bluuebunny/crossref_metadata_2025_split huggingface dataset:
Enhancing pediatric pneumonia diagnosis through masked autoencoders
Authors: Yoon Taeyoung , Kang Daesung 
Published: March 2024

AbstractPneumonia, an inflammatory lung condition primarily triggered by bacteria, viruses, or fungi, presents distinctive challenges in pediatric cases due to the unique characteristics of the respiratory system and the potential for rapid deterioration. Timely diagnosis is crucial, particularly in children under 5, who have immature immune systems, making them more susceptible to pneumonia. While chest X-rays are indispensable for diagnosis, challenges arise from subtle radiographic findings, varied clinical presentations, and the subjectivity of interpretations, especially in pediatric cases. Deep learning, particularly transfer learning, has shown promise in improving pneumonia diagnosis by leveraging large labeled datasets. However, the scarcity of labeled data for pediatric chest X-rays presents a hurdle in effective model training. To address this challenge, we explore the potential of self-supervised learning, focusing on the Masked Autoencoder (MAE). By pretraining the MAE model on adult chest X-ray images and fine-tuning the pretrained model on a pediatric pneumonia chest X-ray dataset, we aim to overcome data scarcity issues and enhance diagnostic accuracy for pediatric pneumonia. The proposed approach demonstrated competitive performance an AUC of 0.996 and an accuracy of 95.89% in distinguishing between normal and pneumonia. Additionally, the approach exhibited high AUC values (normal: 0.997, bacterial pneumonia: 0.983, viral pneumonia: 0.956) and an accuracy of 93.86% in classifying normal, bacterial pneumonia, and viral pneumonia. This study also investigated the impact of different masking ratios during pretraining and explored the labeled data efficiency of the MAE model, presenting enhanced diagnostic capabilities for pediatric pneumonia.
In platelet single donor apheresis, platelet factor 4 levels correlated with donor’s age and decreased during storage
Authors: Duchez Anne Claire , Heestermans Marco , Arthaud Charles-Antoine , Eyraud Marie-Ange , Portier Mailys , Prier Amélie , Hamzeh-Cognasse Hind , Cognasse Fabrice 
Published: March 2024

AbstractThe human population is ageing worldwide. The World Health Organization estimated that the world’s population of people aged 60 years and older will increase to at least 30%, coinciding with a growing frequency of cognitive and cardiovascular disease. Recently, in preclinical studies platelet Factor 4 (PF4) was presented as a pro-cognitive factor. This molecule is released by platelets in the circulation and could be present in blood products destined for transfusion. We wondered if PF4 levels are correlated to the age of the blood donor or to the storage time of platelet concentrates (PCs) intended for transfusion? We observed higher levels of PF4 in PCs from elderly donors compared to younger donors, while PC storage time did not determine PF4 levels expression.
High-performance prediction of epilepsy surgical outcomes based on the genetic neural networks and hybrid iEEG marker
Authors: Sun Lipeng , Feng Chen , Zhang En , Chen Huan , Jin Weifeng , Zhu Junming , Yu Li 
Published: March 2024

AbstractAccurately identification of the seizure onset zone (SOZ) is pivotal for successful surgery in patients with medically refractory epilepsy. The purpose of this study is to improve the performance of model predicting the epilepsy surgery outcomes using genetic neural network (GNN) model based on a hybrid intracranial electroencephalography (iEEG) marker. We extracted 21 SOZ related markers based on iEEG data from 79 epilepsy patients. The least absolute shrinkage and selection operator (LASSO) regression was employed to integrated seven markers, selected after testing in pairs with all 21 biomarkers and 7 machine learning models, into a hybrid marker. Based on the hybrid marker, we devised a GNN model and compared its predictive performance for surgical outcomes with six other mainstream machine-learning models. Compared to the mainstream models, underpinning the GNN with the hybrid iEEG marker resulted in a better prediction of surgical outcomes, showing a significant increase of the prediction accuracy from approximately 87% to 94.3% (P = 0.0412). This study suggests that the hybrid iEEG marker can improve the performance of model predicting the epilepsy surgical outcomes, and validates the effectiveness of the GNN in characterizing and analyzing complex relationships between clinical data variables.
Switching between different cognitive strategies induces switch costs as evidenced by switches between manual and mental object rotation
Authors: Weis Patrick P. , Kunde Wilfried 
Published: March 2024

AbstractSwitching between tasks entails costs when compared to repeating the same task. It is unclear whether switch costs also occur when repeating the same task but switching the underlying cognitive strategy (CS). Here, we investigated whether CS switch costs exist despite overlap in mental processing between CSs and a lack of abstract goal (always “solve task X”) or answer key binding switches. Specifically, we asked participants to judge the identity of two misaligned objects by either mental or manual computer-mediated object rotation. In each trial of Block 1, to measure switch costs without choice-related cognitive processes, a cue indicated which CS (mental/manual) to use. In Block 2, the CS was freely chosen. Participants exhibited considerable CS switch costs for both cued and freely chosen switches. Moreover, Block 1 switch costs moderately predicted Block 2 switch frequency, while an overall tendency for CS repetition was observed. In sum, we found that switch costs are not confined to situations in which tasks are switched but generalize to situations in which the task stays identical and the CS is switched instead. The results have implications for modern computerized cognitive environments in which a multitude of cognitive strategies is available for the same task.
The potential of facial nevi in personal identification
Authors: Cappella Annalisa , Solazzo Riccardo , Mazzarelli Debora , Gibelli Daniele , Dolci Claudia , Sforza Chiarella , Cattaneo Cristina 
Published: March 2024

AbstractForensic anthropologists dealing with personal identification (PI) of human remains have recently stressed the need to explore the potential of “secondary identifiers” for identifying victims who died in particular events for whom images often represent the main antemortem data available. Being the face the part most exposed in images, characteristics as pigmented skin lesions (PSLs), can be crucial if combined with other input. Since no data is available on frequencies and distribution of facial PSLs in the general population, this study aims at systematically collecting such data to verify their potential in PI and to open a debate on the aid that “secondary identifiers”, regardless of their specific nature, can give to the identification of the deceased in specific forensic contexts. A retrospective analysis on three-dimensional facial models of 1039 Italian subjects (from 4 to 84 years old) was conducted to examine the incidence of PSLs discriminated according to size and position in well-defined facial areas. From the collected data we developed a probabilistic approach providing the likelihood ratio (LR) for two settings: (1) the relative frequencies of nevi in the various facial areas, providing the deriving compound probability of owning a certain facial PSLs pattern; and (2) codes describing the facial nevi pattern of each individual of our population, thus testing their uniqueness and so their potential in PI. The calculated LRs mostly proved high identifying strength, particularly when provided by the compound probability-based approach. Data on incidence and position of facial nevi, their generated codes, and the probabilistic approach here presented, all constitute a starting point for advancing secondary identifiers. Nonetheless, although this preliminary study proved facial PSLs as valuable and potentially useful for identification, their significance and validity should be interpreted with caution as we are still at the first theoretical step clearly based on ideal conditions, and thus further investigations are due on the limitations of their use in practical identifying settings. Therefore, being this systematic study only a preliminary one in its nature, it is recommended not to use this kind of approach until further studies will test its validity in several practical conditions.
Defining Porphyromonas gingivalis strains associated with periodontal disease
Authors: Murugaiyan Vijaya , Utreja Simran , Hovey Kathleen M. , Sun Yijun , LaMonte Michael J. , Wactawski‑Wende Jean , Diaz Patricia I. , Buck Michael J. 
Published: March 2024

AbstractPorphyromonas gingivalis, a Gram-negative anaerobic bacterium commonly found in human subgingival plaque, is a major etiologic agent for periodontitis and has been associated with multiple systemic pathologies. Many P. gingivalis strains have been identified and different strains possess different virulence factors. Current oral microbiome approaches (16S or shotgun) have been unable to differentiate P. gingivalis strains. This study presents a new approach that aims to improve the accuracy of strain identification, using a detection method based on sequencing of the intergenic spacer region (ISR) which is variable between P. gingivalis strains. Our approach uses two-step PCR to amplify only the P. gingivalis ISR region. Samples are then sequenced with an Illumina sequencer and mapped to specific strains. Our approach was validated by examining subgingival plaque from 153 participants with and without periodontal disease. We identified the avirulent strain ATCC33277/381 as the most abundant strain across all sample types. The W83/W50 strain was significantly enriched in periodontitis, with 13% of participants harboring that strain. Overall, this approach can have significant implications not only for the diagnosis and treatment of periodontal disease but also for other diseases where P. gingivalis or its toxins have been implicated, such as Alzheimer's disease.
Monitoring and predicting corn grain quality on the transport and post-harvest operations in storage units using sensors and machine learning models
Authors: Rodrigues Dágila Melo , Coradi Paulo Carteri , Teodoro Larissa Pereira Ribeiro , Teodoro Paulo Eduardo , Moraes Rosana dos Santos , Leal Marisa Menezes 
Published: March 2024

AbstractMonitoring the intergranular variables of corn grain mass during the transportation, drying, and storage stages it possible to predict and avoid potential grain quality losses. For monitoring the grain mass along the transport, a probe system with temperature, relative humidity, and carbon dioxide sensors was developed to determine the equilibrium moisture content and the respiration of the grain mass. These same variables were monitored during storage. At drying process, the drying air and grain mass temperatures, as well as the relative humidity, were monitored. For the prediction of the physical and physical–chemical quality of the grains, the results obtained from the monitoring were used as input data for the multiple linear regression, artificial neural networks, decision tree, and random forest models. A Pearson correlation was applied to verify the relationship between the monitored and predicted variables. From the results obtained, we verified that the intergranular relative humidity altered the equilibrium moisture content of the grains, contributing to the increased respiration and hence dry matter losses along the transport. At this stage, the artificial neural network model was the most indicated to predict the electrical conductivity, apparent specific mass, and germination. The random forest model satisfactorily estimated the dry matter loss. During drying, the air temperature caused volumetric contraction and thermal damage to the grains, increasing the electric conductivity index. Artificial neural network and random forest models were the most suitable for predicting the quality of dry grains. During storage, the environmental conditions altered the moisture contents causing a reduction in the apparent specific mass, germination, and crude protein, crude fiber, and fat contents. Artificial neural network and random forest were the best predictors of moisture content and germination. However, the random forest model was the best predictor of apparent specific mass, electrical conductivity, and starch content of stored grains.
Gestational testosterone excess early to mid-pregnancy disrupts maternal lipid homeostasis and activates biosynthesis of phosphoinositides and phosphatidylethanolamines in sheep
Authors: Saadat Nadia , Pallas Brooke , Ciarelli Joseph , Vyas Arpita Kalla , Padmanabhan Vasantha 
Published: March 2024

AbstractGestational hyperandrogenism is a risk factor for adverse maternal and offspring outcomes with effects likely mediated in part via disruptions in maternal lipid homeostasis. Using a translationally relevant sheep model of gestational testosterone (T) excess that manifests maternal hyperinsulinemia, intrauterine growth restriction (IUGR), and adverse offspring cardiometabolic outcomes, we tested if gestational T excess disrupts maternal lipidome. Dimensionality reduction models following shotgun lipidomics of gestational day 127.1 ± 5.3 (term 147 days) plasma revealed clear differences between control and T-treated sheep. Lipid signatures of gestational T-treated sheep included higher phosphoinositides (PI 36:2, 39:4) and lower acylcarnitines (CAR 16:0, 18:0, 18:1), phosphatidylcholines (PC 38:4, 40:5) and fatty acids (linoleic, arachidonic, Oleic). Gestational T excess activated phosphatidylethanolamines (PE) and PI biosynthesis. The reduction in key fatty acids may underlie IUGR and activated PI for the maternal hyperinsulinemia evidenced in this model. Maternal circulatory lipids contributing to adverse cardiometabolic outcomes are modifiable by dietary interventions.
Altered gut microbiota in Taiwanese A97S predominant transthyretin amyloidosis with polyneuropathy
Authors: Chen Chieh-Chang , Tseng Ping-Huei , Hsueh Hsueh-Wen , Chiang Ming-Chang , Tzeng Shiou-Ru , Chiang Tsung Hsien , Wu Ming-Shiang , Hsieh Sung-Tsang , Chao Chi-Chao 
Published: March 2024

AbstractIncreasing evidence suggests that gut microbiota alterations are related to development and phenotypes of many neuropsychiatric diseases. Here, we evaluated the fecal microbiota and its clinical correlates in patients with hereditary transthyretin amyloidosis (ATTRv) and polyneuropathy. Fecal microbiota from 38 ATTRv patients and 39 age-matched controls was analyzed by sequencing 16S V3–V4 ribosomal RNA, and its relationships with clinical characteristics of polyneuropathy and cardiomyopathy were explored. The familial amyloidotic polyneuropathy stage was stage I, II, and III in 13, 18, and 7 patients. 99mTc-PYP SPECT showed a visual score of 2 in 15 and 3 in 21 patients. The gut microbiota of ATTRv patients showed higher alpha diversity (ASV richness and Shannon effective numbers) and dissimilar beta diversity compared to controls. Relative abundance of microbiota was dominated by Firmicutes and decreased in Bacteroidetes in ATTRv patients than in controls. Patients with more myocardial amyloid deposition were associated with increased alpha diversity, and the abundance of Clostridia was significantly correlated with pathophysiology of polyneuropathy in ATTRv patients. These findings demonstrated alterations in the gut microbiota, especially Firmicutes, in ATTRv. The association between altered microbiota and phenotypes of cardiomyopathy and polyneuropathy might suggest potential contributions of gut microbiota to ATTRv pathogenesis.
Climate change unevenly affects the dependence of multiple climate-related hazards in China
Authors: Zong Xuezheng , Yin Yunhe , Yin Mijia 
Published: March 2024

AbstractDue to the complex natural environment and large regional differentiation in China, the dependence of multiple climate-related hazards on compound events (CEs) and their response to climate change are still unclear. Using daily meteorological observations (1961–2020) and climate simulations from the Coupled Model Intercomparison Project Phase 6, statistically strong dependences among hot, dry, and high fire risk are revealed in China. The average temperature from 1991 to 2020 was 1 °C higher than that from 1961 to 1990, and the probabilities of CEs exhibiting extreme hot-dry, dry-high fire risk, and extreme hot-dry-high fire risk increased significantly by 74.8%, 60.5%, and 26.8%, respectively. Although most CEs occur more frequently in China at the end of the 21st century, the increase rates in low emissions are lower. These findings have implications for developing climate adaptation and mitigation strategies to cope with increased CEs in critical geographical regions.
Bio-inspired “Self-denoising” capability of 2D materials incorporated optoelectronic synaptic array
Authors: Islam Molla Manjurul , Rahman Md Sazzadur , Heldmyer Haley , Han Sang Sub , Jung Yeonwoong , Roy Tania 
Published: March 2024

AbstractIn in-sensor image preprocessing, the sensed image undergoes low level processing like denoising at the sensor end, similar to the retina of human eye. Optoelectronic synapse devices are potential contenders for this purpose, and subsequent applications in artificial neural networks (ANNs). The optoelectronic synapses can offer image pre-processing functionalities at the pixel itself—termed as in-pixel computing. Denoising is an important problem in image preprocessing and several approaches have been used to denoise the input images. While most of those approaches require external circuitry, others are efficient only when the noisy pixels have significantly lower intensity compared to the actual pattern pixels. In this work, we present the innate ability of an optoelectronic synapse array to perform denoising at the pixel itself once it is trained to memorize an image. The synapses consist of phototransistors with bilayer MoS2 channel and p-Si/PtTe2 buried gate electrode. Our 7 × 7 array shows excellent robustness to noise due to the interplay between long-term potentiation and short-term potentiation. This bio-inspired strategy enables denoising of noise with higher intensity than the memorized pattern, without the use of any external circuitry. Specifically, due to the ability of these synapses to respond distinctively to wavelengths from 300 nm in ultraviolet to 2 µm in infrared, the pixel array also denoises mixed-color interferences. The “self-denoising” capability of such an artificial visual array has the capacity to eliminate the need for raw data transmission and thus, reduce subsequent image processing steps for supervised learning.
One-pot method for preparing DNA, RNA, and protein for multiomics analysis
Authors: Biedka Stephanie , Alkam Duah , Washam Charity L. , Yablonska Svitlana , Storey Aaron , Byrum Stephanie D. , Minden Jonathan S. 
Published: March 2024

AbstractTypical multiomics studies employ separate methods for DNA, RNA, and protein sample preparation, which is labor intensive, costly, and prone to sampling bias. We describe a method for preparing high-quality, sequencing-ready DNA and RNA, and either intact proteins or mass-spectrometry-ready peptides for whole proteome analysis from a single sample. This method utilizes a reversible protein tagging scheme to covalently link all proteins in a lysate to a bead-based matrix and nucleic acid precipitation and selective solubilization to yield separate pools of protein and nucleic acids. We demonstrate the utility of this method to compare the genomes, transcriptomes, and proteomes of four triple-negative breast cancer cell lines with different degrees of malignancy. These data show the involvement of both RNA and associated proteins, and protein-only dependent pathways that distinguish these cell lines. We also demonstrate the utility of this multiomics workflow for tissue analysis using mouse brain, liver, and lung tissue.
Disparity of cycad leaves dispels the living fossil metaphor
Authors: Coiro Mario , Seyfullah Leyla Jean 
Published: March 2024

AbstractThe living fossil metaphor is tightly linked with the cycads. This group of gymnosperms is supposed to be characterised by long-term morphological stasis, particularly after their peak of diversity and disparity in the Jurassic. However, no formal test of this hypothesis exists. Here, we use a recent phylogenetic framework and an improved character matrix to reconstruct the Disparity Through Time for cycad leaves using a Principal Coordinate Analysis and employing Pre-Ordination Ancestral State Reconstruction to test the impact of sampling on the results. Our analysis shows that the cycad leaf morsphospace expanded up to the present, with numerous shifts in its general positioning, independently of sampling biases. Moreover, they also show that Zamiaceae expanded rapidly in the Early Cretaceous and continued to expand up to the present, while now-extinct clades experienced a slow contraction from their peak in the Triassic. We also show that rates of evolution were constantly high up to the Early Cretaceous, and then experienced a slight decrease in the Paleogene, followed by a Neogene acceleration. These results show a much more dynamic history for cycads, and suggest that the ‘living fossil’ metaphor is actually a hindrance to our understanding of their macroevolution.
Integrated preservation of water activity as key to intensified chemoenzymatic synthesis of bio-based styrene derivatives
Authors: Petermeier Philipp , Bittner Jan Philipp , Jonsson Tobias , Domínguez de María Pablo , Byström Emil , Kara Selin 
Published: March 2024

AbstractThe valorization of lignin-derived feedstocks by catalytic means enables their defunctionalization and upgrading to valuable products. However, the development of productive, safe, and low-waste processes remains challenging. This paper explores the industrial potential of a chemoenzymatic reaction performing the decarboxylation of bio-based phenolic acids in wet cyclopentyl methyl ether (CPME) by immobilized phenolic acid decarboxylase from Bacillus subtilis, followed by a base-catalyzed acylation. Key-to-success is the continuous control of water activity, which fluctuates along the reaction progress, particularly at high substrate loadings (triggered by different hydrophilicities of substrate and product). A combination of experimentation, thermodynamic equilibrium calculations, and MD simulations revealed the change in water activity which guided the integration of water reservoirs and allowed process intensification of the previously limiting enzymatic step. With this, the highly concentrated sequential two-step cascade (400 g·L–1) achieves full conversions and affords products in less than 3 h. The chemical step is versatile, accepting different acyl donors, leading to a range of industrially sound products. Importantly, the finding that water activity changes in intensified processes is an academic insight that might explain other deactivations of enzymes when used in non-conventional media.
Shape programming of liquid crystal elastomers
Authors: Rešetič Andraž 
Published: March 2024

AbstractLiquid crystal elastomers (LCEs) are shape-morphing materials that demonstrate reversible actuation when exposed to external stimuli, such as light or heat. The actuation’s complexity depends heavily on the instilled liquid crystal alignment, programmed into the material using various shape-programming processes. As an unavoidable part of LCE synthesis, these also introduce geometrical and output restrictions that dictate the final applicability. Considering LCE’s future implementation in real-life applications, it is reasonable to explore these limiting factors. This review offers a brief overview of current shape-programming methods in relation to the challenges of employing LCEs as soft, shape-memory components in future devices.