bluuebunny/crossref_metadata_2025_split huggingface dataset:
FracAtlas: A Dataset for Fracture Classification, Localization and Segmentation of Musculoskeletal Radiographs
Authors: Abedeen Iftekharul , Rahman Md. Ashiqur , Prottyasha Fatema Zohra , Ahmed Tasnim , Chowdhury Tareque Mohmud , Shatabda Swakkhar 
Published: August 2023

AbstractDigital radiography is one of the most common and cost-effective standards for the diagnosis of bone fractures. For such diagnoses expert intervention is required which is time-consuming and demands rigorous training. With the recent growth of computer vision algorithms, there is a surge of interest in computer-aided diagnosis. The development of algorithms demands large datasets with proper annotations. Existing X-Ray datasets are either small or lack proper annotation, which hinders the development of machine-learning algorithms and evaluation of the relative performance of algorithms for classification, localization, and segmentation. We present FracAtlas, a new dataset of X-Ray scans curated from the images collected from 3 major hospitals in Bangladesh. Our dataset includes 4,083 images that have been manually annotated for bone fracture classification, localization, and segmentation with the help of 2 expert radiologists and an orthopedist using the open-source labeling platform, makesense.ai. There are 717 images with 922 instances of fractures. Each of the fracture instances has its own mask and bounding box, whereas the scans also have global labels for classification tasks. We believe the dataset will be a valuable resource for researchers interested in developing and evaluating machine learning algorithms for bone fracture diagnosis.
PP2A-B55alpha controls keratinocyte adhesion through dephosphorylation of the Desmoplakin C-terminus
Authors: Perl Abbey L. , Koetsier Jennifer L. , Green Kathleen J. 
Published: August 2023

AbstractCritical for the maintenance of epidermal integrity and function are attachments between intermediate filaments (IF) and intercellular junctions called desmosomes. The desmosomal cytoplasmic plaque protein desmoplakin (DP) is essential for anchoring IF to the junction. DP-IF interactions are regulated by a phospho-regulatory motif within the DP C-terminus controlling keratinocyte intercellular adhesion. Here we identify the protein phosphatase 2A (PP2A)-B55α holoenzyme as the major serine/threonine phosphatase regulating DP’s C-terminus and consequent intercellular adhesion. Using a combination of chemical and genetic approaches, we show that the PP2A-B55α holoenzyme interacts with DP at intercellular membranes in 2D- and 3D- epidermal models and human skin samples. Our experiments demonstrate that PP2A-B55α regulates the phosphorylation status of junctional DP and is required for maintaining strong desmosome-mediated intercellular adhesion. These data identify PP2A-B55α as part of a regulatory module capable of tuning intercellular adhesion strength and a candidate disease target in desmosome-related disorders of the skin and heart.
Spatial predictions and uncertainties of forest carbon fluxes for carbon accounting
Authors: Araza Arnan , de Bruin Sytze , Hein Lars , Herold Martin 
Published: August 2023

AbstractCountries have pledged to different national and international environmental agreements, most prominently the climate change mitigation targets of the Paris Agreement. Accounting for carbon stocks and flows (fluxes) is essential for countries that have recently adopted the United Nations System of Environmental-Economic Accounting - ecosystem accounting framework (UNSEEA) as a global statistical standard. In this paper, we analyze how spatial carbon fluxes can be used in support of the UNSEEA carbon accounts in five case countries with available in-situ data. Using global multi-date biomass map products and other remotely sensed data, we mapped the 2010–2018 carbon fluxes in Brazil, the Netherlands, the Philippines, Sweden and the USA using National Forest Inventory (NFI) and local biomass maps from airborne LiDAR as reference data. We identified areas that are unsupported by the reference data within environmental feature space (6–47% of vegetated country area); cross-validated an ensemble machine learning (RMSE=9–39 Mg C $$\textrm{ha}^{-1}$$

ha

-
1

and $$\textrm{R}^{2}$$

R
2

=0.16–0.71) used to map carbon fluxes with prediction intervals; and assessed spatially correlated residuals (<5 km) before aggregating carbon fluxes from 1-ha pixels to UNSEEA forest classes. The resulting carbon accounting tables revealed the net carbon sequestration in natural broadleaved forests. Both in plantations and in other woody vegetation ecosystems, emissions exceeded sequestration. Overall, our estimates align with FAO-Forest Resource Assessment and national studies with the largest deviations in Brazil and USA. These two countries used highly clustered reference data, where clustering caused uncertainty given the need to extrapolate to under-sampled areas. We finally provide recommendations to mitigate the effect of under-sampling and to better account for the uncertainties once carbon stocks and flows need to be aggregated in relatively smaller countries. These actions are timely given the global initiatives that aim to upscale UNSEEA carbon accounting.
Hyper-fidelity depletion with discrete motion for pebble bed reactors
Authors: Robert Yves , Siaraferas Tatiana , Fratoni Massimiliano 
Published: August 2023

AbstractHyper-fidelity (HxF) depletion of pebble bed reactors (PBRs) is the capability to model depletion for every pebble while accounting for motion through the core. Previous HxF work demonstrated feasibility to deplete hundreds of thousands of stationary pebbles concurrently within reasonable timeframes. This work illustrates the second step towards HxF, coupling depletion with a discrete motion scheme. The model assumes an ordered bed with pebbles occupying fixed positions. Motion is simplified as discrete since pebbles move in straight lines from one set position to another. The methodology was implemented in Serpent 2, combined with its transport and depletion capabilities. Ad-hoc routines were developed ensuring compatibility with domain decomposition and pebble recirculation after each pass based on discharge criteria and fresh pebble insertion. Capabilities of HxF with discrete motion are demonstrated using a full-scale high-temperature gas-cooled reactor model. Specifically, an approach to equilibrium is performed, and example results are shown for in-core and discarded pebbles. The data illustrates how HxF provides unique insights into PBR fuel, producing information on statistical distributions rather than average values only, as obtained by traditional methods that rely on spectral zoning for depletion. Knowledge of these distributions can greatly improve analysis and assessment of PBRs.
Prevalence and risk factors of diabetes mellitus and hypertension in North East Tunisia calling for efficient and effective actions
Authors: Kheriji Nadia , Dakhlaoui Thouraya , Kamoun Rebai Wafa , Maatoug Sonia , Thabet Mohamed Taher , Mellah Thouraya , Mrad Mehdi , Trabelsi Hajer , Soltani Manel , Kabbage Maria , Hassine Hichem Ben , Hadj Salah Bahlous Afef , Mahjoub Faten , Jamoussi Henda , Abid Abdelmajid , Abdelhak Sonia , Kefi Rym 
Published: August 2023

AbstractDiabetes and hypertension are a serious public health problem worldwide. In the last decades, prevalence of these two metabolic diseases has dramatically increased in the Middle East and North Africa region, especially in Tunisia. This study aimed to determine the prevalence of type 2 diabetes (T2D) and High Blood Pressure (HBP) in Zaghouan, a North-East region of Tunisia. To this end, an exploratory study with stratified random sampling of 420 participants has been carried out. Various data were collected. Blood samples and urine were drawn for biochemical assay. Then, all data were analyzed using the statistical R software. Results showed an alarming situation with an inter-regional difference in prevalence of obesity (50.0%, CI 95.0%), HBP (39.0%, CI 95.0%) and T2D (32.0%, CI 95.0%). This study allowed the discovery of 24, 17 and 2 new cases of T2D, HBP and T2D&HBP respectively. The association of some socio-economic factors and biochemical parameters with these chronic diseases has been highlighted. To conclude, the health situation in the governorate of Zaghouan requires urgent interventions to better manage the growing epidemic of non-communicable diseases (NCD) in the region. This study demonstrated the importance of engaging health policy makers in road mapping and implementing national NCD prevention programs.
Machine learning for the development of diagnostic models of decompensated heart failure or exacerbation of chronic obstructive pulmonary disease
Authors: Gálvez-Barrón César , Pérez-López Carlos , Villar-Álvarez Felipe , Ribas Jesús , Formiga Francesc , Chivite David , Boixeda Ramón , Iborra Cristian , Rodríguez-Molinero Alejandro 
Published: August 2023

AbstractHeart failure (HF) and chronic obstructive pulmonary disease (COPD) are two chronic diseases with the greatest adverse impact on the general population, and early detection of their decompensation is an important objective. However, very few diagnostic models have achieved adequate diagnostic performance. The aim of this trial was to develop diagnostic models of decompensated heart failure or COPD exacerbation with machine learning techniques based on physiological parameters. A total of 135 patients hospitalized for decompensated heart failure and/or COPD exacerbation were recruited. Each patient underwent three evaluations: one in the decompensated phase (during hospital admission) and two more consecutively in the compensated phase (at home, 30 days after discharge). In each evaluation, heart rate (HR) and oxygen saturation (Ox) were recorded continuously (with a pulse oximeter) during a period of walking for 6 min, followed by a recovery period of 4 min. To develop the diagnostic models, predictive characteristics related to HR and Ox were initially selected through classification algorithms. Potential predictors included age, sex and baseline disease (heart failure or COPD). Next, diagnostic classification models (compensated vs. decompensated phase) were developed through different machine learning techniques. The diagnostic performance of the developed models was evaluated according to sensitivity (S), specificity (E) and accuracy (A). Data from 22 patients with decompensated heart failure, 25 with COPD exacerbation and 13 with both decompensated pathologies were included in the analyses. Of the 96 characteristics of HR and Ox initially evaluated, 19 were selected. Age, sex and baseline disease did not provide greater discriminative power to the models. The techniques with S and E values above 80% were the logistic regression (S: 80.83%; E: 86.25%; A: 83.61%) and support vector machine (S: 81.67%; E: 85%; A: 82.78%) techniques. The diagnostic models developed achieved good diagnostic performance for decompensated HF or COPD exacerbation. To our knowledge, this study is the first to report diagnostic models of decompensation potentially applicable to both COPD and HF patients. However, these results are preliminary and warrant further investigation to be confirmed.
Clustering-based spatial analysis (CluSA) framework through graph neural network for chronic kidney disease prediction using histopathology images
Authors: Lee Joonsang , Warner Elisa , Shaikhouni Salma , Bitzer Markus , Kretzler Matthias , Gipson Debbie , Pennathur Subramaniam , Bellovich Keith , Bhat Zeenat , Gadegbeku Crystal , Massengill Susan , Perumal Kalyani , Saha Jharna , Yang Yingbao , Luo Jinghui , Zhang Xin , Mariani Laura , Hodgin Jeffrey B. , Rao Arvind 
Published: August 2023

AbstractMachine learning applied to digital pathology has been increasingly used to assess kidney function and diagnose the underlying cause of chronic kidney disease (CKD). We developed a novel computational framework, clustering-based spatial analysis (CluSA), that leverages unsupervised learning to learn spatial relationships between local visual patterns in kidney tissue. This framework minimizes the need for time-consuming and impractical expert annotations. 107,471 histopathology images obtained from 172 biopsy cores were used in the clustering and in the deep learning model. To incorporate spatial information over the clustered image patterns on the biopsy sample, we spatially encoded clustered patterns with colors and performed spatial analysis through graph neural network. A random forest classifier with various groups of features were used to predict CKD. For predicting eGFR at the biopsy, we achieved a sensitivity of 0.97, specificity of 0.90, and accuracy of 0.95. AUC was 0.96. For predicting eGFR changes in one-year, we achieved a sensitivity of 0.83, specificity of 0.85, and accuracy of 0.84. AUC was 0.85. This study presents the first spatial analysis based on unsupervised machine learning algorithms. Without expert annotation, CluSA framework can not only accurately classify and predict the degree of kidney function at the biopsy and in one year, but also identify novel predictors of kidney function and renal prognosis.
Wavelet gated multiformer for groundwater time series forecasting
Authors: Serravalle Reis Rodrigues Vitor Hugo , de Melo Barros Junior Paulo Roberto , dos Santos Marinho Euler Bentes , Lima de Jesus Silva Jose Luis 
Published: August 2023

AbstractDeveloping accurate models for groundwater control is paramount for planning and managing life-sustaining resources (water) from aquifer reservoirs. Significant progress has been made toward designing and employing deep-forecasting models to tackle the challenge of multivariate time-series forecasting. However, most models were initially taught only to optimize natural language processing and computer vision tasks. We propose the Wavelet Gated Multiformer, which combines the strength of a vanilla Transformer with the Wavelet Crossformer that employs inner wavelet cross-correlation blocks. The self-attention mechanism (Transformer) computes the relationship between inner time-series points, while the cross-correlation finds trending periodicity patterns. The multi-headed encoder is channeled through a mixing gate (linear combination) of sub-encoders (Transformer and Wavelet Crossformer) that output trending signatures to the decoder. This process improved the model’s predictive capabilities, reducing Mean Absolute Error by 31.26 % compared to the second-best performing transformer-like models evaluated. We have also used the Multifractal Detrended Cross-Correlation Heatmaps (MF-DCCHM) to extract cyclical trends from pairs of stations across multifractal regimes by denoising the pair of signals with Daubechies wavelets. Our dataset was obtained from a network of eight wells for groundwater monitoring in Brazilian aquifers, six rainfall stations, eleven river flow stations, and three weather stations with atmospheric pressure, temperature, and humidity sensors.
Genetic descriptor search algorithm for predicting hydrogen adsorption free energy of 2D material
Authors: Lee Jaehwan , Shin Seokwon , Lee Jaeho , Han Young-Kyu , Lee Woojin , Son Youngdoo 
Published: August 2023

AbstractTransition metal dichalcogenides (TMDs) have emerged as a promising alternative to noble metals in the field of electrocatalysts for the hydrogen evolution reaction. However, previous attempts using machine learning to predict TMD properties, such as catalytic activity, have been shown to have limitations in their dependence on large amounts of training data and massive computations. Herein, we propose a genetic descriptor search that efficiently identifies a set of descriptors through a genetic algorithm, without requiring intensive calculations. We conducted both quantitative and qualitative experiments on a total of 70 TMDs to predict hydrogen adsorption free energy ($$\Delta G_H$$

Δ

G
H

) with the generated descriptors. The results demonstrate that the proposed method significantly outperformed the feature extraction methods that are currently widely used in machine learning applications.
Plasmodium berghei oocysts possess fatty acid synthesis and scavenging routes
Authors: Saeed Sadia , Tremp Annie Z. , Dessens Johannes T. 
Published: August 2023

AbstractMalaria parasites carry out fatty acid synthesis (FAS) in their apicoplast organelle via a bacterially related (type II) enzymatic pathway. In the vertebrate host, exoerythrocytic Plasmodium stages rely on FAS, whereas intraerythrocytic stages depend on scavenging FA from their environment. In the mosquito, P. falciparum oocysts express and rely on FAS enzymes for sporozoite formation, but P. yoelii oocysts do not express, nor depend on, FAS enzymes and thus rely on FA scavenging to support sporogony. In P. berghei, FAS enzymes are similarly expendable for sporogony, indicating it conforms to the P. yoelii scenario. We show here that P. berghei, unexpectedly, expresses FAS enzymes throughout oocyst development. These findings indicate that P. berghei can employ FAS alongside FA scavenging to maximise sporogony and transmission, and is more similar to P. falciparum than previously assumed with respect to FA acquisition by the oocyst. The ability of oocysts to switch between FAS and scavenging could be an important factor in the non-competitive relationship of resource exploitation between Plasmodium parasites and their mosquito vectors, which shapes parasite virulence both in the insect and vertebrate.
Effect of struvite (Crystal Green) fertilization on soil element content determined by different methods under soybean cultivation
Authors: Jama-Rodzeńska Anna , Gałka Bernard , Szuba-Trznadel Anna , Jandy Anita , Kamińska Joanna A. 
Published: August 2023

AbstractStruvite is regarded as a promising phosphorus fertilizer alternative to mineral fertilizers; however before fertilizing, soil tests should be undertaken to determine fertilizer recommendations. In May 2022, soil was sampled from a pot experiment with the application of phosphorus set up at the Wroclaw University and Environmental and Life Sciences. Chemical analysis of the soil included total and available phosphorus, potassium, magnesium determined by the Egner–Riehm, Mehlich 3 and Yanai methods. The purpose of the article is to compare soil element extraction by three different methods under struvite fertilization and its use in soybean cultivation. The application of these methods indicated an unambiguous increase in soil Mg content after struvite application. Broadcast soybean fertilization affected the phosphorus content of the soil. The results of the study indicated that different extraction methods presented different contents of P from soil. The content of available phosphorus was circa 122–156 mg kg−1 dm, 35.4–67.5 mg kg−1 dm and 100–159 mg kg−1 dm according to the Mehlich, Yanai and Egner–Riehm methods, respectively. A positive correlation was found between the content of Mg and K in soil determined by the Mehlich 3 and Yanai methods, which may suggest that the Yanai method could be introduced into standard soil chemical analysis in Poland. Such a correlation was not found for phosphorus, which is a difficult element to determine due to the multitude of factors affecting its availability.
Variability of blue carbon storage in arid evaporitic environment of two coastal Sabkhas or mudflats
Authors: Al Disi Zulfa Ali , Naja Khaled , Rajendran Sankaran , Elsayed Hadil , Strakhov Ivan , Al-Kuwari Hamad Al Saad , Sadooni Fadhil , Dittrich Maria , Al-Khayat Jassim Abdulla A. 
Published: August 2023

AbstractCoastal Sabkhas are mudflats found in arid coastal regions that are located within the supratidal zone when high rates of evaporation lead to high salinity. While evaporitic minerals often accumulate underneath the surface, the microbial mats are present on the surface of Sabkhas. Coastal Sabkha, an under-studied ecosystem in Qatar, has the potential to store blue carbon. In the present study, we investigated the carbon storage capacity of two Sabkhas from contrasting geological backgrounds. The spatial and temporal variabilities of the carbon stocks were examined. The results showed that both studied Sabkhas exhibit a considerable potential for soil carbon storage with carbon stocks of 109.11 ± 7.07 Mg C ha−1 and 67.77 ± 18.10 Mg C ha−1 in Dohat Faishakh and Khor al Adaid Sabkha respectively. These values fall within the reported range for carbon stocks in coastal Sabkhas in the region (51–194 Mg C ha−1). Interestingly, the carbon stocks in the sediments of the Sabkhas were higher than those in the sediments of Qatari mangroves (50.17 ± 6.27 Mg C ha−1). These finding suggest that coastal Sabkhas can serve as blue carbon ecosystems in arid environments.
A spatio-temporal analysis of fire occurrence patterns in the Brazilian Amazon
Authors: Valente Fernanda , Laurini Márcio 
Published: August 2023

AbstractWildfires in the Amazon significantly impact the forest structure and carbon cycle. Understanding the patterns of fire occurrence is crucial for effective management. A novel spatio-temporal point process framework was used to analyze changes in fire occurrence patterns in the Brazilian Amazon. A dynamic representation of a Log Gaussian Cox process was used to model the intensity function, which was decomposed into trend, seasonality, cycles, covariates, and spatial effects. The results show a marked decrease in long-term fire occurrence movements between the start of the sample and 2012, followed by an increase until the end of the sample, attributed to governance measures and market mechanisms. Spatial variability of fire occurrence rates in the Brazilian Amazon was successfully captured, with regions having more dry seasons experiencing higher fire occurrence rates. This analysis provides valuable insights into fire occurrence patterns in the Amazon region and the factors driving them.
IL-1β transgenic mouse model of inflammation driven esophageal and oral squamous cell carcinoma
Authors: Muthupalani Sureshkumar , Annamalai Damodaran , Feng Yan , Ganesan Suresh M. , Ge Zhongming , Whary Mark T. , Nakagawa Hiroshi , Rustgi Anil K. , Wang Timothy C. , Fox James G. 
Published: August 2023

AbstractChronic inflammation is integral to the development of esophageal adenocarcinoma (EAC) and esophageal squamous cell carcinoma (ESCC), although the latter has not been associated with reflux esophagitis. The L2-IL-1β transgenic mice, expressing human interleukin (IL)-1β in the oral, esophageal and forestomach squamous epithelia feature chronic inflammation and a stepwise development of Barrett’s esophagus-like metaplasia, dysplasia and adenocarcinoma at the squamo-columnar junction. However, the functional consequences of IL-1β-mediated chronic inflammation in the oral and esophageal squamous epithelia remain elusive. We report for the first time that in addition to the previously described Barrett’s esophagus-like metaplasia, the L2-IL-1β mice also develop squamous epithelial dysplasia with progression to squamous cell carcinoma (SCC) in the esophagus and the tongue. L2-IL-1β showed age-dependent progression of squamous dysplasia to SCC with approximately 40% (n = 49) and 23.5% (n = 17) incidence rates for esophageal and tongue invasive SCC respectively, by 12–15 months of age. Interestingly, SCC development and progression in L2-IL-1β was similar in both Germ Free (GF) and Specific Pathogen Free (SPF) conditions. Immunohistochemistry revealed a T cell predominant inflammatory profile with enhanced expression of Ki67, Sox2 and the DNA double-strand break marker, γ-H2AX, in the dysplastic squamous epithelia of L2-IL-1β mice. Pro-inflammatory cytokines, immunomodulatory players, chemoattractants for inflammatory cells (T cells, neutrophils, eosinophils, and macrophages) and oxidative damage marker, iNOS, were significantly increased in the esophageal and tongue tissues of L2-IL-1β mice. Our recent findings have expanded the translational utility of the IL-1β mouse model to aid in further characterization of the key pathways of inflammation driven BE and EAC as well as ESCC and Oral SCC.