Miracle-dz/UniFAR-FTUs huggingface dataset:
RF Heating of Pancreatic Tumours Guided by Hyperthermia Treatment Planning and Limited Thermometry

Hyperthermia (i.e. heating tumour tissue to 39-43°C) is an oncological treatment to enhance the effect of radiotherapy and chemotherapy. RF heating of deep-seated tumours is performed using phased array systems. Antenna settings are usually determined based on clinical experience, assisted by E-field and temperature measurements in natural body cavities (e.g. cervix, bladder, rectum) adjacent to the tumour. When the tumour is not in the direct vicinity of a natural body cavity, e.g. pancreatic tumours, these probes cannot be positioned easily and adequate thermometry is very limited. Treatment planning can be helpful in addition to the limited thermometry available. The usefulness of treatment guidance by hyperthermia treatment planning and limited thermometry was evaluated for RF heating of two pancreatic cancer patients. Median temperatures measured in the duodenum were above 40°C for patient 1 and above 39°C for patient 2. This demonstrates that treatment planning is helpful to determine antenna settings for adequate hyperthermia treatments with limited thermometry.
Cluster-Based Group Paging for Massive Machine Type Communications Under 5G Networks

Machine-type communications (MTCs) have been envisaged to play a key role within the future 5G cellular network. To handle the massive MTCs (mMTCs) and alleviate the congestion of the radio access network, the group paging (GP) scheme was proposed by the third-generation partnership project. However, its performance quickly decreases in the face of massive simultaneous channel accesses. In this paper, we propose a two-phase cluster-based GP (CBGP) scheme. First, owing to the advantages of low-cost, high-access capacity and handy deployment, IEEE 802.11ah is introduced to increase the capability of coping with massive access attempts. The separation of inner cluster data collection and header-based data transmission phases greatly alleviates the access congestion of cellular networks, reducing the access delay and increasing the successful access probability for mMTC devices. Besides, we also derive mathematical models of the CBGP scheme in terms of the successful access probability and average access delay. Moreover, effects from different numbers of clusters on the performance of the CBGP scheme are investigated and the optimal number of clusters is also derived, adaptive to different access scales. At last, numerical results are presented to validate the accuracy of our analytical models, demonstrate the effectiveness of the proposed CBGP scheme, and verify the optimal number of clusters, providing insights for the coming 5G cellular system design.
Influence and safety of electronic apex locators in patients with cardiovascular implantable electronic devices: a systematic review

ABSTRACT The widespread use of cardiovascular implantable electronic devices has increased concerns regarding using electronic apex locators in patients with these devices. This systematic review investigated the effects and safety of using electronic apex locators in patients with cardiovascular implantable electronic devices. Methods: An electronic search in the Cochrane Library, PubMed (MEDLINE), ScienceDirect, and Scientific Electronic Library Online (Scielo) databases for relevant articles published between December 2000 and December 2018 was performed. The search strategy centered on terms related to electronic apex locators use during root canal treatment in patients with cardiovascular implantable electronic devices. Results: Seven studies (five in vitro and two in vivo) fulfilled the inclusion criteria for this review. It was found that electronic apex locators can be used safely in patients with cardiovascular implantable electronic devices, when general precautions are followed. Conclusions: Although the present review suggests that electronic apex locators can be used safely in patients with implantable cardioverter defibrillators, consultation with patients’ cardiologists remains advisable.
Identification of a 4‐mRNA metastasis‐related prognostic signature for patients with breast cancer

Metastasis‐related mRNAs have showed great promise as prognostic biomarkers in various types of cancers. Therefore, we attempted to develop a metastasis‐associated gene signature to enhance prognostic prediction of breast cancer (BC) based on gene expression profiling. We firstly screened and identified 56 differentially expressed mRNAs by analysing BC tumour tissues with and without metastasis in the discovery cohort (GSE102484, n = 683). We then found 26 of these differentially expressed genes were associated with metastasis‐free survival (MFS) in the training set (GSE20685, n = 319). A metastasis‐associated gene signature built using a LASSO Cox regression model, which consisted of four mRNAs, can classify patients into high‐ and low‐risk groups in the training cohort. Patients with high‐risk scores in the training cohort had shorter MFS (hazard ratio [HR] 3.89, 95% CI 2.53‐5.98; P < 0.001), disease‐free survival (DFS) (HR 4.69, 2.93‐7.50; P < 0.001) and overall survival (HR 4.06, 2.56‐6.45; P < 0.001) than patients with low‐risk scores. The prognostic accuracy of mRNAs signature was validated in the two independent validation cohorts (GSE21653, n = 248; GSE31448, n = 246). We then developed a nomogram based on the mRNAs signature and clinical‐related risk factors (T stage and N stage) that predicted an individual's risk of disease, which can be assessed by calibration curves. Our study demonstrated that this 4‐mRNA signature might be a reliable and useful prognostic tool for DFS evaluation and will facilitate tailored therapy for BC patients at different risk of disease.
Feature Extraction for Cortical Sulci Identification

The use of PET in quantitative measurement of brain activity requires the superimposition of some anatomical data coming from other sources, like MRI. In the frame of structural and anatomical data matching for a great number of patients, we developed a method for the automatic identification of cortical sulci on 3D MR images. The knowledge used is automatically extracted from a database containing a few pictures where sulci have been previously recognized. Proportional correction mechanisms, based on Talairach's grid, are proposed. They intend to adapt sulci statistical models to the particular features of any brain, in order to make the recognition easier. Our identification method is efficient and robust for the superficial part of six major sulci.
Analysis of Controlled Packet Departure to Support Ethernet Fronthaul Synchronization via PTP

The synchronization accuracy achieved via the IEEE 1588 Precision Time Protocol (PTP) in packet-based fronthaul networks is substantially impaired by packet delay variation (PDV). Nevertheless, in the particular case of deployment over tree topologies, it is known that PDV can be avoided by controlling the departure of PTP packets such that they experience close to constant delays over the fronthaul. This paper analyzes controlled PTP departure under constraints that are peculiar to a fronthaul scenario of interest and considering that radio traffic itself behaves as background traffic relative to PTP. Since the method involves buffering of radio traffic prior to controlled PTP transmissions, its impact on buffer sizes at the baseband and radio units, and the corresponding increase in fronthaul latency are also analyzed. In the end, results collected through a self-developed FPGA-based testbed are presented.
A murine model of acute lung injury identifies growth factors to promote tissue repair and their biomarkers

Type II alveolar epithelial cells (AEC2s) play a crucial role in the regeneration of type I AECs after acute lung injury. The mechanisms underlying the regeneration of AEC2s are not fully understood. To address this issue, here, we investigated a murine model of acute lung injury using mice expressing human Diphtheria Toxin Receptor (DTR) under the control of Lysozyme M promoter (LysM‐DTR). DT injection induced the depletion of AEC2s, alveolar macrophages, and bone marrow (BM)‐derived myeloid cells in LysM‐DTR mice, and the mice died within 6 days after DT injection. Apoptotic AEC2s and bronchiolar epithelial cells appeared at 24 hr, whereas Ki67‐positive proliferating cells appeared in the alveoli and bronchioles in the lung of LysM‐DTR mice at 72–96 hr after DT injection. Transfer of wild‐type BM cells into LysM‐DTR mice accelerated the regeneration of AEC2s along with the up‐regulation of several growth factors. Moreover, several metabolites were significantly decreased in the sera of LysM‐DTR mice compared with WT mice after DT injection, suggesting that these metabolites might be biomarkers to predict AEC2s injury. Together, LysM‐DTR mice might be useful to identify growth factors to promote lung repair and the metabolites to predict the severity of lung injury.
Energy based indirect adaptive control of robots

An indirect adaptive controller for robots is presented. The control law is a computed torque type which can be calculated using the fast recursive Newton-Euler algorithm. The inertial parameters of the robot are updated using an energy prediction error which is a function of the joint positions and velocities. The calculation cost of the complete adaptive control law for the PUMA robot is given. <<ETX>>
Optimization of a Dynamic Fault Diagnosis Model Based on Machine Learning

A delay dynamic coupled fault diagnosis (DDCFD) model is a kind of effective fault diagnosis model based on probability graph for complex systems. Previous research assumes that its structure and parameters are all known before application. Since the initial model is always constructed by experts, it is possible that it is inconsistent with practical systems, which may impact the diagnostic accuracy. In order to improve the model by applying the practical data, this paper develops a machine learning method for the DDCFD model. A parameter learning method and three structure learning methods are studied. Using this approach, prior knowledge and test data can be combined together to get a better diagnostic model. The methods are tested and compared on simulated examples and an application case. The result indicates that diagnostic accuracy can be improved by means of parameter learning compared to the initial model, and it can be further improved by structure learning. The score-based algorithm is the best choice for learning the structure of the DDCFD model.
Assessment of the stability of the Hyperion SWIR module for hyperspectral mineral mapping using multi-date images from Mount Fitton, Australia

The stability of the SWIR detector area array of the satellite-borne Hyperion hyperspectral system was investigated using six dates of cloud-free imagery collected from the Mount Fitton test site in South Australia. This test site was selected because of its mineralogical diversity, excellent geological exposure and relatively unchanging surface properties. Several aspects of Hyperion's SWIR stability were examined. First, as Hyperion comprises an area array detector of 256 pixels by 242 bands, the nature of any column (along-track) striping was tested. This striping is revealed when the dominating effects of topographic illumination and surface albedo are removed and comprises a minor component of scene-independent column-striping and a major component of scene-dependent striping. Based on scene-derived pixel-band statistics, it was found that both an applied shift and gain for each detector element was required to effectively remove this striping. Scene statistics were generated using scene lengths of 185 km (6170 lines) and 42 km (1400 lines) for comparative purposes. This showed that scene heterogeneity was strongly apparent in the 42 km length scene and only weakly apparent in the 185 km length scene. However, after generating and applying scale and offset corrections to the data, the resultant pixel spectral showed minimal change though the effect of removing the line striping was dramatic in the resulting imagery. Pixel spectra and mineral maps show near identical patterns for all dates of imagery validating the success of the Hyperion system, calibration and destriping process.
Routing in Multi-Hop Cellular Device-to-Device (D2D) Networks: A Survey

In recent years, device-to-device (D2D) communication has attained significant attention in the research community. The advantages of D2D communication can be fully realized in multi-hop communication scenario. The integration of cellular and multi-hop networks not only provides guaranteed quality of service and reliability as a traditional cellular network, but also has the flexibility and adaptability as a multi-hop network. Routing in such multi-hop cellular D2D networks is a critical issue, since the multi-hop network can perform worse than a traditional cellular network if wrong routing decisions are made. This is because routing in these multi-hop networks needs to take care of the node mobility, dynamic network topology, and network fragmentation, which did not exist in traditional cellular networking. This paper provides a comprehensive survey of routing in multi-hop D2D networks. Some future research directions for the routing in D2D networks are also discussed at the end of this paper.
A Subjective Quality of Life Proxy for Older Adults in the PAQUID Cohort Study

Objectives: Quality of life is regarded as a major outcome in epidemiologic research, especially in the older population. Nevertheless, some cohort studies lack a specific instrument to evaluate it. The aim of this study was to propose a subjective quality of life proxy using easily accessible items, available in most epidemiologic studies. Method: We used data from the PAQUID (Personnes Agées Quid) cohort study (1991-1992, France). A subjective quality of life proxy was created based on items on positive affects, subjective health, and life satisfaction. Logistic and linear regression models as well as Cox survival models were used to assess the association between the proxy score and depression, dependence, cognitive complaints, adverse life events, comorbidities, and death. Analyses were replicated in an independent cohort study, AMI (Approche Multidisciplinaire intégrée; 2007-2008, France). All models were adjusted for age, sex, Mini-Mental State Examination score, and place of residence. Results: In the PAQUID sample (n = 2135), we found significant associations between the proxy score and the selected health outcomes. We found the same associations in the AMI cohort. Conclusion: This proxy might be useful when no gold standard for quality of life assessment is available or when in need of a short but reliable instrument that will not require extended administration time.
Antibiotic-resistant indicator bacteria in irrigation water: High prevalence of extended-spectrum beta-lactamase (ESBL)-producing Escherichia coli

Irrigation water is a major source of fresh produce contamination with undesired microorganisms including antibiotic-resistant bacteria (ARB), and contaminated fresh produce can transfer ARB to the consumer especially when consumed raw. Nevertheless, no legal guidelines exist so far regulating quality of irrigation water with respect to ARB. We therefore examined irrigation water from major vegetable growing areas for occurrence of antibiotic-resistant indicator bacteria Escherichia coli and Enterococcus spp. , including extended-spectrum β-lactamase (ESBL)-producing E. coli and vancomycin-resistant Enterococcus spp. Occurrence of ARB strains was compared to total numbers of the respective species. We categorized water samples according to total numbers and found that categories with higher total E. coli or Enterococcus spp. numbers generally had an increased proportion of respective ARB-positive samples. We further detected high prevalence of ESBL-producing E. coli with eight positive samples of thirty-six (22%), while two presumptive vancomycin-resistant Enterococcus spp. were vancomycin-susceptible in confirmatory tests. In disk diffusion assays all ESBL-producing E. coli were multidrug-resistant (n = 21) and whole-genome sequencing of selected strains revealed a multitude of transmissible resistance genes (ARG), with blaCTX-M-1 (4 of 11) and blaCTX-M-15 (3 of 11) as the most frequent ESBL genes. Overall, the increased occurrence of indicator ARB with increased total indicator bacteria suggests that the latter might be a suitable estimate for presence of respective ARB strains. Finally, the high prevalence of ESBL-producing E. coli with transmissible ARG emphasizes the need to establish legal critical values and monitoring guidelines for ARB in irrigation water.
Walloons as General or Specific Others? A Comparison of anti-Walloon and anti-immigrant Attitudes in Flanders

This study attempts to shed light on the structure, the prevalence and the determinants of anti-Walloon attitudes in Flanders. For this purpose, we contrast anti-Walloon prejudice with prejudice against a relatively well-understood and archetypical out-group, namely immigrants. Our theoretical approach draws on insights from two paradigms of intergroup relations: the Group-Focused Enmity approach stressing that specific prejudices have a strong common denominator, and the Differentiated Threat model arguing that specific prejudices are contingent on the context of intergroup relations as well as the involved types of threat. To assess the (dis)similarities in anti-Walloon and anti-immigrant prejudice, we use the Flemish dataset of the Belgian National Election Study (BNES) 2010. Comparable measurement instruments for both forms of prejudice are analyzed by means of structural equation modeling. Our results reveal a nuanced picture regarding the similarities and differences between anti-Walloon and anti-immigrant attitudes in Flanders. One the one hand, anti-Walloon and anti-immigration attitudes are strongly correlated and rooted in economic threat perceptions. On the other hand, anti-Walloon attitudes are less outspoken in the Flemish population than anti-immigrant attitudes, are less founded on cultural threat perceptions and are more closely linked to feelings of identification with the Flemish in-group.
Design Optimization of a Power Generator Soundproofing Enclosure

It is a vital requirement to have passive noise control enclosure in order to depress air borne noise of reciprocating engine type power generators. The design of these enclosures needs to be optimized in terms of the sound pressure level and the designing cost. We have used the existing acoustic equations to obtain the optimization based on the objective functions derived for the sound pressure level and enclosure design cost. Metaheuristic optimization algorithms such as genetic algorithms and particle swarm algorithm are capable of solving these optimization problems which have constraints for different parameters. The results obtained for a real world design problem confirms that particle swarm optimization provides better results than genetic algorithm in terms of optimality of the solutions and also the computational efficiency. Furthermore, it was observed that there is a significant linear relationship (R-Squared = 99.3%, p-value < 0.001) between the minimum enclosure design cost and the sound pressure level of the enclosure $(SPL_{E})$ for the preferred range for $SPL_{E}$ values (65 to 70). The minimum possible enclosure design cost increases linearly with decreasing $SPL_{E}$ value. Therefore, the least possible $SPL_{E}$ value depends on the available financial resources.
UPPAAL-Tiga: Timed Games for Everyone

In 2005 we proposed the first efficient on-the-fly algorithm for solving games based on timed game automata with respect to reachability and safety properties. Since then we have completely re-implemented the first prototype and made dramatic improvements both in terms of performance (several orders of magnitude) and the input language (complete support of all the language features of Uppaal). In addition, the tool supports the new feature of strategy generation with different compactness levels. In this paper we present this new version of Uppaal-Tiga.
Surface parameter estimation using interferometric and polarimetric SAR

In this work the potential of using the interferometric coherence at different polarisations over surface scatterers in order to extract information about surface parameters is investigated. The sensitivity of the individual coherence contributions to surface roughness and moisture conditions is discussed and simulated using a polarimetric surface scattering model. Finally, experimental airborne SAR data are used to demonstrate the discussed effects.
SAND - a hyperspectral sensor for the analysis of dryland degradation

SAND proposes a space-borne imaging spectrometer (hyperspectral) mission measuring bio-geochemical/bio-geophysical variables to determine land degradation processes in semi-arid geographical areas. Arid and semi-arid lands cover approximately one third of the continental surface of the Earth. They include the deserts and their semi-arid and sub-humid dry margins, and the subtropical Mediterranean latitudes. The semi-arid ecosystems provide important land resources for adapted agricultural production and grazing systems. The importance of thoroughly monitoring the state of the environment in areas considered at risk, in the context of global climatic change and worldwide desertification dynamics, has long been recognised. These facts have led to the ratification of the UN Convention to Combat Desertification (UNCCD) by almost 200 nations worldwide. This agreement emphasises the need to assess land degradation and desertification processes. The status of dryland systems is controlled to a large extent by a fragile equilibrium between soil, vegetation and water resources. Precipitation rates are generally low and rainfall events are known to be irregular and of high intensity. Natural ecosystems and traditional land use practices have largely adapted to these conditions over time, and thus, assure an optimum use of available resources. Dryland degradation may be triggered by (global) climatic change and/or human mismanagement. While the former may result in longer and more frequent drought events, the latter one mainly consists of inappropriate land use practices. Both may induce changes in surface properties, impacting the type and density of the vegetation cover. Many of these changes can be detected remotely by measuring the spectral characteristics of the land surfaces. Accordingly, a high-spectral resolution remote sensing system such as the proposed SAND substantially increases these detection capabilities. This paper presents a sensor concept providing global coverage at a reasonable spatial and very high spectral resolution for a quantitative derivation of land surface variables. This includes vegetation parameters and soil-related information such as foliage chemistry, water content, fractional cover, chemical composition, organic matter content and information about the lithological background for selected targets.