB-RF respectively in the first scenario. The obtained results revealed that the sprinkler height had the lowest impact on modeling of the water distribution uniformity.
Proteomic analyses identify HK1 and ATP5A to be overexpressed in distant metastases of lung adenocarcinomas compared to matched primary tumors
Authors: Pasternack Helen , Polzer Mirjam , Gemoll Timo , Kümpers Christiane , Sauer Thorben , Lazar-Karsten Pamela , Hinrichs Sofie , Bohnet Sabine , Perner Sven , Dressler Franz Friedrich , Kirfel Jutta 
Published: November 2023

AbstractLung cancer is the leading cause of cancer-related deaths worldwide with lung adenocarcinoma (LUAD) being the most common type. Genomic studies of LUAD have advanced our understanding of its tumor biology and accelerated targeted therapy. However, the proteomic characteristics of LUAD are still insufficiently explored. The prognosis for lung cancer patients is still mostly determined by the stage of disease at the time of diagnosis. Focusing on late-stage metastatic LUAD with poor prognosis, we compared the proteomic profiles of primary tumors and matched distant metastases to identify relevant and potentially druggable differences. We performed high-performance liquid chromatography (HPLC) and electrospray ionization tandem mass spectrometry (ESI–MS/MS) on a total of 38 FFPE (formalin‐fixed and paraffin‐embedded) samples. Using differential expression analysis and unsupervised clustering we identified several proteins that were differentially regulated in metastases compared to matched primary tumors. Selected proteins (HK1, ATP5A, SRI and ARHGDIB) were subjected to validation by immunoblotting. Thereby, significant differential expression could be confirmed for HK1 and ATP5A, both upregulated in metastases compared to matched primary tumors. Our findings give a better understanding of tumor progression and metastatic spreads in LUAD but also demonstrate considerable inter-individual heterogeneity on the proteomic level.
Radix-4 CORDIC algorithm based low-latency and hardware efficient VLSI architecture for Nth root and Nth power computations
Authors: Changela Ankur , Kumar Yogesh , Woźniak Marcin , Shafi Jana , Ijaz Muhammad Fazal 
Published: November 2023

AbstractIn this article, a low-complexity VLSI architecture based on a radix-4 hyperbolic COordinate Rotion DIgital Computer (CORDIC) is proposed to compute the $$N{{\rm th}}$$

N
th

root and $$N{{\rm th}}$$

N
th

power of a fixed-point number. The most recent techniques use the radix-2 CORDIC algorithm to compute the root and power. The high computation latency of radix-2 CORDIC is the primary concern for the designers. $$N{{\rm th}}$$

N
th

root and $$N{{\rm th}}$$

N
th

power computations are divided into three phases, and each phase is performed by a different class of the proposed modified radix-4 CORDIC algorithms in the proposed architecture. Although radix-4 CORDIC can converge faster with fewer recurrences, it demands more hardware resources and computational steps due to its intricate angle selection logic and variable scale factor. We have employed the modified radix-4 hyperbolic vectoring (R4HV) CORDIC to compute logarithms, radix-4 linear vectoring (R4LV) to perform division, and the modified scaling-free radix-4 hyperbolic rotation (R4HR) CORDIC to compute exponential. The criteria to select the amount of rotation in R4HV CORDIC is complicated and depends on the coordinates $$X^j$$

X
j

and $$Y^j$$

Y
j

of the rotating vector. In the proposed modified R4HV CORDIC, we have derived the simple selection criteria based on the fact that the inputs to R4HV CORDIC are related. The proposed criteria only depend on the coordinate $$Y^j$$

Y
j

that reduces the hardware complexity of the R4HV CORDIC. The R4HR CORDIC shows the complex scale factor, and compensation of such scale factor necessitates the complex hardware. The complexity of R4HR CORDIC is reduced by pre-computing the scale factor for initial iterations and by employing scaling-free rotations for later iterations. Quantitative hardware analysis suggests better hardware utilization than the recent approaches. The proposed architecture is implemented on a Virtex-6 FPGA, and FPGA implementation demonstrates $$19\%$$

19
%

less hardware utilization with better error performance than the approach with the radix-2 CORDIC algorithm.
Tolerance evaluation and genetic relationship analysis among some economically important chestnut cultivars in Türkiye using drought-associated SSR and EST-SSR markers
Authors: Kara Didem , Orhan Emine 
Published: November 2023

AbstractThe aim of this study was to evaluate drought tolerance and genetic relationships among some important chestnut cultivars for Türkiye by using drought-related genomic simple sequence repeat (SSR) markers and genic expressed sequence tag-simple sequence repeat (EST-SSR) markers. Using five SSR markers, the average number of alleles (avNa), mean heterozygosity (Havp) and polymorphism information content (PIC) were determined to be 9.22, 0.395 and 0.375, respectively. In addition, using eight EST-SSR markers, the values of avNa, Havp and PIC were determined to be 7.75, 0.309 and 0.262, respectively. All microsatellite markers used in this study showed 100% polymorphism among chestnut cultivars. In UPGMA dendrograms obtained with both SSR and EST-SSR markers, the Erfelek and Hacıömer chestnut cultivars were determined to be the most similar cultivars. Some assessments are discussed regarding drought tolerance for specific alleles obtained from the EST-SSR markers GOT045, GOT021, GOT004, FIR094 and VIT033 in chestnut cultivars. Some preliminary results regarding drought tolerance in chestnut cultivars were obtained in our study with the help of these markers. Our study also characterized the genetic relationships among chestnut cultivars of great importance using drought-related character-specific markers.
Discordant pair analysis for sample efficient model evaluation
Authors: Musgrove Donald , Radtke Andrew , Haddad Tarek 
Published: November 2023

AbstractWe present a new technique for assessing the effectiveness of a classification algorithm using discordant pair analysis. This method utilizes a known performance baseline algorithm and a large unlabeled dataset with an assumed class distribution to obtain overall performance estimates by only assessing the subset of examples that the algorithms classify discordantly. Our approach offers an efficient way to evaluate the performance of an algorithm that minimizes the human adjudications needed while also maintaining precision in the evaluation and in some cases improving the evaluation quality by reducing human adjudication errors. This approach is a computationally efficient alternative to the traditional exhaustive method of performance evaluation and has the potential to improve the accuracy of performance estimates. Simulation studies show that the discordant pair method reduces the number of adjudications by over 90%, while maintaining the same level of sensitivity and specificity.
Long-term pulmonary and neurodevelopmental impairment in a fetal growth restriction rabbit model
Authors: Valenzuela Ignacio , Regin Yannick , Gie Andre , Basurto David , Emam Doaa , Scuglia Marianna , Zapletalova Katerina , Greyling Marnel , Deprest Jan , van der Merwe Johannes 
Published: November 2023

AbstractFetal growth restriction (FGR) remains one of the main obstetrical problems worldwide, with consequences beyond perinatal life. Animal models with developmental and structural similarities to the human are essential to understand FGR long-term consequences and design novel therapeutic strategies aimed at preventing or ameliorating them. Herein, we described the long-term consequences of FGR in pulmonary function, structure, and gene expression, and characterized neurodevelopmental sequelae up to preadolescence in a rabbit model. FGR was induced at gestational day 25 by surgically reducing placental blood supply in one uterine horn, leaving the contralateral horn as internal control. Neonatal rabbits born near term were assigned to foster care in mixed groups until postnatal day (PND) 21. At that time, one group underwent pulmonary biomechanical testing followed by lung morphometry and gene expression analysis. A second group underwent longitudinal neurobehavioral assessment until PND 60 followed by brain harvesting for multiregional oligodendrocyte and microglia quantification. FGR was associated with impaired pulmonary function and lung development at PND 21. FGR rabbits had higher respiratory resistance and altered parenchymal biomechanical properties in the lungs. FGR lungs presented thicker alveolar septal walls and reduced alveolar space. Furthermore, the airway smooth muscle content was increased, and the tunica media of the intra-acinar pulmonary arteries was thicker. In addition, FGR was associated with anxiety-like behavior, impaired memory and attention, and lower oligodendrocyte proportion in the frontal cortex and white matter. In conclusion, we documented and characterized the detrimental pulmonary function and structural changes after FGR, independent of prematurity, and beyond the neonatal period for the first time in the rabbit model, and describe the oligodendrocyte alteration in pre-adolescent rabbit brains. This characterization will allow researchers to develop and test therapies to treat FGR and prevent its sequelae.
Discriminative machine learning for maximal representative subsampling
Authors: Hauptmann Tony , Fellenz Sophie , Nathan Laksan , Tüscher Oliver , Kramer Stefan 
Published: November 2023

AbstractBiased population samples pose a prevalent problem in the social sciences. Therefore, we present two novel methods that are based on positive-unlabeled learning to mitigate bias. Both methods leverage auxiliary information from a representative data set and train machine learning classifiers to determine the sample weights. The first method, named maximum representative subsampling (MRS), uses a classifier to iteratively remove instances, by assigning a sample weight of 0, from the biased data set until it aligns with the representative one. The second method is a variant of MRS – Soft-MRS – that iteratively adapts sample weights instead of removing samples completely. To assess the effectiveness of our approach, we induced artificial bias in a public census data set and examined the corrected estimates. We compare the performance of our methods against existing techniques, evaluating the ability of sample weights created with Soft-MRS or MRS to minimize differences and improve downstream classification tasks. Lastly, we demonstrate the applicability of the proposed methods in a real-world study of resilience research, exploring the influence of resilience on voting behavior. Through our work, we address the issue of bias in social science, amongst others, and provide a versatile methodology for bias reduction based on machine learning. Based on our experiments, we recommend to use MRS for downstream classification tasks and Soft-MRS for downstream tasks where the relative bias of the dependent variable is relevant.
The developmental process of suicidal ideation among adolescents: social and psychological impact from a nation-wide survey
Authors: Tintori Antonio , Pompili Maurizio , Ciancimino Giulia , Corsetti Gianni , Cerbara Loredana 
Published: November 2023

AbstractSuicidal ideation is a multifactorial phenomenon that is increasingly prevalent among adolescents, especially following the impact of Covid 19 pandemic on their mental health. Its analysis necessitates an interdisciplinary approach that simultaneously considers sociological and psychological perspectives, especially looking at the role of interpersonal relationships and structural inequalities. The present study, based on a face-to-face survey conducted with a representative sample of 4288 adolescents, aims to identify the factors that most differentiate individuals with and without suicidal ideation, proposing a descriptive model of development process of this phenomenon with reference to the Italian context. We analysed variables related to socio-demographic status, relational status, social interactions, and psychological well-being using multiple correspondence analysis and logistic regression models. The results provide evidence for the existence of a direct association between negative psychological status and suicidal ideation but clarify that the psychological aspects are associated with sociodemographic characteristics and have their origins in the social sphere.
Attentional, emotional, and behavioral response toward spiders, scorpions, crabs, and snakes provides no evidence for generalized fear between spiders and scorpions
Authors: Landová E. , Štolhoferová I. , Vobrubová B. , Polák J. , Sedláčková K. , Janovcová M. , Rádlová S. , Frynta D. 
Published: November 2023

AbstractSpiders are among the animals evoking the highest fear and disgust and such a complex response might have been formed throughout human evolution. Ironically, most spiders do not present a serious threat, so the evolutionary explanation remains questionable. We suggest that other chelicerates, such as scorpions, have been potentially important in the formation and fixation of the spider-like category. In this eye-tracking study, we focused on the attentional, behavioral, and emotional response to images of spiders, scorpions, snakes, and crabs used as task-irrelevant distractors. Results show that spider-fearful subjects were selectively distracted by images of spiders and crabs. Interestingly, these stimuli were not rated as eliciting high fear contrary to the other animals. We hypothesize that spider-fearful participants might have mistaken crabs for spiders based on their shared physical characteristics. In contrast, subjects with no fear of spiders were the most distracted by snakes and scorpions which supports the view that scorpions as well as snakes are prioritized evolutionary relevant stimuli. We also found that the reaction time increased systematically with increasing subjective fear of spiders only when using spiders (and crabs to some extent) but not snakes and scorpions as distractors. The maximal pupil response covered not only the attentional and cognitive response but was also tightly correlated with the fear ratings of the picture stimuli. However, participants’ fear of spiders did not affect individual reactions to scorpions measured by the maximal pupil response. We conclude that scorpions are evolutionary fear-relevant stimuli, however, the generalization between scorpions and spiders was not supported in spider-fearful participants. This result might be important for a better understanding of the evolution of spider phobia.
Explaining COVID-19 related mortality disparities in American Indians and Alaska Natives
Authors: Slutske Wendy S. , Conner Karen L. , Kirsch Julie A. , Smith Stevens S. , Piasecki Thomas M. , Johnson Adrienne L. , McCarthy Danielle E. , Nez Henderson Patricia , Fiore Michael C. 
Published: November 2023

AbstractAmerican Indian and Alaska Native (AI/AN) individuals are more likely to die with COVID-19 than other groups, but there is limited empirical evidence to explain the cause of this inequity. The objective of this study was to determine whether medical comorbidities, area socioeconomic deprivation, or access to treatment can explain the greater COVID-19 related mortality among AI/AN individuals. The design was a retrospective cohort study of harmonized electronic health record data of all inpatients with COVID-19 from 21 United States health systems from February 2020 through January 2022. The mortality of AI/AN inpatients was compared to all Non-Hispanic White (NHW) inpatients and to a matched subsample of NHW inpatients. AI/AN inpatients were more likely to die during their hospitalization (13.2% versus 7.1%; odds ratio [OR] = 1.98, 95% confidence interval [CI] = 1.48, 2.65) than their matched NHW counterparts. After adjusting for comorbidities, area social deprivation, and access to treatment, the association between ethnicity and mortality was substantially reduced (OR 1.59, 95% CI 1.15, 2.22). The significant residual relation between AI/AN versus NHW status and mortality indicate that there are other important unmeasured factors that contribute to this inequity. This will be an important direction for future research.
Improved electrochemical performance of bio-derived plasticized starch/ reduced graphene oxide/ molybdenum disulfide ternary nanocomposite for flexible energy storage applications
Authors: Mahmud Eashika , Islam Muhammad Rakibul 
Published: November 2023

AbstractA ternary nanocomposite of plasticized starch (PS), reduced graphene oxide (rGO), and molybdenum disulfide (MoS2) was prepared via a solution casting process, with MoS2 concentrations ranging from 0.25 to 1.00 wt%. The structural, surface morphological, optical, and electrochemical properties of the nanocomposites were studied. FTIR analysis reveals the formation of new chemical bonds between PS, rGO, and MoS2, indicating strong interactions among them. The XRD analysis showed a reduction in the crystallinity of the nanocomposite from 40 to 21% due to the incorporation of nanofiller. FESEM micrograph showed an increment of the surface roughness due to the incorporation of rGO-MoS2 layers. UV–vis spectroscopy demonstrated a reduction of optical bandgap from 4.71 to 2.90 eV, resulting from enhanced charge transfer between the layers and defect states due to the addition of nanofillers. The incorporation of MoS2 increase the specific capacitance of the PS from 2.78 to 124.98 F g−1 at a current density of 0.10 mA g−1. The EIS analysis revealed that the nanofiller significantly reduces the charge transfer resistance from 4574 to 0 Ω, facilitating the ion transportation between the layers. The PS/rGO/MoS2 nanocomposite also exhibited excellent stability, retaining about 85% of its capacitance up to 10,000 charging-discharging cycles. These biocompatible polymer-based nanocomposites with improved electrochemical performance synthesized from an easy and economical route may offer a promising direction to fabricate a nature-friendly electrode material for energy storage applications.
GPCR molecular dynamics forecasting using recurrent neural networks
Authors: López-Correa Juan Manuel , König Caroline , Vellido Alfredo 
Published: November 2023

AbstractG protein-coupled receptors (GPCRs) are a large superfamily of cell membrane proteins that play an important physiological role as transmitters of extracellular signals. Signal transmission through the cell membrane depends on conformational changes in the transmembrane region of the receptor, which makes the investigation of the dynamics in these regions particularly relevant. Molecular dynamics (MD) simulations provide a wealth of data about the structure, dynamics, and physiological function of biological macromolecules by modelling the interactions between their atomic constituents. In this study, a Recurrent and Convolutional Neural Network (RNN) model, namely Long Short-Term Memory (LSTM), is used to predict the dynamics of two GPCR states and three specific simulations of each one, through their activation path and focussing on specific receptor regions. Active and inactive states of the GPCRs are analysed in six scenarios involving APO, Full Agonist (BI 167107) and Partial Inverse Agonist (carazolol) of the receptor. Four Machine Learning models with increasing complexity in terms of neural network architecture are evaluated, and their results discussed. The best method achieves an overall RMSD lower than 0.139 Å and the transmembrane helices are the regions showing the minimum prediction errors and minimum relative movements of the protein.
A novel RF-CEEMD-LSTM model for predicting water pollution
Authors: Ruan Jinlou , Cui Yang , Song Yuchen , Mao Yawei 
Published: November 2023

AbstractAccurate water pollution prediction is an important basis for water environment prevention and control. The uncertainty of input variables and the nonstationary and nonlinear characteristics of water pollution series hinder the accuracy and reliability of water pollution prediction. This study proposed a novel water pollution prediction model (RF-CEEMD-LSTM) to improve the performance of water pollution prediction by combining advantages of the random forest (RF) and Long short-term memory (LSTM) models and Complementary ensemble empirical mode decomposition (CEEMD). The experimental results based on measured data show that the proposed RF-CEEMD-LSTM model can accurately predict water pollution trends, with a mean ab-solute percentage error (MAPE) of less than 8%. The RMSE of the RF-CEEMD-LSTM model is reduced by 62.6%, 39.9%, and 15.5% compared to those of the LSTM, RF-LSTM, and CEEMD-LSTM models, respectively, proving that the proposed method has good advantages in predicting non-linear and nonstationary water pollution sequences. The driving force analysis results showed that TN has the most significant impact on water pollution prediction. The research results could provide references for identifying and explaining water pollution variables and improving water pollution prediction method.