bluuebunny/crossref_metadata_2025_split huggingface dataset:
Variabilities and similarities of adult stem cells derived intestinal organoids originating from different intestinal segments in pig
Authors: Kar Soumya K , Pas Marinus F. W. te , Rikkers Roxann , Madsen Ole , Taverne Nico , Ellen Esther D , Wells Jerry M , Schokker Dirkjan 
Published: February 2023

Abstract
Background
Organoids are in vitro model systems generated from tissues. Organoids express specific physiological functions associated with their original tissue location and they express tissue-segment-specific genes. The aim of this study was to investigate the resemblance of duodenum, ileum (with or without Peyer’s Patches (PP) – PP could be recognized visually), and colon tissue-derived organoids with the tissue of origin.
Methods and Results
We studied the variability of multiple adjacent sampling sites based on histology and transcriptome profiles. The transcriptome profiles of the in vivo tissues and the derived organoids showed high resemblance for all intestinal segments. Transcriptomic cluster analysis showed that it is important to use tissue-specific genes to show the resemble between tissue and their derived organoids. The transcriptome profiles clearly separated the intestinal segments, and samples of the same segment from adjacent tissue locations showed high transcriptome profile similarity. Ileum samples with and without PP were also separated. Pathway analysis of differentially expressed genes from PP compared with non-PP suggested the importance of several aspects of cell cycle progression regulation, including DNA metabolism, chromatin organization, regulation of mitotic stage progression, and regulation of inflammation.
Conclusions
Organoids reflect the sampled intestinal segment and that organoids derived from adjacent sampling sites in an intestinal tissue segment showed low variability. The results from the ileum indicate that organoids have potential to study intestinal immune processes.
Glycan-shielded homodimer structure and dynamical features of the canine distemper virus hemagglutinin relevant for viral entry and efficient vaccination
Authors: Fukuhara Hideo , Yumoto Kohei , Sako Miyuki , Kajikawa Mizuho , Ose Toyoyuki , Kawamura Mihiro , Yoda Mei , Chen Surui , Ito Yuri , Takeda Shin , Mwaba Mwila , Wang Jiaqi , Hashiguchi Takao , Kamishikiryo Jun , Maita Nobuo , Kitatsuji Chihiro , Takeda Makoto , Kuroki Kimiko , Maenaka Katsumi 
Published: February 2023

Abstract

Canine distemper virus (CDV) belongs to morbillivirus, including measles virus (MeV), which causes serious immunological and neurological disorders in carnivores, but their vaccines are highly effective. The attachment glycoprotein hemagglutinin (CDV-H) at the CDV surface utilizes signaling lymphocyte activation molecule (SLAM) and Nectin-4 as entry receptors. Although fusion models have been proposed, the molecular mechanism of morbillivirus fusion entry is poorly understood. Here, we determined the crystal structure of the globular head domain of CDV-H vaccine strain at 3.2 Å resolution, revealing that CDV-H exhibits a highly tilted homodimeric form with a six-bladed β-propeller fold. While the predicted Nectin-4 binding site is well-conserved with that of MeV-H, that of SLAM is similar but partially different, which contributes to host specificity. Five N-linked sugars covered a broad area of the CDV-H surface to expose receptor binding sites only, supporting the effective production of neutralizing antibodies. These features are common to MeV-H, although the glycosylation sites are completely different. Furthermore, real-time observation using high-speed atomic force microscopy revealed highly mobile features of the CDV-H dimeric head. These results suggest that sugar-shielded tilted-homodimeric structure and dynamic changes are common characteristics of morbilliviruses and ensure effective fusion entry and vaccination.
Detection, Visualization and Quantification of Protein Complexes in Human Alzheimer’s Disease Brains using Proximity Ligation Assay
Authors: Romero-Fernandez Wilber , Carvajal-Tapia Cristian , Prusky Alex , Katdare Ketaki , Wang Emmeline , Shostak Alena , Ventura-Antunes Lissa , Harmsen Hannah , Lippmann Ethan , Borroto-Escuela Dasiel , MacGurn Jason , Fuxe Kjell , Schrag Matthew 
Published: February 2023

Abstract
Examination of healthy and diseased human brain is essential to translational neuroscience. Protein-protein interactions play a pivotal role in physiological and pathological processes, but their detection is difficult, especially in aged and fixed human brain tissue. We used the proximity ligation assay (PLA) to broaden the range of molecular interactions assessable in-situ in human neuropathology. We adapted fluorescent in-situ PLA to detect ubiquitin-modified proteins in human brains with Alzheimer’s disease (AD), including approaches for the management of autofluorescence and quantification using a high-content image analysis system. We confirmed that hyperphosphorylated microtubule-associated protein tau (Serine202, Threonine205) aggregates were modified by ubiquitin and that phospho-tau-ubiquitin complexes were increased in hippocampal and frontal cortex regions in AD compared to non-AD brains. Overall, we refined PLA for use in human neuropathology, which has revealed a profound change in the distribution of ubiquitin in AD brain and its association with characteristic tau pathologies.
Efficient Continuous kNN Join over Dynamic High-dimensional Data
Authors: Ukey Nimish , Zhang Guangjian , Yang Zhengyi , Li Binghao , Li Wei , Zhang Wenjie 
Published: February 2023

Abstract

Given a user dataset U and an object dataset I, a kNN join query in high-dimensional space returns the k nearest neighbors of each object in dataset U from the object dataset I. The kNN join is a basic and necessary operation in many applications, such as databases, data mining, computer vision, multi-media, machine learning, recommendation systems, and many more. In the real world, datasets frequently update dynamically as objects are added or removed. In this paper, we propose novel methods of continuous kNN join over dynamic high-dimensional data. We firstly propose the HDR+ Tree which supports more efficient insertion, deletion, and batch update. Further observed that the existing methods rely on globally correlated datasets for effective dimen-sionality reduction, we then propose the HDR Forest. It clusters the dataset and constructs multiple HDR Trees to capture local correlations among the data. As a result, our HDR Forest is able to process non-globally correlated dataset efficiently. Two novel optimisations are applied to the proposed HDR Forest, including the precomputation of the PCA states of data items and pruning-based kNN recomputation during item deletion. For the completeness of the work, we also present the proof of computing distances in reduced dimensions of PCA in HDR Tree. Extensive experiments on real-world datasets show that the proposed methods and optimisations outperform the baseline algorithms of naive RkNN join and HDR Tree.
Lysosomal Storage Dysfunction, a Germline Variants Affecting Pancreatic Ductal Adenocarcinoma Development and Progression
Authors: Park Joo Kyung , Koh Youngil , Kim Hyemin , Song Seulki , Choi Young Hoon , Joo So Young , Kim Hyung Rae , Moon Byul , Byun Jamin , Hong Junshik , Shin Dong-Yeop , Park Solip , Lee Kwang Hyuck , Lee Kyu Taek , Lee Jong Kyun , Park Daechan , Jang Jin-Young , Lee Hyunsook , Kim Jung-Ae , Yoon Sung-Soo 
Published: February 2023

Abstract
Lysosome is closely linked to autophagy, which plays a vital role in pancreatic adenocarcinoma (PDAC) tumor biology. This study investigated whether lysosome storage dysfuncton(LSD) contributes to PDAC development. Germline putative pathogenic variants (PPVs) in genes involved in lysosome functions were compared between PDAC patients (N=418) and healthy controls (N=845). Furthermore, Galc-knockout mouse pancreas organoids and human PDAC organoids were used to evaluate the consequences of PPV status in PDAC development and establishment. LSD PPVs were enriched in PDAC patients (Log2OR=1.65, P=3.08×10−3). PPV carriers diagnosed with PDAC were younger than non-carriers (61.5 vs. 65.3 years, P=0.031). Hampered autophagolysosome activity with increased autophagy flux and elevated Ki-67 index were observed following GALC downregulation. RNA sequencing of human PDAC organoids revealed upregulation of metabolism related to LSD. Genetically defined lysosome dysfunction is frequently observed in young-age onset PDACs. Lysosome dysfunction might contribute to PDAC development via altered metabolism and impaired autophagolysosome activity.
Prediction of rainfall time series using the hybrid DWT-SVR-Prophet model
Authors: Li Dongsheng , Ma Jinfeng , Rao Kaifeng , Wang Xiaoyan , Li Ruonan , Yang Yanzheng , Zheng Hua 
Published: February 2023

Abstract
The discrete wavelet method can be used to decompose rainfall time series into subseries of different frequencies. It would be worthwhile to investigate whether combining forecasting results from different frequency subseries could improve the accuracy of rainfall prediction. A novel DWT-SVR-Prophet (DSP) hybrid model for rainfall prediction is proposed in this paper. First, the rainfall time series is decomposed into high-frequency and low-frequency subseries using discrete wavelet transform (DWT). The SVR and Prophet models are then used to predict high-frequency and low-frequency subsequences, respectively. Finally, the predicted rainfall is determined by summing the predicted values of each subsequence. A case study in China is conducted from January 1, 2014, to June 30, 2016. The results show that the DSP model provides excellent prediction, with RMSE, MAE, R2 values of 6.17, 3.3, and 0.75, respectively. The DSP model yields higher prediction accuracy than the three baseline models considered, with the prediction accuracy ranking as follows: DSP > SSP > Prophet > SVR. In addition, the DSP model is quite stable, and can achieve good results when applied to rainfall data from various climate types, with RMSEs ranging from 1.24 to 7.31, MAEs ranging from 0.52 to 6.14 and R2 values ranging from 0.62 to 0.75. The proposed model may provide a noval approach for rainfall forecasting and is readily adaptable to other time series predictions.
Association of lifestyle factors and breast cancer risk in Vietnamese women: A matched case-control study
Authors: Nguyen Hien D. , Phung An HT. , Do Thanh C. , Nguyen Quynh HN , Tran Thach S. , Nguyen Tuan V. , Ho-Pham Lan T. 
Published: February 2023

Abstract
Purpose
Lifestyle factors might play a role in the development of breast cancer. This study aimed to examine the relationship between lifestyle factors and the risk of breast cancer in Vietnamese women.
Methods
This study included 370 newly diagnosed breast cancer cases and 370 age-matched controls recruited between September 2019 and March 2020 in Ho Chi Minh City. Breast cancer diagnoses were confirmed through pathological examination, while controls were randomly selected from a non-cancer population. Lifestyle factors, including smoking status, sleep duration, and physical activity level, were collected through a face-to-face interview. The association between lifestyle factors and breast cancer risk was analyzed using adjusted conditional logistic regression, and a case impact number (CIN) was calculated to determine the effect of these factors on breast cancer risk..
Results
Passive smokers were associated with an almost two-fold greater risk of breast cancer compared to non-smokers (adjusted odd ratios 1.69, 95%CI: 1.09-2.63). The CIN was 4, meaning that passive smoking was a contributing factor to one out of every four breast cancer cases. Physical activity level and daily sleep duration were not significantly associated with breast cancer risk.
Conclusion
Passive smoking was associated with breast cancer in Vietnamese women, highlighting the potential for implementing a passive smoking prevention policy to reduce breast cancer.
Palliative care in rural and regional health care settings: A qualitative study of enabling factors and implementation challenges
Authors: Aregay Atsede , O'Connor Margaret , Stow Jill , Ayers Nicola , Lee Susan 
Published: February 2023

Abstract
Background: Palliative care is limited or non-existent in Ethiopia, particularly in rural areas where, more than 78% of the population live. Current initiatives and research are focused on urban settings and are primarily donor dependent. This study aims to explore the status of palliative care, enabling factors and implementation challenges in Ethiopia’s rural and regional health care settings.
Methods:A qualitative regional case study was conducted with health professionals drawn from different health care settings, academic institutions and included health planners and practitioners. Focus groups were conducted with rural community members and face- to face- individual interviews were conducted with health professionals working in numerous roles as well as academic leaders.
Results: Ethiopia’s national policy and guidelines on palliative care, are not integrated into the national and regional health care system. The existing diploma and a few post-graduate curricula at universities and colleges contain limited palliative care content. Data analysis indicated that policymakers, professionals and community members lacked awareness towards palliative care, which started with caring for Human Immunodeficiency Virus (HIV) patients. Those working at all health care levels explained that chronically ill patients other than those with HIV received inadequate care, limited to physical care, pain management, and psychosocial support rather than comprehensive palliative care. Some participants perceived that palliative care was not within the remit of their service, so families and patients were forced to seek alternative or informal care, including from traditional healers.
Conclusions: Enablers for the improvement of palliative care access in rural and regional health care were identified, including better integration of palliative care into the national health care plan and guidelines; palliative care content in university and college courses; and use of mobile phone technology to facilitate care. And policy makers and responsible stakeholders could consider the palliative care implementation in rural and regional health care settings through a combination of home, community and facility-based models.
Deep Learning Based Low-Complexity andHigh-Speed Coding Unit Partition Decisionfor High Efficiency Video Coding
Authors: Pattimi Hari , B.K.N Srinivasa Rao 
Published: February 2023

Abstract
High-Efficiency Video Coding (HEVC/H.265) is the current video coding standard and it achieves a 50% greater bitrate reduction than H.264.The Coding Tree Unit (CTU) is the main processing unit that definesthe HEVC decoding process. Due to recursive search method used inHEVC encoder to find the Coding Unit (CU) partitions, it increasescomputational complexity and time for processing. This paper suggeststwo approaches for coding unit partitioning, one is based on machinelearning and the other is based on deep learning. The parameterslike Edge Magnitude (EM), Rough Mode Decision cost (RMD), RateDistortion cost (RD), and Most Probable Modes (MPM) are used toestimate the complexity of CU splitting. These parameters are utilised bymachine learning methods, such as Fischer’s Linear Discriminant Analysis (FLDA), Support Vector Machine (SVM), and Logistic Regression(LR), to classify split and un-split samples of the coding unit partitioning. The machine learning algorithms increased the coding efficiencyand decreased its computing complexity in comparison to the traditionalHEVC encoder. However, machine learning algorithms also used the iterative procedure in determining the splitting of the coding units. Thispaper proposes an alternative method using deep learning technique toaddress the recursive search issue. Using a modified version of the LeNet-5 architecture, our proposed deep learning method simplifies coding unitpartitioning because neither recursive searching nor rate-distortion optimization are required to identify the coding unit partitioning in modifiedLeNet-5 architectures. As a result, the coding unit partition speed is raised in all intra-configurations without changing the computational costof HEVC. According to simulation results, the proposed techniques LR,SVM, and modified LeNet-5 could individually reduce nearly 50.09%,63.78%, and 86.06% of the time required to encode various test videos.
A weak solution for the fractional N-Laplacian flow
Authors: CHOI Q-HEUNG , JUNG TACKSUN 
Published: February 2023

Abstract
We deal with the nonlinear parabolic problems given by the fractional N-Laplacian operator and time derivative of functions on the fractional Orlicz-Sobolev spaces. We get a result which shows existence of a weak solution for these fractional N-Laplacian heat flows. We obtain this result by using the approximation method. We first obtain a unique sequence of the approximating weak solutions from the difference fractional N-Laplacian problems, and then get some sequences of the variant approximating weak solutions. We next show the convergence of the sequences of the approximating weak solutions to the limits and the limit of some sequence of the approximating weak solutions is a weak solution of this problem.
AMS 2000 Mathematics Subject Classifications: 35A01, 35A25, 35D30, 35B50, 35K35, 46E30
Distinct modulation of cellular immunopeptidome by the allosteric regulatory site of ER aminopeptidase 1
Authors: Temponeras Ioannis , Samiotaki Martina , Koumantou Despoina , Nikopaschou Martha , Kuiper Jonas J. W. , Panayotou George , Stratikos Efstratios 
Published: February 2023

Abstract
ER aminopeptidase 1 (ERAP1) is an ER-resident aminopeptidase that excises N-terminal residues off peptides that then bind onto Major Histocompatibility Complex I molecules (MHC-I) and indirectly modulates adaptive immune responses. ERAP1 contains an allosteric regulatory site that accommodates the C-terminus of at least some peptide substrates, raising questions about its exact influence on antigen presentation and the potential of allosteric inhibition for cancer immunotherapy. We used an inhibitor that targets this regulatory site to study its effect on the immunopeptidome of a human cancer cell line. The immunopeptidomes of allosterically inhibited and ERAP1 knockout cells contain high-affinity peptides with sequence motifs consistent with the cellular HLA class I haplotypes, but were strikingly different in peptide composition. Compared to knockout cells, allosteric inhibition did not affect the length distribution of peptides and skewed the peptide repertoire both in terms of sequence motifs and HLA allele utilization, indicating significant mechanistic differences between the two ways of disrupting ERAP1 function. These findings suggest that the regulatory site of ERAP1 plays distinct roles in antigenic peptide selection, which should be taken into consideration when designing therapeutic interventions targeting the cancer immunopeptidome.
Jordan and local multipliers on certain Banach algebras are multipliers
Authors: Zivari-Kazempour Abbas , Minapoor Ahmad 
Published: February 2023

Abstract
‎In this paper‎, ‎we prove that every continuous Jordan multiplier T from a‎ C*-algebra or group algebra A into a Banach A-bimodule X is exact a multiplier‎. We also characterize a continuous linear map on C*-algebras and standard operator algebra‎s ‎through the action on zero products‎. ‎Additionally‎, ‎we show that every continuous local multiplier T from a Banach algebra A with the property $(\mathbb{B})$ into a Banach A-bimodule X is a multiplier‎.
2010 Mathematics Subject Classification. Primary 47B47, 47B49 Secondary 15A86, 46H25.
Construction of a predictive model to predict the incidence of cerebral white matter lesions in young patients: a large retrospective study
Authors: Wen Haitao , Mei Haibo , Li Jie 
Published: February 2023

Abstract
Objectives
Recently, many studies have focused on the occurrence and development of cerebral white matter lesions in the elderly, while we focused on the risk factors leading to cerebral white matter lesions in younger patients and constructed a predictive model for predicting the incidence of lesions in younger patients.
Materials and methods
We studied patients younger than 60 years old who visited hospitals in Shin Takeo Hospital between april 1, 2016 and october 30, 2017. We randomised the data into test group and validation group. The model was developed by the test group and then validated by the validation group. The ROC curve, DCA curve (clinical decision curve) and clinical impact curve were used to verify the accuracy and clinical practical value of the model.
Results
1050 patients were included in this study, including 363 patients with cerebral white matter lesions and 687 patients with non-cerebral white matter lesions. Multivariate analysis show that age(1.08(1.06, 1.11)), gender(1.69(1.27, 2.27)), carotid plaque score(1.19(1.07, 1.33)), diastolic blood pressure(1.02(1.01, 1.03)), Red_bp_med(1.50(1.01, 2.26)) were significantly associated with white matter lesions. The area under the curve (AUC) was 0.726 for the training group and 0.694 for the validation group. Our prediction model has high accuracy and clinical application value.
Conclusions
Age, gender, diastolic blood pressure, carotid plaque score and the use of antihypertensive drugs were independent risk factors for cerebral white matter lesions. The predictive model we constructed has good accuracy and clinical application value.
Detection &amp; Mitigation of DDOS Attack
Authors: Hasan Muhammad Zulkifl , Hussain Muhammad Zunnurain , Nazar Muhammad Zeeshan , A Mahelak , Anjum Fatima , Yaqub Muhammad Atif , Mustafa Muzzamil , Chuhan Saad Hussain , Mubarak Zaima , Siddiqui Adeel Ahmad , Qureshi Ali Moiz , Mustafa Ghulam 
Published: February 2023

Abstract
The DDoS (Distributed Denial of Service) attack is a type of Cyberattack in which multiple attackers aim to attack different network resources like a server or a website. Although many statistical methods have already been designed for DDoS attack detection, designing a real-time detector with low computational overhead is still one of the main concerns. The already existing datasets are highly important and can be used for constructing and checking new solutions. It is the most dangerous attack against IPv6 networks today. The attack uses Internet Control Message Protocol version 6 (ICMPv6) messages. DDoS attack can be detected in various ways like a sudden fluctuation in the traffic of a website or unreal raise in the requests to resources. DDoS attacks are among the four most malicious attacks, like social engineering, ransomware, and supply chain attacks. It’s relatively easy to confuse DDoS attacks with other cyber threats. As for now most of our application and infrastructure resides on the cloud. As for cloud providers, the services provider must facilitate some tools to prevent the attack on their services and their user. Some of the major cloud providers give us this type of facility (AWS, Azure, and GCPThis cloud service provider offers cloud DDoS mitigation and prevention that operates entirely outside of your current network, inside the Internet cloud, and can identify and stop DDoS attacks before they even get to you. For bigger installations, routing is utilised to ensure that all network traffic, regardless of type, is filtered before delivery via a clean pipe. Domain name system (DNS) is used to direct inbound traffic through a scrubbing centre before delivery to the server. DDoS mitigation and prevention in the cloud is not only speedy, but also incredibly effective at stopping DDoS attacks.