bluuebunny/crossref_metadata_2025_split huggingface dataset:
Verifiable Attribute-Based Keyword Search Scheme over Encrypted Data for Personal Health Records in Cloud
Authors: Sun Yuqin , Han Lidong , Bi Jingguo , Tan Xiao , Xie Qi 
Published: January 2023

Abstract
Personal health record (PHR) is a medical model in which patients can upload their medical records and define the access control by themselves. Since the limited local storage and the development of cloud computing, many PHR services have been outsourced to the cloud. In order to ensure the privacy of electronic medical records, patients intend to encrypt their health records before uploading them. However, encrypted PHR can not be accessed directly and not be retrieved by the legitimate users. To solve these issues, in this article we propose a new searchable encryption scheme with ciphertext-policy attributes, which achieves fine-grained access control and exact keyword search over encrypted PHRs. Moreover, in our proposed scheme, the receiver can verify the integrity of search result which the cloud server returns. Finally, we simulate our scheme, and the experiments show that our scheme has high practicability for cloud-based healthcare systems and has high efficiency in aspects of keyword search and results verification.
Developing dosiomics models for the prediction of postoperative radiotherapy-induced esophagitis in patients with non-small cell lung cancer
Authors: Ma Zeliang , Liang Bin , Wei Ran , Liu Yunsong , Bao Yongxing , Yuan Meng , Men Yu , Wang Jianyang , Deng Lei , Zhai Yirui , Bi Nan , Wang Luhua , Dai Jianrong , Hui Zhouguang 
Published: January 2023

Abstract
Background: Radiotherapy-induced esophagitis (RE)diminishes quality of life and involves treatment interruption in patients with non-small cell lung cancer (NSCLC) undergoing postoperative radiotherapy. Dosimetric models showed limited capability in predicting RE. We aimed to develop dosiomics models to predict RE.
Methods: Patients with NSCLC who underwent resection followed by postoperative radiotherapy between 2006 and 2015 were enrolled. The endpoint was grade ≥ 2 RE. Oesophageal three-dimensional dose distribution features were extracted using handcrafted and convolutional neural network (CNN)methods, screened using an entropy-based method, and selected using minimum redundancy maximum relevance. Prediction models were built using logistic regression. The area under the receiver operating characteristic curve (AUC) and precision-recall curve were used to evaluate prediction model performance. A dosimetric model was built for comparison.
Results: Models were trained and validated using respective (n = 190) and prospective (n = 103) cohorts, respectively. Using handcrafted and CNN methods, 107 and 4,096 features were derived, respectively. Three handcrafted, four CNN-extracted, and three dosimetric features were selected. AUCs of training and test sets were 0.737 and 0.655 for the dosimetric features, 0.730 and 0.724 for handcrafted features, and 0.812 and 0.785 for CNN-extracted features, respectively. Precision-recall curves revealed that CNN-extracted features outperformed dosimetric and handcrafted features.
Conclusions: Prediction models may identify patients at high risk of developing RE. Dosiomics models outperformed the dosimetric-feature model in predicting RE. CNN-extracted features were more predictive but less interpretable than handcrafted features.
Lite General Network and MagFace CNN for Micro-Expression Spotting in Long Videos
Authors: Gu Quan-Lin , Yang Sai , Cao Pan , Yu Tianxing 
Published: January 2023

Abstract
Facial expressions, especially spontaneous micro-expressions, as an intuitive reflection of human emotions, have come through much concern along with rapid advances in computer vision recently. Micro-expressions are small in amplitude and short in duration and often appear together with macro-expressions, making micro-expression spotting in long videos as a challenging task. In this article, we propose intersection over minimum labelling method combined with a Lite General Network and MagFace CNN (LGNMNet) model to predict the possibility of video frames belonging to an micro-expression interval, which balances easy and difficult samples to improve the learning effect of training process. Experimental results show that our method achieves state-of-the-art performance in spotting micro-expressions in long videos of both the CAS(ME) 2 and SAMM-LV datasets (with F1-scores of 0.2474 and 0.2555, respectively). Additionally, a new pair-merge way of combining nearby detected apex frames to construct micro-expression intervals in post-processing stage has been devised and analysed, providing a feasible solution for the task of macro- and micro-spotting in long videos.
Stricter correction of lower extremity length is required during total hip arthroplasty in patients with ankylosing spondylitis
Authors: Im Chae-Jin , Park Kyung-Soon , Beom Jae Young , Kim Min-Gwang , Yoon Taek-Rim 
Published: January 2023

Abstract
Background: Patients with ankylosing spondylitis often have fusions in the spine and sacroiliac joints, such that it is difficult to compensate for the difference in length of the lower extremities.
Methods: We retrospectively measured the difference in lower extremity length after total hip arthroplasty(THA) in 89 patients with ankylosing spondylitis from June 2004 to February 2021 at Our institute. Patients were divided into two groups based on a difference in lower extremity length of 5 mm or 10 mm. Clinical outcomes were investigated using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) and Harris Hip Score (HHS). In addition, these points are investigated : patient satisfaction with the operation; whether there was a current difference in leg length; and whether there was a limping gait.
Results: The group with a leg length difference of 5–10 mm rather than <5 mm had significantly worse WOMAC pain and stiffness. The survey revealed statistically significant differences in patient satisfaction with the operation, limping gait, and whether back pain had improved.
Conclusion: For patients with ankylosing spondylitis, reducing the difference in length of the lower extremities to <5 mm, which is more accurate than the current standard of <10 mm, may produce greater improvement in clinical outcomes after hip arthroplasty.
Genome Wide Association Studies of early fitness traits in Drosophila melanogaster unveil plasticity and decoupling of different aspects of phenotype
Authors: Zappala María Alejandra Petino , Mensch Julian , Carreira Valeria , Soto Ignacio , Fanara Juan José 
Published: January 2023

Abstract
Great efforts have been sustained to explain the relationships between genotype and phenotype for developmental fitness traits through the study of their genetic architecture. However, crucial aspects of functional architecture influencing the maintenance of genetic variability, and thus the capacity for evolutionary change, are still unexplored.
Here we performed Genome-wide Association Studies for phenotypic variability, plasticity and within-line canalization at two temperatures for Larval Developmental Time (LDT), Pupal Developmental Time (PDT), Larval Viability (LV), Pupal Viability (PV), and Pupal Height (PH) in lines derived from a natural population of Drosophila melanogaster. Results suggest changes in genetic networks linked to resource acquisition and allocation underlying variability for all traits. However, we found low genetic pleiotropy between traits and for different aspects of phenotype (means, plasticity, within-line canalization) within each trait. Their genetic bases were also temperature-specific: we found no variants showing an effect for the same trait at both temperatures. Moreover, a genetic decoupling between larval and pupal traits was confirmed, as there were no candidate variants significantly associated to phenotypic variability for the same trait across stages. We found evidence of genetic antagonistic pleiotropy for several loci affecting larval and pupal traits.
The high degree of modularity at various levels would allow for the independent evolution of distinct aspects of the phenotype in different environments and ontogenetic stages. This may explain why genetic variation for these adaptive traits is not extinguished in natural populations and may entail important implications regarding evolvability.
The role of transient elastography and novel serum biomarkers in assessment of liver fibrosis in patients with psoriasis and rheumatic disease
Authors: Wong Christina Sze Man , Mak Loey Lung Yi , Chung Ho Yi , Chung Man Ho , Ng Ling Ling , Chan Shirley Chiu Wai , Cheung Ka Shing , Yeung Chi Keung , Yuen Man Fung , Chan Henry Hin Lee 
Published: January 2023

AbstractBackgroundMethotrexate (MTX) and leflunomide are commonly used among patients with psoriasis and rheumatoid arthritis (RA) and are implicated in hepatotoxicity. We aimed to determine the prevalence of significant liver fibrosis and its risk factors in patients with psoriasis and RA. We also explored the role of novel serum biomarkers to identify significant liver fibrosis in these patients.MethodsA total of 318 patients attending dermatology-rheumatology clinics in Queen Mary Hospital, with clinical diagnosis of psoriasis or RA were recruited from August 2020 to July 2022. Liver fibrosis was assessed by transient elastography (TE) and serum biomarkers for liver fibrosis, namely autotaxin and matrix metalloproteinase (MMP), were measured. Risk factors associated with significant liver fibrosis (defined as liver stiffness [LS] ≥7.1kPa) were analyzed by multivariate regression models.ResultsA total of 67 (21.1%) patients with psoriasis or RA had significant liver fibrosis. Body mass index (OR 1.14, 95%CI 1.04-1.24), diabetes mellitus (OR 1.93, 95%CI 1.25-2.98) and PASI (OR 1.13, 95%CI 1.05-1.27), but not cumulative dosage (CD) of MTX or leflunomide, were independently associated with significant liver fibrosis (all p<0.01). Serum MMP 3,8,9 and autotaxin levels were significantly higher among patients with advanced liver fibrosis (LS≥14 kPa). Serum autotaxin showed modest correlation with LS (r=0.31, p=0.026) and CD of MTX (r=0.30, p<0.001).ConclusionSignificant liver fibrosis in patients with psoriasis and RA is related to the underlying metabolic risk factors and independent of MTX and leflunomide CD. Minimising hepatic risks by tight control of metabolic risk factors should be considered.
Analysis of factors influencing college students' online class anxiety in the COVID-19 epidemic
Authors: Huang Hai-Yi , Li Qin-Fang , Chen Jia-Shen , Wu Yu-Qian , Huang Dan-Yi , Zhong Ying-Ting , Pan Hai-yan , Pan Congcong 
Published: January 2023

Abstract
Objective: This research analyzed the current anxiety situation and its influencing factors of online home classes among college students in the context of the COVID-19 epidemic. The study aims to prevent psychological disorders and improve the efficiency of college students online classes.
Methods: We developed a questionnaire on the factors affecting anxiety in online classes for college students. A total of 225 college students participated in the survey. Factor analysis and cluster analysis were used to analyze the data.
Results : The anxiety score of college students was (97.19 ± 18.60). Factor analysis on the 25 questions in the questionnaire according to the KMO statistic 0.904 and Bartlett's spherical test (p < 0.05). Six common factors: epidemic (13. 914%), online course model (12.601%), the external environment (11.397%), psychological factors (10.477%), pedagogical factors (7.716%), and self-adjustment factors (7.404%), with a cumulative contribution of 63.51%. On this basis, the total scores of factor analysis by K-means cluster analysis and four types of college students with different degrees of online course anxiety, normal (6.13%), mild anxiety (28.30%), moderate anxiety (45.75%), severe anxiety (19.81%).
Conclusions： College students have mild to moderate anxiety in home-based online classes. Male, first-year, and non-medical students are more likely to have anxiety. Factor analysis and cluster analysis can summarize the main influencing factors of college students' online class anxiety, which can provide a practical reference for alleviating college students' online class anxiety and then taking measures.
Experience and future perspective of artificial intelligence (AI) in postoperative gastric cancer treatment recommendation: a retrospective, observational, cross-validation study with multidisciplinary team board
Authors: Park Yong-Eun , Chae Hyundong 
Published: January 2023

Abstract
Background: Recently, owing to significant growth in the amount of information produced by cancer research, staying abreast of the developments has become a challenging task. Artificial intelligence (AI) can learn, reason, and understand the enormous corpus of literature available to the scientific community. However, large-scale cross-validation studies comparing the recommendations of AI and multidisciplinary tumor boards (MTB) in gastric cancer treatment have rarely been performed. Therefore, we retrospectively conducted a real-world study to assess the level of concordance between AI and MTB treatment recommendations.
Methods: We retrospectively analyzed the treatment recommendations of Watson for Oncology (WFO) and MTB for 322 patients with gastric cancer from January 2015 to December 2018 and compared the degree of agreement between them. The patients were divided into concordance and non-concordance groups. The factors affecting the concordance rate were analyzed.
Results: The concordance rate between AI and MTB was 86.96% at consideration level (280/322). The concordance rate for stage I gastric cancer was the highest (96.93 %). The concordance rates for stages II and III were 88.89% and 90.91%, respectively, which were close to 90%; however, the concordance rate for stage IV was the lowest at 45.83%. In the multivariate analysis, age, performance status, and stage IV gastric cancer had a significant effect on concordance between MTB and WFO.
Conclusions: The factors affecting the concordance rate were age, performance status, and stage IV gastric cancer. For increasing the validity of future medical AI systems for gastric cancer treatment, their supplementation of the local guidelines and the ability to comprehensively understand individual patients is essential.
Determinants of Women heads non-farm participation in Western Ethiopia; Empirical Evidence in Horo Guduru Wollega zone
Authors: Dufera Wakuma , Bedemo Amsalu , Kebede Temesgen 
Published: January 2023

Abstract

Women have an important role in business participation that has a great impact on improving households’ livelihoods. The main objective of this study was to describe factors determining women's non-farm participation in non-farm activities. In order to identify the determining factor of women’s participation in non -agricultural activities, an English questionnaire was translated into Afan Oromo in a way that respondents and data collectors could understand. For analyzing the data, the researcher used the binary logit model, and the result of the study shows that the determinants of women's participation in non-agricultural activities were age, family size, household property ownership, land size, non-farm training, lack of access to credit, membership in Idir, women's participation in local community affairs, and distance from the market location. Among these determinant variables, land size, family size, and distance from market location negatively influence women's participation, while the rest positively influence women's participation in non-agricultural activities. Furthermore, socioeconomic factors such as society's backward attitude toward women and women's low participation in education have been identified as major issues. Accordingly, the study recommended that the government, NGOs, and various religious leaders work together to minimize the socio-economic impact on women, and the women's and children's affairs office in the study area should work with the university researchers to identify the problems faced by women.
Staged Surgical Treatment of Patients with Destructive Pulmonary Tuberculosis with pre-XDR Using Anterior Mediastinal Plasty
Authors: Tarasov R. V. , Krasnikova E. V. , Lepekha L. N. , Sadovnikova S. S. , Bagirov M. A. 
Published: January 2023

The presented observation describes an example of effective treatment of the patient with disseminated destructive pulmonary tuberculosis with preXDR. The treatment included staged surgery using the original method of anterior mediastinum plasty with a mesh implant.The treatment resulted in the following outcomes: stabilization of the tuberculous disease, persistent sputum conversion, minimization of overdistension of the only lung, absence of mediastinal pulmonary hernia and no deterioration of respiratory function.
The Alternative of Sensor Placement in Multi-Story Buildings through the Metric Dimension Approach: A Representation of Generalized Petersen Graphs
Authors: Asmiati Asmiati , Junaidi Akmal , Aldino Ahmad Ari , Munandar Arif 
Published: January 2023

In a public facility or private office where many people can get together, a fire detection device is a mandatory tool as an emergency alarm in the facility. However, the expense of the installation of the device is a troublesome matter. So, optimization is needed to minimize the number of these devices. The way to implement is to select the appropriate position to place the devices in public facilities. The research discussed the placement of the sensors in multi-story buildings. The multi-story buildings could be represented as cube composition graphs with the number of rooms, and the connectivity between the floor and its rooms was equal. The concept of this multi-story building was modeled into a generalized Petersen graph where a vertex represented a room, and an edge was the connectivity of rooms. The basis obtained on that metric dimension was represented as a sensor placed on the building. Then, the optimization of device placement was seen as determining the metric dimensions of the Petersen graph. In the research, the alternative sensor placements were computed using the graph metric dimension approach implemented in Python. The research successfully implements the metric dimension of to using Python code to obtain the alternative of its basis. A basic alternative indicates the location of the device placement like fire detectors, network access points, or other sensors inside a building.
Real-time water quality detection based on fluctuation feature analysis with the LSTM model
Authors: Wang Lixiang , Dong Huilin , Cao Yuqi , Hou Dibo , Zhang Guangxin 
Published: January 2023

AbstractSignal analysis and anomaly detection for water pollution early warning systems are important and necessary. In view of the nonlinear and volatile characteristics of water quality time series, this paper proposes a new method for water anomaly detection based on fluctuation feature analysis. The method has two steps. First, the water quality time series data are used to calculate the residuals between the observed value and the predicted value with the long short-term memory (LSTM) network. Second, the dynamic features are extracted by sliding time window and described by the Approximate Entropy (ApEn) which are input to the anomaly detection model with Isolation Forest. Compared with traditional anomaly detection methods, the results obtained by the proposed method show better performance in distinguishing water quality anomalies. The proposed method can be applied to real-time water quality anomaly detection and early warning.
Quality monitoring of inland water bodies using Google Earth Engine
Authors: Sherjah P. Y. , Sajikumar N. , Nowshaja P. T. 
Published: January 2023

Abstract
Regular quality monitoring of inland water bodies is vital for identifying the areas with deteriorating water quality. Satellite remote sensing has been used for obtaining long-term trends that require the processing of many images. The computational load of processing a large number of satellite imageries can be eased by utilizing the cloud computing capabilities of Google Earth Engine (GEE). The present study explores the possibility of using the GEE platform for mapping the Trophic State Index (TSI) of an inland water body. The bottom of atmosphere (BOA) reflectance retrieved by the SIAC algorithm (used in the GEE platform) is assessed for its accuracy. The algorithm could retrieve only BOA reflectance at bands B3 and B4 of Sentinel 2L1C (S2) with reasonable accuracy. The study has identified the Normalized Difference of B3 and B4 bands of S2 (i.e., ND34) as the tool for mapping TSI of a water body using GEE. TSI from six imageries of three lakes was estimated with a mean error &lt;17%. The capability of GEE as a rapid water quality monitoring tool is demonstrated by displaying the temporal and spatial variations of water quality across Vembanad Lake for the period 2016–2021.
Apparent temperature as a modelling substitute for evapotranspiration: Ramanathapuram, Tamil Nadu, India
Authors: Sivapragasam Chandrasekaran , Natarajan Narayanan , Prabavathy Irulraj , Ragasudha Dommati 
Published: January 2023

Abstract
Estimating evapotranspiration is very important in calculating crop water requirements. The Penman–Montieth (P–M) method is the most commonly used. This study is an attempt to simplify the P–M application using apparent temperature (AT) as a substitute for meteorological parameters. Genetic programming (GP) was used to model paddy crop evapotranspiration for six stations in Tamil Nadu, India, with two input sets. In one the inputs were mean temperature, wind speed, relative humidity and antecedent evapotranspiration. In the other model, the input was AT, an agglomeration of meteorological parameters and antecedent evapotranspiration. The GP model, using AT, proved capable of predicting evapotranspiration better than the P–M-based model, indicating that AT reflects the effects of other meteorological parameters and can be used in estimating evapotranspiration. Like any other data-driven technique, the training dataset in GP should include the lowest and highest modelling parameter values, if the model developed is to be robust.