bluuebunny/crossref_metadata_2025_split huggingface dataset:
Prioritising quality: investigating the influence of image quality on forensic facial comparison
Authors: Bacci Nicholas , Briers Nanette , Steyn Maryna 
Published: February 2024

AbstractMorphological analysis in forensic facial comparison was recently validated for judicial use. However, no image quality assurance systems exist for this purpose, despite image triage being considered the best practice approach. Hence, this study aimed at testing a semi-quantitative scoring method to assess image quality and investigated facial image resolution and lighting quality quantitatively in a context of forensic facial comparison. For this purpose, 400 facial comparison photographic and CCTV image pools developed from the Wits Face Database were used. These facial images were analysed in prior studies that investigated the validity of morphological analysis. A semi-quantitative image quality scoring system was adapted and tested on the above sample and compared across correct and incorrect matches obtained as part of previous studies using a logistic regression model. In addition, facial images were cropped to the closest pixel comprising the face, head and neck areas; then, a face-to-image pixel proportion was calculated as an estimator of resolution quality; and pixel exposure qualities were obtained to be compared to facial comparison outcomes. Ideal and high image quality scores were related to correctness of matches, while low-quality scores were related to incorrect matches. High pixel proportions were related to true matches and low exposure was related to false positives, while high exposure was related to false negatives. These results suggest that an easy method for image triage could be employed by scoring image quality. Quantitative measures should be investigated further for thresholding quality suitability for confidence of facial comparisons.
A novel mouse model for familial hypocalciuric hypercalcemia (FHH1) reveals PTH-dependent and independent CaSR defects
Authors: Küng Catharina J. , Daryadel Arezoo , Fuente Rocio , Haykir Betül , de Angelis Martin Hrabĕ , Hernando Nati , Rubio-Aliaga Isabel , Wagner Carsten A. 
Published: February 2024

AbstractThe Calcium-sensing receptor (CaSR) senses extracellular calcium, regulates parathyroid hormone (PTH) secretion, and has additional functions in various organs related to systemic and local calcium and mineral homeostasis. Familial hypocalciuric hypercalcemia type I (FHH1) is caused by heterozygous loss-of-function mutations in the CaSR gene, and is characterized by the combination of hypercalcemia, hypocalciuria, normal to elevated PTH, and facultatively hypermagnesemia and mild bone mineralization defects. To date, only heterozygous Casr null mice have been available as model for FHH1. Here we present a novel mouse FHH1 model identified in a large ENU-screen that carries an c.2579 T > A (p.Ile859Asn) variant in the Casr gene (CasrBCH002 mice). In order to dissect direct effects of the genetic variant from PTH-dependent effects, we crossed CasrBCH002 mice with PTH deficient mice. Heterozygous CasrBCH002 mice were fertile, had normal growth and body weight, were hypercalcemic and hypermagnesemic with inappropriately normal PTH levels and urinary calcium excretion replicating some features of FHH1. Hypercalcemia and hypermagnesemia were independent from PTH and correlated with higher expression of claudin 16 and 19 in kidneys. Likewise, reduced expression of the renal TRPM6 channel in CasrBCH002 mice was not dependent on PTH. In bone, mutations in Casr rescued the bone phenotype observed in Pth null mice by increasing osteoclast numbers and improving the columnar pattern of chondrocytes in the growth zone. In summary, CasrBCH002 mice represent a new model to study FHH1 and our results indicate that only a part of the phenotype is driven by PTH.
Unlocking allelic variation in circadian clock genes to develop environmentally robust and productive crops
Authors: Dwivedi Sangam Lal , Quiroz Luis Felipe , Spillane Charles , Wu Rongling , Mattoo Autar K. , Ortiz Rodomiro 
Published: February 2024

Abstract
Main conclusion
Molecular mechanisms of biological rhythms provide opportunities to harness functional allelic diversity in core (and trait- or stress-responsive) oscillator networks to develop more climate-resilient and productive germplasm.

Abstract
The circadian clock senses light and temperature in day–night cycles to drive biological rhythms. The clock integrates endogenous signals and exogenous stimuli to coordinate diverse physiological processes. Advances in high-throughput non-invasive assays, use of forward- and inverse-genetic approaches, and powerful algorithms are allowing quantitation of variation and detection of genes associated with circadian dynamics. Circadian rhythms and phytohormone pathways in response to endogenous and exogenous cues have been well documented the model plant Arabidopsis. Novel allelic variation associated with circadian rhythms facilitates adaptation and range expansion, and may provide additional opportunity to tailor climate-resilient crops. The circadian phase and period can determine adaptation to environments, while the robustness in the circadian amplitude can enhance resilience to environmental changes. Circadian rhythms in plants are tightly controlled by multiple and interlocked transcriptional–translational feedback loops involving morning (CCA1, LHY), mid-day (PRR9, PRR7, PRR5), and evening (TOC1, ELF3, ELF4, LUX) genes that maintain the plant circadian clock ticking. Significant progress has been made to unravel the functions of circadian rhythms and clock genes that regulate traits, via interaction with phytohormones and trait-responsive genes, in diverse crops. Altered circadian rhythms and clock genes may contribute to hybrid vigor as shown in Arabidopsis, maize, and rice. Modifying circadian rhythms via transgenesis or genome-editing may provide additional opportunities to develop crops with better buffering capacity to environmental stresses. Models that involve clock gene‒phytohormone‒trait interactions can provide novel insights to orchestrate circadian rhythms and modulate clock genes to facilitate breeding of all season crops.
Comparing of brain tumor diagnosis with developed local binary patterns methods
Authors: Gül Mehmet , Kaya Yılmaz 
Published: February 2024

AbstractA brain tumor is one of the most lethal diseases that can affect human health and cause death. Invasive biopsy techniques are one of the most common methods of identifying brain tumor disease. As a result of this procedure, bleeding may occur during the procedure, which could harm some brain functions. Consequently, this invasive biopsy process may be extremely dangerous. To overcome such a dangerous process, medical imaging techniques, which can be used by experts in the field, can be used to conduct a thorough examination and obtain detailed information about the type and stage of the disease. Within the scope of the study, the dataset was examined, and this dataset consisted of brain images with tumors and brain images of normal patients. Numerous studies on medical images were conducted and obtained with high accuracy within the hybrid model algorithms. The dataset's images were enhanced using three distinct local binary patterns (LBP) algorithms in the developed model within the scope of the study: the LBP, step-LBP (nLBP), and angle-LBP (αLBP) algorithms. In the second stage, classification algorithms were used to evaluate the results from the LBP, nLBP and αLBP algorithms. Among the 11 classification algorithms used, four different classification algorithms were chosen as a consequence of the experimental process since they produced the best results. The classification algorithms with the best outcomes are random forest (RF), optimized forest (OF), rotation forest (RF), and instance-based learner (IBk) algorithms, respectively. With the developed model, an extremely high success rate of 99.12% was achieved within the IBk algorithm. Consequently, the clinical service can use the developed method to diagnose tumor-based medical images.
Prostate cancer grading framework based on deep transfer learning and Aquila optimizer
Authors: Balaha Hossam Magdy , Shaban Ahmed Osama , El-Gendy Eman M. , Saafan Mahmoud M. 
Published: February 2024

AbstractProstate cancer is the one of the most dominant cancer among males. It represents one of the leading cancer death causes worldwide. Due to the current evolution of artificial intelligence in medical imaging, deep learning has been successfully applied in diseases diagnosis. However, most of the recent studies in prostate cancer classification suffers from either low accuracy or lack of data. Therefore, the present work introduces a hybrid framework for early and accurate classification and segmentation of prostate cancer using deep learning. The proposed framework consists of two stages, namely classification stage and segmentation stage. In the classification stage, 8 pretrained convolutional neural networks were fine-tuned using Aquila optimizer and used to classify patients of prostate cancer from normal ones. If the patient is diagnosed with prostate cancer, segmenting the cancerous spot from the overall image using U-Net can help in accurate diagnosis, and here comes the importance of the segmentation stage. The proposed framework is trained on 3 different datasets in order to generalize the framework. The best reported classification accuracies of the proposed framework are 88.91% using MobileNet for the “ISUP Grade-wise Prostate Cancer” dataset and 100% using MobileNet and ResNet152 for the “Transverse Plane Prostate Dataset” dataset with precisions 89.22% and 100%, respectively. U-Net model gives an average segmentation accuracy and AUC of 98.46% and 0.9778, respectively, using the “PANDA: Resized Train Data (512 × 512)” dataset. The results give an indicator of the acceptable performance of the proposed framework.
Eco-friendly spark-generated CoxOy nanoparticle-modified graphite screen-printed sensing surfaces for the determination of H2O2 in energy drinks
Authors: Siampani Maria , Lazanas Alexandros Ch. , Spyrou Konstantinos , Prodromidis Mamas I. 
Published: February 2024

Abstract The modification of graphite screen-printed electrodes (SPEs) is reported using an eco-friendly and extremely fast method based on the direct cobalt pin electrode-to-SPE spark discharge at ambient conditions. This approach does not utilize any liquids or chemical templates, does not produce any waste, and allows the in-situ generation of CoxOy nanoparticles onto the electrode surface and the development of efficient electrocatalytic sensing surfaces for the determination of H2O2. Co-spark SPEs were characterized using scanning electron microscopy, energy-dispersive X-ray spectroscopy and x-ray photoelectron spectroscopy (XPS), revealing the formation of surface confined CoxOy nanoparticles and the diverse oxidation states of cobalt species. Co-spark SPEs were also characterized with cyclic voltammetry and electrochemical impedance spectroscopy. Redox transitions of the surface confined electrocatalysts are demonstrated by electrochemical polarization studies, showing the formation of different oxides (CoxOy), varying the XPS results. Amperometric measurements at 0.3 V vs. Ag/AgCl revealed a linear relationship between the current response and the concentration of H2O2 over the range 1 − 102 μM, achieving a limit of detection (3σ/m) of 0.6 μM. The interference effect of various electroactive species was effectively addressed by employing dual measurements in the absence and presence of the enzyme catalase. The analytical utility of the method was evaluated in antioxidant rich real-world samples, such as energy drinks, demonstrating sufficient recovery.
Graphical Abstract
Dosimetry in cranial photobiomodulation therapy: effect of cranial thickness and bone density
Authors: Castaño-Castaño Sergio , Zorzo Candela , Martínez-Esteban Juan Á. , Arias Jorge L. 
Published: February 2024

Abstract
This research aims to examine the influence of human skull bone thickness and density on light penetration in PBM therapy across different wavelengths, focusing on how these bone characteristics affect the absorption of therapeutic light. Analyses explored the effect of skull bone density and thickness on light penetration in PBM, specifically using Low-Level Laser Therapy (LLLT) for efficacy prediction. Measurements of bone thickness and density were taken using precise tools. This approach emphasizes LLLT's significance in enhancing PBM outcomes by assessing how bone characteristics influence light penetration. The study revealed no significant correlation between skull bone density and thickness and light penetration capability in photobiomodulation (PBM) therapy, challenging initial expectations. Wavelengths of 405 nm and 665 nm showed stronger correlations with bone density, suggesting a significant yet weak impact. Conversely, wavelengths of 532 nm, 785 nm, 810 nm, 830 nm, 980 nm, and 1064 nm showed low correlations, indicating minimal impact from bone density variations. However, data variability (R2 < 0.4) suggests that neither density nor thickness robustly predicts light power traversing the bone, indicating penetration capability might be more influenced by bone thickness at certain wavelengths. The study finds that the effectiveness of photobiomodulation (PBM) therapy with bone isn't just based on bone density and thickness but involves a complex interplay of factors. These include the bone's chemical and mineral composition, light's wavelength and energy dose, treatment duration and frequency, and the precise location where light is applied on the skull.
3D Genome Reconstruction from Partially Phased Hi-C Data
Authors: Cifuentes Diego , Draisma Jan , Henriksson Oskar , Korchmaros Annachiara , Kubjas Kaie 
Published: February 2024

AbstractThe 3-dimensional (3D) structure of the genome is of significant importance for many cellular processes. In this paper, we study the problem of reconstructing the 3D structure of chromosomes from Hi-C data of diploid organisms, which poses additional challenges compared to the better-studied haploid setting. With the help of techniques from algebraic geometry, we prove that a small amount of phased data is sufficient to ensure finite identifiability, both for noiseless and noisy data. In the light of these results, we propose a new 3D reconstruction method based on semidefinite programming, paired with numerical algebraic geometry and local optimization. The performance of this method is tested on several simulated datasets under different noise levels and with different amounts of phased data. We also apply it to a real dataset from mouse X chromosomes, and we are then able to recover previously known structural features.
Political Business Cycles in Türkiye: A Chaotic Perspective
Authors: Sonustun Fulya Ozaksoy , Bildirici Melike 
Published: February 2024

Abstract
This paper has the goal of analyzing the chaotic structure of political business cycles in Türkiye for the period from 1986 March to 2020 January. To investigate the existence of uncertainty, the chaotic dynamics of the analyzed variables was examined using the Lyapunov exponent, Shannon entropy, and Hurst entropy. The Fourier VAR model was applied to determine the effects of unemployment, inflation, economic growth, and the minimum wage on voting behavior in Türkiye. The Granger Causality approach was also used to assess the direction of the causal relationship between macroeconomic factors and political party groups. Granger causality analysis findings of this paper reveal a bi-directional Granger causality link between the center-right party and economic growth, inflation, unemployment, and the minimum wage. Furthermore, there is also a uni-directional causality relation from inflation, unemployment, and economic growth to the center-left party and a bi-directional causality relation between the minimum wage and the center-left party. In this regard, one of the most remarkable evaluations of this paper is that the sensitivity findings of party groups to macroeconomic factors in Türkiye do not align with the core assumptions of the Partisan Theory concerning the right-left party division. Therefore, contrary to the related theory, the analysis results indicate that it is not eligible to make a sharp and distinct distinction between right-wing and left-wing parties in Türkiye. Additionally, Lyapunov exponent, Shannon entropy, and Hurst entropy findings point to the existence of chaotic structure and uncertainty in political cyclical fluctuations in Türkiye. Accordingly, it is figured out that the existence of chaotic structure and uncertainty in party groups and analyzed variables allows only short-term and not long-term forecasting.
Improving glacio-hydrological model calibration and model performance in cold regions using satellite snow cover data
Authors: Mohammadi Babak , Gao Hongkai , Pilesjö Petter , Duan Zheng 
Published: February 2024

AbstractHydrological modeling realism is a central research question in hydrological studies. However, it is still a common practice to calibrate hydrological models using streamflow as a single hydrological variable, which can lead to large parameter uncertainty in hydrological simulations. To address this issue, this study employed a multi-variable calibration framework to reduce parameter uncertainty in a glacierized catchment. The current study employed multi-variable calibration using three different calibration schemes to calibrate a glacio-hydrological model (namely the FLEXG) in northern Sweden. The schemes included using only gauged streamflow data (scheme 1), using satellite snow cover area (SCA) derived from MODIS data (scheme 2), and using both gauged streamflow data and satellite SCA data as references for calibration (scheme 3) of the FLEXG model. This study integrated the objective functions of satellite-derived SCA and gauged streamflow into one criterion for the FLEXG model calibration using a weight-based approach. Our results showed that calibrating the FLEXG model based on solely satellite SCA data (from MODIS) produced an accurate simulation of SCA but poor simulation of streamflow. In contrast, calibrating the FLEXG model based on the measured streamflow data resulted in minimum error for streamflow simulation but high error for SCA simulation. The promising results were achieved for glacio-hydrological simulation with acceptable accuracy for simulation of both streamflow and SCA, when both streamflow and SCA data were used for calibration of FLEXG. Therefore, multi-variable calibration in a glacierized basin could provide more realistic hydrological modeling in terms of multiple glacio-hydrological variables.
Calibration and evaluation of the FAO AquaCrop model for canola (Brassica napus) under full and deficit irrigation in a semi-arid region
Authors: Khorsand Afshin , Dehghanisanij Hossein , Heris Abolfazl Majnooni , Asgarzadeh Hossein , Rezaverdinejad Vahid 
Published: February 2024

AbstractField experiments were conducted in the cropping years 2012 and 2013 with four irrigation treatments, I1, I2, I3, and I4, respectively, corresponding to full irrigation, 20, 35, and 50% less than the crop water requirement in a semi-arid region of Iran. Data were used for calibration (2012) and validation (2013) of biomass (B), grain yield (GY), soil water content (SWC), canopy cover (CC), and evapotranspiration (ET) of canola by the AquaCrop model. Model coefficients were calibrated for I1 treatment in 2012, and the calibrated parameters were used for other treatments in both years. Based on the results, the NRMSE value for SWC ranged from 10 to 20%. Simulated and measured SWC for the four water treatments showed that AquaCrop has good accuracy. Overall, the results of the model in simulating SWC for both years were satisfactory. AquaCrop provided overestimation in the calibration phase of ET and underestimation in the ET validation phase. The results also showed that AquaCrop is overestimated in the simulation and prediction of CC and is less satisfactory under water stress. The model evaluation showed that AquaCrop can simulate B and GY with high accuracy. In general, AquaCrop was identified as a good tool for studying irrigation management, crop B and GY, CC, SWC, and other factors affecting the canola growth in the studied fields and climate.
Targeting glutamine metabolism exhibits anti-tumor effects in thyroid cancer
Authors: Zhang G.-Q. , Xi C. , Ju N.-T. , Shen C.-T. , Qiu Z.-L. , Song H.-J. , Luo Q.-Y. 
Published: February 2024

Abstract
Background
Effective treatment for patients with advanced thyroid cancer is lacking. Metabolism reprogramming is required for cancer to undergo oncogenic transformation and rapid tumorigenic growth. Glutamine is frequently used by cancer cells for active bioenergetic and biosynthetic needs. This study aims to investigate whether targeting glutamine metabolism is a promising therapeutic strategy for thyroid cancer.

Methods
The expression of glutaminase (GLS) and glutamate dehydrogenase (GDH) in thyroid cancer tissues was evaluated by immunohistochemistry, and glutamine metabolism-related genes were assessed using real time-qPCR and western blotting. The effects of glutamine metabolism inhibitor 6-diazo-5-oxo-l-norleucine (DON) on thyroid cancer cells were determined by CCK-8, clone formation assay, Edu incorporation assay, flow cytometry, and Transwell assay. The mechanistic study was performed by real time-qPCR, western blotting, Seahorse assay, and gas chromatography–mass spectrometer assay. The effect of DON prodrug (JHU-083) on thyroid cancer in vivo was assessed using xenograft tumor models in BALB/c nude mice.

Results
GLS and GDH were over-expressed in thyroid cancer tissues, and GLS expression was positively associated with lymph-node metastasis and TNM stage. The growth of thyroid cancer cells was significantly inhibited when cultured in glutamine-free medium. Targeting glutamine metabolism with DON inhibited the proliferation of thyroid cancer cells. DON treatment did not promote apoptosis, but increased the proportion of cells in the S phase, accompanied by the decreased expression of cyclin-dependent kinase 2 and cyclin A. DON treatment also significantly inhibited the migration and invasion of thyroid cancer cells by reducing the expression of N-cadherin, Vimentin, matrix metalloproteinase-2, and matrix metalloproteinase-9. Non-essential amino acids, including proline, alanine, aspartate, asparagine, and glycine, were reduced in thyroid cancer cells treated with DON, which could explain the decrease of proteins involved in migration, invasion, and cell cycle. The efficacy and safety of DON prodrug (JHU-083) for thyroid cancer treatment were verified in a mouse model. In addition to suppressing the proliferation and metastasis potential of thyroid cancer in vivo, enhanced innate immune response was also observed in JHU-083-treated xenograft tumors as a result of decreased expression of cluster of differentiation 47 and programmed cell death ligand 1.

Conclusions
Thyroid cancer exhibited enhanced glutamine metabolism, as evidenced by the glutamine dependence of thyroid cancer cells and high expression of multiple glutamine metabolism-related genes. Targeting glutamine metabolism with DON prodrug could be a promising therapeutic option for advanced thyroid cancer.