bluuebunny/crossref_metadata_2025_split huggingface dataset:
Magnetic and optical properties of ZnO nanoparticles and nanorods synthesized by green chemistry
Authors: Reyes-Damián C , Álvarez-Chimal R , Ascencio F , León-Flores J , Arenas-Alatorre J 
Published: April 2024

Abstract
ZnO nanostructures have attracted considerable attention because of their physicochemical properties and applications as antibacterial agents, photocatalytic reactions for pollutant removal, and electronics. Hence, efficient production and knowledge of their properties under different synthesis conditions are essential. Biosynthesis has emerged as an excellent growth-directing method for synthesizing nanomaterials, representing a soft and cleaner alternative for their production. In this study, we synthesized different ZnO nanostructures using a soft chemistry method at different growth temperatures, from 200 to 800 °C every 200 °C. The crystalline structure was estudied by x-ray Diffraction (XRD) and High-Resolution Transmission Electron Microscopy (HRTEM). The shape and size were studied by Field Emission Scanning Electron Microscopy (FESEM) and Transmission Electron Microscopy (TEM), which revealed a ZnO hexagonal phase with two shapes: nanoparticles (NPs) with irregular shapes and nanorods of different sizes. The optical properties were studied by Raman and UV-visible spectroscopy, and optical absorption measurements showed bandgap tuning of the produced nanostructures. Finally, the magnetic characteristics of the samples demonstrated magnetic anisotropy due to the preference for crystalline formation and the size of the nanoparticles. The magnetic interaction between the two types of NPs increased the diamagnetism associated with the nanorods.
Diet choices determine mercury exposure risks for people living in gold mining regions of Peru
Authors: Marchese Melissa J , Gerson Jacqueline R , Berky Axel J , Driscoll Charles , Fernandez Luis E , Hsu-Kim Heileen , Lansdale Kelsey N , Letourneau Eliza , Montesdeoca Mario , Pan William K , Robie Emily , Vega Claudia , Bernhardt Emily S 
Published: April 2024

Abstract
Artisanal and small-scale gold mining (ASGM) is the largest global anthropogenic mercury (Hg) source and is widespread in the Peruvian Amazon. Consuming Hg-laden foods exposes people to this potent neurotoxin. While numerous studies have examined fish Hg content near ASGM, Hg accumulation in other commonly consumed animal-and plant-based foods from terrestrial environments is often overlooked. In this study, we aim to address understudied dietary Hg exposures.
To understand Hg exposure from food staples in the Peruvian Amazon, we measured total and methyl Hg in local crops, fish, chicken meat, chicken feathers, and eggs from ASGM-impacted and upstream (reference) communities. Diet surveys were used to estimate probable weekly Hg intake from each food. Fish and chicken stable carbon and nitrogen isotope signatures were analyzed to evaluate trophic magnification.
Though few crops exceeded food safety recommendations, rice methyl Hg proportions were high (84%). Trophic level was an expected key predictor of fish Hg content. 81% (17 of 21) of local carnivorous fish exceeded WHO and EPA recommendations. Compared to upstream communities, mining-impacted communities demonstrated elevated total Hg in crops (1.55 (interquartile ranges (IQR): 0.60–3.03) μg kg−1 upstream versus 3.38 (IQR: 1.62–11.58) in mining areas), chicken meats (2.69 (IQR: BDL–9.96) μg kg−1 versus 19.68 (IQR: 6.33–48.1)), and feathers (91.20 (IQR: 39.19–216.13) μg kg−1 versus 329.99 (IQR: 173.22–464.99)). Chicken meats from mining areas exhibited over double the methyl Hg concentrations of those upstream. Methyl Hg fractions in chicken muscle tissue averaged 93%. Egg whites and livers exceeded Hg recommendations most frequently. Proximity to mining, but not trophic position, was a predictor of chicken Hg content.
Our results demonstrate that terrestrial and aquatic foods can accumulate Hg from mining activity, introducing additional human Hg exposure routes. However, locally sourced carnivorous fish was the largest contributor to an estimated three-fold exceedance of the provisional tolerable weekly Hg intake.
SPDesign: protein sequence designer based on structural sequence profile using ultrafast shape recognition
Authors: Wang Hui , Liu Dong , Zhao Kailong , Wang Yajun , Zhang Guijun 
Published: April 2024

Abstract
Protein sequence design can provide valuable insights into biopharmaceuticals and disease treatments. Currently, most protein sequence design methods based on deep learning focus on network architecture optimization, while ignoring protein-specific physicochemical features. Inspired by the successful application of structure templates and pre-trained models in the protein structure prediction, we explored whether the representation of structural sequence profile can be used for protein sequence design. In this work, we propose SPDesign, a method for protein sequence design based on structural sequence profile using ultrafast shape recognition. Given an input backbone structure, SPDesign utilizes ultrafast shape recognition vectors to accelerate the search for similar protein structures in our in-house PAcluster80 structure database and then extracts the sequence profile through structure alignment. Combined with structural pre-trained knowledge and geometric features, they are further fed into an enhanced graph neural network for sequence prediction. The results show that SPDesign significantly outperforms the state-of-the-art methods, such as ProteinMPNN, Pifold and LM-Design, leading to 21.89%, 15.54% and 11.4% accuracy gains in sequence recovery rate on CATH 4.2 benchmark, respectively. Encouraging results also have been achieved on orphan and de novo (designed) benchmarks with few homologous sequences. Furthermore, analysis conducted by the PDBench tool suggests that SPDesign performs well in subdivided structures. More interestingly, we found that SPDesign can well reconstruct the sequences of some proteins that have similar structures but different sequences. Finally, the structural modeling verification experiment indicates that the sequences designed by SPDesign can fold into the native structures more accurately.
Improving drug response prediction via integrating gene relationships with deep learning
Authors: Li Pengyong , Jiang Zhengxiang , Liu Tianxiao , Liu Xinyu , Qiao Hui , Yao Xiaojun 
Published: April 2024

Abstract
Predicting the drug response of cancer cell lines is crucial for advancing personalized cancer treatment, yet remains challenging due to tumor heterogeneity and individual diversity. In this study, we present a deep learning-based framework named Deep neural network Integrating Prior Knowledge (DIPK) (DIPK), which adopts self-supervised techniques to integrate multiple valuable information, including gene interaction relationships, gene expression profiles and molecular topologies, to enhance prediction accuracy and robustness. We demonstrated the superior performance of DIPK compared to existing methods on both known and novel cells and drugs, underscoring the importance of gene interaction relationships in drug response prediction. In addition, DIPK extends its applicability to single-cell RNA sequencing data, showcasing its capability for single-cell-level response prediction and cell identification. Further, we assess the applicability of DIPK on clinical data. DIPK accurately predicted a higher response to paclitaxel in the pathological complete response (pCR) group compared to the residual disease group, affirming the better response of the pCR group to the chemotherapy compound. We believe that the integration of DIPK into clinical decision-making processes has the potential to enhance individualized treatment strategies for cancer patients.
TripHLApan: predicting HLA molecules binding peptides based on triple coding matrix and transfer learning
Authors: Wang Meng , Lei Chuqi , Wang Jianxin , Li Yaohang , Li Min 
Published: April 2024

Abstract
Human leukocyte antigen (HLA) recognizes foreign threats and triggers immune responses by presenting peptides to T cells. Computationally modeling the binding patterns between peptide and HLA is very important for the development of tumor vaccines. However, it is still a big challenge to accurately predict HLA molecules binding peptides. In this paper, we develop a new model TripHLApan for predicting HLA molecules binding peptides by integrating triple coding matrix, BiGRU + Attention models, and transfer learning strategy. We have found the main interaction site regions between HLA molecules and peptides, as well as the correlation between HLA encoding and binding motifs. Based on the discovery, we make the preprocessing and coding closer to the natural biological process. Besides, due to the input being based on multiple types of features and the attention module focused on the BiGRU hidden layer, TripHLApan has learned more sequence level binding information. The application of transfer learning strategies ensures the accuracy of prediction results under special lengths (peptides in length 8) and model scalability with the data explosion. Compared with the current optimal models, TripHLApan exhibits strong predictive performance in various prediction environments with different positive and negative sample ratios. In addition, we validate the superiority and scalability of TripHLApan’s predictive performance using additional latest data sets, ablation experiments and binding reconstitution ability in the samples of a melanoma patient. The results show that TripHLApan is a powerful tool for predicting the binding of HLA-I and HLA-II molecular peptides for the synthesis of tumor vaccines. TripHLApan is publicly available at https://github.com/CSUBioGroup/TripHLApan.git.
Coding genomes with gapped pattern graph convolutional network
Authors: Wang Ruo Han , Ng Yen Kaow , Zhang Xianglilan , Wang Jianping , Li Shuai Cheng 
Published: April 2024

Abstract

Motivation
Genome sequencing technologies reveal a huge amount of genomic sequences. Neural network-based methods can be prime candidates for retrieving insights from these sequences because of their applicability to large and diverse datasets. However, the highly variable lengths of genome sequences severely impair the presentation of sequences as input to the neural network. Genetic variations further complicate tasks that involve sequence comparison or alignment.

Results
Inspired by the theory and applications of “spaced seeds,” we propose a graph representation of genome sequences called “gapped pattern graph.” These graphs can be transformed through a Graph Convolutional Network to form lower-dimensional embeddings for downstream tasks. On the basis of the gapped pattern graphs, we implemented a neural network model and demonstrated its performance on diverse tasks involving microbe and mammalian genome data. Our method consistently outperformed all the other state-of-the-art methods across various metrics on all tasks, especially for the sequences with limited homology to the training data. In addition, our model was able to identify distinct gapped pattern signatures from the sequences.

Availability and implementation
The framework is available at https://github.com/deepomicslab/GCNFrame.
DrivR-Base: a feature extraction toolkit for variant effect prediction model construction
Authors: Francis Amy , Campbell Colin , Gaunt Tom R 
Published: April 2024

Abstract

Motivation
Recent advancements in sequencing technologies have led to the discovery of numerous variants in the human genome. However, understanding their precise roles in diseases remains challenging due to their complex functional mechanisms. Various methodologies have emerged to predict the pathogenic significance of these genetic variants. Typically, these methods employ an integrative approach, leveraging diverse data sources that provide important insights into genomic function. Despite the abundance of publicly available data sources and databases, the process of navigating, extracting, and pre-processing features for machine learning models can be highly challenging and time-consuming. Furthermore, researchers often invest substantial effort in feature extraction, only to later discover that these features lack informativeness.

Results
In this article, we introduce DrivR-Base, an innovative resource that efficiently extracts and integrates molecular information (features) related to single nucleotide variants. These features encompass information about the genomic positions and the associated protein positions of a variant. They are derived from a wide array of databases and tools, including structural properties obtained from AlphaFold, regulatory information sourced from ENCODE, and predicted variant consequences from Variant Effect Predictor. DrivR-Base is easily deployable via a Docker container to ensure reproducibility and ease of access across diverse computational environments. The resulting features can be used as input for machine learning models designed to predict the pathogenic impact of human genome variants in disease. Moreover, these feature sets have applications beyond this, including haploinsufficiency prediction and the development of drug repurposing tools. We describe the resource’s development, practical applications, and potential for future expansion and enhancement.

Availability and implementation
DrivR-Base source code is available at https://github.com/amyfrancis97/DrivR-Base.
scDAC: deep adaptive clustering of single-cell transcriptomic data with coupled autoencoder and Dirichlet process mixture model
Authors: An Sijing , Shi Jinhui , Liu Runyan , Chen Yaowen , Wang Jing , Hu Shuofeng , Xia Xinyu , Dong Guohua , Bo Xiaochen , He Zhen , Ying Xiaomin 
Published: April 2024

Abstract

Motivation
Clustering analysis for single-cell RNA sequencing (scRNA-seq) data is an important step in revealing cellular heterogeneity. Many clustering methods have been proposed to discover heterogenous cell types from scRNA-seq data. However, adaptive clustering with accurate cluster number reflecting intrinsic biology nature from large-scale scRNA-seq data remains quite challenging.

Results
Here, we propose a single-cell Deep Adaptive Clustering (scDAC) model by coupling the Autoencoder (AE) and the Dirichlet Process Mixture Model (DPMM). By jointly optimizing the model parameters of AE and DPMM, scDAC achieves adaptive clustering with accurate cluster numbers on scRNA-seq data. We verify the performance of scDAC on five subsampled datasets with different numbers of cell types and compare it with 15 widely used clustering methods across nine scRNA-seq datasets. Our results demonstrate that scDAC can adaptively find accurate numbers of cell types or subtypes and outperforms other methods. Moreover, the performance of scDAC is robust to hyperparameter changes.

Availability and implementation
The scDAC is implemented in Python. The source code is available at https://github.com/labomics/scDAC.
A supervised Bayesian factor model for the identification of multi-omics signatures
Authors: Gygi Jeremy P , Konstorum Anna , Pawar Shrikant , Aron Edel , Kleinstein Steven H , Guan Leying 
Published: April 2024

Abstract

Motivation
Predictive biological signatures provide utility as biomarkers for disease diagnosis and prognosis, as well as prediction of responses to vaccination or therapy. These signatures are identified from high-throughput profiling assays through a combination of dimensionality reduction and machine learning techniques. The genes, proteins, metabolites, and other biological analytes that compose signatures also generate hypotheses on the underlying mechanisms driving biological responses, thus improving biological understanding. Dimensionality reduction is a critical step in signature discovery to address the large number of analytes in omics datasets, especially for multi-omics profiling studies with tens of thousands of measurements. Latent factor models, which can account for the structural heterogeneity across diverse assays, effectively integrate multi-omics data and reduce dimensionality to a small number of factors that capture correlations and associations among measurements. These factors provide biologically interpretable features for predictive modeling. However, multi-omics integration and predictive modeling are generally performed independently in sequential steps, leading to suboptimal factor construction. Combining these steps can yield better multi-omics signatures that are more predictive while still being biologically meaningful.

Results
We developed a supervised variational Bayesian factor model that extracts multi-omics signatures from high-throughput profiling datasets that can span multiple data types. Signature-based multiPle-omics intEgration via lAtent factoRs (SPEAR) adaptively determines factor rank, emphasis on factor structure, data relevance and feature sparsity. The method improves the reconstruction of underlying factors in synthetic examples and prediction accuracy of coronavirus disease 2019 severity and breast cancer tumor subtypes.

Availability and implementation
SPEAR is a publicly available R-package hosted at https://bitbucket.org/kleinstein/SPEAR.
Teaching scientific evidence and critical thinking for policy making
Authors: Pasternak Taschner Natalia , Almeida Paulo 
Published: April 2024

Abstract
While there is worldwide tendency to promote the use of scientific evidence to inform policy making, little has been done to train scientists and policy makers for this interaction. If we want to bridge the gap between academia, scientific knowledge, and policy, we must begin by providing formal training and skill building for actors and stakeholders. Scientists are not trained to communicate and inform policy, and policy makers are not trained to understand scientific process and assess evidence. Building an environment where this collaboration can flourish depends on teaching competencies and abilities specific for decision-making processes. As professors of policy with a background in science, we have started teaching preliminary courses on the use of scientific evidence in policy making. Feedback from students and institutions has been positive, paving the way for similar courses in other schools and institutions and maybe even new career paths. This article is intended to share our experience in designing and teaching courses aimed at training policy makers. Moving forward we plan to include training for science majors, thus encompassing the two main sides of this dialogue and opening new career opportunities for scientists and policy makers.
Tazarotene is as effective and well-tolerated as imiquimod in the treatment of verruca plana: a comparative randomized controlled trial
Authors: Nofal Hagar , Omran Fatma , ElKholy Basma , Nofal Sara , Nofal Ahmad 
Published: April 2024

Abstract

Background
Plane warts, when multiple and recurrent, present a therapeutic challenge acting as a source of reinfection, causing frustration and affecting a patient’s quality of life. For large numbers of lesions in cosmetically significant sites, topical treatment is preferred to avoid potential sequelae.

Objectives
To evaluate and compare the efficacy and tolerability of tazarotene 0.1% gel vs. imiquimod 5% cream for the treatment of plane warts.

Methods
In a parallel three-arm randomized controlled trial, 60 patients were randomized to imiquimod, tazarotene or placebo groups. Patients applied the corresponding treatment once daily at night for a maximum of 12 weeks. Primary outcomes were the percentage of respondents with complete clearance in the three studied groups, and the type and frequency of side-effects in each group.

Results
Both active treatments resulted in significant improvement compared with baseline and the placebo group (P = 0.001). The imiquimod 5% treated group showed complete clearance in 50% (10/20) of patients, partial response in 15% (3/20), and no response in 35% (7/20). Tazarotene 0.1% gel showed complete clearance in 40% (8/20) of patients, partial response in 40% (8/20), and no response in 20% (4/20). No significant difference was detected between the imiquimod and tazarotene groups (P = 0.19).

Conclusions
Compared with imiquimod, tazarotene 0.1% gel for the treatment of plane warts seems to offer an equivalent treatment response, it maintained efficacy without recurrence and had a safer profile regarding dyspigmentation with an advantageous cheaper cost.
Behaviorally meaningful functional networks mediate the effect of Alzheimer’s pathology on cognition
Authors: Ziontz Jacob , Harrison Theresa M , Chen Xi , Giorgio Joseph , Adams Jenna N , Wang Zehao , Jagust William ,   Alzheimer’s Disease Neuroimaging Initiative
Published: April 2024

AbstractTau pathology is associated with cognitive impairment in both aging and Alzheimer’s disease, but the functional and structural bases of this relationship remain unclear. We hypothesized that the integrity of behaviorally meaningful functional networks would help explain the relationship between tau and cognitive performance. Using resting state fMRI, we identified unique networks related to episodic memory and executive function cognitive domains. The episodic memory network was particularly related to tau pathology measured with positron emission tomography in the entorhinal and temporal cortices. Further, episodic memory network strength mediated the relationship between tau pathology and cognitive performance above and beyond neurodegeneration. We replicated the association between these networks and tau pathology in a separate cohort of older adults, including both cognitively unimpaired and mildly impaired individuals. Together, these results suggest that behaviorally meaningful functional brain networks represent a functional mechanism linking tau pathology and cognition.
Aerobic fitness as a moderator of acute aerobic exercise effects on executive function
Authors: Dai Yuan-Fu , Zhong Xiao-Ke , Gao Xiao-Yan , Huang Chen , Leng Wen-Wu , Chen Han-Zhe , Jiang Chang-Hao 
Published: April 2024

Abstract
This study aimed to investigate the moderating role of aerobic fitness on the effect of acute exercise on improving executive function from both behavioral and cerebral aspects. Thirty-four young individuals with motor skills were divided into high- and low-fitness groups based on their maximal oxygen uptake. Both groups completed 30 min of moderate-intensity aerobic exercise on a power bike. Executive function tests (Flanker, N-back, More-odd-shifting) were performed before and after exercise and functional near-infrared spectroscopy was used to monitor prefrontal cerebral blood flow changes during the tasks. The results indicated significant differences between the two groups regarding executive function. Participants with lower aerobic fitness performed better than their higher fitness counterparts in inhibitory control and working memory, but not in cognitive flexibility. This finding suggests that the aerobic fitness may moderate the extent of cognitive benefits gained from acute aerobic exercise. Furthermore, the neuroimaging data indicated negative activation in the frontopolar area and dorsolateral prefrontal cortex in response to three complex tasks. These findings underscore the importance of considering individual aerobic fitness when assessing the cognitive benefits of exercise and could have significant implications for tailoring fitness programs to enhance cognitive performance.