Miracle-dz/UniFAR-FTUs huggingface dataset:
A statistically optimized regional thresholding method (SORT) for bone lesion detection in 18F-NaF PET/CT imaging

Identification of individual lesions on 18F-NaF PET bone scans is a time-consuming and often subjective process that makes accurate characterization of disease burden challenging. Current automated methods either underestimate disease or struggle with high false positive rates. We developed a statistically optimized regional thresholding (SORT) method that optimizes detection of bone lesions. This study assessed 18F-NaF PET/CT scans of 37 bone metastatic prostate cancer patients. Each PET image was divided into 19 skeletal regions. Areas of disease in each skeletal region were identified by an experienced nuclear medicine physician. A region of interest (ROI) was placed at each disease location and local maxima were extracted for both healthy and diseased ROIs. Secondary physician review was performed after identification of suspicious local maxima. Region-specific SUV thresholds were determined based on receiver operating characteristic (ROC) analysis optimized for detection of malignant disease. The detection performance of the SORT thresholds were compared to commonly used SUV > 10 g ml−1 (SUV10) and SUV > 15 g ml−1 (SUV15) global thresholds. The sensitivity of the SORT thresholds to various factors was evaluated, such as the number of subjects evaluated or image reconstruction settings. 1751 lesions were manually identified by the nuclear medicine physician. SORT identified different thresholds in each skeletal region (SUV range: 3–13 g ml−1). Region-specific SORT thresholding resulted in higher sensitivity (95.8%) than commonly used global thresholds (82.8% for SUV10 and 58.4% for SUV15) while maintaining a high specificity (97.1%, compared to 97.3% for SUV10 and 100.0% for SUV15). Factors, such as reconstruction settings, had minimal impact on threshold optimization, resulting in an average change of 10% (range: 2%–17%) in thresholds for each factor. Region-specific SUV thresholding of NaF PET images for bone lesion detection in metastatic prostate patients was found to be superior to current global thresholding methods.
Trypanosoma cruzi diversity in naturally infected nonhuman primates in Louisiana assessed by deep sequencing of the mini-exon gene

BACKGROUND Trypanosoma cruzi is a zoonotic pathogen of increasing relevance in the USA, with a growing number of autochthonous cases identified in recent years. The identification of parasite genotypes is key to understanding transmission cycles and their dynamics and consequently human infection. Natural T. cruzi infection is present in captive nonhuman primate colonies in the southern USA. METHODS We investigated T. cruzi genetic diversity through a metabarcoding and next-generation sequencing approach of the mini-exon gene to characterize the parasite genotypes circulating in nonhuman primates in southern Louisiana. RESULTS We confirmed the presence of T. cruzi in multiple tissues of 12 seropositive animals, including heart, liver, spleen and gut. The TcI discrete typing unit (DTU) predominated in these hosts, and specifically TcIa, but we also detected two cases of coinfections with TcVI and TcIV parasites, unambiguously confirming the circulation of TcVI in the USA. Multiple mini-exon haplotypes were identified in each host, ranging from 6 to 11. CONCLUSIONS The observation of multiple T. cruzi sequence haplotypes in each nonhuman primate indicates possible multiclonal infections. These data suggest the participation of these nonhuman primates in local parasite transmission cycles and highlight the value of these naturally infected animals for the study of human Chagas disease.
Simulations of blood as a suspension predicts a depth dependent hematocrit in the circulation throughout the cerebral cortex

Recent advances in modeling oxygen supply to cortical brain tissue have begun to elucidate the functional mechanisms of neurovascular coupling. While the principal mechanisms of blood flow regulation after neuronal firing are generally known, mechanistic hemodynamic simulations cannot yet pinpoint the exact spatial and temporal coordination between the network of arteries, arterioles, capillaries and veins for the entire brain. Because of the potential significance of blood flow and oxygen supply simulations for illuminating spatiotemporal regulation inside the cortical microanatomy, there is a need to create mathematical models of the entire cerebral circulation with realistic anatomical detail. Our hypothesis is that an anatomically accurate reconstruction of the cerebrocirculatory architecture will inform about possible regulatory mechanisms of the neurovascular interface. In this article, we introduce large-scale networks of the murine cerebral circulation spanning the Circle of Willis, main cerebral arteries connected to the pial network down to the microcirculation in the capillary bed. Several multiscale models were generated from state-of-the-art neuroimaging data. Using a vascular network construction algorithm, the entire circulation of the middle cerebral artery was synthesized. Blood flow simulations indicate a consistent trend of higher hematocrit in deeper cortical layers, while surface layers with shorter vascular path lengths seem to carry comparatively lower red blood cell (RBC) concentrations. Moreover, the variability of RBC flux decreases with cortical depth. These results support the notion that plasma skimming serves a self-regulating function for maintaining uniform oxygen perfusion to neurons irrespective of their location in the blood supply hierarchy. Our computations also demonstrate the practicality of simulating blood flow for large portions of the mouse brain with existing computer resources. The efficient simulation of blood flow throughout the entire middle cerebral artery (MCA) territory is a promising milestone towards the final aim of predicting blood flow patterns for the entire brain.
Comparison of 2016–17 and Previous Epizootics of Highly Pathogenic Avian Influenza H5 Guangdong Lineage in Europe

We analyzed the highly pathogenic avian influenza (HPAI) H5 epizootic of 2016–17 in Europe by epidemiologic and genetic characteristics and compared it with 2 previous epizootics caused by the same H5 Guangdong lineage. The 2016–17 epizootic was the largest in Europe by number of countries and farms affected and greatest diversity of wild birds infected. We observed significant differences among the 3 epizootics regarding region affected, epidemic curve, seasonality, and outbreak duration, making it difficult to predict future HPAI epizootics. However, we know that in 2005–06 and 2016–17 the initial peak of wild bird detections preceded the peak of poultry outbreaks within Europe. Phylogenetic analysis of 2016–17 viruses indicates 2 main pathways into Europe. Our findings highlight the need for global surveillance of viral changes to inform disease preparedness, detection, and control.
GuitarSet: A Dataset for Guitar Transcription

The guitar is a popular instrument for a variety of reasons, including its ability to produce polyphonic sound and its musical versatility. The resulting variability of sounds, however, poses signiﬁcant challenges to automated meth-ods for analyzing guitar recordings. As data driven meth-ods become increasingly popular for difﬁcult problems like guitar transcription, sets of labeled audio data are highly valuable resources. In this paper we present Gui-tarSet, a dataset that provides high quality guitar recordings alongside rich annotations and metadata. In particular, by recording guitars using a hexaphonic pickup, we are able to not only provide recordings of the individual strings but also to largely automate the expensive annotation process. The dataset contains recordings of a variety of musical excerpts played on an acoustic guitar, along with time-aligned annotations of string and fret positions, chords, beats, downbeats, and playing style. We conclude with an analysis of new challenges presented by this data, and see that it is interesting for a wide variety of tasks in addition to guitar transcription, including performance analysis, beat/downbeat tracking, and chord estimation.
Giant Intrapericardial Myxoma Adjacent to the Left Main Coronary Artery

A 62-years-old woman was admitted to the hospital because of chronic cough, expectoration of thick mucus, hoarseness and tightness in the precordial area. Computed Tomography (CT) examination revealed the presence of a giant intrapericardial tumor with the dimensions of 80 × 38 × 32 mm. It was located anteriorly and laterally to the left atrium, posteriorly to the pulmonary trunk and the ascending aorta. This hypodense change modeled the left atrium without evidence of invasion. CT coronary angiography and 3-dimensional reconstruction were applied to enable precise planning of cardiac surgery. CT evaluation confirmed that it is possible to remove the tumor without damage to the adjacent left main coronary artery. The patient underwent cardiac surgery with sternotomy and cardiopulmonary bypass. A cohesive, smooth, vascularized tumor pedunculated to the left atrial epicardium was visualized. The location and dimensions corresponded to those determined by CT scan examination. The entire tumor was successfully dissected together with adjacent adipose and fibrous tissue. Histological evaluation revealed the presence of myxoid cells, blood vessels, degenerative changes, and microcalcifications embedded in profuse hyalinized stroma. Those histological features enabled identification of the intrapericardial tumor as a myxoma. Follow-up CT examination did not demonstrate any signs of recurrence of the myxoma. According to our knowledge, a myxoma located inside the pericardial sac has never been described before.
Wireless Sensor Networks Running on Harvested Power

Tese de doutoramento em Engenharia Eletrotecnica e de Computadores, no ramo de especializacao de Computadores e Electronica, apresentada ao Departamento de Engenharia Eletrotecnica e de Computadores da Faculdade de Ciencias e Tecnologia da Universidade de Coimbra
Data Quality in Content Analysis. The Case of the Comparative Manifestos Project

While textbooks offer numerous devices for enhancing and testing the data quality of content analysis, all tools must be tailored in line with the contexts of the text and the analytical concepts of research. This is particularly the case in a long-term project such as ours that has continued for three dec ades to code election programs of all significant parliamentary parties in old and new representative democracies since World-War II for the purpose of measuring policy preferences of political parties. This article starts with a dis cussion of the strengths and weaknesses of the two basic types of quantitative approaches - human-based and computer-based content analysis. The basic features of our classical human-based approach for estimating parties' policy preferences are outlined by reference to Krippendorfs (2006) typologies of reliability and validity. The conclusions highlight implications of the contexts of manifestos and the concepts applied to them for providing high quality
Smoothed Analysis of Edge Elimination for Euclidean TSP

One way to speed up the calculation of optimal TSP tours in practice is eliminating edges that are certainly not in the optimal tour as a preprocessing step. In order to do so several edge elimination approaches have been proposed in the past. In this work we investigate two of them in the scenario where the input consists of $n$ independently distributed random points with bounded density function from above and below by arbitrary positive constants. We show that after the edge elimination procedure of Hougardy and Schroeder the expected number of remaining edges is $\Theta(n)$, while after that of Jonker and Volgenant the expected number of remaining edges is $\Theta(n^2)$.
Deep Reinforcement Learning for Resource Management in Network Slicing

Network slicing is born as an emerging business to operators by allowing them to sell the customized slices to various tenants at different prices. In order to provide better-performing and cost-efficient services, network slicing involves challenging technical issues and urgently looks forward to intelligent innovations to make the resource management consistent with users’ activities per slice. In that regard, deep reinforcement learning (DRL), which focuses on how to interact with the environment by trying alternative actions and reinforcing the tendency actions producing more rewarding consequences, is assumed to be a promising solution. In this paper, after briefly reviewing the fundamental concepts of DRL, we investigate the application of DRL in solving some typical resource management for network slicing scenarios, which include radio resource slicing and priority-based core network slicing, and demonstrate the advantage of DRL over several competing schemes through extensive simulations. Finally, we also discuss the possible challenges to apply DRL in network slicing from a general perspective.
Automatic face recognition for home safety using video-based side-view face images

Face recognition from side-view positions is an essential task for recognition systems with real-world scenarios. Most of the existing face recognition methods rely on alignment of face images into some canonical form. However, alignment in side-view faces can be challenging due to lack of symmetry and a small number of reliable reference points. To the best of the author's knowledge, only a few of the existing methods deal with video-based face recognition from side-view images, and not many databases include sufficient video footage to study this task. Here, the authors propose an automatic side-view face recognition system designed for home safety applications. They first contribute a newly collected video face database, named UT-DOOR, where 98 subjects were recorded with four cameras attached at doorposts as they pass through doors. Secondly, they propose a face recognition system, where they automatically detect and recognise faces using side-view images in videos. One of the attractive properties of this system is that they use cameras with limited view angle to preserve the privacy of the people. They review several databases and test their system both on the CMU Multi-PIE database and the UT-DOOR database for comparison. Experimental results show that their system can successfully recognise side-view faces from videos.
Human mummified brain from a medieval tomb with lime‐soil mixture barrier of the Joseon Dynasty, Korea

Although all of the Korean medieval mummies found up to the present time have shown relatively well-preserved brains, relevant biomedical information has been difficult to acquire owing to the obstacles to obtaining permission to conduct invasive investigations. However, recently we were able to secure permission to investigate, through various forms of examination including dissection, a well-preserved brain found in a lime-soil mixture barrier (LSMB) tomb in Yongin, Korea, because the remains found within the tomb were mostly skeletal. As in the cases of mummified brains from tombs found in other countries, the current Yongin case showed well-preserved grey and white matter within well demarcated brain lobes, on which sulci and gyri could be identified. On histological examination, we found that the remaining brain tissue was composed mainly of lipids, which seems to correspond to the preserved myelin sheath. The present paper is the first report on the general preservation status of a mummified brain found in Korea. Moreover, because mummified brains are found quite frequently in LSMB medieval tombs, this study could provide a good basis upon which further such palaeo-neuropathological studies can be progressed. Copyright © 2008 John Wiley & Sons, Ltd.
Ear recognition in 3D using 2D curvilinear features

This study presents a novel approach for human recognition using co-registered three-dimensional (3D) and 2D ear images. The proposed technique is based on local feature detection and description. The authors detect feature key-points in 2D ear images utilising curvilinear structure and map them to the 3D ear images. Considering a neighbourhood around each mapped key-point in 3D, a feature descriptor vector is computed. To match a probe 3D ear image with a gallery 3D ear image for recognition, first highly similar feature key-points of these images are used as correspondence points for an initial alignment. Afterwards, a fine iterative closest point matching is performed on entire data of the 3D ear images being matched. An extensive experimental analysis is performed to demonstrate the recognition performance of the proposed approach in the presence of noise and occlusions, and compared with the available state-of-the-art 3D ear recognition techniques. The recognition rate of the proposed technique is found to be 98.69% on the University of Notre Dame-Collection J2 dataset with an equal error rate of 1.53%.
Analogy Based Effort Estimation by using Use Cases and Actors

Analogy based efforts estimation is one of the very popular method of estimation . In analogy we estimate the efforts of new project on the basis of the old project. The problem with analogy based estimation is how to select a project from the many old projects. This paper is suggesting the way of selecting a project from the old project. The selection of project depends on what project information is stored in the database. This paper is also suggesting that what information of project should be stored in the database. This paper is suggesting that projects can be compared on the basis of Use Case and Actor instead of comparison on the basis of features and functions. Almost every project using Use Case diagram in expressing the requirement and it also provides the basis for the other design diagrams. Use Case shows the functional requirement of the system. For the comparison with next projects we need to store name and description of the Use Case, the description contains the functionality in detail. One thing must be considered that Use Case should be simplified, means each use case should describe the one requirement; it should not have the relationship like generalization, include and extend. Each use case must be simple and specific. The same rule is applicable for the Actors also.
Automatic annotation of tennis action for content-based retrieval by collaborating audio and visual information

In this paper, the author proposes a scheme for automatically annotating tennis action for content-based retrieval by using video and audio information collaboratively. Conventionally, annotation for content-based retrieval had been associated with the action events to be recognized by analyzing information such as the locus or relative position and their transitions for each target object within the video. However, these methods have the drawback that when only video information is used, there are parts for which important times or positions basically cannot be identified because of tracking errors due to occlusion of the target object, which results in action events not being detected or detection errors occurring. The proposed method, which is applied to tennis video, uses audio information to extract the times when a player strikes the ball and recognizes basic actions of the player such as a forehand swing or overhead swing from the positional relationship between the player and ball at those times. The authors performed experiments to check the degree to which the recognition of the basic action of a player was affected by whether or not audio information was used to extract the ball impact time. The results verified the effectiveness of the approach by showing that the proposed scheme could prevent several action identification errors that could not be avoided by using only video information. © 2004 Wiley Periodicals, Inc. Electron Comm Jpn Pt 3, 87(11): 57–72, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjc.20120
Research on Grouping Methods in CoMP Networks

Coordinated multi-point (CoMP) networks are inter-cell cooperation mechanisms specifically proposed to enhance transmission of users at cell edge. Therefore, selection appropriate group of cells to provide better transmission service is an important research topic in CoMP networks. In this paper, we proposed two grouping methods in CoMP networks including: (1) the arriving user accesses the same empty channels of his best and second best link quality cells and (2) if there are no same empty channels from his best and second best link quality cells, allow the arriving user to access one of the empty channels from his best or second best link quality cells. Simulation Results show that the proposed model can effectively evaluate influences of system performance of these two grouping methods.
Machine vision for automatic calibration of analog display instruments

This paper presents a general process for an automatic reading of analog instruments. The calibration of analog watermeters serves for a demonstration of this algorithm. Reading a measuring instrument means detecting the position of scales and pointers in the intensity image in order to determine the value displayed by the measuring instrument. The reading process involves the detection of its boundaries, scales, pointers and lettering elements by using pattern recognition algorithms and a-priori information about the geometry and the elements of the meter. First, a validation for the correct type has to be done by checking the actual, detected elements with the elements defined in the type-description. Following the determination of the indicated measurement value, the intensity image of the watermeter is compressed and stored to ensure a given quality standard by protocolling each measurement step. The calibration process consists of five reading phases. A series of watermeters of the same type is calibrated in one run with water quality measurement. The outlined automated approach to the problem is faster, more reliable, stable and cheaper than the manual reading and checking of the meters performed now. Results concerning time, accuracy and reliability are given at the end of the paper.
Thai Automatic signature verification System Employing Textural Features

This study focuses on a comprehensive study of Automatic Signature Verification (ASV) for off-line Thai signatures; an investigation was carried out to characterise the challenges in Thai ASV and to baseline the performance of Thai ASV employing baseline features, being Local Binary Pattern, Local Directional Pattern, Local Binary and Directional Patterns combined (LBDP), and the baseline shape/feature-based hidden Markov model. As there was no publicly available Thai signature database found in the literature, the authors have developed and proposed a database considering real-world signatures from Thailand. The authors have also identified their latent challenges and characterised Thai signature-based ASV. The database consists of 5,400 signatures from 100 signers. Thai signatures could be bi-script in nature, considering the fact that a single signature can contain only Thai or Roman characters or contain both Roman and Thai, which poses an interesting challenge for script-independent SV. Therefore, along with the baseline experiments, the investigation on the influence and nature of bi-script ASV was also conducted. From the equal error rates and Bhattacharyya distance, the score achieved in the experiments indicate that the Thai SV scenario is a script-independent problem. The open research area on this subject of research has also been addressed.