bluuebunny/crossref_metadata_2025 huggingface dataset:
Integrating Iso-seq and RNA-seq data for the reannotation of the greater amberjack genome
Authors: Zhao Yuanli , Chen Zonggui , Hu Meidi , Liu Hairong , Zhao Haiping , Huang Yang , Jiang Mouyan , Li Shengkang , Li Guangli , Zhu Chunhua , Hu Wei , Luo Daji
Published: June 2024

The greater amberjack is a very important fishery species with high commercial value, and it is distributed worldwide. Transcriptome-based studies on S. dumerili have been limited by an inadequate reference genome and a lack of well-annotated full-length transcripts. In this study, a total of 12 tissues from juvenile and adult fish both sexes were collected for next-generation RNA sequencing (RNA-seq) and full-length isoform sequencing (Iso-seq). For Iso-seq, a total of 163,218, 149,716, and 189,169 high-quality unique transcript sequences were obtained, with an N50 of 5,441, 5,255, and 5,939, from juvenile, adult male and adult female S. dumerili, respectively. We integrated the Iso-seq and RNA-seq data to construct a comprehensive gene annotation and systematically profiled the dynamics of gene expression across the 12 tissues. Our gene models had greater detail and accuracy than those from NCBI and Ensembl, with more precise polyA locations. These resources serve as a foundation for functional genomic studies and provide valuable insights into the molecular mechanisms underlying the development, reproduction and commercial traits of amberjack.
Validation and refinement of cropland map in southwestern China by harnessing ten contemporary datasets
Authors: Cui Yifeng , Dong Jinwei , Zhang Chao , Yang Jilin , Chen Na , Guo Peng , Di Yuanyuan , Chen Mengxi , Li Aiwen , Liu Ronggao
Published: June 2024

Accurate cropland map serves as the cornerstone of effective agricultural monitoring. Despite the continuous enrichment of remotely sensed cropland maps, pervasive inconsistencies have impeded their further application. This issue is particularly evident in areas with limited valid observations, such as southwestern China, which is characterized by its complex topography and fragmented parcels. In this study, we constructed multi-sourced samples independent of the data producers, taking advantage of open-source validation datasets and sampling to rectify the accuracy of ten contemporary cropland maps in southwestern China, decoded their inconsistencies, and generated a refined cropland map (CroplandSyn) by leveraging ten state-of-the-art remotely sensed cropland maps released from 2021 onwards using the self-adaptive threshold method. Validations, conducted at both prefecture and county scales, underscored the superiority of the refined cropland map, aligning more closely with national land survey data. The refined cropland map and samples are publicly available to users. Our study offers valuable insights for improving agricultural practices and land management in under-monitored areas by providing high-quality cropland maps and validation datasets.
A thermosurvey dataset: Older adults’ experiences and adaptation to urban heat and climate change
Authors: Jancewicz Barbara , Wrotek Małgorzata
Published: June 2024

We introduce the thermosurvey dataset, a comprehensive collection focusing on the thermal comfort, heat-related experiences, health, socioeconomic status, and perceptions of older adults (aged 65 and over) in Warsaw and Madrid. The two cities differ greatly in their heat experiences, but due to climate change, both face increasing temperatures. The study aimed to understand how heat affects cities’ older adult population and how we can better adapt to rising temperatures. We call the study a thermosurvey because it connects traditional survey data with temperature and humidity measurements done before, after and during the interview, offering a holistic view of the participants’ thermal environments. The dataset can be used to better understand thermal comfort, the interplay of health and heat experiences, and the relationship between experiences and climate change views. We hope our data will enable scholars to analyse the impact of climate change on older adults and to develop strategies to help them adapt to a warming climate.
Chromosome-level genome assembly of Cnidium monnieri, a highly demanded traditional Chinese medicine
Authors: Wang Zixuan , He Jiaxin , Qi Qi , Wang Kaixuan , Tang Huanying , Feng Yimeng , Zhao Xinyue , Yi Shanyong , Zhao Yucheng , Xu Dingqiao
Published: June 2024

Cnidium monnieri, a medicinal herb of the Cnidium genus and the Apiaceae family, is among the most important traditional Chinese medicines and is widely distributed in China. However, to date, no C. monnieri-related genomic information has been described. In this study, we assembled the C. monnieri genome of approximately 1210.23 Mb with a contig N50 of 83.14 Mb. Using PacBio HiFi and Hi-C sequencing data, we successfully anchored 93.86% of the assembled sequences to 10 pseudochromosomes (2n = 20). We predicted a total of 37,460 protein-coding genes, with 97.02% of them being functionally annotated in Non-Redundant, Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and other databases. In addition, we identified 2,778 tRNAs, 4,180 rRNAs, 258 miRNAs, and 1,700 snRNAs in the genome. This is the first reported C. monnieri genome. Hopefully, the availability of this chromosome-level reference genome provides a significant basis for upcoming natural product-related biosynthetic pathway assessment in C. monnieri.
Synthetic data generation for a longitudinal cohort study – evaluation, method extension and reproduction of published data analysis results
Authors: Kühnel Lisa , Schneider Julian , Perrar Ines , Adams Tim , Moazemi Sobhan , Prasser Fabian , Nöthlings Ute , Fröhlich Holger , Fluck Juliane
Published: June 2024

Access to individual-level health data is essential for gaining new insights and advancing science. In particular, modern methods based on artificial intelligence rely on the availability of and access to large datasets. In the health sector, access to individual-level data is often challenging due to privacy concerns. A promising alternative is the generation of fully synthetic data, i.e., data generated through a randomised process that have similar statistical properties as the original data, but do not have a one-to-one correspondence with the original individual-level records. In this study, we use a state-of-the-art synthetic data generation method and perform in-depth quality analyses of the generated data for a specific use case in the field of nutrition. We demonstrate the need for careful analyses of synthetic data that go beyond descriptive statistics and provide valuable insights into how to realise the full potential of synthetic datasets. By extending the methods, but also by thoroughly analysing the effects of sampling from a trained model, we are able to largely reproduce significant real-world analysis results in the chosen use case.
New insights into the anti-inflammatory and anti-melanoma mechanisms of action of azelaic acid and other Fusarium solani metabolites via in vitro and in silico studies
Authors: Ismail Mona , Hassan Marwa H. A. , Mohamed Enas I. A. , Azmy Ahmed F. , Moawad Abeer , Mohammed Rabab , Zaki Mohamed A.
Published: June 2024

Metabolites exploration of the ethyl acetate extract of Fusarium solani culture broth that was isolated from Euphorbia tirucalli root afforded five compounds; 4-hydroxybenzaldehyde (1), 4-hydroxybenzoic acid (2), tyrosol (3), azelaic acid (4), malic acid (5), and fusaric acid (6). Fungal extract as well as its metabolites were evaluated for their anti-inflammatory and anti-hyperpigmentation potential via in vitro cyclooxygenases and tyrosinase inhibition assays, respectively. Azelaic acid (4) exhibited powerful and selective COX-2 inhibition followed by fusaric acid (6) with IC50 values (2.21 ± 0.06 and 4.81 ± 0.14 μM, respectively). As well, azelaic acid (4) had the most impressive tyrosinase inhibitory effect with IC50 value of 8.75 ± 0.18 μM compared to kojic acid (IC50 = 9.27 ± 0.19 μM). Exclusive computational studies of azelaic acid and fusaric acid with COX-2 were in good accord with the in vitro results. Interestingly, this is the first time to investigate and report the potential of compounds 3–6 to inhibit cyclooxygenase enzymes. One of the most invasive forms of skin cancer is melanoma, a molecular docking study using a set of enzymes related to melanoma suggested pirin to be therapeutic target for azelaic acid and fusaric acid as a plausible mechanism for their anti-melanoma activity.
Site selection and prediction of urban emergency shelter based on VGAE-RF model
Authors: Wang Yong , Han Yaoyao , Luo An , Xu Shenghua , Chen Jian , Liu Wangwang
Published: June 2024

As urban development accelerates and natural disasters occur more frequently, the urgency of developing effective emergency shelter planning strategies intensifies. The shelter location selection method under the traditional multi-criteria decision-making framework suffers from issues such as strong subjectivity and insufficient data support. Artificial intelligence offers a robust data-driven approach for site selection; however, many methods neglect the spatial relationships of site selection targets within geographical space. This paper introduces an emergency shelter site selection model that combines a variational graph autoencoder (VGAE) with a random forest (RF), namely VGAE-RF. In the constructed urban spatial topological graph, based on network geographic information, this model captures both the latent features of geographic unit coupling and integrates explicit and latent features to forecast the likelihood of emergency shelters in the construction area. This study takes Beijing, China, as the experimental area and evaluates the reliability of different model methods using a confusion matrix, Receiver Operating Characteristic (ROC) curve, and Imbalance Index of spatial distribution as evaluation indicators. The experimental results indicate that the proposed VGAE-RF model method, which considers spatial semantic associations, displays the best reliability.
Optical, structural and antibacterial properties of phase heterostructured Fe2O3–CuO–CuFe2O4 nanocomposite
Authors: Alnehia Adnan , Hadi Muhammad , Alnahari Hisham , Al-Sharabi Annas
Published: June 2024

The synthesis of the Fe2O3–CuO–CuFe2O4 nanocomposite was effectively achieved through the sol–gel technique, utilizing ethanol as a reactive fuel. Investigation of the nanocomposite’s structure via X-ray Diffraction confirmed the coexistence of Fe2O3, CuO, and CuFe2O4 phases within the material. The Scherrer equation was applied to determine an average crystallite size ranging from 60 to 95 nm. UV–visible spectroscopy studies suggested the material possesses an approximate energy bandgap of 4 eV. Scanning Electron Microscopy provided insights into the nanocomposite’s surface morphology, which exhibited a porous and heterogeneous aggregation of particles in various sizes and shapes. When tested for antibacterial efficacy, the nanocomposite exhibited activity against gram-positive S. aureus with a maximum zone of inhibition (ZOI) measuring 9 mm at the highest concentration, whereas no inhibitory effect was detected against gram-negative E.coli.
Development of a single-chain variable antibody fragment against a conserved region of the SARS-CoV-2 spike protein
Authors: Gao Tingyu , Irie Atsushi , Kouwaki Takahisa , Oshiumi Hiroyuki
Published: June 2024

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has prolonged the duration of the pandemic because of the continuous emergence of new variant strains. The emergence of these mutant strains makes it difficult to detect the virus with the existing antibodies; thus, the development of novel antibodies that can target both the variants as well as the original strain is necessary. In this study, we generated a high-affinity monoclonal antibody (5G2) against the highly conserved region of the SARS-CoV-2 spike protein to detect the protein variants. Moreover, we generated its single-chain variable antibody fragment (sc5G2). The sc5G2 expressed in mammalian and bacterial cells detected the spike protein of the original SARS-CoV-2 and variant strains. The resulting sc5G2 will be a useful tool to detect the original SARS-CoV-2 and variant strains.
Uncertainty of treatment efficacy moderates placebo effects on reinforcement learning
Authors: Augustat Nick , Endres Dominik , Mueller Erik M.
Published: June 2024

The placebo-reward hypothesis postulates that positive effects of treatment expectations on health (i.e., placebo effects) and reward processing share common neural underpinnings. Moreover, experiments in humans and animals indicate that reward uncertainty increases striatal dopamine, which is presumably involved in placebo responses and reward learning. Therefore, treatment uncertainty analogously to reward uncertainty may affect updating from rewards after placebo treatment. Here, we address whether different degrees of uncertainty regarding the efficacy of a sham treatment affect reward sensitivity. In an online between-subjects experiment with N = 141 participants, we systematically varied the provided efficacy instructions before participants first received a sham treatment that consisted of listening to binaural beats and then performed a probabilistic reinforcement learning task. We fitted a Q-learning model including two different learning rates for positive (gain) and negative (loss) reward prediction errors and an inverse gain parameter to behavioral decision data in the reinforcement learning task. Our results yielded an inverted-U-relationship between provided treatment efficacy probability and learning rates for gain, such that higher levels of treatment uncertainty, rather than of expected net efficacy, affect presumably dopamine-related reward learning. These findings support the placebo-reward hypothesis and suggest harnessing uncertainty in placebo treatment for recovering reward learning capabilities.
Utilizing Bayesian inference in accelerated testing models under constant stress via ordered ranked set sampling and hybrid censoring with practical validation
Authors: Hashem Atef F. , Alotaibi Naif , Alyami Salem A. , Abdelkawy Mohamed A. , Elgawad Mohamed A. Abd , Yousof Haitham M. , Abdel-Hamid Alaa H.
Published: June 2024

This research investigates the application of the ordered ranked set sampling (ORSSA) procedure in constant-stress partially accelerated life-testing (CSPALTE). The study adopts the assumption that the lifespan of a specific item under operational stress follows a half-logistic probability distribution. Through Bayesian estimation methods, it concentrates on estimating the parameters, utilizing both asymmetric loss function and symmetric loss function. Estimations are conducted using ORSSAs and simple random samples, incorporating hybrid censoring of type-I. Real-world data sets are utilized to offer practical context and validate the theoretical discoveries, providing concrete insights into the research findings. Furthermore, a rigorous simulation study, supported by precise numerical calculations, is meticulously conducted to gauge the Bayesian estimation performance across the two distinct sampling methodologies. This research ultimately sheds light on the efficacy of Bayesian estimation techniques under varying sampling strategies, contributing to the broader understanding of reliability analysis in CSPALTE scenarios.
An elastic net regression model for predicting the risk of ICU admission and death for hospitalized patients with COVID-19
Authors: Zou Wei , Yao Xiujuan , Chen Yizhen , Li Xiaoqin , Huang Jiandong , Zhang Yong , Yu Lin , Xie Baosong
Published: June 2024

This study aimed to develop and validate prediction models to estimate the risk of death and intensive care unit admission in COVID-19 inpatients. All RT-PCR-confirmed adult COVID-19 inpatients admitted to Fujian Provincial Hospital from October 2022 to April 2023 were considered. Elastic Net Regression was used to derive the risk prediction models. Potential risk factors were considered, which included demographic characteristics, clinical symptoms, comorbidities, laboratory results, treatment process, prognosis. A total of 1906 inpatients were included finally by inclusion/exclusion criteria and were divided into derivation and test cohorts in a ratio of 8:2, where 1526 (80%) samples were used to develop prediction models under a repeated cross-validation framework and the remaining 380 (20%) samples were used for performance evaluation. Overall performance, discrimination and calibration were evaluated in the validation set and test cohort and quantified by accuracy, scaled Brier score (SbrS), the area under the ROC curve (AUROC), and Spiegelhalter-Z statistics. The models performed well, with high levels of discrimination (AUROCICU [95%CI]: 0.858 [0.803,0.899]; AUROCdeath [95%CI]: 0.906 [0.850,0.948]); and good calibrations (Spiegelhalter-ZICU: − 0.821 (p-value: 0.412); Spiegelhalter-Zdeath: 0.173) in the test set. We developed and validated prediction models to help clinicians identify high risk patients for death and ICU admission after COVID-19 infection.
Succinate promotes pulmonary fibrosis through GPR91 and predicts death in idiopathic pulmonary fibrosis
Authors: He Yijun , Han Yuanyuan , Zou Lijun , Yao Tingting , Zhang Yan , Lv Xin , Jiang Mao , Long Lingzhi , Li Mengyu , Cheng Xiaoyun , Jiang Guoliang , Peng Zhangzhe , Tao Lijian , Meng Jie , Xie Wei
Published: June 2024

Idiopathic pulmonary fibrosis (IPF) is believed to be associated with a notable disruption of cellular energy metabolism. By detecting the changes of energy metabolites in the serum of patients with pulmonary fibrosis, we aimed to investigate the diagnostic and prognostic value of energy metabolites in IPF, and further elucidated the mechanism of their involvement in pulmonary fibrosis. Through metabolomics research, it was discovered that the TCA cycle intermediates changed dramatically in IPF patients. In another validation cohort of 55 patients with IPF compared to 19 healthy controls, it was found that succinate, an intermediate product of TCA cycle, has diagnostic and prognostic value in IPF. The cut-off levels of serum succinate were 98.36 μM for distinguishing IPF from healthy controls (sensitivity, 83.64%; specificity, 63.16%; likelihood ratio, 2.27, respectively). Moreover, a high serum succinate level was independently associated with higher rates of disease progression (OR 13.087, 95%CI (2.819–60.761)) and mortality (HR 3.418, 95% CI (1.308–8.927)). In addition, accumulation of succinate and increased expression of the succinate receptor GPR91 were found in both IPF patients and BLM mouse models of pulmonary fibrosis. Reducing succinate accumulation in BLM mice alleviated pulmonary fibrosis and 21d mortality, while exogenous administration of succinate can aggravate pulmonary fibrosis in BLM mice. Furthermore, GPR91 deficiency protected against lung fibrosis caused by BLM. In vitro, succinate promoted the activation of lung fibroblasts by activating ERK pathway through GPR91. In summary, succinate is a promising biomarker for diagnosis and prognosis of IPF. The accumulation of succinate may promote fibroblast activation through GPR91 and pulmonary fibrosis.
Estimating the COVID-19 prevalence from wastewater
Authors: Mohring Jan , Leithäuser Neele , Wlazło Jarosław , Schulte Marvin , Pilz Maximilian , Münch Johanna , Küfer Karl-Heinz
Published: June 2024

Wastewater based epidemiology has become a widely used tool for monitoring trends of concentrations of different pathogens, most notably and widespread of SARS-CoV-2. Therefore, in 2022, also in Rhineland–Palatinate, the Ministry of Science and Health has included 16 wastewater treatment sites in a surveillance program providing biweekly samples. However, the mere viral load data is subject to strong fluctuations and has limited value for political deciders on its own. Therefore, the state of Rhineland–Palatinate has commissioned the University Medical Center at Johannes Gutenberg University Mainz to conduct a representative cohort study called SentiSurv, in which an increasing number of up to 12,000 participants have been using sensitive antigen self-tests once or twice a week to test themselves for SARS-CoV-2 and report their status. This puts the state of Rhineland–Palatinate in the fortunate position of having time series of both, the viral load in wastewater and the prevalence of SARS-CoV-2 in the population. Our main contribution is a calibration study based on the data from 2023-01-08 until 2023-10-01 where we identified a scaling factor ($$0.208 \pm 0.031$$

0.208
±
0.031

) and a delay ($$5.07 \pm 2.30$$

5.07
±
2.30

days) between the virus load in wastewater, normalized by the pepper mild mottle virus (PMMoV), and the prevalence recorded in the SentiSurv study. The relation is established by fitting an epidemiological model to both time series. We show how that can be used to estimate the prevalence when the cohort data is no longer available and how to use it as a forecasting instrument several weeks ahead of time. We show that the calibration and forecasting quality and the resulting factors depend strongly on how wastewater samples are normalized.
Predicting chronic wasting disease in white-tailed deer at the county scale using machine learning
Authors: Ahmed Md Sohel , Hanley Brenda J. , Mitchell Corey I. , Abbott Rachel C. , Hollingshead Nicholas A. , Booth James G. , Guinness Joe , Jennelle Christopher S. , Hodel Florian H. , Gonzalez-Crespo Carlos , Middaugh Christopher R. , Ballard Jennifer R. , Clemons Bambi , Killmaster Charlie H. , Harms Tyler M. , Caudell Joe N. , Benavidez Westrich Kathryn M. , McCallen Emily , Casey Christine , O’Brien Lindsey M. , Trudeau Jonathan K. , Stewart Chad , Carstensen Michelle , McKinley William T. , Hynes Kevin P. , Stevens Ashley E. , Miller Landon A. , Cook Merril , Myers Ryan T. , Shaw Jonathan , Tonkovich Michael J. , Kelly James D. , Grove Daniel M. , Storm Daniel J. , Schuler Krysten L.
Published: June 2024

Continued spread of chronic wasting disease (CWD) through wild cervid herds negatively impacts populations, erodes wildlife conservation, drains resource dollars, and challenges wildlife management agencies. Risk factors for CWD have been investigated at state scales, but a regional model to predict locations of new infections can guide increasingly efficient surveillance efforts. We predicted CWD incidence by county using CWD surveillance data depicting white-tailed deer (Odocoileus virginianus) in 16 eastern and midwestern US states. We predicted the binary outcome of CWD-status using four machine learning models, utilized five-fold cross-validation and grid search to pinpoint the best model, then compared model predictions against the subsequent year of surveillance data. Cross validation revealed that the Light Boosting Gradient model was the most reliable predictor given the regional data. The predictive model could be helpful for surveillance planning. Predictions of false positives emphasize areas that warrant targeted CWD surveillance because of similar conditions with counties known to harbor CWD. However, disagreements in positives and negatives between the CWD Prediction Web App predictions and the on-the-ground surveillance data one year later underscore the need for state wildlife agency professionals to use a layered modeling approach to ensure robust surveillance planning. The CWD Prediction Web App is at https://cwd-predict.streamlit.app/.
Three-dimensional atrous inception module for crowd behavior classification
Authors: Choi Jong-Hyeok , Kim Jeong-Hun , Nasridinov Aziz , Kim Yoo-Sung
Published: June 2024

Recent advances in deep learning have led to a surge in computer vision research, including the recognition and classification of human behavior in video data. However, most studies have focused on recognizing individual behaviors, whereas recognizing crowd behavior remains a complex problem because of the large number of interactions and similar behaviors among individuals or crowds in video surveillance systems. To solve this problem, we propose a three-dimensional atrous inception module (3D-AIM) network, which is a crowd behavior classification model that uses atrous convolution to explore interactions between individuals or crowds. The 3D-AIM network is a 3D convolutional neural network that can use receptive fields of various sizes to effectively identify specific features that determine crowd behavior. To further improve the accuracy of the 3D-AIM network, we introduced a new loss function called the separation loss function. This loss function focuses the 3D-AIM network more on the features that distinguish one type of crowd behavior from another, thereby enabling a more precise classification. Finally, we demonstrate that the proposed model outperforms existing human behavior classification models in terms of accurately classifying crowd behaviors. These results suggest that the 3D-AIM network with a separation loss function can be valuable for understanding complex crowd behavior in video surveillance systems.
Exploratory risk prediction of type II diabetes with isolation forests and novel biomarkers
Authors: Yousef Hibba , Feng Samuel F. , Jelinek Herbert F.
Published: June 2024

Type II diabetes mellitus (T2DM) is a rising global health burden due to its rapidly increasing prevalence worldwide, and can result in serious complications. Therefore, it is of utmost importance to identify individuals at risk as early as possible to avoid long-term T2DM complications. In this study, we developed an interpretable machine learning model leveraging baseline levels of biomarkers of oxidative stress (OS), inflammation, and mitochondrial dysfunction (MD) for identifying individuals at risk of developing T2DM. In particular, Isolation Forest (iForest) was applied as an anomaly detection algorithm to address class imbalance. iForest was trained on the control group data to detect cases of high risk for T2DM development as outliers. Two iForest models were trained and evaluated through ten-fold cross-validation, the first on traditional biomarkers (BMI, blood glucose levels (BGL) and triglycerides) alone and the second including the additional aforementioned biomarkers. The second model outperformed the first across all evaluation metrics, particularly for F1 score and recall, which were increased from 0.61 ± 0.05 to 0.81 ± 0.05 and 0.57 ± 0.06 to 0.81 ± 0.08, respectively. The feature importance scores identified a novel combination of biomarkers, including interleukin-10 (IL-10), 8-isoprostane, humanin (HN), and oxidized glutathione (GSSG), which were revealed to be more influential than the traditional biomarkers in the outcome prediction. These results reveal a promising method for simultaneously predicting and understanding the risk of T2DM development and suggest possible pharmacological intervention to address inflammation and OS early in disease progression.
Improved prediction of anti-angiogenic peptides based on machine learning models and comprehensive features from peptide sequences
Authors: Lee Yun-Chen , Yu Jen-Chieh , Ni Kuan , Lin Yu-Chuan , Chen Ching-Tai
Published: June 2024

Angiogenesis is a key process for the proliferation and metastatic spread of cancer cells. Anti-angiogenic peptides (AAPs), with the capability of inhibiting angiogenesis, are promising candidates in cancer treatment. We propose AAPL, a sequence-based predictor to identify AAPs with machine learning models of improved prediction accuracy. Each peptide sequence was transformed to a vector of 4335 numeric values according to 58 different feature types, followed by a heuristic algorithm for feature selection. Next, the hyperparameters of six machine learning models were optimized with respect to the feature subset. We considered two datasets, one with entire peptide sequences and the other with 15 amino acids from peptide N-termini. AAPL achieved Matthew’s correlation coefficients of 0.671 and 0.756 for independent tests based on the two datasets, respectively, outperforming existing predictors by a range of 5.3% to 24.6%. Further analyses show that AAPL yields higher prediction accuracy for peptides with more hydrophobic residues, and fewer hydrophilic and charged residues. The source code of AAPL is available athttps://github.com/yunzheng2002/Anti-angiogenic.
Spatio-temporal evolution and prediction of carbon balance in the Yellow River Basin and zoning for low-carbon economic development
Authors: Dong Linlin
Published: June 2024

Studying the carbon effect of land use in watersheds is important for mitigating global warming, promoting coordinated emission reduction in different regions within the watersheds, and realizing high-quality development of the watersheds. Although a number of scholars have carried out relevant studies in the past, they mainly focused on carbon emissions, rarely involved the carbon balance formed by carbon sources and sinks, and lacked relevant studies on the development of low-carbon economy sub-region. Based on this, this study takes the Yellow River Basin as an example, explores the spatial and temporal evolution of carbon emissions from land use in counties in the Yellow River Basin from 1980 to 2020, and predicts the spatial pattern of carbon income and expenditure from land use under natural conditions in 2030 and 2060 using the PLUS model; and then superimposes on the main functional area planning, divides 735 counties in the Yellow River Basin into six low-carbon economic development subregions, and analyzes their economic development The model of their economic development is analyzed. The results show that: (1) the spatial and temporal differentiation of land use carbon balance in the Yellow River Basin has changed greatly over the past 40 years, (2) the spatial distribution pattern of land use carbon balance in the natural context in 2030 and 2060 is more similar to that in 1990, (3) the carbon emission reduction potentials and pattern optimization of the different low-carbon economic development subregions differ greatly, and they have different low-carbon economic development patterns. The results of this study provide a theoretical basis for scientifically and rationally formulating economic policies for low-carbon development in the counties of the Yellow River Basin, and also provide an important reference for related studies in other similar basins or regions in the world.
Comparative evaluation of screening tools for sarcopenia in patients with axial spondyloarthritis
Authors: Chaiamnuay Sumapa , Kanjanavaikoon Natnicha , Saisirivechakun Pannarat
Published: June 2024

Sarcopenia is linked to chronic inflammation and muscle wasting. This research aims to compare the screening accuracy of tools for sarcopenia in axial spondyloarthritis (axSpA). A cross-sectional study involving 104 axSpA patients was conducted at Phramongkutklao Hospital between January 2020 and February 2021. Sarcopenia was diagnosed according to the AWGS 2019 criteria. Appendicular skeletal muscle mass was measured using DXA. SARC-F, SARC-CalF, and SARC-F+EBM, muscle strength, and physical performance were assessed. The screening tests were evaluated using ROC curves. The optimal cutoffs were identified with the Youden index. Most patients were male (74%), with a mean (SD) age and disease duration of 42.6 (12.22) and 8.3 (8.5), respectively. The prevalence of sarcopenia was 22.1%. The AUCs (95% CI) for calf circumference, SARC-F, SARC-CalF, SARC-F+EBM, handgrip strength, chair stand time, gait speed, and time and go test were 0.830 (0.734, 0.925), 0.509 (0.373–0.645), 0.782 (0.670–0.894), 0.856 (0.758–0.954), 0.710 (0.594–0.825), 0.640 (0.508–0.772), 0.689 (0.539–0.839), and 0.711 (0.576–0.846), respectively. The optimal cutoffs for SARC-F, SARC-CalF, and SARC-F+EBM were 1, 10, and 10, with sensitivity/specificity of 81.0%/29.7%, 90.5%/68.9%, and 77.3%/87.2%, respectively. Calf circumference, SARC-CalF, and SARC-F+EBM had the best performance to screen for sarcopenia in axSpA patients. Lowering the thresholds would potentially enhance the performances of SARC-CalF and SARC-F+EBM.
Bacteriophage ISP eliminates Staphylococcus aureus in planktonic phase, but not in the various stages of the biofilm cycle
Authors: Verheul Mariëlle , Mulder Aat A. , van Dun Sven C. J. , Merabishvili Maia , Nelissen Rob G. H. H. , de Boer Mark G. J. , Pijls Bart G. , Nibbering Peter H.
Published: June 2024

Metal-implant associated bacterial infections are a major clinical problem due to antibiotic treatment failure. As an alternative, we determined the effects of bacteriophage ISP on clinical isolates of Staphylococcus aureus in various stages of its life cycle in relation to biofilm formation and maturation. ISP effectively eliminated all planktonic phase bacteria, whereas its efficacy was reduced against bacteria attached to the metal implant and bacteria embedded within biofilms. The biofilm architecture hampered the bactericidal effects of ISP, as mechanical disruption of biofilms improved the efficacy of ISP against the bacteria. Phages penetrated the biofilm and interacted with the bacteria throughout the biofilm. However, most of the biofilm-embedded bacteria were phage-tolerant. In agreement, bacteria dispersed from mature biofilms of all clinical isolates, except for LUH15394, tolerated the lytic activity of ISP. Lastly, persisters within mature biofilms tolerated ISP and proliferated in its presence. Based on these findings, we conclude that ISP eliminates planktonic phase Staphylococcus aureus while its efficacy is limited against bacteria attached to the metal implant, embedded within (persister-enriched) biofilms, and dispersed from biofilms.
Physical unclonable function using photonic spin Hall effect
Authors: Divyanshu Divyanshu , Goyal Amit Kumar , Massoud Yehia
Published: June 2024

This study presents a novel method leveraging surface wave-assisted photonic spin Hall effect (PSHE) to construct physical unclonable functions (PUFs). PUFs exploit inherent physical variations to generate unique Challenge–Response pairs, which are critical for hardware security and arise from manufacturing discrepancies, device characteristics, or timing deviations. We explore PSHE generation-based PUF design, expanding existing design possibilities. With recent applications in precise sensing and computing, PSHE offers promising performance metrics for our proposed PUFs, including an inter-Hamming distance of 47.50% , an average proportion of unique responses of 62.5% , and a Pearson correlation coefficient of − 0.198. The PUF token demonstrates robustness to simulated noise. Additionally, we evaluate security using a machine learning-based attack model, employing a multi-layer perceptron (MLP) regression model with a randomized search method. The average accuracy of successful attack prediction is 9.70% for the selected dataset. Our novel PUF token exhibits high non-linearity due to the PSHE effect, resilience to MLP-based attacks, and sensitivity to process variation.
Observational study of frailty in older Japanese patients with non-valvular atrial fibrillation receiving anticoagulation therapy
Authors: Matsui Kunihiko , Kusano Kengo , Akao Masaharu , Tsuji Hikari , Hiramitsu Shinya , Hatori Yutaka , Odakura Hironori , Ogawa Hisao
Published: June 2024

The number of patients with atrial fibrillation is increasing, and frailty prevalence increases with age, posing challenges for physicians in prescribing anticoagulants to such patients because of possible harm. The effects of frailty on anticoagulant therapy in older Japanese patients with nonvalvular atrial fibrillation (NVAF) are unclear. Herein, we prescribed rivaroxaban to Japanese patients with NVAF and monitored for a mean of 2.0 years. The primary endpoint was stroke or systemic embolism. The secondary endpoints were all-cause or cardiovascular death, composite endpoint, and major or non-major bleeding. Frailty was assessed using the Japanese long-term care insurance system. A multiple imputation technique was used for missing data. The propensity score (PS) was obtained to estimate the treatment effect of frailty and was used to create two PS-matched groups. Overall, 5717 older patients had NVAF (mean age: 73.9 years), 485 (8.5%) were classified as frail. After PS matching, background characteristics were well-balanced between the groups. Rivaroxaban dosages were 10 and 15 mg/day for approximately 80% and the remaining patients, respectively. Frailty was not associated with the primary endpoint or secondary endpoints. In conclusion, frailty does not affect the effectiveness or safety of rivaroxaban anticoagulant therapy in older Japanese patients with NVAF.

Trial registration: UMIN000019135, NCT02633982.
Satellite data is revealing long time changes in the world largest lakes
Authors: Kutser Tiit , Soomets Tuuli
Published: June 2024

Lakes are a crucial source of drinking water, provide ecological services from fisheries and aquaculture to tourism and are also a critical part of the global carbon cycle. Therefore, it is important to understand how lakes are changing over time. The ESA Ocean Colour Climate Change Initiative (OC-CCI) database allows to study changes in the largest lakes over 1997–2023 period. The Caspian Sea and ten next largest lakes were under investigation. Changes in the phytoplankton biomass (Chl-a), the concentration of particulate matter (bbp(555)), the colored dissolved organic matter, CDOM (adg(412)), and the light diffuse attenuation coefficient in water (Kd(490)) were analyzed. Both increasing and decreasing trends (or no significant trend at all) of studied parameters were observed in these lakes over the study period. In some of the Laurentian Great Lakes the changes in CDOM over the study period were found to be in accordance with the lake water level changes i.e. with the inflow from the catchment. There was difference between the trends of Chl-a and bbp(555) in lakes Michigan and Huron indicating that there may have been shift in phytoplankton community that took place around 2005. The study demonstrated that remote sensing products, like the ones created by ESA OC-CCI, are valuable tools to study behavior of large lakes ecosystems over time.
360-degree virtual reality video to teach neonatal resuscitation: an exploratory development study
Authors: Tachejian Sevag , Moussa Ahmed
Published: June 2024

Simulation is an effective training method for neonatal resuscitation (NR). However, the limitations brought about by the COVID-19 pandemic, and other resource constraints, have necessitated exploring alternatives. Virtual reality (VR), particularly 360-degree VR videos, have gained attraction in medical training due to their immersive qualities. The primary objectives of the study were to produce a high quality 360-degree virtual reality (VR) video capturing NR simulation and to determine if it could be an acceptable adjunct to teach NR. The secondary objective was to determine which aspects of NR could benefit from the incorporation of such a video in training. This was an exploratory development study. The first part consisted of producing the video using a GoPro action camera, Adobe Premiere Pro, and Unity Editor. In the second part participants were recruited, based on level of experience, to watch the video and answer questionnaires to determine acceptability (user experience and cognitive load) and aspects of NR which could benefit from the video. The video was successfully developed. Forty-six participants showed a strong general appreciation. User experience revealed high means (> 6) in the positive subscales and low means (< 4) for immersion side effect, with no difference between groups. Cognitive load was higher than anticipated. Participants indicated that this video could be effective for teaching crisis resource management principles, human and environment interactions, and procedural skills. The 360-degree VR video could be a potential new simulation adjunct for NR. Future studies are needed to evaluate learning outcomes of such videos.
A digital image analysis approach to understand the microscopic and macroscopic phenomena in dissolved air flotation
Authors: Taghizadeh Mohammadi Mohammad Javad , Movahedirad Salman
Published: June 2024

Dissolved air flotation (DAF) is an effective method for separating suspended oil and solid particles from wastewater by utilizing small air bubbles. This study aims to investigate the impact of key factors, such as saturating pressure and water flow rate, on the separation of fine oil droplets from a water stream. The macroscopic flow patterns within the cell were analyzed using particle image velocimetry (PIV), while Digital Image Analysis (DIA) was employed to study microscopic phenomena, including oil droplet rising velocity and oil-bubble contact mechanisms. Our findings propose a safe operating window (specifically, water flow rate and saturation pressure) for the effective separation of oil droplets without any oil escaping into the clean water stream. It was found that the oil droplet rising velocity increases with the saturation pressure up to 200 kPa. However, a further increase in the pressure of the air saturating chamber leads to a decrease in oil droplet rising velocity. Additionally, we identified a peak in rising velocity at an oil droplet size of approximately 200 µm. Below this threshold, the rising velocity increases with droplet size, while for droplet sizes exceeding 200 µm, the rising velocity decreases with size. This behavior can be explained by the conflicting effects of droplet size increment according to the Stokes law for the rising velocity of oil droplets. As the droplet size increases, the average density of the bubbles/droplet aggregate rises, reducing the ∆ρ in the Stokes law and subsequently lowering the aggregate rising rate. However, as per the Stokes law, the oil droplet rising velocity increases proportionally to the square of its size.
Dietary incorporation of brown seaweed spent oyster mushroom substrate alters growth performance, physiological responses and meat quality parameters in Boschveld roosters
Authors: Mhlongo Godfrey , Mnisi Caven M.
Published: June 2024

Use of brown seaweed (Ecklonia maxima) as a nutraceutical source in indigenous chicken diets is limited by high dietary fibre levels. Inoculating seaweeds with oyster mushroom (Pleurotus ostreatus) spawn (OMS) could enhance the utility of the spent mushroom substrate (SMS). This study investigated the effect of feeding incremental levels of brown seaweed SMS on growth performance, physiological responses, and meat quality parameters in Boschveld roosters. A total of 324, 4-week-old Boschveld roosters were weighed and randomly allotted to 36 pens (9 birds per pen) to produce six replicates per dietary treatment. The diets were formulated as follows: a standard grower diet (CON); and CON containing 150 g/kg of brown seaweed inoculated with OMS at 0 (SMS0), 20 (SMS20), 30 (SMS30), 40 (SMS40) and 50% (SMS50). Birds fed diet CON had the least feed intake (p < 0.05) than all the other SMS treatment levels in weeks 7, 8, 12, 14 and 15. Diet SMS40 promoted higher (p < 0.05) body weight gain (BWG) than CON in weeks 6, 7, 9 and 14. Gain-to-feed ratio linearly increased in weeks 7 [R2 = 0.288; p = 0.010], 11 [R2 = 0.581, p = 0.0001] and 14 [R2 = 0.389, p = 0.004], respectively. Quadratic responses (p < 0.05) were observed for BWG in week 5, white blood cells, heterophils, platelets, lymphocytes, monocytes, and relative spleen and large intestine weights as OMS levels increased. Linear increases were recorded for slaughter [R2 = 0.197, p = 0.017] and breast weights [R2 = 0.197, p = 0.020] as OMS levels increased. Diet SMS0 promoted higher (p < 0.05) relative caeca weights than the CON and SMS treatment groups. Neither quadratic nor linear responses (p > 0.05) were observed for breast meat quality parameters. In conclusion, feeding brown seaweed SMS improved growth performance and slaughter weight, altered some blood parameters and internal organs, without affecting breast meat quality of Boschveld roosters. Based on the quadratic response for BWG, the optimum OMS level was deduced at 20% in a brown seaweed-based Boschveld rooster diet.
Alternative crop residue management practices to mitigate the environmental and economic impacts of open burning of agricultural residues
Authors: Prateep Na Talang Rutjaya , Na Sorn Warangluck , Polruang Sucheela , Sirivithayapakorn Sanya
Published: June 2024

Deliberate open burning of crop residues emits greenhouse gases and toxic pollutants into the atmosphere. This study investigates the environmental impacts (global warming potential, GWP) and economic impacts (net cash flow) of nine agricultural residue management schemes, including open burning, fertilizer production, and biochar production for corn residue, rice straw, and sugarcane leaves. The environmental assessment shows that, except the open burning schemes, fossil fuel consumption is the main contributor of the GWP impact. The fertilizer and biochar schemes reduce the GWP impact including black carbon by 1.88–1.96 and 2.46–3.22 times compared to open burning. The biochar schemes have the lowest GWP (− 1833.19 to − 1473.21 kg CO2-eq/ton). The economic assessment outcomes reveal that the biochar schemes have the highest net cash flow (222.72—889.31 US$2022/ton or 1258.15–13409.16 US$2022/ha). The expenditures of open burning are practically zero, while the biochar schemes are the most costly to operate. The most preferable agricultural residue management type is the biochar production, given the lowest GWP impact and the highest net cash flow. To discourage open burning, the government should tailor the government assistance programs to the needs of the farmers and make the financial assistance more accessible.
Topography of the GLP-1/GLP-1 receptor system in the spinal cord of male mice
Authors: Ruska Yvette , Csibi Andrea , Dorogházi Beáta , Szilvásy-Szabó Anett , Mohácsik Petra , Környei Zsuzsanna , Dénes Ádám , Kádár Andrea , Puskár Zita , Hrabovszky Erik , Gereben Balázs , Wittmann Gábor , Fekete Csaba
Published: June 2024

Glucagon-like peptide-1 receptor (GLP-1R) agonists are now commonly used to treat type 2 diabetes and obesity. GLP-1R signaling in the spinal cord has been suggested to account for the mild tachycardia caused by GLP-1R agonists, and may also be involved in the therapeutic effects of these drugs. However, the neuroanatomy of the GLP-1/GLP-1R system in the spinal cord is still poorly understood. Here we applied in situ hybridization and immunohistochemistry to characterize this system, and its relation to cholinergic neurons. GLP-1R transcript and protein were expressed in neuronal cell bodies across the gray matter, in matching distribution patterns. GLP-1R-immunolabeling was also robust in dendrites and axons, especially in laminae II–III in the dorsal horn. Cerebrospinal fluid-contacting neurons expressed GLP-1R protein at exceedingly high levels. Only small subpopulations of cholinergic neurons expressed GLP-1R, including a subset of sympathetic preganglionic neurons at the rostral tip of the intermediolateral nucleus. GLP-1 axons innervated all regions where GLP-1R neurons were distributed, except laminae II–III. Scattered preproglucagon (Gcg) mRNA-expressing neurons were identified in the cervical and lumbar enlargements. The results will facilitate further studies on how GLP-1 regulates the sympathetic system and other autonomic and somatic functions via the spinal cord.