bluuebunny/crossref_metadata_2025_split huggingface dataset:
Optimal control strategies for toxoplasmosis disease transmission dynamics via harmonic mean-type incident rate
Authors: Khan Usman , Ali Farhad , Alqasem Ohud A. , Elwahab Maysaa E. A. , Khan Ilyas , Rahimzai Ariana Abdul 
Published: June 2024

AbstractToxoplasma infection in humans is considered due to direct contact with infected cats. Toxoplasma infection (an endemic disease) has the potential to affect various organs and systems (brain, eyes, heart, lungs, liver, and lymph nodes). Bilinear incidence rate and constant population (birth rate is equal to death rate) are used in the literature to explain the dynamics of Toxoplasmosis disease transmission in humans and cats. The goal of this study is to consider the mathematical model of Toxoplasma disease with harmonic mean type incident rate and also consider that the population of humans and cats is not equal (birth rate and the death rate are not equal). In examining Toxoplasma transmission dynamics in humans and cats, harmonic mean incidence rates are better than bilinear incidence rates. The disease dynamics are first schematically illustrated, and then the law of mass action is applied to obtain nonlinear ordinary differential equations (ODEs). Analysis of the boundedness, positivity, and equilibrium points of the system has been analyzed. The reproduction number is calculated using the next-generation matrix technique. The stability of disease-free and endemic equilibrium are analyzed. Sensitivity analysis is also done for reproduction number. Numerical simulation shows that the infection is spread in the population when the contact rate $$\beta_{h}$$

β
h

and $$\beta_{c}$$

β
c

increases while the infection is reduced when the recovery rate $$\delta_{h}$$

δ
h

increases. This study investigates the impact of various optimal control strategies, such as vaccinations for the control of disease and the awareness of disease awareness, on the management of disease.
Prevalence and genetic diversity of Anaplasma and Ehrlichia in ticks and domesticated animals in Suizhou County, Hubei Province, China
Authors: Tang Ju , Xu Jiao , Liu Xiao-hui , Lv Fang-zhi , Yao Qiu-ju , Zhou Xiao-Fan , Lu Hui-ya , Yu Tian-mei , Jiang Ze-zheng , Jin Xiao-zhou , Guo Fang , Yu Xue-jie 
Published: June 2024

AbstractAnaplasma and Ehrlichia are tick-borne bacterial pathogens that cause anaplasmoses and ehrlichioses in humans and animals. In this study, we examined the prevalence of Anaplasma and Ehrlichia species in ticks and domesticated animals in Suizhou County, Hubei Province in the central China. We used PCR amplification and DNA sequencing of the 16S rRNA, groEL, and gltA genes to analyze. We collected 1900 ticks, including 1981 Haemaphysalis longicornis and 9 Rhipicephalus microplus, 159 blood samples of goats (n = 152), cattle (n = 4), and dogs (n = 3) from May to August of 2023. PCR products demonstrated that Anaplasma bovis, Anaplasma capra, and an Ehrlichia species were detected in the H. longicornis with the minimum infection rates (MIR) of 1.11%, 1.32%, and 0.05%, respectively; A. bovis, A. capra, and unnamed Anaplasma sp. were detected in goats with an infection rate of 26.31%, 1.31% and 1.97%, respectively. Anaplasma and Ehrlichia species were not detected from cattle, dogs and R. microplus ticks. The genetic differences in the groEL gene sequences of the Anaplasma in the current study were large, whereas the 16S rRNA and gltA gene sequences were less disparate. This study shows that ticks and goats in Suizhou County, Hubei Province carry multiple Anaplasma species and an Ehrlichia species, with relatively higher infection rate of A. bovis in goats. Our study indicates that multiple Anaplasma and Ehrlichia species exist in ticks and goats in the central China with potential to cause human infection.
A study on deep learning model based on global–local structure for crowd flow prediction
Authors: Go HeounMo , Park SangHyun 
Published: June 2024

AbstractCrowd flow prediction has been studied for a variety of purposes, ranging from the private sector such as location selection of stores according to the characteristics of commercial districts and customer-tailored marketing to the public sector for social infrastructure design such as transportation networks. Its importance is even greater in light of the spread of contagious diseases such as COVID-19. In many cases, crowd flow can be divided into subgroups by common characteristics such as gender, age, location type, etc. If we use such hierarchical structure of the data effectively, we can improve prediction accuracy of crowd flow for subgroups. But the existing prediction models do not consider such hierarchical structure of the data. In this study, we propose a deep learning model based on global–local structure of the crowd flow data, which utilizes the overall(global) and subdivided by the types of sites(local) crowd flow data simultaneously to predict the crowd flow of each subgroup. The experiment result shows that the proposed model improves the prediction accuracy of each sub-divided subgroup by 5.2% (Table 5 Cat #9)—45.95% (Table 11 Cat #5), depending on the data set. This result comes from the comparison with the related works under the same condition that use target category data to predict each subgroup. In addition, when we refine the global data composition by considering the correlation between subgroups and excluding low correlated subgroups, the prediction accuracy is further improved by 5.6–48.65%.
Measuring global cerebrovascular pulsatility transmission using 4D flow MRI
Authors: Dempsey Sergio , Safaei Soroush , Holdsworth Samantha J. , Maso Talou Gonzalo D. 
Published: June 2024

AbstractPulse wave encephalopathy (PWE) is hypothesised to initiate many forms of dementia, motivating its identification and risk assessment. As candidate pulsatility based biomarkers for PWE, pulsatility index and pulsatility damping have been studied and, currently, do not adequately stratify risk due to variability in pulsatility and spatial bias. Here, we propose a locus-independent pulsatility transmission coefficient computed by spatially tracking pulsatility along vessels to characterise the brain pulse dynamics at a whole-organ level. Our preliminary analyses in a cohort of 20 subjects indicate that this measurement agrees with clinical observations relating blood pulsatility with age, heart rate, and sex, making it a suitable candidate to study the risk of PWE. We identified transmission differences between vascular regions perfused by the basilar and internal carotid arteries attributed to the identified dependence on cerebral blood flow, and some participants presented differences between the internal carotid perfused regions that were not related to flow or pulsatility burden, suggesting underlying mechanical differences. Large populational studies would benefit from retrospective pulsatility transmission analyses, providing a new comprehensive arterial description of the hemodynamic state in the brain. We provide a publicly available implementation of our tools to derive this coefficient, built into pre-existing open-source software.
Structural, dynamic behaviour, in-vitro and computational investigations of Schiff’s bases of 1,3-diphenyl urea derivatives against SARS-CoV-2 spike protein
Authors: Ullah Saeed , Ullah Atta , Waqas Muhammad , Halim Sobia Ahsan , Pasha Anam Rubbab , Shafiq Zahid , Mali Suraj N. , Jawarkar Rahul D. , Khan Ajmal , Khalid Asaad , Abdalla Ashraf N. , Kashtoh Hamdy , Al-Harrasi Ahmed 
Published: June 2024

AbstractThe COVID-19 has had a significant influence on people's lives across the world. The viral genome has undergone numerous unanticipated changes that have given rise to new varieties, raising alarm on a global scale. Bioactive phytochemicals derived from nature and synthetic sources possess lot of potential as pathogenic virus inhibitors. The goal of the recent study is to report new inhibitors of Schiff bases of 1,3-dipheny urea derivatives against SARS COV-2 spike protein through in-vitro and in-silico approach. Total 14 compounds were evaluated, surprisingly, all the compounds showed strong inhibition with inhibitory values between 79.60% and 96.00% inhibition. Here, compounds 3a (96.00%), 3d (89.60%), 3e (84.30%), 3f (86.20%), 3g (88.30%), 3h (86.80%), 3k (82.10%), 3l (90.10%), 3m (93.49%), 3n (85.64%), and 3o (81.79%) exhibited high inhibitory potential against SARS COV-2 spike protein. While 3c also showed significant inhibitory potential with 79.60% inhibition. The molecular docking of these compounds revealed excellent fitting of molecules in the spike protein receptor binding domain (RBD) with good interactions with the key residues of RBD and docking scores ranging from − 4.73 to − 5.60 kcal/mol. Furthermore, molecular dynamics simulation for 150 ns indicated a strong stability of a complex 3a:6MOJ. These findings obtained from the in-vitro and in-silico study reflect higher potency of the Schiff bases of 1,3-diphenyl urea derivatives. Furthermore, also highlight their medicinal importance for the treatment of SARS COV-2 infection. Therefore, these small molecules could be a possible drug candidate.
Diagnostic value of combined ultrasound contrast and elastography for differentiating benign and malignant thyroid nodules: a meta-analysis
Authors: Liu Funing , Wang Yihan , Xiong Yu , Li Xin , yao Jun , Ju Hao , Ren Fu , Zhang Luwei , Wang Hongbo 
Published: June 2024

AbstractThe diagnostic value of contrast-enhanced ultrasound combined with ultrasound elastography for benign and malignant thyroid nodules is still controversial, so we used meta-analysis to seek controversial answers. The PubMed, OVID, and CNKI databases were searched according to the inclusion and exclusion criteria. The literature was selected from the establishment of each database to February 2024. The QUADAS-2 tool assessed diagnostic test accuracy. SROC curves and Spearman's correlation coefficient were made by Review Manager 5.4 software to assess the presence of threshold effects in the literature. Meta-Disc1.4 software was used for Cochrane-Q and χ2 tests, which be used to evaluate heterogeneity, with P-values and I2 indicating heterogeneity levels. The appropriate effect model was selected based on the results of the heterogeneity test. Stata18.0 software was used to evaluate publication bias. The diagnostic accuracy of contrast-enhanced ultrasound combined with ultrasound elastography for benign and malignant thyroid nodules was evaluated by calculating the combined sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, DOR, and area under the SROC curve. A total of 31 studies included 3811 patients with 4718 nodules were analyzed. There is no heterogeneity caused by the threshold effect, but there is significant non-threshold heterogeneity. Combined diagnostic metrics were: sensitivity = 0.93, specificity = 0.91, DOR = 168.41, positive likelihood ratio = 10.60, and negative likelihood ratio = 0.07. The SROC curve area was 0.97. Contrast-enhanced ultrasound and elastography show high diagnostic accuracy for thyroid nodules, offering a solid foundation for early diagnosis and treatment.Trial registration. CRD42024509462.
Mitochondrial RNA modification-based signature to predict prognosis of lower grade glioma: a multi-omics exploration and verification study
Authors: Zhou Xingwang , Ling Yuanguo , Cui Junshuan , Wang Xiang , Long Niya , Teng Wei , Liu Jian , Xiang Xin , Yang Hua , Chu Liangzhao 
Published: June 2024

AbstractMitochondrial RNA modification (MRM) plays a crucial role in regulating the expression of key mitochondrial genes and promoting tumor metastasis. Despite its significance, comprehensive studies on MRM in lower grade gliomas (LGGs) remain unknown. Single-cell RNA-seq data (GSE89567) was used to evaluate the distribution functional status, and correlation of MRM-related genes in different cell types of LGG microenvironment. We developed an MRM scoring system by selecting potential MRM-related genes using LASSO regression analysis and the Random Survival Forest algorithm, based on multiple bulk RNA-seq datasets from TCGA, CGGA, GSE16011, and E-MTAB-3892. Analysis was performed on prognostic and immunological features, signaling pathways, metabolism, somatic mutations and copy number variations (CNVs), treatment responses, and forecasting of potential small-molecule agents. A total of 35 MRM-related genes were selected from the literature. Differential expression analysis of 1120 normal brain tissues and 529 LGGs revealed that 22 and 10 genes were upregulated and downregulated, respectively. Most genes were associated with prognosis of LGG. METLL8, METLL2A, TRMT112, and METTL2B were extensively expressed in all cell types and different cell cycle of each cell type. Almost all cell types had clusters related to mitochondrial RNA processing, ribosome biogenesis, or oxidative phosphorylation. Cell–cell communication and Pearson correlation analyses indicated that MRM may promoting the development of microenvironment beneficial to malignant progression via modulating NCMA signaling pathway and ICP expression. A total of 11 and 9 MRM-related genes were observed by LASSO and the RSF algorithm, respectively, and finally 6 MRM-related genes were used to establish MRM scoring system (TRMT2B, TRMT11, METTL6, METTL8, TRMT6, and TRUB2). The six MRM-related genes were then validated by qPCR in glioma and normal tissues. MRM score can predict the malignant clinical characteristics, abundance of immune infiltration, gene variation, clinical outcome, the enrichment of signaling pathways and metabolism. In vitro experiments demonstrated that silencing METTL8 significantly curbs glioma cell proliferation and enhances apoptosis. Patients with a high MRM score showed a better response to immunotherapies and small-molecule agents such as arachidonyl trifluoromethyl ketone, MS.275, AH.6809, tacrolimus, and TTNPB. These novel insights into the biological impacts of MRM within the glioma microenvironment underscore its potential as a target for developing precise therapies, including immunotherapeutic approaches.
Estimation of rice yield using multivariate analysis techniques based on meteorological parameters
Authors: Sharma Ajay , Kumar Joginder , Redhu Mandeep , Kumar Parveen , Godara Mohit , Ghiyal Pushpa , Fu Pingping , Rahimi Mehdi 
Published: June 2024

Abstract
This study aims to develop predictive models for rice yield by applying multivariate techniques. It utilizes stepwise multiple regression, discriminant function analysis and logistic regression techniques to forecast crop yield in specific districts of Haryana. The time series data on rice crop have been divided into two and three classes based on crop yield. The yearly time series data of rice yield from 1980–81 to 2020–21 have been taken from various issues of Statistical Abstracts of Haryana. The study also utilized fortnightly meteorological data sourced from the Agrometeorology Department of CCS HAU, India. For comparing various predictive models' performance, evaluation of measures like Root Mean Square Error, Predicted Error Sum of Squares, Mean Absolute Deviation and Mean Absolute Percentage Error have been used. Results of the study indicated that discriminant function analysis emerged as the most effective to predict the rice yield accurately as compared to logistic regression. Importantly, the research highlighted that the optimum time for forecasting the rice yield is 1 month prior to the crops harvesting, offering valuable insight for agricultural planning and decision-making. This approach demonstrates the fusion of weather data and advanced statistical techniques, showcasing the potential for more precise and informed agricultural practices.
Accelerating 3D genomics data analysis with Microcket
Authors: Zhao Yu , Yang Mengqi , Gong Fanglei , Pan Yuqi , Hu Minghui , Peng Qin , Lu Leina , Lyu Xiaowen , Sun Kun 
Published: June 2024

AbstractThe three-dimensional (3D) organization of genome is fundamental to cell biology. To explore 3D genome, emerging high-throughput approaches have produced billions of sequencing reads, which is challenging and time-consuming to analyze. Here we present Microcket, a package for mapping and extracting interacting pairs from 3D genomics data, including Hi-C, Micro-C, and derivant protocols. Microcket utilizes a unique read-stitch strategy that takes advantage of the long read cycles in modern DNA sequencers; benchmark evaluations reveal that Microcket runs much faster than the current tools along with improved mapping efficiency, and thus shows high potential in accelerating and enhancing the biological investigations into 3D genome. Microcket is freely available at https://github.com/hellosunking/Microcket.
Dynamic molecular portraits of ion-conducting pores characterize functional states of TRPV channels
Authors: Trofimov Yury A. , Krylov Nikolay A. , Minakov Alexander S. , Nadezhdin Kirill D. , Neuberger Arthur , Sobolevsky Alexander I. , Efremov Roman G. 
Published: June 2024

AbstractStructural biology is solving an ever-increasing number of snapshots of ion channel conformational ensembles. Deciphering ion channel mechanisms, however, requires understanding the ensemble dynamics beyond the static structures. Here, we present a molecular modeling-based approach characterizing the ion channel structural intermediates, or their “dynamic molecular portraits”, by assessing water and ion conductivity along with the detailed evaluation of pore hydrophobicity and residue packing. We illustrate the power of this approach by analyzing structures of few vanilloid-subfamily transient receptor potential (TRPV) channels. Based on the pore architecture, there are three major states that are common for TRPVs, which we call α-closed, π-closed, and π-open. We show that the pore hydrophobicity and residue packing for the open state is most favorable for the pore conductance. On the contrary, the α-closed state is the most hydrophobic and always non-conducting. Our approach can also be used for structural and functional classification of ion channels.
Automated federated learning‐based adversarial attack and defence in industrial control systems
Authors: Zeng Guo‐Qiang , Shao Jun‐Min , Lu Kang‐Di , Geng Guang‐Gang , Weng Jian 
Published: June 2024

AbstractWith the development of deep learning and federated learning (FL), federated intrusion detection systems (IDSs) based on deep learning have played a significant role in securing industrial control systems (ICSs). However, adversarial attacks on ICSs may compromise the ability of deep learning‐based IDSs to accurately detect cyberattacks, leading to serious consequences. Moreover, in the process of generating adversarial samples, the selection of replacement models lacks an effective method, which may not fully expose the vulnerabilities of the models. The authors first propose an automated FL‐based method to generate adversarial samples in ICSs, called AFL‐GAS, which uses the principle of transfer attack and fully considers the importance of replacement models during the process of adversarial sample generation. In the proposed AFL‐GAS method, a lightweight neural architecture search method is developed to find the optimised replacement model composed of a combination of four lightweight basic blocks. Then, to enhance the adversarial robustness, the authors propose a multi‐objective neural architecture search‐based IDS method against adversarial attacks in ICSs, called MoNAS‐IDSAA, by considering both classification performance on regular samples and adversarial robustness simultaneously. The experimental results on three widely used intrusion detection datasets in ICSs, such as secure water treatment (SWaT), Water Distribution, and Power System Attack, demonstrate that the proposed AFL‐GAS method has obvious advantages in evasion rate and lightweight compared with other four methods. Besides, the proposed MoNAS‐IDSAA method not only has a better classification performance, but also has obvious advantages in model adversarial robustness compared with one manually designed federated adversarial learning‐based IDS method.
Programmable cancer treatments: Engineering biology approaches for living cures
Authors: Biarnes Carrera Marc , Sevko Alexandra , Glanville Nicholas , Deban Livija 
Published: June 2024

AbstractLiving cures are cell‐based, programmable therapies that integrate the latest learnings in immunology and synthetic biology. Although Adoptive Cell Therapies (ACTs) have transformed the treatment landscape of haematological malignancies by harnessing the powerful anti‐tumour properties of immune cells, commercialisation and ensuring access is challenging. Their application in solid tumour treatment has been hindered by the immunosuppressive tumour microenvironment (TME) and its associated physical barriers. Conversely, bacterial immunotherapies offer cost‐effective solutions by utilising tumour‐colonising bacteria that trigger localised inflammatory responses within the TME. The authors briefly examine advancements in ACT and propose bacterial immunotherapies as an alternative or complementary treatment modality with potential use either as standalone therapies or in conjunction with other treatments.
Beyond central-local relations: the introduction of a new perspective on China’s environmental governance model
Authors: Chen Haisong , Feng Li , Sun Xiaoyu 
Published: June 2024

AbstractIn recent years, the Chinese government has initiated assertive centralization efforts in its approach to environmental governance. However, the efficacy of these initiatives demonstrates marked variability across different sectors. While the central-local relational framework has traditionally been employed to elucidate these disparities, its explanatory power is showing signs of strain. This paper, through policy analysis and field interviews, investigates regulatory conflicts in land use for ecological and agricultural purposes within China. The findings highlight instances of overlapping jurisdictions and discordant policy objectives among agencies responsible for delineating ecological spaces and agricultural zones. While these conflicts seem to stem from the dynamics between the central and local governments, they more accurately reflect the inherent characteristics of fragmented authoritarianism. This paper aims to expand the theory of fragmented authoritarianism by incorporating the dimension of inter-departmental competition into environmental governance, moving beyond the traditional binary analysis framework of central-local relations. It seeks to understand and critique its limitations from a broader perspective, emphasizing the role of departmental competition within fragmented authoritarianism. By analyzing this internal mechanism, the paper reveals a more nuanced governance landscape, essentially replacing the traditional central-local paradigm with a model that situates departmental competition within the overall context of fragmented authoritarianism. We propose two models for delineating competition among governmental institutions: the bureaucratic model and the charisma model, thereby advancing and deepening the application of fragmented authoritarianism theory in China’s environmental governance. This provides new theoretical insights for understanding the current challenges and developments in China’s environmental governance.