bluuebunny/crossref_metadata_2025 huggingface dataset:
Tumour stemness and poor clinical outcomes in haemochromatosis patients with hepatocellular carcinoma
Authors: Di Capua Daniel M , Shanahan William , Bourke Michele , Ramlaul Navneet , Appel Josh , Canney Aoife , Docherty Neil G , McGrath Erinn , Ring Eabha , Jones Fiona , Boyle Marie , McCormack Janet , Gallagher Tom , Hoti Emir , Nolan Niamh , Ryan John D , Houlihan Diarmaid D , Fabre Aurelie
Published: May 2023

Aims

Patients with haemochromatosis (HFE) are known to have an increased risk of developing hepatocellular carcinoma (HCC). Available data are conflicting on whether such patients have poorer prognosis, and there is lack of data regarding the biology of HFE-HCC. We compared the course of HFE-HCC with a matched non-HFE-HCC control group and examined tumour characteristics using immunohistochemistry.

Methods

In this tertiary care-based retrospective analysis, 12 patients with HFE and 34 patients with alcohol/non-alcoholic steatohepatitis who underwent initially successful curative HCC therapy with ablation or resection were identified from our registry. Time to tumour progression was compared. Resected liver tissue from a separate cohort of 11 matched patients with HFE-HCC and without HFE-HCC was assessed for the expression of progenitor and epithelial–mesenchymal transition markers using immunohistochemistry.

Results

The median follow-up was 24.39 and 24.28 months for patients with HFE-HCC and those without HFE-HCC, respectively (p>0.05). The mean time to progression was shorter in the HFE group compared with the non-HFE group (12.87 months vs 17.78 months; HR 3.322, p<0.05). Patients with HFE-HCC also progressed to more advanced disease by the end of follow-up (p<0.05). Immunohistochemical analysis of matched HFE-HCC and non-HFE-HCC explants demonstrated increased expression of the cancer stem cell markers EpCAM (epithelial cell adhesion molecule) and EpCAM/SALL4 (spalt-like transcription factor 4) coexpression in HFE-HCC specimens (p<0.05). There was a high frequency of combined tumour subtypes within the HFE cohort.

Conclusions

This study demonstrates that the clinical course of patients with HFE-HCC is more aggressive and provides the first data indicating that their tumours have increased expression of progenitor markers. These findings suggest patients with HFE-HCC may need to be considered for transplant at an earlier stage.
Flow diversion for basilar quadrifurcation aneurysms
Authors: Srinivasan Visish M , Jubran Jubran H , Stonnington Henry O , Catapano Joshua S , Scherschinski Lea , Hendricks Benjamin K , Winkler Ethan A , Rudy Robert F , Nguyen Brandon A , Dabrowski Stephen J , Jadhav Ashutosh P , Ducruet Andrew F , Albuquerque Felipe C
Published: May 2023

Background

Flow-diverting devices (FDDs), such as the Pipeline Embolization Device, have been gaining traction for treating challenging posterior circulation aneurysms. Few previous studies have focused on using FDDs to treat aneurysms of the basilar quadrifurcation.

Methods

We retrospectively reviewed the use of FDDs to treat patients with basilar quadrifurcation aneurysms. Patients were assessed for aneurysm type, previous aneurysm treatment, technical success, periprocedural complications, and long-term aneurysm occlusion.

Results

34 patients were assessed; aneurysms of the basilar apex (n=23) or superior cerebellar artery (SCA) (n=7), or both (n=1), and posterior cerebral artery (PCA) (n=3). The mean (SD) largest aneurysm dimension was 8.7 (6.1) mm (range 1.9–30.8 mm). 14 aneurysms were previously surgically clipped or endovascularly coiled. All aneurysms had a saccular morphology. Complete or near-complete occlusion was achieved in 30 of 34 patients (88%) at final angiographic follow-up, a mean (SD) of 6.6 (5.4) months (range 0–19 months) postoperatively. No patient experienced postoperative symptomatic occlusions of the SCA or PCA; 4 patients developed asymptomatic posterior communicating artery occlusions; 28 patients (82%) experienced no complications; whereas 3 (9%) experienced major complications and 3 (9%) experienced minor complications; and 1 patient died as a result of subarachnoid hemorrhage.

Conclusion

Flow diversion may be a safe and effective option to treat basilar quadrifurcation aneurysms. Previously treated basilar quadrifurcation aneurysms with recurrence or residual lesion may benefit from additional treatment with an FDD. Further prospective studies should be directed toward validating these findings.
Extent of illicit cigarette sales in Nepal: findings from a retail survey
Authors: Shakya Sangita , Lamichhane Anjana , Karki Pranav , Gurung Jaya Kumar , Pradhan Pranil Man Singh
Published: May 2023

Introduction

Increasing the tax on cigarettes is widely considered the most effective method to reduce its consumption. However, Nepal has a low cigarette tax as policymakers in Nepal are concerned about illicit trade of cigarettes if taxes are increased.

Methods

The study employed a retail survey approach used in India suitable for countries with prevalent loose cigarette sales, with improved methodology. In 2021, empty cigarette packs generated in a day’s loose cigarette sales were collected directly from cigarette retailers from 23 primary sampling units covering rural/urban, geographic divisions, border/non-border to India and tobacco factory locations. The central points of each primary sampling unit were identified, and retailers were selected for the survey. A cigarette pack was classified as illicit if it had at least one of the following attributes: (a) no authentic excise duty sticker, (b) no graphic health warning, (c) no mention of ‘maximum retail price/MRP’ and (d) no production date, name, address and trademark.

Findings

We collected 4307 empty cigarette packs from 1204 retailers and 0.33% of them were classified as illicit. The estimates varied across location with the highest prevalence of illicit packs in Kathmandu (1.25%). All the illicit cigarettes were imported and were high-priced brands (>90%), mostly found in urban areas and not bordering India.

Conclusion

Our estimate of the illicit cigarette market share of 0.33% suggests that the industry’s statement of 25% is grossly overstated.
Evolution of channel flow and Darcy’s law beyond the critical Reynolds number
Authors: Deng Xiaohui , Sheng Ping
Published: May 2023

For incompressible channel flow, there is a critical state, characterized by a critical Reynolds number Rec and a critical wavevector mc along the channel direction, beyond which the channel flow becomes unstable in the linear regime. In this work, we investigate the channel flow beyond the critical state and find the existence of a new fluctuating, quasi-stationary flow that comprises the laminar Poiseuille flow superposed with a counter-flow component, accompanied by vortices and anti-vortices. The net flow rate is reduced by  ~ 15% from the linear, laminar regime. Our study is facilitated by the analytical solution of the linearized, incompressible, three-dimensional (3D) Navier–Stokes (NS) equation in the channel geometry, with the Navier boundary condition, alternatively denoted as the hydrodynamic modes (HMs). By using the HMs as the complete mathematical basis for expanding the velocity in the NS equation, the Rec is evaluated to 5-digit accuracy when compared to the well-known Orszag result, without invoking the standard Orr-Sommerfeld equation. Beyond Rec, the analytical solution is indispensable in offering physical insight to those features of the counter-flow component that differs from any of the pressure-driven channel flows. In particular, the counter flow is found to comprise multiple HMs, some with opposite flow direction, that can lead to a net boundary reaction force along the counter-flow direction. The latter is analyzed to be necessary for satisfying Newton’s law. Experimental verification of the predictions is discussed.

Graphical Abstract
Printed circuit heat exchangers and fast neutron radiography
Authors: Vrban B. , Čerba Š. , Lüley J. , Filová V. , Nečas V.
Published: May 2023

Non-destructive testing is capable of detecting defects of important components, where a failure could pose a significant hazard and cause severe economic losses. Currently, imaging techniques utilizing gamma or X-ray sources are mainly used and allow to examine of devices ranging in size from units to tens of centimetres, but for industrial-sized large components, these methods usually fail. The utilization of fast neutrons in radiography is a promising alternative, especially for industrial applications. Currently, due to the higher neutron-matter interaction probabilities, most neutron-imaging systems use thermal neutrons originating from research reactors. Due to the intention of on-site inspection of revealing the possible ruptures and failures, this option is not applicable. The paper presents the newly developed neutronic models of printed circuit heat exchangers where the specific ruptures are defined. Next, neutron transport simulations are performed to investigate the different irradiation geometries, various neutron sources, and backfill materials to access the principal detectability of these ruptures in the industrial environment. Finally, conclusions are drawn, and the best backfill material and neutron source are identified in the simplified model.
Exact definition of optical phenomena and characteristics of ultrathin crystalline molecular films
Authors: Vučenović Siniša M. , Šetrajčić-Tomić Ana J. , Vojnović Nikola R. , Ilić Dušan I. , Šetrajčić Jovan P.
Published: May 2023

A complete and fully developed theory of all optical phenomena (refraction, reflection, absorption, and transparency) and the corresponding optical properties of ultrathin crystalline films (optical indices) are presented in this paper, especially along the direction in which the structure is spatially limited (perpendicular to surfaces). While these indices depend on the position of the crystallographic plane (where the mentioned optical phenomena occur) with respect to the two interfaces, these values can be measured/ determined in experiments only for the film as a whole. For these reasons, it is important to answer the question of how to define these optical indices precisely.
Entrepreneurial Orientation, Commitment to Learning and Family Business Resilience– Exploring the Moderating Effect of Family-to-Firm Identity Fit
Authors: Abdelwahed Yosra , Basly Sami
Published: May 2023

The ability of an organisation to endure in the face of critical changes in the business environment and resist disruptions and catastrophic events is a key factor explaining how to be able to manage organisational crises. The resilience of family firms is considered a major capacity that allows them to pass through difficult times and to be sustainable (Miller and Le Breton-Miller, 2005). Although the academic literature argues that family firms are more resilient than non-family firms, the reasons for this presumed superiority are still not clearly identified. In addition, while family firms are a significant component of the economic landscape, research has evidenced variations in this organisational form such that some family firms seem to be more resilient than others. In this research, we highlight two entrepreneurial features that may explain why family firms are resilient in times of crises (and why family firms are heterogeneous in terms of their resilience capacity). We propose a model that aims to test the effect of entrepreneurial orientation and commitment to learning on a family firm’s resilience while considering the moderating effect of a family-to-firm identity fit. More precisely, we first hypothesize that entrepreneurial orientation and commitment to learning positively influence a family firm’s resilience capacity. Then, we suggest that family-to-firm identity fit would negatively moderate these relationships. The empirical investigation is based on a sample of 76 Tunisian family firms. Data were collected through a survey and analysed through Smart PLS modelling. Findings show that the family firm’s commitment to learning is positively associated with family firm’s resilience. However, contrary to expectations, entrepreneurial orientation has no significant effect on family firm’s resilience. Finally and unexpectedly, we found that the more the family is oriented towards preserving family members’ identification with the firm the more commitment to learning would positively influence a family firm’s resilience.
About this title - Geological Hazards in the UK: Their Occurrence, Monitoring and Mitigation – Engineering Group Working Party Report
Authors: Giles D. P. , Griffiths J. S.
Published: May 2023

The UK is perhaps unique globally in that it presents the full spectrum of geological time, stratigraphy and associated lithologies within its boundaries. With this wide range of geological assemblages comes a wide range of geological hazards, whether they be geophysical (earthquakes, effects of volcanic eruptions, tsunami, landslides), geotechnical (collapsible, compressible, liquefiable, shearing, swelling and shrinking soils), geochemical (dissolution, radon and methane gas hazards) or georesource related (coal, chalk and other mineral extraction). An awareness of these hazards and the risks that they pose is a key requirement of the engineering geologist.

The Geological Society considered that a Working Party Report would help to put the study and assessment of geohazards into the wider social context, helping the engineering geologist to better communicate the issues concerning geohazards in the UK to the client and the public. This volume sets out to define and explain these geohazards, to detail their detection, monitoring and management and to provide a basis for further research and understanding.
Distributed GPU Joins on Fast RDMA-capable Networks
Authors: Thostrup Lasse , Doci Gloria , Boeschen Nils , Luthra Manisha , Binnig Carsten
Published: May 2023

In this paper, we present a novel pipelined GPU join that accelerates the performance of distributed DBMSs by leveraging GPU resources on fast networks. A key insight is that we enable pipelined join execution by overlapping the network shuffling with the build and probe phases, thereby significantly reducing the GPU idle time. To demonstrate this, we propose novel algorithms for distributed pipelined GPU joins with RDMA and GPUDirect for both arbitrarily large probe- and build-side tables. In our evaluation, we show our pipelined distributed GPU join can reduce the overall runtime of a full query by up to 6× against a state-of-the-art CPU-only join.
Foreign Keys Open the Door for Faster Incremental View Maintenance
Authors: Svingos Christoforos , Hernich Andre , Gildhoff Hinnerk , Papakonstantinou Yannis , Ioannidis Yannis
Published: May 2023

Serverless cloud-based warehousing systems enable users to create materialized views in order to speed up predictable and repeated query workloads. Incremental view maintenance (IVM) minimizes the time needed to bring a materialized view up-to-date. It allows the refresh of a materialized view solely based on the base table changes since the last refresh. In serverless cloud-based warehouses, IVM uses computations defined as SQL scripts that update the materialized view based on updates to its base tables. However, the scripts set up for materialized views with inner joins are not optimal in the presence of foreign key constraints. For instance, for a join of two tables, the state of the art IVM computations use a UNION ALL operator of two joins - one computing the contributions to the join from updates to the first table and the other one computing the remaining contributions from the second table. Knowing that one of the join keys is a foreign-key would allow us to prune all but one of the UNION ALL branches and obtain a more efficient IVM script. In this work, we explore ways of incorporating knowledge about foreign key into IVM in order to speed up its performance. Experiments in Redshift showed that the proposed technique improved the execution times of the whole refresh process up to 2 times, and up to 2.7 times the process of calculating the necessary changes that will be applied into the materialized view.
Spatio-Temporal Denoising Graph Autoencoders with Data Augmentation for Photovoltaic Data Imputation
Authors: Fan Yangxin , Yu Xuanji , Wieser Raymond , Meakin David , Shaton Avishai , Jaubert Jean-Nicolas , Flottemesch Robert , Howell Michael , Braid Jennifer , Bruckman Laura , French Roger , Wu Yinghui
Published: May 2023

The integration of the global Photovoltaic (PV) market with real time data-loggers has enabled large scale PV data analytical pipelines for power forecasting and reliability assessment of PV fleets. Nevertheless, the performance of PV data analysis depends on the quality of PV timeseries data. We propose a novel Spatio-Temporal Denoising Graph Autoencoder STD-GAE framework to impute missing PV Power Data. STD-GAE exploits temporal correlation, spatial coherence, and value dependencies from domain knowledge to recover missing data. It is empowered by two modules. (1) To cope with sparse yet various scenarios of missing data, STD-GAE incorporates a domain-knowledge aware data augmentation module to create plausible variations of missing data patterns. This generalizes STD-GAE to robust imputation over different seasons and environment. (2) STD-GAE nontrivially integrates spatiotemporal graph convolution layers and denoising autoencoder to improve the accuracy of imputation accuracy at PV fleet level. Experimental results on two PV datasets show that STD-GAE can achieve a gain of 43.14% in imputation accuracy and remains less sensitive to missing rate, different seasons, and missing scenarios, compared with state-of-the-art data imputation methods.
An Effective and Differentially Private Protocol for Secure Distributed Cardinality Estimation
Authors: Wang Pinghui , Yang Chengjin , Xie Dongdong , Zhao Junzhou , Li Hui , Tao Jing , Guan Xiaohong
Published: May 2023

Counting the number of distinct elements distributed over multiple data holders is a fundamental problem with many real-world applications ranging from crowd counting to network monitoring. Although a number of space and computationally efficient sketch methods (e.g., the Flajolet-Martin sketch and the HyperLogLog sketch) for cardinality estimation have been proposed to solve the above problem, these sketch methods are insecure when considering privacy concerns related to the use of each data holder's personal dataset. Despite a recently proposed protocol that successfully implements the well-known Flajolet-Martin (FM) sketch on a secret-sharing based multiparty computation (MPC) framework for solving the problem of private distributed cardinality estimation (PDCE), we observe that this MPC-FM protocol is not differentially private. In addition, the MPC-FM protocol is computationally expensive, which limits its applications to data holders with limited computation resources. To address the above issues, in this paper we propose a novel protocol DP-DICE, which is computationally efficient and differentially private for solving the problem of PDCE. Experimental results show that our DP-DICE achieves orders of magnitude speedup and reduces the estimation error by several times in comparison with state-of-the-arts under the same security requirements.
The RLR-Tree: A Reinforcement Learning Based R-Tree for Spatial Data
Authors: Gu Tu , Feng Kaiyu , Cong Gao , Long Cheng , Wang Zheng , Wang Sheng
Published: May 2023

Learned indexes have been proposed to replace classic index structures like B-Tree with machine learning (ML) models. They require to replace both the indexes and query processing algorithms currently deployed by the databases, and such a radical departure is likely to encounter challenges and obstacles. In contrast, we propose a fundamentally different way of using ML techniques to build a better R-Tree without the need to change the structure or query processing algorithms of traditional R-Tree. Specifically, we develop reinforcement learning (RL) based models to decide how to choose a subtree for insertion and how to split a node when building and updating an R-Tree, instead of relying on hand-crafted heuristic rules currently used by the R-Tree and its variants. Experiments on real and synthetic datasets with up to more than 100 million spatial objects show that our RL based index outperforms the R-Tree and its variants in terms of query processing time.
DBPA: A Benchmark for Transactional Database Performance Anomalies
Authors: Huang Shiyue , Wang Ziwei , Zhang Xinyi , Tu Yaofeng , Li Zhongliang , Cui Bin
Published: May 2023

Anomaly diagnosis is vital to the performance of online transaction processing (OLTP) systems. In the meanwhile, machine learning techniques can reason complex relationships beyond human abilities and perform well on such problems. However, they rely on a large number of training samples for anomalies, which are in serious shortage in both industry and academia due to the difficulty of collection. The problem raises the demand of a benchmark for anomaly reproduction and data collection.

In this paper, we propose DBPA, a benchmark for transactional database performance anomalies. Specifically, we identify nine common anomalies rooted in the diverse influence factors. For each anomaly, we carefully design a reproduction procedure, which consists with its root cause in real-world databases. With the reproduction procedures, users can easily generate a dataset in a new environment and extend new anomaly types. For compound anomalies, we provide a generation algorithm that allows users to generate compound anomalies data of any possible combinations with existing collected data. We also provide a large dataset of both normal and anomalous monitoring data collected from various environments, facilitating the training of machine learning models and the evaluation of new algorithms for anomaly diagnosis.
HAIPipe: Combining Human-generated and Machine-generated Pipelines for Data Preparation
Authors: Chen Sibei , Tang Nan , Fan Ju , Yan Xuemi , Chai Chengliang , Li Guoliang , Du Xiaoyong
Published: May 2023

Data preparation is crucial in achieving optimized results for machine learning (ML). However, having a good data preparation pipeline is highly non-trivial for ML practitioners, which is not only domain-specific, but also dataset-specific. There are two common practices. Human-generated pipelines (HI-pipelines) typically use a wide range of any operations or libraries but are highly experience- and heuristic-based. In contrast, machine-generated pipelines (AI-pipelines), a.k.a. AutoML, often adopt a predefined set of sophisticated operations and are search-based and optimized. These two common practices are mutually complementary. In this paper, we study a new problem that, given an HI-pipeline and an AI-pipeline for the same ML task, can we combine them to get a new pipeline (HAI-pipeline) that is better than the provided HI-pipeline and AI-pipeline? We propose HAIPipe, a framework to address the problem, which adopts an enumeration-sampling strategy to carefully select the best performing combined pipeline. We also introduce a reinforcement learning (RL) based approach to search an optimized AI-pipeline. Extensive experiments using 1400+ real-world HI-pipelines (Jupyter notebooks from Kaggle) verify that HAIPipe can significantly outperform the approaches using either HI-pipelines or AI-pipelines alone.
Efficient and Effective Cardinality Estimation for Skyline Family
Authors: Miao Xiaoye , Wu Yangyang , Peng Jiazhen , Gao Yunjun , Yin Jianwei
Published: May 2023

Cardinality estimation, predicting the query result size, is a fundamental problem in databases. Existing skyline cardinality estimation methods are computationally infeasible for massive skyline queries over the large-scale database. In this paper, we introduce a unified skyline family w.r.t. various skyline variants. We propose an efficient and effective skyline family cardinality estimation model, named EECE, in an end-to-end manner. EECE consists of two modules, unsupervised data distribution learning (DDL) and supervised monotonic cardinality estimation (MCE). DDL leverages the mixture data guided transformer to learn the distribution of database and query parameters for model pre-training. MCE further incorporates supervised learning and parameter clamping to enhance the estimation under monotonicity guarantees. We develop an efficient incremental learning algorithm for EECE to adapt the database and query logs update. Extensive experiments on several real-world and synthetic datasets demonstrate that, EECE speeds up the cardinality estimation by six orders of magnitude, with more than 39% accuracy gain, compared to the state-of-the-art approaches.
When Tree Meets Hash: Reducing Random Reads for Index Structures on Persistent Memories
Authors: Wang Ke , Yang Guanqun , Li Yiwei , Zhang Huanchen , Gao Mingyu
Published: May 2023

Indexing structures are widely used in modern data-processing applications to support high-performance queries, and there are a variety of recent designs specifically optimized for the newly available persistent memory (PM). The primary focus of previous PM indexes is on reducing the expensive PM writes for persisting data. However, we find that in tree-based PM indexes, because of the smaller performance gap between writes and random reads on real PM devices, the read-intensive tree traversal phase dominates the overall latency. This observation calls for further optimizations on existing indexing structures for PM.

In this paper, we propose Extendible Radix Tree (ERT), an efficient indexing structure for PM that significantly reduces tree heights to minimize random reads, while still maintaining fast in-node search speed. The key idea is to use extendible hashing for each node in a radix tree. This design allows us to have a relatively large fanout of the radix tree to keep the tree height small, and also to realize constant-time lookups within a node. Using extendible hashing also allows for incremental node modification without excessive writes during inserts and updates. Range queries are efficiently and robustly handled by enforcing partial ordering among the keys in the hash table of each node without introducing more hash collisions. Our experiments on both synthetic and real-world data sets demonstrate that ERT achieves up to 2.65×, 4.41×, and 2.43× speedups for search, insert, and range queries over the respectively state-of-the-art PM index.
Tissue Specificity of DNA Damage and Repair
Authors: Hoch Nicolas C.
Published: May 2023

DNA is a remarkable biochemical macromolecule tasked with storing the genetic information that instructs life on planet Earth. However, its inherent chemical instability within the cellular milieu is incompatible with the accurate transmission of genetic information to subsequent generations. Therefore, biochemical pathways that continuously survey and repair DNA are essential to sustain life, and the fundamental mechanisms by which different DNA lesions are repaired have remained well conserved throughout evolution. Nonetheless, the emergence of multicellular organisms led to profound differences in cellular context and physiology, leading to large variations in the predominant sources of DNA damage between different cell types and in the relative contribution of different DNA repair pathways toward genome maintenance in different tissues. While we continue to make large strides into understanding how individual DNA repair mechanisms operate on a molecular level, much less attention is given to these cell type-specific differences. This short review aims to provide a broad overview of DNA damage and repair mechanisms to nonspecialists and to highlight some fundamental open questions in tissue and cell-type-specificity of these processes, which may have profound implications for our understanding of important pathophysiological processes such as cancer, neurodegeneration, and aging.
COVID-19 Vaccine Acceptance among Ajman Undergraduate Dental Students: A Cross-Sectional Study
Authors: Jaber M. A. , Abdelmagied M. , El-Ameen E. M. , Afrashtehfar K. I.
Published: May 2023

Background. Achieving widespread coronavirus disease 2019 (COVID-19) vaccination is crucial in controlling the pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This cross-sectional study aimed to identify factors associated with the willingness of dental medicine students to receive the COVID-19 vaccine. Objectives. The study sought to assess the knowledge, attitudes, and behaviors of undergraduate dental students toward COVID-19 vaccines and to identify determinants, motivators, and barriers to vaccine uptake and booster receipt. Methods. A web-based survey was distributed to all 882 undergraduate dental surgery students in January 2022, and 70.7% of the students responded. The survey used χ2 tests and logistic regression analysis to examine the association among the variables. The significance level was set at α = 0.05. Results. Most participants (72.4%) reported having adequate knowledge of COVID-19. The vaccine acceptance rate was higher among male and older trainees, with no significant difference compared to women and younger trainees with no significant difference (

p
=
0.849

). Acceptance of the vaccine varied according to study level (5-year program), ranging from 44.8% to 73.0%, in the following order 4th > 1st > 3rd > 5th > 2nd year. Social media (76.8%), government websites (66.5%), and family and friends (57.2%) were the main sources of COVID-19-related information. Among hesitant and unwilling participants, the main concerns were side effects (34.0%) and lack of understanding about the vaccine’s mechanism (67.3%). Conclusions. Ajman dental students had moderate knowledge of COVID-19 and obtained information mainly from social media, government websites, and family and friends. Age, sex, and study year influenced vaccine acceptance. The main reasons for refusal were lack of knowledge, fear of side effects, and complications. Education campaigns are needed to increase vaccine acceptance among dental students.
Left Ventricular Recovery after Transapical Transcatheter Aortic Valve Implantation Compared with Conventional Aortic Valve Replacement in Patients with Aortic Regurgitation and Reduced Ejection Fraction
Authors: Lin Zhiqin , Xu Zheng , Dai Xiaofu , Chen Liangwan
Published: May 2023

Background and Aim of the Study. To evaluate differences in left ventricular recovery after transapical transcatheter aortic valve implantation and conventional aortic valve replacement in patients with aortic regurgitation and reduced left ventricular ejection fraction. Methods. All patients with reduced left ventricular ejection fraction who underwent aortic valve surgery for AR at our institution between January 2015 and November 2021 were retrospectively reviewed. Generalized estimating equations were used to compare left ventricular recovery and remodeling outcomes between the patient groups. Results. A total of 87 cases were included in this study, 36 patients for TA-TAVI and 51 patients for C-AVR. Transapical transcatheter aortic valve implantation was associated with better and faster recovery of left ventricular ejection fraction and left ventricular end-diastolic dimension (adjusted β = 0.002, 95% CI: 0.000 to 0.003, and

p
=
0.046

; adjusted β = 0.330, 95% CI: 0.185 to 0.474, and

p
<
0.001

, respectively) within the first 3 months postoperatively compared with left ventricular ejection fraction, with the same improvement in New York Heart Association function class (adjusted β = 0.381, 95% CI: −0.349 to 1.111, and

p
=
0.306

). Conclusions. This study highlights patients who underwent transapical transcatheter aortic valve implantation for aortic regurgitation with reduced left ventricular ejection fraction. However, future randomized controlled prospective clinical trials with longer follow-up durations are required.
Pore Structure and Permeability Variations during Gas Displacement in Unconsolidated Sandstone Reservoirs through CT Reconstruction Analysis
Authors: Zha Yuqiang , Cao Bao , Li Fengying , Ye Qing , Zhu Shaopeng , Zhou Wei , Xiong Runfu
Published: May 2023

The continuous gas displacement in unconsolidated sandstone gas reservoirs will necessarily result in the pore structure and rock permeability variations, which cannot be neglected in the gas development process. However, the variations have not been comprehensively addressed yet, especially for the rock structure in pore scale. This work presented the quantitative results of pore structure in microscale and permeability variations during gas displacement in unconsolidated sandstone reservoirs through computed tomography (CT) reconstruction analysis. The results indicated that a more than 3% increase in porosity after gas displacement resulted from the enlargement of the pore and throat with a diameter of more than 20 μm and 3 μm, respectively, owing to the release and migration of clay and fine particles, in spite of the distribution frequency decline of both pore and throat with a small diameter. The pore connectivity would be enhanced by the increase of the connected pores as well as the enlargement of the pore and throat sizes. However, the pore-throat coordination number could only change with slight improvement. In terms of permeability and relative permeability changes with pore structure, the improvement of permeability after gas displacement was higher than that of porosity, and the continuous gas displacement would broaden gas-water flow region and lower irreducible water saturation and residual gas saturation, and then, the equal phase relative permeability point would shift to the right. These investigations will contribute to more accurate reserve evaluation and productivity prediction.
Review on Creep Phenomenon and Its Model in Aircraft Engines
Authors: Yuan Lin , Chenglong Wang , Yongchao Sun , Mingbo Sun , Yu Yuan , Zhan Gao , Yiwen Xiao
Published: May 2023

The aircraft is subjected to high-temperature and high-pressure conditions during flight, which renders it susceptible to the occurrence of creep phenomenon. Several academics have conducted extensive research on this issue. This research paper provides a comprehensive overview of the existing literature on creep phenomena in aircraft engines. First, several classical creep calculation models are enumerated and categorized as creep life calculation, creep-fatigue life calculation, and creep deformation calculation. Studies on creep phenomena are conducted in various components of aircraft engines, such as the engine’s turbine blades, turbine disks, and combustion chambers. The creep behavior of turbine blades in aircraft engines has been extensively researched. Furthermore, the protective measures aimed at mitigating creep are presented. Materials with high creep resistance can be used, and alternative fuels could be implemented. This paper provides an in-depth analysis of the advantages of creep in aircraft, presented in a favorable perspective. Finally, the prospective future research direction is discussed.
Drilling Parameter Optimization of Shale Gas Wells Based on Saw-Tooth Genetic Algorithm to Reduce Drilling Costs
Authors: Bai Kai , Dong Abing , Zhan Ce , Zhang WanXing , Tu BingRui
Published: May 2023

It is difficult to optimize the drilling parameters, which makes it difficult to reduce drilling costs in the process of drilling shale gas wells in the Fuling region of Chongqing, China. This paper takes the optimization combination of drilling parameters in drilling engineering as the research object and uses a saw-tooth genetic algorithm to perform the objective function. To solve the problem, the algorithm proposes a variable population size with periodic reinitialization, which follows a saw-tooth scheme with unequal amplitude and change period (saw-tooth GA), which can optimize the drilling parameters under constraints. According to the actual situation of the wells in the Fuling region, the three algorithms are compared for the vertical section’s drilling parameters. Example calculations show that the algorithm can achieve the best economic benefits at different revolutions per minute and WOB to reduce drilling costs. Compared with other methods, this algorithm has the characteristics of fast convergence, is easy to understand, and is simple to implement. The proposed algorithm is tested for the drilling parameter optimization of shale gas wells and from which it becomes evident that the saw-tooth scheme enhances the overall performance of GAs.
Current Regulation and Initial Considerations for Successful Development and Commercialization of Microbiome Therapies
Authors: Thanush D. , Basavaraj H. C. , Gowrav M. P.
Published: May 2023

The gut microbiome is frequently referred to as the “second brain” or “second genome,” referring to the impact of the gut microbiota on our health. The microbiome is formed at birth, grows with the host, and is impacted by environmental variables such as nutrition, antibiotics, and lifestyle. Understanding the host health-gut microbiota correlation opens the possibility of modifying the gut microbiome to manage an individual’s health. Several techniques, such as probiotic, prebiotic, synbiotic, and faecal microbiota transplantation (FMT), are explored to alleviate the dysbiosis-related negative consequences and restore the gut microbiota in humans. While microbiome-based medicines have made remarkable progress in the last decade, from prebiotics and probiotics to live biotherapeutics, there are still safety concerns and regulatory issues to be addressed. The FMT treatment is currently experimental and lacks authorization, posing numerous ethical, legal, and social challenges that must be resolved as part of an effective regulatory policy response. This study gives an outline of our current understanding of microbiome restoration approaches as well as safety concerns regarding how these approaches are regulated. It presents an outline of the contemporary gut microbiome therapeutic development landscape and an assessment of the commercialization hurdles encountered.
Dynamic Task Assignment Framework for Mobile Crowdsensing with Deep Reinforcement Learning
Authors: Fu Yanming , Qi Kangheng , Shi Yuanquan , Shen Yuming , Xu Liqiang , Zhang Xian
Published: May 2023

Task assignment is a key issue in mobile crowdsensing (MCS). Previous task assignment methods were mainly static offline assignment. However, the MCS platform needs to process dynamically changing workers and tasks online in the actual assignment process. Hence, a reliable dynamic assignment strategy is crucial to improving the platform’s efficiency. This paper proposes an MCS dynamic task assignment framework to solve the task maximization assignment problem with spatiotemporal properties. First, a single worker is modeled for the Markov decision process, and a deep reinforcement learning algorithm (DDQN) is used to perform offline learning on historical task data. Then, in the dynamic assignment process, we consider the impact of current decisions on future decisions. Use the maximum flow model to maximize the number of tasks completed in each period while maximizing the expected

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value of all workers to achieve the optimal global assignment. Experiments show that the strategy proposed in this paper has good performance compared with the baseline strategy under different conditions.
The Comprehensive Analysis Illustrates the Role of CDCA5 in Breast Cancer: An Effective Diagnosis and Prognosis Biomarker
Authors: Gao Yang , Liu Shuting , Yang Junyuan , Su Min , Xu Jingjing , Wang Hua , Zhang Jingwei
Published: May 2023

Background. Several studies have been conducted to investigate the role of cell division cycle-associated 5 (CDCA5) in cancer. Its role in breast cancer, however, remains unknown. Methods. The Gene Expression Omnibus and Cancer Genome Atlas Program databases provided the open-access information needed for the research. The CCK8 and colony formation assays were used to measure cell proliferation. The capacity of breast cancer cells to invade and migrate was assessed using the transwell assay. Results. In our study, CDCA5 was identified as the interested gene through a series of bioinformatics analysis. We found a higher CDCA5 expression level in tissue and cells of breast cancer. Meanwhile, CDCA5 has been linked to increased proliferation, invasion, and migration of breast cancer cells, which was also associated with worse clinical features. The biochemical pathways, in which CDCA5 was engaged, were identified using biological enrichment analysis. Immune infiltration research revealed that CDCA5 was linked to enhanced activity of several immune function terms. Meanwhile, DNA methylation might be responsible for the aberrant level of CDCA5 in tumor tissue. In addition, CDCA5 could significantly increase the paclitaxel and docetaxel sensitivity, indicating that it has the potential for clinical application. Also, we found that CDCA5 is mainly localized in cell nucleoplasm. Moreover, in the breast cancer microenvironment, we found that CDCA5 is mainly expressed in malignant cells, proliferation T cells, and neutrophils. Conclusion. Overall, our findings suggest that CDCA5 is a potential prognostic indicator and target for breast cancer, which can indicate the direction of the relevant research.
The Application of Knowledge‐Based Clinical Decision Support Systems to Enhance Adherence to Evidence‐Based Medicine in Chronic Disease
Authors: Gholamzadeh Marsa , Abtahi Hamidreza , Safdari Reza
Published: May 2023

Among the technology‐based solutions, clinical decision support systems (CDSSs) have the ability to keep up with clinicians with the latest evidence in a smart way. Hence, the main objective of our study was to investigate the applicability and characteristics of CDSSs regarding chronic disease. The Web of Science, Scopus, OVID, and PubMed databases were searched using keywords from January 2000 to February 2023. The review was completed according to the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses checklist. Then, an analysis was done to determine the characteristics and applicability of CDSSs. The quality of the appraisal was assessed using the Mixed Methods Appraisal Tool checklist (MMAT). A systematic database search yielded 206 citations. Eventually, 38 articles from sixteen countries met the inclusion criteria and were accepted for final analysis. The main approaches of all studies can be classified into adherence to evidence‐based medicine (84.2%), early and accurate diagnosis (81.6%), identifying high‐risk patients (50%), preventing medical errors (47.4%), providing up‐to‐date information to healthcare providers (36.8%), providing patient care remotely (21.1%), and standardizing care (71.1%). The most common features among the knowledge‐based CDSSs included providing guidance and advice for physicians (92.11%), generating patient‐specific recommendations (84.21%), integrating into electronic medical records (60.53%), and using alerts or reminders (60.53%). Among thirteen different methods to translate the knowledge of evidence into machine‐interpretable knowledge, 34.21% of studies utilized the rule‐based logic technique while 26.32% of studies used rule‐based decision tree modeling. For CDSS development and translating knowledge, diverse methods and techniques were applied. Therefore, the development of a standard framework for the development of knowledge‐based decision support systems should be considered by informaticians.
Optimization of Water Management for Green Pepper Production in a Water-Limiting Tropical Savanna Agroecological Zone Based on Crop Water Productivity
Authors: Alegewe Rose , Shaibu Abdul-Ganiyu , Zakaria Yakubu Saaka
Published: May 2023

This study aimed to determine the optimum levels of irrigation regime and irrigation schedule based on crop water productivity for the sustainable production of green pepper in a water-limiting tropical savannah agroecological zone. The study was conducted at the Hydro Farm of MotorKing Company Limited in the Tamale Metropolis, Northern Region, Ghana. The experimental design was a 2 × 3 factorial experiment laid out in a randomized complete block and replicated five times. Irrigation schedule at two levels (one-time daily application and split daily application at 60% morning and 40% evening) and irrigation regime at three levels (100% ETc, 80% ETc, and 60% ETc) were the factors. The “Yolo Wonder” variety of green pepper was the test crop. The crop was planted at a planting distance of 0.3 m within rows and 0.5 m between rows. Treatments were applied using a drip irrigation system. Crop water requirements (ETc) of green pepper were estimated using the CROPWAT model. Crop yield and water applied under each treatment were determined. Crop yield was measured at harvest as the total weight of fruits per hectare. Crop water productivity was determined under each treatment as crop yield per unit of water consumed. Data analysis was done in Genstat (12th edition). Analysis of variance (ANOVA) and Duncan’s multiple range test at 5% level of significance were employed to separate differences in treatment means. The results suggest that both irrigation regime and irrigation schedule have significant influence on the yield and crop water productivity of green pepper. Irrigating at 60% ETc and split irrigation (60% morning and 40% evening) gave significantly higher yields and crop water productivity compared to the other levels of the factors. This study demonstrated that irrigation schedule and irrigation regime are important factors to consider in the optimization of water management for green pepper; however, further research is needed to identify the optimal levels of these factors and the most effective irrigation strategies for the crop in different environments.
The DRD2 Antagonist Haloperidol Mediates Autophagy-Induced Ferroptosis to Increase Temozolomide Sensitivity by Promoting Endoplasmic Reticulum Stress in Glioblastoma
Authors: Shi Linyong , Chen Hanning , Chen Kunxiang , Zhong Chengzong , Song Chong , Huang Yifeng , Wang Tong , Chen Lei , Li Chiyang , Huang Annie , Qi Songtao , Li Hong , Lu Yuntao
Published: May 2023

Purpose:

Temozolomide resistance remains a major obstacle in the treatment of glioblastoma (GBM). The combination of temozolomide with another agent could offer an improved treatment option if it could overcome chemoresistance and prevent side effects. Here, we determined the critical drug that cause ferroptosis in GBM cells and elucidated the possible mechanism by which drug combination overcomes chemoresistance.

Experimental Design:

Haloperidol/temozolomide synergism was assessed in GBM cell lines with different dopamine D2 receptor (DRD2) expression in vitro and in vivo. Inhibitors of ferroptosis, autophagy, endoplasmic reticulum (ER) stress and cyclic adenosine monophosphate (cAMP)/protein kinase A (PKA) were used to validate the specific mechanisms by which haloperidol and temozolomide induce ferroptosis in GBM cells.

Results:

In the present work, we demonstrate that the DRD2 level is increased by temozolomide in a time-dependent manner and is inversely correlated with temozolomide sensitivity in GBM. The DRD2 antagonist haloperidol, a butylbenzene antipsychotic, markedly induces ferroptosis and effectively enhances temozolomide efficacy in vivo and in vitro. Mechanistically, haloperidol suppressed the effect of temozolomide on cAMP by antagonizing DRD2 receptor activity, and the increases in cAMP/PKA triggered ER stress, which led to autophagy and ferroptosis. Furthermore, elevated autophagy mediates downregulation of FTH1 expression at the posttranslational level in an autophagy-dependent manner and ultimately leads to ferroptosis.

Conclusions:

Our results provide experimental evidence for repurposing haloperidol as an effective adjunct therapy to inhibit adaptive temozolomide resistance to enhance the efficacy of chemoradiotherapy in GBM, a strategy that may have broad prospects for clinical application.