Miracle-dz/UniFAR-FTUs huggingface dataset:
The multidimensional burden of psoriasis

dermoscopy and RCM to study MIS, and compare them with naevi. Dermoscopic atypical network and regression were identified as independent predicting factors for the diagnosis of MIS. Significant confocal predictors for malignancy were epidermal pagetoid spread and junctional cytological atypia. The authors provide a multistep diagnostic algorithm that is able to predict MIS with a sensitivity of 92 5% and a specificity of 61%. Compared with former algorithms designed for invasive melanoma, the present combined dermoscopic RCM algorithm has a better accuracy for MIS. On the other hand, the MIS algorithm remains highly sensitive for thin invasive melanomas, an important issue when examining a flat pigmented lesion suspected as a MIS, when a thin invasive melanoma cannot be ruled out. Shortly after the initial online publication of the aforementioned study, Lallas et al. reported a dermoscopic study of MIS. The multivariate analysis revealed five positive dermoscopic indicators of MIS: atypical network, regression, irregular hyperpigmented areas, prominent skin markings and angulated lines. It would now be interesting to assess the accuracy of these new dermoscopic criteria for the diagnosis of MIS. Furthermore, it would be interesting to determine by the benefit of two techniques combined compared with a single technique. Previous studies comparing dermoscopy and confocal microscopy show variable results. Langley et al. found no significant difference between the sensitivities (89 2% dermoscopy, 97 3% RCM) or specificities (84 1% dermoscopy, 83% RCM) of the two methods. Guitera et al. found that RCM had superior specificity (68%) for the diagnosis of melanoma compared with dermoscopy (68% RCM, 32% dermoscopy), while showing no difference in sensitivity (91% RCM, 88% dermoscopy). Likewise, Alarcon et al. found no significant improvement in sensitivity when comparing the combination of dermoscopy and RCM with dermoscopy (94 6% dermoscopy with RCM vs. 97 8% RCM). However, the differences between specificities were statistically significant, favouring the combination of dermoscopy and RCM over dermoscopy. Although the study was not designed on purpose, it seemed that RCM was even more specific when used alone. Finally, Cinotti et al. compared dermoscopy and RCM for the diagnosis of lentigo maligna. In contrast to previous studies, RCM was more sensitive (80% vs. 61%) and less specific (81% vs. 92%) than dermoscopy. In conclusion, the combination of dermoscopy and RCM for the diagnosis of melanoma in situ is promising; however, head-to-head comparison for each single technique is still needed, before we can state: dermoscopy and confocal microscopy: come together, right now!
Esclerose múltipla e qualidade de vida: contributos para a melhoria das práticas de saúde

Introduction: Multiple Sclerosis (MS) is a chronic disease that affects the fibers of the central nervous system and can lead to severe neurological disability, often with impaired quality of life (QoL). The QoL is subjective and multidimensional, their measurement brings the perspective of patients in improving health care. Objective: To evaluate the QoL of people with MS; and analyze the relationship between QoL of these people and sociodemographic and clinical aspects. Methods: This is an analytical cross-sectional study based on a random and representative sample of 67 people with MS accompanied the external consultation of a northern institution of the country. We used a characterization questionnaire and sociodemographic and clinical health questionnaire, MOS SF-36v2 (Ferreira, 2000a,b). Results: Majority are women (82%), mean age 42.09 years ± 11.76 years. The highest scores were or obtained QoL scale in Mental Health and the lower the vitality domain. younger people have a better QoL in dimensions Physical Function (t= 4.148, p <0.001), Physical Performance (t=2.581, p= 0.012), Emotional Performance (t=2.714, p= 0.009) and Social Function (t= 3.188; p = 0.002). People with fatigue have worse QoL in all dimensions, except for Physical Pain and General Health. Conclusions The creation of a query, the training of professionals and the implementation of a telephone helpline were some of the measures already adopted.
Editorial: being out of shape may impact more than your figure

1. Graziadei IW, Wiesner RH, Batts KP, et al. Recurrence of primary sclerosing cholangitis following liver transplantation. Hepatology. 1999;29:1050-1056. 2. Broom e U, Bergquist A. Primary sclerosing cholangitis, inflammatory bowel disease, and colon cancer. Semin Liver Dis. 2006;26:031-041. 3. Weism€ uller TJ, Trivedi PJ, Bergquist A, et al. Patient age, sex, and inflammatory bowel disease phenotype associate with course of primary sclerosing cholangitis. Gastroenterology. 2017;152:19751984.e8. 4. Pavlides M, Cleland J, Rahman M, et al. Outcomes after ileal pouch anal anastomosis in patients with primary sclerosing cholangitis. J Crohns Colitis. 2014;8:662-670. 5. Trivedi PJ, Reece J, Laing RW, et al. The impact of ileal-pouch-anal anastomosis on graft survival following liver transplantation for primary sclerosing cholangitis. Aliment Pharmacol Ther. 2018;48:322-332.
An Improved Gauss-Seidel Algorithm and Its Efficient Architecture for Massive MIMO Systems

Compared to most of the linear detection algorithms in massive multiple-input multiple-output (MIMO) systems, the Gauss-Seidel algorithm with initialization (GSI) promises a faster convergence rate. We improve the GSI algorithm by employing the first three items of Taylor expansion to approximate the reciprocals of diagonal entries and using the group inversion to obtain the inversion of the lower triangle matrix, which is feasible and efficient to implement. Simulation results show that the bit error rate performance of the improved GSI algorithm is similar to that of the minimum mean square error equalization for a MIMO system with 128 base station (BS) antennas and eight users. The algorithm can be applied to 64 and 32 BS antennas by employing two and three iterations, respectively. Based on the above improved algorithm, we develop a high-throughput and area-efficient architecture, which is further implemented on a Xilinx Virtex-7 XC7VX690T FPGA. The data are interleaved during the pipeline processing so that we can make the best use of the hardware. The presented architecture can achieve a maximum throughput of 127 Mb/s, which is 2.6 times higher than that of the existing architecture based on the GS algorithm. When ten received vectors are considered at a time, the throughput of the proposed architecture can reach up to 607 Mb/s, and it outperforms the optimized coordinate descent method in terms of resource utilization efficiency (throughput per look-up tables) by two times.
Multi-RPN Fusion-based Sparse PCA-CNN Approach to Object Detection and Recognition for Robot-aided Visual System

Object detection and recognition plays an important role in robot-aided visual system. However, when the object's scale varied largely, current object detection algorithms based on convolutional neural network (CNN or ConvNet) (i.e. Fast R-CNN or Faster R-CNN) always cause a high misdetection rate and a low computation speed. In addition, these methods also have the inherent problem: over-fitting. Therefore, we proposed a new multi-RPN fusion-based sparse PCA-CNN detection algorithm to solve the above problems. Firstly, a multi-RPN (Region proposal network) fusion method is adopted to generate candidate windows. Then the new sparse PCA-CNN algorithm is proposed to detect and recognize objects from images or video sequence. Finally, an experiment is adopted to verify the proposed algorithm. And, compared with the existing methods, the results indicate that the proposed method has a better performance.
Term Set Expansion based NLP Architect by Intel AI Lab

We present SetExpander, a corpus-based system for expanding a seed set of terms into a more complete set of terms that belong to the same semantic class. SetExpander implements an iterative end-to-end workflow. It enables users to easily select a seed set of terms, expand it, view the expanded set, validate it, re-expand the validated set and store it, thus simplifying the extraction of domain-specific fine-grained semantic classes. SetExpander has been used successfully in real-life use cases including integration into an automated recruitment system and an issues and defects resolution system.
Mobility of Antiflorigen and PEBP mRNAs in Tomato-Tobacco Heterografts1[OPEN]

Long-distance movement of tobacco NsCET1 and PEBP mRNAs suggests that acquisition of RNA mobility is an early evolutionary event. Photoperiodic floral induction is controlled by the leaf-derived and antagonistic mobile signals florigen and antiflorigen. In response to photoperiodic variations, florigen and antiflorigen are produced in leaves and translocated through phloem to the apex, where they counteract floral initiation. Florigen and antiflorigen are encoded by a pair of homologs belonging to FLOWERING LOCUS T (FT)- or TERMINAL FLOWER1 (TFL1)-like clades in the phosphatidylethanolamine-binding domain protein (PEBP) family. The PEBP gene family contains FT-, TFL1-, and MOTHER OF FT AND TFL1 (MFT)-like clades. Evolutionary analysis suggests that FT- and TFL1-like clades arose from an ancient MFT-like clade. The protein movement of the PEBP family is an evolutionarily conserved mechanism in many plants; however, the mRNA movement of the PEBP family remains controversial. Here, we examined the mRNA movement of PEBP genes in different plant species. We identified a tobacco (Nicotiana sylvestris) CENTRORADIALIS-like1 gene, denoted NsCET1, and showed that NsCET1 is an ortholog of the Arabidopsis (Arabidopsis thaliana) antiflorigen ATC. In tobacco, NsCET1 acts as a mobile molecule that non-cell-autonomously inhibits flowering. Grafting experiments showed that endogenous and ectopically expressed NsCET1 mRNAs move long distances in tobacco and Arabidopsis. Heterografts of tobacco and tomato (Solanum lycopersicum) showed that, in addition to NsCET1, multiple members of the FT-, TFL1-, and MFT-like clades of tobacco and tomato PEBP gene families are mobile mRNAs. Our results suggest that the mRNA mobility is a common feature of the three clades of PEBP-like genes among different plant species.
Monosomy X rescue explains discordant NIPT results and leads to uniparental isodisomy

Noninvasive prenatal testing accurately detects trisomy for chromosomes 13, 21, and 18, but has a significantly lower positive predictive value for monosomy X. Discordant monosomy X results are often assumed to be due to maternal mosaicism, usually without maternal follow‐up. We describe a case of monosomy X‐positive noninvasive prenatal testing that was discordant with the 46,XX results from amniocentesis and postnatal testing. This monosomy X pregnancy doubled the single X chromosome, leading to 45,X/46,XX mosaicism in the placenta and uniparental isodisomy X in the amniotic fluid. Thus, at least some discordant monosomy X results are due to true mosaicism in the pregnancy, which has important implications for clinical outcome and patient counseling.
Convex Relaxation for Maximum-Likelihood Network Localization Using Distance and Direction Data

A reliable and accurate positioning technology is crucial for a large variety of wireless services and applications. High-resolution estimates of distance and direction data are available in most current and emerging wireless systems. Combining these two sensing modalities can improve the estimation performance and identifiability of the localization problem. However, the problem of cooperative localization using joint distance and direction estimates is still a largely unexplored problem. A novel convex relaxation of the maximum likelihood (ML) estimator for this problem called Semidefinite Programming Hybrid Localization (SDHL) algorithm is proposed in this paper. Numerical results are presented showing that the localization error is significantly reduced in almost every simulation scenario compared to the state of the art. This improvement in localization performance is due to the close approximation of the ML estimator.
Learning Spatial–Spectral Features for Hyperspectral Image Classification

Combining spatial information with spectral information for classifying hyperspectral images can dramatically improve the performance. This paper proposes a simple but innovative framework to automatically generate spatial–spectral features for hyperspectral image classification. Two unsupervised learning methods—<inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula>-means and principal component analysis—are utilized to learn the spatial feature bases in each decorrelated spectral band. The spatial feature representations are extracted with these spatial feature bases. Then, spatial–spectral features are generated by concatenating the spatial feature representations in all/principal spectral bands. The experimental results indicate that the proposed method is flexible enough to generate rich spatial–spectral features and can outperform the other state-of-the-art methods.
Neural Successive Cancellation Decoding of Polar Codes

Neural network (NN) based decoders have appeared as potential candidates to replace successive cancellation (SC) based and belief propagation (BP) decoders for polar codes, due to their one-shot-decoding property. Partitioned NN (PNN) decoder has provided a solution to make use of multiple NN decoders which are connected with BP decoding, with the presence of insufficient training data for practical-length polar codes. However, PNN decoder requires BP iterations that detrimentally affect the decoding latency as compared to noniterative approaches. In this paper, we propose a neural SC (NSC) decoder to overcome the issue associated with PNN. Unlike PNN, the NSC decoder is constructed by multiple NN decoders connected with SC decoding. Compared to a PNN decoder for a polar code of length 128 and rate 0.5, the proposed NSC decoder achieves the same decoding performance, while reducing the decoding latency by 42.5%.
Obstacle avoidance of aerial vehicle based on monocular vision

Collision-free autonomous navigation is extremely important for quadcopter and other flying robots. The implementation of autonomic moving capabilities can contribute significantly to their promotion and usage in fields such as goods delivering, aerial photos shooting, and monitoring. In order to realize the autonomous flight without crash, the obstacle avoidance problem demands a prompt solution. Also, with the concern of cost and endurance, using only single camera to perform this task would be a better choice for low-cost flying robots. Thus, this paper focuses on achieving quadcopter's collision avoidance in unknown stable (rarely changes, such as high sky or inner room) environment only by single camera. The algorithm proposed by this paper is composed of PTAM (Parallel Tracking and Mapping), DTAM (Dense Tracking and Mapping in Real-Time) algorithm and CNN (Convolutional Neural Network). PTAM is used to create the 3D map of the environment. DTAM is used to obtain the depth map of those image frames. And the CNN is used to train and get a model used for automatically avoidance. Finally, this algorithm is proved to be valid by an experiment.
Cellular age in the manifestation of disease‐relevant phenotypes in Huntington's disease

Transgenic animals and rodent primary neuronal cultures have provided valuable information regarding the molecular mechanisms underlying Huntington’s disease (HD), but therapies developed using these models have been disappointing in human clinical trials. The discovery of induced pluripotent stem cell (iPSC) technology from a variety of somatic cell types including fibroblasts has provided exciting new opportunities to develop in vitro HD models that more accurately reflect the human disease. Victor and colleagues generated medium spiny neurons (MSNs) from HD patients (HD-MSN) fibroblasts through microRNA-based direct neuronal conversion, bypassing the induction of pluripotency and retaining age signatures of the original fibroblasts. They found that patient MSNs consistently exhibited mutant HTT (mHTT) aggregates and mHTTdependent DNA damage. The authors first tested the efficacy of miR-9/9*-124-CDMbased neuronal conversion in fibroblasts from 10 symptomatic HD patients. HD fibroblasts could be directly reprogrammed to MSNs regardless of age or CAG repeat number. They validated MSN conversion in 3 independent HD-patient fibroblast samples containing 40, 43, or 44 CAG repeats and their respective ageand sex-matched healthy controls with 19, 17, or 18 CAG repeats. On postinduction day 30, HD-MSNs expressed the neuronal markers, the MSN marker, and GABA. The authors also carried out whole-cell recordings to determine functional properties of HD-MSNs compared with control-MSNs and detected increased current responses with high-voltage stimulus in HD-MSNs, reflecting their increased excitability and firing complexity. The authors also provided evidence that cellular age is an essential component underlying the manifestations of HD phenotypes. They induced HD fibroblasts into iPSCs and redifferentiated them back into embryonic fibroblasts for neuronal conversion and discovered that age-associated reduction in protein homeostasis levels was primarily responsible for mHTT aggregation in adult HD-MSNs. Further, they also observed that despite comparable levels of mHTT aggregates, MSNs reprogrammed from presymptomatic HD patients were less vulnerable to mHTTinduced toxicity than MSNs reprogrammed from symptomatic patients. However, this observation did not give proof of age-dependent change, as cellular pathology varied based on the presence or absence of clinical symptoms. To conclude, these results show that directly converted HD-MSNs provide a human cellular model for HD pathogenesis. Although there are limitations in using MSNs as an HD model, as it is not clear how well they reflect network-based HD pathologies and whether they play any role in cognitive and behavioral disturbances symptoms, still they provide a means to gain mechanistic insight into the pathogenesis of HD.
The Position of Neutrophils-To-Lymphocytes and Lymphocytes-To-Platelets Ratio as Predictive Markers of Progression and Prognosis in Patients with Non-Small Cell Lung Cancer

BACKGROUND: Non-small cell lung cancer (NSCLC) is an insidious metastasis condition of the lungs often presenting no symptoms at the onset. Defining markers for quick determination of prognosis is essential for building up a treatment strategy. AIM: The aim of this study is to define the role of the Neutrophils-to-Lymphocytes ratio (NLR) and Platelets-to- Lymphocytes ratio (PLR) as biomarkers in patients with NSCLC, according to the stage and prognosis of the disease. METHODS: We investigated 20 patients with NSCLC. NLR and PLR are calculated and are evaluated according to the presence or absence of metastasis, stage of the disease, histological type and survival rate. RESULTS: We found that thirteen of the patients had low NLR, while the rest 7 had high NLR (mean 3.15). By analysing PLR we found that 11 patients have low and 9 have high level of PLR (mean 1.42). After the correlations have been made we discovered that in 90.1% of the patients with low PLR no lymph metastasises were detected, while in 50% of the patients with high PLR lymph metastasises were observed (χ2 = 3.99; P = 0.046). We also discovered that in 84.6% of the patients with low NLR lymph metastases were absent, while in 42.9% with high NLR lymph metastasises were present (χ 2 = 1.83; P = 0.176). CONCLUSION: In conclusion, NLR and PLR were discovered as prominent biomarkers which provide relatively fast determination for prognosis in patients with NSCLC.
6D Pose Estimation Based on Multiple Appearance Features from Single Color Image

Template matching methods are widely used to recognize instances of an untextured object and determine the poses. But in many practical cases the false positive rate is high because of similar interference characteristic in images especially in complex and clutter scene. A method based on multiple appearance features is presented in the paper to recognize poorly textured 3D objects and estimate their 6D pose in single color image. The object is distinguished from cluttered environment by multi-features which include the color, size and aspect ratio. Image pyramid is introduced here to improve efficiency. And the CAD model of the object is used to generate a hierarchical shape model in the offline phase for estimating the 6D pose accurately. Lastly the approach is evaluated in our application and publicly available dataset, and achieves good performance both in speed and recognition rate compared with state-of-the-art methods.
On the Inductive Coupling Between Two Parallel Thin-Wire Circular Loop Antennas

The magnetic coupling between two circular coils is a classical electromagnetic problem which is of interest in many different fields. While existing analytical models make quite strong assumptions on the relative position and assume a quasi-static approximation, the present contribution aims to provide an analytical full-wave model for the magnetic coupling between two parallel thin-wire circular loop antennas. The proposed model is tested by comparison with the results available in literature under the above-mentioned limitations and by numerical integration showing accuracy and reduced computation time.
Activation Process of ONU in EPON/GPON Networks

This article presents a numerical implementation of the activation process for Gigabit passive optical networks and Ethernet passive optical networks. Both are completely different because Gigabit PON was developed by International Telecommunication Union and Ethernet PON by Institute of Electrical and Electronics Engineers. The speed of an activation process is the most important in blackout scenario because end optical units have a timer after expiration transmission parameters are discarded. Our results prove lower time duration activation process (thanks to lower frame duration in Ethernet PON) in Ethernet based PON but the maximum split ratio is only 1:32 instead of un to 1:128 for Gizabit PON.
Development of the Sensory Network for the Vibration-based Fault Detection and Isolation in the Multirotor UAV Propulsion System

In this paper, a fault detection and isolation (FDI) system for propeller impairments of the multirotor UAV is presented. The algorithm is based on the processing of signal vectors from the set of vibration sensors located close to the propulsion units. Axial and radial vibrations are measured using MEMS accelerometers that provide data for the feature extraction based on the Fast Fourier Transform (FFT). Characteristic fault signatures extracted from vibration signals are used to detect and localize damaged blades using the set of random decision trees. A method was evaluated with data gathered during numerous test flights and validated in relation to signal acquisition time and number of classifiers in the ensemble. Results show over 95% sensitivity in detecting and isolating faulty rotor states. The presented approach is an effective and low-cost solution, very versatile to implement in the arbitrary UAV.