Miracle-dz/UniFAR-FTUs huggingface dataset:
Prognostic Significance of Regional/Systemic Metabolic Parameters on 18F-FDG PET in Pulmonary Lymphoepithelioma-Like Carcinoma

Background Pulmonary lymphoepithelioma-like carcinoma (LELC) is a rare subtype of lung cancer with less than 700 cases being reported in the literature, and no specialized treatment guidelines have been established. The prognostic significance of metabolic parameters on 18F-FDG PET in pulmonary LELC still remains unknown. Methods From July 2011 to September 2020, 76 pulmonary LELC patients undergoing pre-treatment 18F-FDG PET imaging were enrolled, and PET parameters including maximum standard uptake value (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) were calculated. In addition, whole-body tumor burdens were categorized into primary tumor lesion (PRL), thoracic lymph-node lesion (TRLN), and distant metastasis (DM) for respective metabolic parameters acquisition. ROC curves were generated to evaluate the predictive performance of the PET parameters, and correlations between tumor burdens of the different regional lesions were analyzed using linear correlation analysis. The prognostic significance for progression-free survival (PFS) and overall survival (OS) was assessed using univariate and multivariate survival analyses. Results Tumor stage, pre-/post-treatment serum EBV-DNA copies, SUVmax (cutoff 17.5), MTV, and TLG were significantly associated with PFS and OS in univariate analysis. MTV and TLG (AUC = 0.862 and 0.857, respectively) showed significantly higher predictive value than SUVmax (AUC = 0.754) and remained independent prognostic indicators for PFS in multivariate analysis (P = 0.026 and 0.019, respectively). Besides, non-colinearity was detected between metabolic burdens of the different regional lesions. MTV-PRL, MTV-DM, TLG-PRL, and TLG-DM were identified to be independent prognostic factors for PFS and OS, whereas MTV-TRLN and TLG-TRLN were not. Conclusion The study demonstrated that MTV and TLG had independent prognostic significance for pulmonary LELC, which supported the incorporation of 18F-FDG PET imaging into clinical treatment protocols for pulmonary LELC and implied multi-disciplinary cooperation for primary and distant metastatic lesions to further improve prognosis.
Using application benchmark call graphs to quantify and improve the practical relevance of microbenchmark suites

Performance problems in applications should ideally be detected as soon as they occur, i.e., directly when the causing code modification is added to the code repository. To this end, complex and cost-intensive application benchmarks or lightweight but less relevant microbenchmarks can be added to existing build pipelines to ensure performance goals. In this paper, we show how the practical relevance of microbenchmark suites can be improved and verified based on the application flow during an application benchmark run. We propose an approach to determine the overlap of common function calls between application and microbenchmarks, describe a method which identifies redundant microbenchmarks, and present a recommendation algorithm which reveals relevant functions that are not covered by microbenchmarks yet. A microbenchmark suite optimized in this way can easily test all functions determined to be relevant by application benchmarks after every code change, thus, significantly reducing the risk of undetected performance problems. Our evaluation using two time series databases shows that, depending on the specific application scenario, application benchmarks cover different functions of the system under test. Their respective microbenchmark suites cover between 35.62% and 66.29% of the functions called during the application benchmark, offering substantial room for improvement. Through two use cases—removing redundancies in the microbenchmark suite and recommendation of yet uncovered functions—we decrease the total number of microbenchmarks and increase the practical relevance of both suites. Removing redundancies can significantly reduce the number of microbenchmarks (and thus the execution time as well) to ~10% and ~23% of the original microbenchmark suites, whereas recommendation identifies up to 26 and 14 newly, uncovered functions to benchmark to improve the relevance. By utilizing the differences and synergies of application benchmarks and microbenchmarks, our approach potentially enables effective software performance assurance with performance tests of multiple granularities.
Perception of Person-Centred Maternity Care and Its Associated Factors Among Post-Partum Women: Evidence From a Cross-Sectional Study in Enugu State, Nigeria

Objectives: This study validated a person-centred maternity care (PCMC) scale and assessed perception of PCMC and its associated factors among post-partum women. Methods: A cross-sectional study was conducted among 450 post-partum women in two districts in Enugu State, Nigeria, using a 30-item PCMC scale. Exploratory and confirmatory factor analyses, descriptive, bivariate and Generalized Linear Models analyses were conducted. Results: Twenty-two items were retained in the PCMC scale with high internal reliability and goodness-of-fit indices. About 25% of women received high PCMC. Marrying at 20–29 years (β = 3.46, ρ = 0.017) and 30–49 years (β = −5.56, ρ = 0.020); self-employment (β = −7.50, ρ = 0.005); marrying government worker (β = 7.12, ρ = 0.020); starting antenatal care in the third trimester (β = −6.78, ρ = 0.003); high participation in decision-making (β = −10.41, ρ < 0.001); domestic violence experience (β = 3.60, ρ = 0.007); delivery at health centre (β = 18.10, ρ < 0.001), private/mission hospital (β = 4.01, ρ = 0.003), by non-skilled attendant (β = −16.55, ρ < 0.001) and community health worker (β = −10.30, ρ < 0.001); and pregnancy complication (β = 4.37, ρ = 0.043) influenced PCMC. Conclusion: The PCMC scale is valid and reliable in Nigeria. PCMC requires improvement in Enugu State. This study identified factors that may be considered for inclusion in intervention strategies.
Digital Support to Multimodal Community-Based Prehabilitation: Looking for Optimization of Health Value Generation

Prehabilitation has shown its potential for most intra-cavity surgery patients on enhancing preoperative functional capacity and postoperative outcomes. However, its large-scale implementation is limited by several constrictions, such as: i) unsolved practicalities of the service workflow, ii) challenges associated to change management in collaborative care; iii) insufficient access to prehabilitation; iv) relevant percentage of program drop-outs; v) need for program personalization; and, vi) economical sustainability. Transferability of prehabilitation programs from the hospital setting to the community would potentially provide a new scenario with greater accessibility, as well as offer an opportunity to effectively address the aforementioned issues and, thus, optimize healthcare value generation. A core aspect to take into account for an optimal management of prehabilitation programs is to use proper technological tools enabling: i) customizable and interoperable integrated care pathways facilitating personalization of the service and effective engagement among stakeholders; ii) remote monitoring (i.e. physical activity, physiological signs and patient-reported outcomes and experience measures) to support patient adherence to the program and empowerment for self-management; and, iii) use of health risk assessment supporting decision making for personalized service selection. The current manuscript details a proposal to bring digital innovation to community-based prehabilitation programs. Moreover, this approach has the potential to be adopted by programs supporting long-term management of cancer patients, chronic patients and prevention of multimorbidity in subjects at risk.
Hypofunction of Circulating Endothelial Progenitor Cells and Aggravated Severity in Elderly Male Patients With Non-ST Segment Elevation Myocardial Infarction: Its Association With Systemic Inflammation

Background: Aging patients easily suffer from non-ST segment elevation myocardial infarction (NSTEMI). Our previous studies revealed declined function of endothelial progenitor cells (EPCs) in the elderly. However, the impact of aging on EPC function and severity in male NSTEMI patients and its possible mechanism is unclear until now. Methods: We measured the circulating EPC function including migration, proliferation, and adhesion in aging or young male patients with NSTEMI. The GRACE and TIMI risk score were evaluated. Plasma levels of interleukin-6 (IL-6) and interleukin-17 (IL-17) were also detected in all patients. Results: Compared with the young group, the old male patients with NSTEMI had higher GRACE score and TIMI score and decreased function of circulating EPCs. EPC function was negatively correlated with GRACE score and TIMI score. IL-6 and IL-17 level were higher in the old group than those in the young group. There was a significant negative correlation between EPC function and IL-6 or IL-17. Moreover, IL-6 and IL-17 positively correlated with GRACE and TIMI score. Age was positively related with GRACE or TIMI score and plasma level of IL-6 or IL-17, but inversely correlated with EPC function. Conclusions: The current study firstly illustrates that the age-related decrement in EPC function is related to the severity of NSTEMI in male patients, which may be connected with systemic inflammation. These findings provide novel insights into the pathogenetic mechanism and intervention target of aging NSTEMI.
Examining the Factors Influencing the Intention to Adopt Cloud ERP Systems by SMEs in Jordan

This study aims to explore the factors influencing the intention to adopt cloud enterprise resource planning (ERP) systems by small and medium-sized enterprises (SMEs) in Jordan. The research proposes a new model by extending the technology acceptance model (TAM) with four additional factors, including computer self-efficacy, complexity, compatibility, and organizational support. Using a questionnaire, data was collected from 394 subjects. An adaptive neuro fuzzy inference system (ANFIS) was utilized to analyze the data. The results revealed that perceived usefulness and perceived ease-of-use of cloud ERP systems have a low-direct effect on the intention to adopt cloud ERP systems. Also, the study found that computer self-efficacy, complexity, compatibility, and organizational support have a low-direct effect on perceived usefulness and perceived ease-of-use of cloud ERP systems and, in turn, on the intention to adopt cloud ERP systems.
Exact Likelihood Inference for a Competing Risks Model with Generalized Type II Progressive Hybrid Censored Exponential Data

In many situations of survival and reliability test, the withdrawal of units from the test is pre-planned in order to to free up testing facilities for other tests, or to save cost and time. It is known that several risk factors (RiFs) compete for the immediate failure cause of items. In this paper, we derive an inference for a competing risks model (CompRiM) with a generalized type II progressive hybrid censoring scheme (GeTy2PrHCS). We derive the conditional moment generating functions (CondMgfs), distributions and confidence interval (ConfI) of the scale parameters of exponential distribution (ExDist) under GeTy2PrHCS with CompRiM. A real data set is analysed to illustrate the validity of the method developed here. From the data, it can be seen that the conditional PDFs of MLEs is almost symmetrical.
LeLePhid: An Image Dataset for Aphid Detection and Infestation Severity on Lemon Leaves

Aphids are small insects that feed on plant sap, and they belong to a superfamily called Aphoidea. They are among the major pests causing damage to citrus crops in most parts of the world. Precise and automatic identification of aphids is needed to understand citrus pest dynamics and management. This article presents a dataset that contains 665 healthy and unhealthy lemon leaf images. The latter are leaves with the presence of aphids, and visible white spots characterize them. Moreover, each image includes a set of annotations that identify the leaf, its health state, and the infestation severity according to the percentage of the affected area on it. Images were collected manually in real-world conditions in a lemon plant field in Junín, Manabí, Ecuador, during the winter, by using a smartphone camera. The dataset is called LeLePhid: lemon (Le) leaf (Le) image dataset for aphid (Phid) detection and infestation severity. The data can facilitate evaluating models for image segmentation, detection, and classification problems related to plant disease recognition.
iRG-4mC: Neural Network Based Tool for Identification of DNA 4mC Sites in Rosaceae Genome

DNA N4-Methylcytosine is a genetic modification process which has an essential role in changing different biological processes such as DNA conformation, DNA replication, DNA stability, cell development and structural alteration in DNA. Due to its negative effects, it is important to identify the modified 4mC sites. Further, methylcytosine may develop anywhere at cytosine residue, however, clonal gene expression patterns are most likely transmitted just for cytosine residues in strand-symmetrical sequences. For this reason many different experiments are introduced but they proved not to be viable choice due to time limitation and high expenses. Therefore, to date there is still need for an efficient computational method to deal with 4mC sites identification. Keeping it in mind, in this research we have proposed an efficient model for Fragaria vesca (F. vesca) and Rosa chinensis (R. chinensis) genome. The proposed iRG-4mC tool is developed based on neural network architecture with two encoding schemes to identify the 4mC sites. The iRG-4mC predictor outperformed the existing state-of-the-art computational model by an accuracy difference of 9.95% on F. vesca (training dataset), 8.7% on R. chinesis (training dataset), 6.2% on F. vesca (independent dataset) and 10.6% on R. chinesis (independent dataset). We have also established a webserver which is freely accessible for the research community.
Morphometric Evaluation of Detailed Asymmetry for the Proximal Humerus in Korean Population

Computer-assisted orthopedic surgery and patient-specific instruments are widely used in orthopedic fields that utilize contralateral side bone data as a template to restore the affected side bone. The essential precondition for these techniques is that the left and right bone features are similar. Although proximal humerus fracture accounts for 4% to 8% of all fractures, the bilateral asymmetry of the proximal humerus is not fully understood. The aim of this study is to investigate anthropometric differences of the bilateral proximal humerus. One hundred one pairs of Korean humerus CT data from 51 females and 50 males were selected for this research. To investigate bilateral shape differences, we divided the proximal humerus into three regions and the proximal humerus further into five sections in each region. The distance from the centroid to the cortical outline at every 10 degrees was measured in each section. Differences were detected in all regions of the left and right proximal humerus; however, males had a larger number of significant differences than females. Large bilateral differences were measured in the greater tubercle. Nevertheless, using contralateral data as a template for repairing an affected proximal humerus might be possible.
A method of knowledgebase curation using RDF Knowledge Graph and SPARQL for a knowledge-based clinical decision support system

Clinical decisions are considered crucial and lifesaving. At times, healthcare workers are overworked and there could be lapses in judgements or decisions that could lead to tragic consequences. The clinical decision support systems are very important to assist heath workers. But in spite of a lot of effort in building a perfect system for clinical decision support, such a system is yet to see the light of day. Any clinical decision support system is as good as its knowledgebase. So, the knowledgebase should be consistently maintained with updated knowledge available in medical literature. The challenge in doing it lies in the fact that there is huge amount of data in the web in varied format. A method of knowledgebase curation is proposed in the article using RDF Knowledge Graph and SPARQL queries.
Learning a Diagnostic Strategy on Medical Data With Deep Reinforcement Learning

In recent years, Artificial Intelligence based disease diagnosis has drawn considerable attention both in academia and industry. In medical scenarios, a well-trained classifier can effectively detect a disease with sufficient features associating with medical tests. However, such features are not always readily available due to the high cost of time and money associating with medical tests. To address this, this study identifies the diagnostic strategy learning problem and proposes a novel framework consisting of three components to learn a diagnostic strategy with limited features. First, as we often encounter incomplete medical records of the patients, a sequence encoder is designed to encode any set of information in various sizes into fixed-length vectors. Second, taking the output of the encoder as the input, a feature selector based on reinforcement learning techniques is proposed to learn the best feature sequence for diagnosis. Finally, with the best feature sequence, an oracle classifier is used to give the final diagnosis. To evaluate the performance of the proposed method, experiments are conducted on nine real medical datasets. The results suggest that the proposed method is effective for providing personalized diagnostic strategies and makes better diagnoses with fewer features compared with existing methods.
Visual-Multi-Sensor Odometry with Application in Autonomous Driving

Relative localization in GNSS-denied environments is an essential task in advanced driver assistance systems or autonomous driving. In this paper, we present a loosely coupled visual-multi-sensor odometry algorithm for relative localization. Inertial Measurement Unit (IMU), vehicle speed, and steering angle measurements are fused in an Unscented Kalman Filter (UKF) to estimate the vehicle’s pose and velocity as well as the IMU biases. Relative pose estimates between two camera frames are used to update the UKF and further increase precision in the estimated localization. Our system is able to localize a vehicle in real-time from arbitrary states such as an already moving car which is a challenging scenario. We evaluate our visual-multi-sensor algorithm on real-world datasets recorded in inner-city and rural areas and compare it two state-of-the-art Visual-Inertial Odometry (VIO) algorithms. We report a lower relative odometry error in particular at the start of motion estimation with lower computational cost.
SMT-Based Consistency Checking of Configuration-Based Components Specifications

Cyber-Physical Systems (CPSs) are engineered systems that are built from, and depend upon, the seamless integration of computational algorithms and physical components. CPSs are widely used in many safety-critical domains, making it crucial to ensure that they operate safely without causing harm to people and the environment. Therefore, their design should be robust enough to deal with unexpected conditions and flexible to answer to the high scalability and complexity of systems. Nowadays, it is well-established that formal verification has a great potential in reinforcing safety of critical systems, but nevertheless its application in the development of industrial products may still be a challenging activity. In this paper, we describe an approach based on Satisfiability Modulo Theories (SMT) to formally verify, at the design stage, the consistency of the system design – expressed in a given domain-specific language, called QRML, which is specifically designed for CPSs – with respect to some given property constraints, with the purpose to reduce inconsistencies during the system development process. To this end, we propose an SMT-based approach for checking the consistency of configuration based-components specifications and we report the results of the experimental analysis using three different state-of-the-art SMT solvers. The main goal of the experimental analysis is to test the scalability of the selected SMT solvers and thus to determine which SMT solver is the best in checking the satisfiability of the properties.
Fully-Transparent TFT Sensor Array with IGZO/Nanorods Enhancing Structure

Operational stability and sensitivity are key issues in the practical application thin film transistor (TFT) of sensors, and the interdigital electrode is an important element that can be applied to TFT. In this study, a completely transparent interdigital electrode was printed on the glass plated with indium tin oxide (ITO) material using a laser etching machine. The produced interdigital electrode has the characteristics of high density, small line spacing, high sensitivity, and laser etching can be large. The amplitude reduces the production time of the device. Use a semiconductor tester to test the electrical transfer characteristics of the interdigital electrodes. The photoelectric generation spectrum and absorption spectrum of the interdigital electrode array plated with indium gallium zinc oxide (IGZO) material were tested. After plated with IGZO material, the ability to absorb light was greatly enhanced. The interdigital electrodes plated with IGZO material can be applied to the driving and test circuits of flexible wearable spectroscopic industrial detection sensors. It can amplify the circuit signal. After being plated with IGZO material, the power consumption of the circuit can be greatly reduced, and the measurement with low power consumption, high stability and high accuracy can be realized.
Variational Autoencoders and Wasserstein Generative Adversarial Networks for Improving the Anti-Money Laundering Process

There has been much recent work on fraud and Anti Money Laundering (AML) detection using machine learning techniques. However, most algorithms are based on supervised techniques. Studies show that supervised techniques often have the limitation of not adapting well to new irregular fraud patterns when the dataset is highly imbalanced. Instead, unsupervised learning can have a better capability to find anomalous and irregular patterns in new transaction. Despite this, unsupervised techniques also have the disadvantage of not being able to give state-of-the-art detection results. We propose a suite of unsupervised and deep learning techniques to implement an anti-money laundering and fraud detection system to resolve this limitation. The system leverages three deep learning models: autoencoder (AE), variational autoencoder (VAE), and a generative adversarial network. We preprocess the given dataset to separate the Transaction Date attribute into its base components to capture time-related fraud patterns. Also, Wasserstein Generative Adversarial Network (WGAN) is used to generate fraud transactions, which are then mixed with the base dataset to form a more balanced mixed dataset. These two datasets are used to train the AE and VAE models. We built two versions of the AE model (single-loss and multi-loss) besides a novel method of calculating the anomaly score threshold, called Recall-First Threshold (RFT), which helps enhance the model’s performance. Experimental results demonstrated that the False Positive Rate (FPR) drops down to as low as 7% in the proposed multi-loss AE model. In comparison, we achieved an accuracy of 93%, with 100% of the fraud transactions recalled successfully.