Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of potentially variable RBE in liver proton therapy

Currently, the relative biological effectiveness (RBE) is assumed to be constant with a value of 1.1 in proton therapy. Although trends of RBE variations are well known, absolute values in patients are associated with considerable uncertainties. This study aims to evaluate the impact of a variable proton RBE in proton therapy liver trials using different fractionation schemes. Sixteen liver cancer cases were evaluated assuming two clinical schedules of 40 Gy/5 fractions and 58.05 Gy/15 fractions. The linear energy transfer (LET) and physical dose distribution in patients were simulated using Monte Carlo. The variable RBE distribution was calculated using a phenomenological model, considering the influence of the LET, fraction size and α/β value. Further, models to predict normal tissue complication probability (NTCP) and tumor control probability (TCP) were used to investigate potential RBE effects on outcome predictions. Applying the variable RBE model to the 5 and 15 fractions schedules results in an increase in mean fraction-size equivalent dose (FED) to the normal liver of 5.0% and 9.6% respectively. For patients with a mean FED to the normal liver larger than 29.8 Gy, this results in a non-negligible increase in the predicted NTCP of the normal liver averaging 11.6%, ranging from 2.7% to 25.6%. On the other hand, decrease in TCP was less than 5% for both fractionation regimens for all patients when assuming a variable RBE instead of constant. Consequently, the difference in TCP between the two fractionation schedules did not change significantly assuming a variable RBE while the impact on the NTCP difference was highly case specific. In addition, both the NTCP and TCP decrease with increasing α/β value for both fractionation schemes, with the decreases being more pronounced when using a variable RBE compared to using RBE = 1.1. Assuming a constant RBE of 1.1 most likely overestimates the therapeutic ratio in proton therapy for liver cancer, predominantly due to underestimation of the RBE-weighted dose to the normal liver. The impact of applying a variable RBE (as compared to RBE = 1.1) on the NTCP difference of the two fractionation regimens is case dependent. A variable RBE results in a slight increase in TCP difference. Variations in patient radiosensitivity increase when using a variable RBE.
Factors hindering the adherence to clinical practice guideline for diabetes mellitus in the Palestinian primary healthcare clinics: a qualitative study

Objective Despite a high number of the internationally produced and implemented clinical guidelines, the adherence with them is still low in healthcare. This study aimed at exploring the perspectives and experiences of senior doctors and nurses towards the barriers of adherence to diabetes guideline. Setting The Palestinian Primary Health Care-Ministry of Health (PHC-MoH) and Primary Health Care-United Nations Relief and Works Agency for Palestine Refugees in the Near East (PHC- UNRWA) in Gaza Strip. Participants Individual face-to-face in-depth interviews were conducted with 20 senior doctors and nurses who were purposefully selected. Methods Qualitative design was employed using the theoretical framework by Cabana et al to develop an interview guide. Semi-structural and audio-recorded interviews were conducted. Data were transcribed verbatim and thematically analysed. Results The key theme barriers identified by participants that emerged from the analysed data were in regard of the PHC-MoH lack reimbursement, lack of resources and lack of the guideline trustworthiness, and in regard of PHC-UNRWA the time constraints and the lack of the guideline trustworthiness. The two key subthemes elicited from the qualitative analysis were the outdated guideline and lack of auditing and feedback. Conclusion The analysis identified a wide range of barriers against the adherence to diabetes guideline within the PHC-MoH and PHC-UNRWA. The environmental-related and guideline-related barriers were the most prominent factors influencing the guideline adherence. Our study can inform the policy makers and senior managers to develop a tailored interventions that can target the elicited barriers through a multifaceted implementation strategy.
Perceptual Quality Evaluation of 3D Triangle Mesh: A Technical Review

During mesh processing operations (e.g. simplifications, compression, and watermarking), a 3D triangle mesh is subject to various visible distortions on mesh surface which result in a need to estimate visual quality. The necessity of perceptual quality evaluation is already established, as in most cases, human beings are the end users of 3D meshes. To measure such kinds of distortions, the metrics that consider geometric measures integrating human visual system (HVS) is called perceptual quality metrics. In this paper, we direct an expansive study on 3D mesh quality evaluation mostly focusing on recently proposed perceptual based metrics. We limit our study on greyscale static mesh evaluation and attempt to figure out the most workable method for real-time evaluation by making a quantitative comparison. This paper also discusses in detail how to evaluate objective metric's performance with existing subjective databases. In this work, we likewise research the utilization of the psychometric function to expel non-linearity between subjective and objective values. Finally, we draw a comparison among some selected quality metrics and it shows that curvature tensor based quality metrics predicts consistent result in terms of correlation.
Gradient Tree Boosting-Based Positioning Method for Monolithic Scintillator Crystals in Positron Emission Tomography

Monolithic crystals are considered as an alternative for complex segmented scintillator arrays in positron emission tomography systems. Monoliths provide high sensitivity, good timing, and energy resolution while being cheaper than highly segmented arrays. Furthermore, monoliths enable intrinsic depth of interaction capabilities and good spatial resolutions (SRs) mostly based on statistical calibrations. To widely translate monoliths into clinical applications, a time-efficient calibration method and a positioning algorithm implementable in system architecture such as field-programmable gate arrays (FPGAs) are required. We present a novel positioning algorithm based on gradient tree boosting (GTB) and a fast fan beam calibration requiring less than 1 h per detector block. GTB is a supervised machine learning technique building a set of sequential binary decisions (decision trees). The algorithm handles different sets of input features, their combinations and partially missing data. GTB models are strongly adaptable influencing both the positioning performance and the memory requirement of trained positioning models. For an FPGA-implementation, the memory requirement is the limiting aspect. We demonstrate a general optimization and propose two different optimization scenarios: one without compromising on positioning performance and one optimizing the positioning performance for a given memory restriction. For a 12 mm high LYSO-block, we achieve an SR better than 1.4 mm FWHM.
Reconstruction and Registration of Large-Scale Medical Scene Using Point Clouds Data from Different Modalities

Sensing the medical scenario can ensure the safety during the surgical operations. So, in this regard, a monitor platform which can obtain the accurate location information of the surgery room is desperately needed. Compared to 2D camera image, 3D data contains more information of distance and direction. Therefore, 3D sensors are more suitable to be used in surgical scene monitoring. However, each 3D sensor has its own limitations. For example, Lidar (Light Detection and Ranging) can detect large-scale environment with high precision, but the point clouds or depth maps are very sparse. As for commodity RGBD sensors, such as Kinect, can accurately capture denser data, but limited to a small range from 0.5 to 4.5m. So, a proper method which can address these problems for fusing different modalities data is important. In this paper, we proposed a method which can fuse different modalities 3D data to get a large-scale and dense point cloud. The key contributions of our work are as follows. First, we proposed a 3D data collecting system to reconstruct the medical scenes. By fusing the Lidar and Kinect data, a large-scale medical scene with more details can be reconstructed. Second, we proposed a location-based fast point clouds registration algorithm to deal with different modality datasets.
Application of UAV Imagery for Inventory Mapping – A Case of Industrial Estate

The paper presents the results of the accuracy assessment of UAV/UAS imagery and digital processing for inventory mapping. The test object was an industrial estate in the Stryków municipality. The photo block (581 photos, GSD was 4.6 cm) registration was performed using Trimble®UX5 UAS equipped with Sony NEX-5T 16 MP digital system camera. The automatic aerial triangulation with self-calibration using the bundle block adjustment was performed and dense point clouds, digital surface model (DSM) and finally an orthomosaic were generated in Agisoft Photoscan and in Pix4Dmapper Pro software. The results of photogrammetric 3D data collection with an existing base map, which was elaborated using direct geodetic detailed surveys, were compared in AutoCAD Civil 3D 2017 suite. The generated DSM's were additionally analyzed using CloudCompare application. The obtained accuracy of photogrammetric 2D vectorization fulfils the accuracy requirements of the 1 st survey group of terrain details, specified in Polish geodetic technical norm.
CFS Based Feature Subset Selection for Enhancing Classification of Similar Looking Food Grains- A Filter Approach

Feature selection plays an important role in machine learning. The best selection of significant feature set results inbetter performance of classifiers. In this work feature selection using filter approach for classification of 10 different similar looking food grains has been carried out. As part of the work, firstly texture features of food grain images are extracted using color based GLCM. As part of feature subset selection, the Correlation-based Feature Selection (CFS) filter approach has been applied with 5 different search methods. The evaluation of feature selected is done using 6 different classifiers. Result shows the feature set selected by the CFS filter have indeed enhanced the classification performance of all the 6 classifiers when compared to their performance with the original feature set.
Optimal antero-posterior position of the maxillary central incisors and its relationship to the forehead in adult African American females.

OBJECTIVES: To determine an optimal anteroposterior (AP) position of the maxillary central incisors and their relationship to the forehead in adult African American (AA) females. MATERIALS AND METHODS: Smile profile photographs of 150 AA females were acquired and divided into an optimal control group (N = 48) and a study group (N = 102) based on the position of the maxillary central incisors, as judged by a panel of orthodontists and orthodontic residents. The AP position of the maxillary central incisors and the forehead inclination (FI) were measured relative to Glabella vertical (GV). A two-sample t-test was used to compare the incisor AP position and the FI between the two groups. Linear regression was used to quantify the relationship between the incisor AP position and the FI. RESULTS: In all groups, the maxillary incisors were anterior to GV. However, a significant difference was found in the incisor AP position between the groups (8.58 ± 3.96 mm for the control group and 11.2 ± 4.48 mm for the study group; P = .001). Furthermore, the control group demonstrated a positive association between the optimal AP position of the maxillary central incisors and FI ( P < .0001). CONCLUSIONS: GV is a reliable landmark with which to access the AP maxillary incisor position in AA females. The optimal AP position of the maxillary central incisors is significantly associated with FI; the greater the FI, the more anterior the optimal maxillary incisor position. A prediction equation to determine the optimal position of the maxillary incisors relative to GV for AA females is proposed.
AES Design Improvements Towards Information Security Considering Scan Attack

With the rapid development and globalization of semiconductor industry, data security is becoming a more critical issue for highly confidential devices, especially for cryptography related applications. Advanced Encryption Standard (AES) is widely used for information security. For AES, the most important data are plaintext and keys, which are the targets of attacks. In this paper, AES security vulnerabilities are analyzed first. Information leakage would be a major concern for AES. Hence one of the most common types of attacks that could leak information at the AES implementation, inserted into AES and utilizing scan chains in or around AES to extract keys or plaintext, is discussed. To deal with the attacks and improve AES circuit's information security, one protection, namely Registered Data Obfuscation, is presented. Experiment results show that with the proposed protection, the scan-based attack is invalidated to leak the critical data. Meanwhile, the proposed protection can also disalbe key Trojan attack introduced in [1, 2]. The cost analysis shows that the additional area and power overhead incurred by the proposed protection are 1.09% and 0.46%, respectively.
Visual Mitosis Detection and Cell Tracking Using Labeled Multi-Bernoulli Filter

Cell life-cycle and motility analysis is one of the fundamental tasks in many biological research activities, and its automation is a challenging problem. To solve the cell tracking problem using a Bayesian stochastic filter, one needs to properly incorporate all the information available about the cell behavior within the filter. This includes its movements, changes during mitosis process (up to splitting) and death. This paper demonstrates an effective way to perform this task for a particular family of cells (Chinese Hamster Ovarian (CHO) cells) that are known to have a commonly elliptic shape, and elongate during their mitosis. We model this by incorporating an ellipse-based state variable, a particular spawning process and an intuitive adaptive (measurement-driven) birth process that are added to the prediction step of a multi-Bernoulli filter. Our numerical experimental results involving microscopic images of living cells demonstrate significant improvement in tracking performance as a result of the proposed additions.
Sequence‐Based In‐silico Discovery, Characterisation, and Biocatalytic Application of a Set of Imine Reductases

Imine reductases (IREDs) have recently become a primary focus of research in biocatalysis, complementing other classes of amine‐forming enzymes such as transaminases and amine dehydrogenases. Following in the footsteps of other research groups, we have established a set of IRED biocatalysts by sequence‐based in silico enzyme discovery. In this study, we present basic characterisation data for these novel IREDs and explore their activity and stereoselectivity using a panel of structurally diverse cyclic imines as substrates. Specific activities of >1 U/mg and excellent stereoselectivities (ee>99 %) were observed in many cases, and the enzymes proved surprisingly tolerant towards elevated substrate loadings. Co‐expression of the IREDs with an alcohol dehydrogenase for cofactor regeneration led to whole‐cell biocatalysts capable of efficiently reducing imines at 100 mM initial concentration with no need for the addition of extracellular nicotinamide cofactor. Preparative biotransformations on gram scale using these ‘designer cells’ afforded chiral amines in good yield and excellent optical purity.
Performance Analysis of MMSE Pre-Coding Aided Spatial Modulation

Pre-coding aided spatial modulation (PSM) constitutes a flexible closed-loop multiple-input multiple-output transmission scheme, which attracts substantial research interest recently. In this paper, an upper bound of the average bit error probability (ABEP) for the PSM system based on the minimum mean square error (MMSE) pre-coding is investigated. In addition, a theoretical analysis is given based on the assumption that the numbers of transmit and receive antennas are large but their ratio <inline-formula> <tex-math notation="LaTeX">$\theta $ </tex-math></inline-formula> is constrained. Furthermore, the theoretical analysis shows that the ABEP upper bound of the PSM system based on MMSE pre-coding depends only on ratio <inline-formula> <tex-math notation="LaTeX">$\theta $ </tex-math></inline-formula>. Simulation results demonstrate that the upper bound is asymptotically tight as the signal-to-noise ratio increases, and the proposed system outperforms its benchmark that is based on zero-forcing pre-coding.
Patch-Sorted Deep Feature Learning for High Resolution SAR Image Classification

Synthetic aperture radar (SAR) image classification is a fundamental process for SAR image understanding and interpretation. The traditional SAR classification methods extract shallow and handcrafted features, which cannot subtly depict the abundant modal information in high resolution SAR image. Inspired by deep learning, an effective feature learning tool, a novel method called patch-sorted deep neural network (PSDNN) to implement unsupervised discriminative feature learning is proposed. First, the randomly selected patches are measured and sorted by the meticulously designed patch-sorted strategy, which adopts instance-based prototypes learning. Then the sorted patches are delivered to a well-designed dual-sparse autoencoder to obtain desired weights in each layer. Convolutional neural network is followed to extract high-level spatial and structural features. At last, the features are fed to a linear support vector machine to generate predicted labels. The experimental results in three broad SAR images of different satellites confirm the effectiveness and generalization of our method. Compared with three traditional feature descriptors and four unsupervised deep feature descriptors, the features learned in PSDNN appear powerful discrimination and the PSDNN achieves desired classification accuracy and a good visual appearance.
A Fast Track to Track Association Algorithm by Sequence Processing of Target States

Track association and fusion posses great importance in distributed sensor systems. In this study, we propose a novel statistical method based on temporal covariance estimation of local sensor tracks in a time interval and an association cost based on Mahalanobis distance is derived to associate tracks from different sensors. Contrary to associating the sensor tracks at each scan, we use a sequence of statistical features obtained from a state-space based tracking algorithm and process them in blocks to reduce false association probability. The effectiveness of the proposed method is illustrated by various three-dimensional multi-target tracking simulation scenarios. The algorithm is fast. As the number of tracks to associate increases, the computation time increases only linearly while the degradation in the association performance is small.
Identification of miRNAs associated with the mechanical response of hepatic stellate cells by miRNA microarray analysis

It has been suggested that hepatic stellate cells (HSCs) could be used in the regulation of liver microcirculation and portal hypertension. The effects of tensile strain on the microRNA (miRNA) profile of HSCs are largely unknown. In this study, we aimed to explore the changes of miRNA expression in tensile strain-treated HSCs. The purity and activation of HSCs were determined by immunofluorescence staining with antibody against desmin and a-SMA, respectively. miRNA profile analysis was performed on HSCs with and without tensile strain treatment (n=3) using microarray analysis. We identified 6 significantly differentially expressed miRNAs (DEMs), including 1 downregulated (rno-miR-125b-2-3p) and 5 upregulated (rno-miR-1224, rho-miR-188-5p, rho-miR-211-3p, rho-miR-3584-5p and rho-miR-466b-5p), which were validated by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) experiments. Further analysis of the DEMs revealed that many important biological processes and signal pathways were triggered in tensile strain-treated HSCs. These include the signal transduction mechanisms associated with protein binding, apoptosis, proliferation, and the FoxO and Wnt signaling pathways. In conclusion, this study presents the specific DEMs in tensile strain-treated HSCs. Our study provide novel miRNA-based information that may enhance our understanding of the pathophysiological processes leading to portal hypertension.
OCEANS-18: monitoring undetected vessels in high risk maritime areas

OCEANS-18 (Optical Camera for Earth and Naval Sighting) is a project approved and financed by the University of Padua. Its aim is to locate vessels who are deprived of localization instrumentation, in particular refugee vessels, that in the past years have become a concerning matter in terms of security and have moved governments to spend millions of euros attempting to prevent accidents on the Mediterranean Sea. The project consists in the assembling and evaluation of the engineering model of a CubeSat (3U), whose goal is to provide monitoring data that will be post-processed, and the detection algorithm, which will be developed with data provided by third party companies. The test will be made through a stratospheric balloon flight on July 2018. The project is currently under development and the final results will be delivered by the end of September 2018 to the University; the expected results are the detection, broadcasting and cross-referencing of the spotted vessels in order to identify the ones who do not figure on the radar.
Significance of Radius in the Phase-Difference-Based Approach to the Estimation of Glottal Closure Instants

The phase-difference (PD)-based approach to the estimation of glottal closure instants (Gels), basically considers the given speech signal to be the Fourier Transform (FT) of an arbitrary even signal, such that the valleys corresponding to GCCCCCCCCls in the signal would now correspond to zeros lying outside the unit circle. The angular locations of these zeros can be easily identified from the phase-difference (PD) spectrum, which has a value of 2π at these locations. However, measuring the FT on the unit circle, as is conventionally done, would result in spurious spikes in the PD spectrum, thereby increasing the ambiguity with which GCls can be detected. In order to circumvent this, the FT could be measured outside the unit circle, that is, at a radius greater than one. This radius has to be chosen carefully, since measuring the FT at too high a radius would result in missing Gels and too low a radius would result in spurious GCls. Therefore, the current work focuses on analyzing the significance of the radius in the accurate estimation of GCls and on automatically identifying the most suitable radius, at which the PD spectrum could be derived, based on the characteristics of a given speech signal.