bluuebunny/crossref_metadata_2025_split huggingface dataset:
Metagenomics datasets of water and sediments from eutrophication-impacted artificial lakes in South Africa
Authors: Ijoma Grace Nkechinyere , Ogola Henry Joseph Oduor , Pillay Preenan , Tshisekedi Kalonji Abondance , Tekere Memory 
Published: May 2024

AbstractWe present metagenomes of 16 samples of water and sediment from two lakes, collected from eutrophic and non-eutrophic areas, including pooled samples enriched with phosphate and nitrate. Additionally, we assembled 167 bacterial metagenome-assembled genomes (MAGs). These MAGs were de-replicated into 83 unique genomes representing different species found in the lakes. All the MAGs exhibited >70% completeness and <10% contamination, with 79 MAGs being classified as ‘nearly complete’ (completeness >90%), while 54 falling within 80–90% range and 34 between 75–80% complete. The most abundant MAGs identified across all samples were Proteobacteria (n = 80), Firmicutes_A (n = 35), Firmicutes (n = 13), and Bacteriodota (n = 22). Other groups included Desulfobacteria_I (n = 2), Verrucomicrobiota (n = 4), Campylobacterota (n = 4) and Actinobacteriota (n = 6). Importantly, phylogenomic analysis identified that approximately 50.3% of the MAGs could not be classified to known species, suggesting the presence of potentially new and unknown bacteria in these lakes, warranting further in-depth investigation. This study provides valuable new dataset on the diverse and often unique microbial communities living in polluted lakes, useful in developing effective strategies to manage pollution.
Conventional and frugal methods of estimating COVID-19-related excess deaths and undercount factors
Authors: Dedhe Abhishek M. , Chowkase Aakash A. , Gogate Niramay V. , Kshirsagar Manas M. , Naphade Rohan , Naphade Atharv , Kulkarni Pranav , Naik Mrunmayi , Dharm Aarya , Raste Soham , Patankar Shravan , Jogdeo Chinmay M. , Sathe Aalok , Kulkarni Soham , Bapat Vibha , Joshi Rohinee , Deshmukh Kshitij , Lele Subhash , Manke-Miller Kody J. , Cantlon Jessica F. , Pandit Pranav S. 
Published: May 2024

AbstractAcross the world, the officially reported number of COVID-19 deaths is likely an undercount. Establishing true mortality is key to improving data transparency and strengthening public health systems to tackle future disease outbreaks. In this study, we estimated excess deaths during the COVID-19 pandemic in the Pune region of India. Excess deaths are defined as the number of additional deaths relative to those expected from pre-COVID-19-pandemic trends. We integrated data from: (a) epidemiological modeling using pre-pandemic all-cause mortality data, (b) discrepancies between media-reported death compensation claims and official reported mortality, and (c) the “wisdom of crowds” public surveying. Our results point to an estimated 14,770 excess deaths [95% CI 9820–22,790] in Pune from March 2020 to December 2021, of which 9093 were officially counted as COVID-19 deaths. We further calculated the undercount factor—the ratio of excess deaths to officially reported COVID-19 deaths. Our results point to an estimated undercount factor of 1.6 [95% CI 1.1–2.5]. Besides providing similar conclusions about excess deaths estimates across different methods, our study demonstrates the utility of frugal methods such as the analysis of death compensation claims and the wisdom of crowds in estimating excess mortality.
Synthesis and in silico inhibitory action studies of azo-anchored imidazo[4,5-b]indole scaffolds against the COVID-19 main protease (Mpro)
Authors: Geedkar Deepika , Kumar Ashok , Sharma Pratibha 
Published: May 2024

AbstractThe present work elicits a novel approach to combating COVID-19 by synthesizing a series of azo-anchored 3,4-dihydroimidazo[4,5-b]indole derivatives. The envisaged methodology involves the l-proline-catalyzed condensation of para-amino-functionalized azo benzene, indoline-2,3-dione, and ammonium acetate precursors with pertinent aryl aldehyde derivatives under ultrasonic conditions. The structures of synthesized compounds were corroborated through FT-IR, 1H NMR, 13C NMR, and mass analysis data. Molecular docking studies assessed the inhibitory potential of these compounds against the main protease (Mpro) of SARS-CoV-2. Remarkably, in silico investigations revealed significant inhibitory action surpassing standard drugs such as Remdesivir, Paxlovid, Molnupiravir, Chloroquine, Hydroxychloroquine (HCQ), and (N3), an irreversible Michael acceptor inhibitor. Furthermore, the highly active compound was also screened for cytotoxicity activity against HEK-293 cells and exhibited minimal toxicity across a range of concentrations, affirming its favorable safety profile and potential suitability. The pharmacokinetic properties (ADME) of the synthesized compounds have also been deliberated. This study paves the way for in vitro and in vivo testing of these scaffolds in the ongoing battle against SARS-CoV-2.
Improved random forest classification model combined with C5.0 algorithm for vegetation feature analysis in non-agricultural environments
Authors: Wang Tianyu 
Published: May 2024

AbstractIn response to the challenges posed by the high computational complexity and suboptimal classification performance of traditional random forest algorithms when dealing with high-dimensional and noisy non-agricultural vegetation satellite data, this paper proposes an enhanced random forest algorithm based on the C5.0 algorithm. The paper focuses on the Liaohe Plain, selecting two distinct non-agricultural landscape patterns in Shenbei New District and Changtu County as research objects. High-resolution satellite data from GF-2 serves as the experimental dataset. This paper introduces an ensemble feature method based on the bagging concept to improve the original random forest classification model. This method enhances the likelihood of selecting features beneficial to classifying positive class samples, avoiding excessive removal of useful features from negative samples. This approach ensures feature importance and model diversity. The C5.0 algorithm is then employed for feature selection, and the enhanced vegetation index (EVI) is utilized for vegetation coverage estimation. Results indicate that employing a multi-scale parameter selection tool, combined with limited field-measured data, facilitates the identification and classification of plant species in forest landscapes. The C5.0 algorithm effectively selects classification features, minimizing information redundancy. The established object-oriented random forest classification model achieves an impressive accuracy of 94.02% on the aerial imagery for forest classification dataset, with EVI-based vegetation coverage estimation demonstrating high accuracy. In experiments on the same test set, the proposed algorithm attains an average accuracy of 90.20%, outperforming common model algorithms such as bidirectional encoder representation from transformer, FastText, and convolutional neural network, which achieve average accuracies ranging from 84.41 to 88.33% in identifying non-agricultural artificial habitat vegetation features. The proposed algorithm exhibits a competitive edge compared to other algorithms. These research findings contribute scientific evidence for protecting agricultural ecosystems and restoring agricultural ecosystem biodiversity.
Scenarios for the Altamira cave CO2 concentration from 1950 to 2100
Authors: Sáez Marina , Benavente David , Cuezva Soledad , Huc Mireille , Fernández-Cortés Ángel , Mialon Arnaud , Kerr Yann , Sánchez-Moral Sergio , Mangiarotti Sylvain 
Published: May 2024

AbstractA data-driven approach insensitive to the initial conditions was developed to extract governing equations for the concentration of CO2in the Altamira cave (Spain) and its two main drivers: the outside temperature and the soil moisture. This model was then reformulated in order to use satellite observations and meteorological predictions, as a forcing. The concentration of CO2inside the cave was then investigated from 1950 to 2100 under various scenarios. It is found that extreme levels of CO2were reached during the period 1950–1972 due to the massive affluence of visitors. It is demonstrated that it is possible to monitor the CO2in the cave in real time using satellite information as an external forcing. For the future, it is shown that the maximum values of CO2will exceed the levels reached during the 1980s and the 1990s when the CO2introduced by the touristic visits, although intentionally reduced, still enhanced considerably the micro corrosion of walls and pigments.
Essentiality of dietary cholesterol and its interactions with phospholipid in juvenile slipper lobster (Thenus australiensis)
Authors: Landman Michael J. , Codabaccus Basseer M. , Nichols David S. , Carter Chris G. , Fitzgibbon Quinn P. , Smith Gregory G. 
Published: May 2024

AbstractThis study was conducted to verify the essentiality of dietary cholesterol for early juvenile slipper lobster, Thenus australiensis (initial weight 4.50 ± 0.72 g, mean ± SD, CV = 0.16), and to explore the potential for interactions between dietary cholesterol and phospholipid. An 8-week experiment was conducted using six experimental feeds containing three supplemental cholesterol concentrations (0, 0.2 and 0.4% dry matter) at two supplemental phospholipid concentrations (0% and 1.0% dry matter). Dietary cholesterol concentrations of ≥ 0.2% resulted in up to threefold greater weight gain compared to 0% dietary cholesterol, but without any significant main or interactive dietary phospholipid effect. An interaction was observed for lobster survival with lowest survival (46%) recorded for combined 0% cholesterol and 0% phospholipid compared to every other treatment (71–100%). However, all surviving lobsters at 0% dietary cholesterol, regardless of dietary phospholipid level, were in poor nutritional condition. Apparent feed intake (AFI) was significantly higher at dietary cholesterol ≥ 0.2% but was lower for each corresponding dietary cholesterol level at 1% dietary phospholipid. This implied that the feed conversion ratio was improved with supplemental phospholipid. In conclusion, this study confirms the essential nature of dietary cholesterol and that dietary phospholipid can provide additional benefits.
Trefoil factor 3 can stimulate Th17 cell response in the development of type 2 diabetes mellitus
Authors: Lin Ziyang , Zhang Jinyuan , Duan Tingting , Yang Junzheng , Yang Yiqi 
Published: May 2024

AbstractThis study aims to evaluate the role of trefoil factor 3 (TFF3) peptides in type 2 diabetes mellitus (T2DM) from an inflammatory perspective. The focus was on exploring how TFF3 affects the function of T cells. TFF3 overexpression model was constructed using lentivirus in Jurkat cell lines. We evaluated the impact of TFF3 on the proliferation, apoptosis, and IL-17A levels of Jurkat cells cultured in high glucose. The T2DM model was induced in TFF3 knockout (KO) mice through streptozotocin combined with high-fat diet. The measurements included glucose tolerance, insulin tolerance, inflammation markers, Th17 cell proportion, and pancreatic pathological changes. The T2DM modeling led to splenomegaly in mice, and increased expression of TFF3 in their spleens. Overexpression of TFF3 increased the proportion of IL-17+ T cells and the levels of Th17-related cytokines in Jurkat cells. There was no difference in body weight and blood glucose levels between wild-type and TFF3 KO mice. However, T2DM mice lacking the TFF3 gene showed improved glucose utilization, ameliorated pancreatic pathology, decreased inflammation levels, and reduced Th17 cell ratio. TFF3 may be involved in the chronic inflammatory immune response in T2DM. Its mechanism may be related to the regulation of the RORγt/IL-17 signaling pathway and its impact on T cell proliferation and apoptosis.
Neurological features of Hansen disease: a retrospective, multicenter cohort study
Authors: Chen Xiaohua , Di Li , Qian Min , Shen Dongchao , Feng Xinhong , Zhang Xiqing 
Published: May 2024

AbstractTo elucidate the neurological features of Hansen disease. The medical records of patients with confirmed Hansen disease transferred from the neurology department were reviewed, and all medical and neurological manifestations of Hansen disease were assessed. Eleven patients with confirmed Hansen disease, 10 with newly detected Hansen disease and 1 with relapsed Hansen disease, who visited neurology departments were enrolled. The newly detected patients with Hansen disease were classified as having lepromatous leprosy (LL, n = 1), borderline lepromatous leprosy (BL, n = 2), borderline leprosy (BB, n = 2), borderline tuberculoid leprosy (BT, n = 1), tuberculoid leprosy (TT, n = 2), or pure neural leprosy (PNL, n = 2). All of the patients with confirmed Hansen were diagnosed with peripheral neuropathy (100.00%, 11/11). The symptoms and signs presented were mainly limb numbness (100.00%, 11/11), sensory and motor dysfunction (100.00%, 11/11), decreased muscle strength (90.90%, 10/11), and skin lesions (81.81%, 9/11). Nerve morphological features in nerve ultrasonography (US) included peripheral nerve asymmetry and segmental thickening (100.00%, 9/9). For neuro-electrophysiology feature, the frequency of no response of sensory nerves was significantly higher than those of motor nerves [(51.21% 42/82) vs (24.70%, 21/85)(P = 0.0183*)] by electrodiagnostic (EDX) studies. Nerve histological features in nerve biopsy analysis included demyelination (100.00%, 5/5) and axonal damage (60.00%, 3/5). In addition to confirmed diagnoses by acid-fast bacteria (AFB) staining (54.54%, 6/11) and skin pathology analysis (100.00%, 8/8), serology and molecular technology were positive in 36.36% (4/11) and 100.00% (11/11) of confirmed patients of Hansen disease, respectively. It is not uncommon for patients of Hansen disease to visit neurology departments due to peripheral neuropathy. The main pathological features of affected nerves are demyelination and axonal damage. The combination of nerve US, EDX studies, nerve biopsy, and serological and molecular tests can improve the diagnosis of Hansen disease.
Future groundwater potential mapping using machine learning algorithms and climate change scenarios in Bangladesh
Authors: Sarkar Showmitra Kumar , Rudra Rhyme Rubayet , Talukdar Swapan , Das Palash Chandra , Nur Md. Sadmin , Alam Edris , Islam Md Kamrul , Islam Abu Reza Md. Towfiqul 
Published: May 2024

AbstractThe aim of the study was to estimate future groundwater potential zones based on machine learning algorithms and climate change scenarios. Fourteen parameters (i.e., curvature, drainage density, slope, roughness, rainfall, temperature, relative humidity, lineament density, land use and land cover, general soil types, geology, geomorphology, topographic position index (TPI), topographic wetness index (TWI)) were used in developing machine learning algorithms. Three machine learning algorithms (i.e., artificial neural network (ANN), logistic model tree (LMT), and logistic regression (LR)) were applied to identify groundwater potential zones. The best-fit model was selected based on the ROC curve. Representative concentration pathways (RCP) of 2.5, 4.5, 6.0, and 8.5 climate scenarios of precipitation were used for modeling future climate change. Finally, future groundwater potential zones were identified for 2025, 2030, 2035, and 2040 based on the best machine learning model and future RCP models. According to findings, ANN shows better accuracy than the other two models (AUC: 0.875). The ANN model predicted that 23.10 percent of the land was in very high groundwater potential zones, whereas 33.50 percent was in extremely high groundwater potential zones. The study forecasts precipitation values under different climate change scenarios (RCP2.6, RCP4.5, RCP6, and RCP8.5) for 2025, 2030, 2035, and 2040 using an ANN model and shows spatial distribution maps for each scenario. Finally, sixteen scenarios were generated for future groundwater potential zones. Government officials may utilize the study’s results to inform evidence-based choices on water management and planning at the national level.
Designing of a new transdermal antibiotic delivery polymeric membrane modified by functionalized SBA-15 mesoporous filler
Authors: Samari Mahya , Kashanian Soheila , Zinadini Sirus , Derakhshankhah Hossein 
Published: May 2024

AbstractA new drug delivery system using an asymmetric polyethersulfone (PES) membrane modified by SBA-15 and glutamine-modified SBA-15 (SBA-Q) was prepared in this study by the aim of azithromycin delivery enhancement in both in vitro and ex vivo experiments. The research focused on optimizing membrane performance by adjusting critical parameters including drug concentration, membrane thickness, modifier percentage, polymer percentage, and pore maker percentage. To characterize the fabricated membranes, various techniques were employed, including scanning electron microscopy, water contact angle, and tensile strength assessments. Following optimization, membrane composition of 17% PES, 2% polyvinylpyrrolidone, 1% SBA-15, and 0.5% SBA-Q emerged as the most effective. The optimized membranes demonstrated a substantial increase in drug release (906 mg/L) compared to the unmodified membrane (440 mg/L). The unique membrane structure, with a dense top layer facilitating sustained drug release and a porous sub-layer acting as a drug reservoir, contributed to this improvement. Biocompatibility assessments, antibacterial activity analysis, blood compatibility tests, and post-diffusion tissue integrity evaluations confirmed the promising biocompatibility of the optimized membranes. Moreover, long-term performance evaluations involving ten repeated usages underscored the reusability of the optimized membrane, highlighting its potential for sustained and reliable drug delivery applications.
Oral microbiome dysbiosis among cigarette smokers and smokeless tobacco users compared to non-users
Authors: Chattopadhyay Suhana , Malayil Leena , Chopyk Jessica , Smyth Eoghan , Kulkarni Prachi , Raspanti Greg , Thomas Stephen B. , Sapkota Amir , Mongodin Emmanuel F. , Sapkota Amy R. 
Published: May 2024

AbstractTobacco use significantly influences the oral microbiome. However, less is known about how different tobacco products specifically impact the oral microbiome over time. To address this knowledge gap, we characterized the oral microbiome of cigarette users, smokeless tobacco users, and non-users over 4 months (four time points). Buccal swab and saliva samples (n = 611) were collected from 85 participants. DNA was extracted from all samples and sequencing was carried out on an Illumina MiSeq, targeting the V3–V4 region of the 16S rRNA gene. Cigarette and smokeless tobacco users had more diverse oral bacterial communities, including a higher relative abundance of Firmicutes and a lower relative abundance of Proteobacteria, when compared to non-users. Non-users had a higher relative abundance of Actinomyces, Granulicatella, Haemophilus, Neisseria, Oribacterium, Prevotella, Pseudomonas, Rothia, and Veillonella in buccal swab samples, compared to tobacco users. While the most abundant bacterial genera were relatively constant over time, some species demonstrated significant shifts in relative abundance between the first and last time points. In addition, some opportunistic pathogens were detected among tobacco users including Neisseria subflava, Bulleidia moorei and Porphyromonas endodontalis. Overall, our results provide a more holistic understanding of the structure of oral bacterial communities in tobacco users compared to non-users.
Pseudo-class part prototype networks for interpretable breast cancer classification
Authors: Choukali Mohammad Amin , Amirani Mehdi Chehel , Valizadeh Morteza , Abbasi Ata , Komeili Majid 
Published: May 2024

AbstractInterpretability in machine learning has become increasingly important as machine learning is being used in more and more applications, including those with high-stakes consequences such as healthcare where Interpretability has been regarded as a key to the successful adoption of machine learning models. However, using confounding/irrelevant information in making predictions by deep learning models, even the interpretable ones, poses critical challenges to their clinical acceptance. That has recently drawn researchers’ attention to issues beyond the mere interpretation of deep learning models. In this paper, we first investigate application of an inherently interpretable prototype-based architecture, known as ProtoPNet, for breast cancer classification in digital pathology and highlight its shortcomings in this application. Then, we propose a new method that uses more medically relevant information and makes more accurate and interpretable predictions. Our method leverages the clustering concept and implicitly increases the number of classes in the training dataset. The proposed method learns more relevant prototypes without any pixel-level annotated data. To have a more holistic assessment, in addition to classification accuracy, we define a new metric for assessing the degree of interpretability based on the comments of a group of skilled pathologists. Experimental results on the BreakHis dataset show that the proposed method effectively improves the classification accuracy and interpretability by respectively $$8 \%$$

8
%

and $$18 \%$$

18
%

. Therefore, the proposed method can be seen as a step toward implementing interpretable deep learning models for the detection of breast cancer using histopathology images.
Automated machine learning model for fundus image classification by health-care professionals with no coding experience
Authors: Zago Ribeiro Lucas , Nakayama Luis Filipe , Malerbi Fernando Korn , Regatieri Caio Vinicius Saito 
Published: May 2024

AbstractTo assess the feasibility of code-free deep learning (CFDL) platforms in the prediction of binary outcomes from fundus images in ophthalmology, evaluating two distinct online-based platforms (Google Vertex and Amazon Rekognition), and two distinct datasets. Two publicly available datasets, Messidor-2 and BRSET, were utilized for model development. The Messidor-2 consists of fundus photographs from diabetic patients and the BRSET is a multi-label dataset. The CFDL platforms were used to create deep learning models, with no preprocessing of the images, by a single ophthalmologist without coding expertise. The performance metrics employed to evaluate the models were F1 score, area under curve (AUC), precision and recall. The performance metrics for referable diabetic retinopathy and macular edema were above 0.9 for both tasks and CFDL. The Google Vertex models demonstrated superior performance compared to the Amazon models, with the BRSET dataset achieving the highest accuracy (AUC of 0.994). Multi-classification tasks using only BRSET achieved similar overall performance between platforms, achieving AUC of 0.994 for laterality, 0.942 for age grouping, 0.779 for genetic sex identification, 0.857 for optic, and 0.837 for normality with Google Vertex. The study demonstrates the feasibility of using automated machine learning platforms for predicting binary outcomes from fundus images in ophthalmology. It highlights the high accuracy achieved by the models in some tasks and the potential of CFDL as an entry-friendly platform for ophthalmologists to familiarize themselves with machine learning concepts.