Miracle-dz/UniFAR-FTUs huggingface dataset:
Complete metabolic response to therapy of hepatic epithelioid hemangioendothelioma evaluated with 18F-fluorodeoxyglucose positron emission tomography/contrast-enhanced computed tomography

Rationale: Hepatic epithelioid hemangioendothelioma (EHE) is a rare malignant vascular tumor of endothelial origin with a highly variable clinical presentation and natural history. Given its vascular origin, new therapies with inhibitors of vascular endothelial growth factor (VEGF) have been introduced in the treatment of these patients and have shown promising results. Few reports have described the role of 18F-Fluorodeoxyglucose positron emission tomography/contrast-enhanced computed tomography (18F-FDG PET/CT) in the evaluation of this tumor after treatment with anti-angiogenic agents. Our case reports how 18F-FDG PET-CT scan was critical in the assessment of this tumor after treatment with an anti-angiogenic agent, Pazopanib, demonstrating complete metabolic response. Patient concerns: A 30-year-old man with no previous significant medical history presented with pain in the right upper quadrant for over a year. Diagnoses: Multiple hepatic masses were found on abdominal ultrasound. Liver biopsy confirmed the diagnosis of epithelioid hemangioendothelioma. 18F-FDG PET/CT was performed for staging. Multiple FDG-avid hepatic, splenic, and lymph nodes lesions were detected on 18F-FDG PET/CT. A subsequent spleen biopsy confirmed splenic involvement. Immunohistochemistry was positive for CD31, CD34, and ERG, supporting the diagnosis of epithelioid hemangioendothelioma. Interventions: A 1-year cyclophosphamide treatment was provided followed by Pazopanib for 17 months. Outcomes: Six years after the first 18F-FDG PET/CT, 18F-FDG PET/CT performed for restaging demonstrated complete metabolic response to therapy. Follow-up CT demonstrated no interval changes in size of some of the treated lesions. Lesson: 18F-FDG PET/CT is useful for baseline assessment and posttreatment follow-up of this rare cancer.
Low-Consumption Neuromorphic Memristor Architecture Based on Convolutional Neural Networks

With the rapid development of VLSI industry, the research of intelligent applications moves towards IoT edge computing. While the power consumption and area cost of deep neural networks usually exceed the hardware limitation of edge devices. In this paper, we propose a low-power neural network architecture to address such problem. We simplify the current popular convolutional neural networks structure, and utilize the memristor crossbar to store weights to execute convolution operation in parallel, and we present the spiking convolutional neural networks. At the same time, we proposed a performance metrics V to help provide design guidelines for choosing the parameters of the network.
Improving Speech Separation with Adversarial Network and Reinforcement Learning

In contrast to the conventional deep neural network for single-channel speech separation, we propose a separation framework based on adversarial network and reinforcement learning. The purpose of the adversarial network inspired by the generative adversarial network is to make the separated result and ground-truth with the same data distribution by evaluating the discrepancy between them. Meanwhile, in order to enable the model to bias the generation towards desirable metrics and reduce the discrepancy between training loss (such as mean squared error) and testing metric (such as SDR), we present the future success based on reinforcement learning. We directly optimize the performance metric to accomplish exactly that. With the combination of adversarial network and reinforcement learning, our model is able to improve the performance of single-channel speech separation.
Heterogeneous spectrum of EXT gene mutations in Chinese patients with hereditary multiple osteochondromas

Abstract Hereditary multiple osteochondroma (HMO) is one of the most common genetic skeletal disorders. It is caused by mutations in either EXT1 or EXT2 resulting in abnormal skeletal growth and morphogenesis. However, the spectrum and frequency of EXT1 and EXT2 mutations in Chinese patients with HMO was not previously investigated. Mutations were identified by performing Sanger sequencing analysis of the complete coding regions and flanking intronic sequences of EXT1 and EXT2, followed by multiplex ligation-dependent probe amplification (MLPA) analysis to detect gene deletions or duplications that could not be identified by the Sanger sequencing method. The present study identified pathogenic mutations in 93% (68/73) of unrelated HMO probands from 73 pedigrees. Mutations in EXT1 and EXT2 were identified in 53% (39/73) and 40% (29/73) of families. We identified 58 distinct mutations in EXT1 and EXT2, including 20 frameshift mutations, 16 nonsense mutations, 7 missense mutations, 9 splice site mutations, 5 large deletions, and 1 in-frame deletion mutation. Twenty-six of these mutations were novel and 32 were previously reported. Most of the mutations in EXT1 were base deletions or insertions (21/33), whereas the majority of those in EXT2 were single base substitution (18/25). Complete sequencing of both the EXT1 and EXT2 followed by MLPA analysis is recommended for genetic analysis of Chinese patients with HMO. This study provides a comprehensive characterization of the genetic aberrations found in Chinese patients with HMO and highlights the diagnostic value of molecular genetic analysis in this particular disease.
Diacritics Restoration Using Deep Neural Networks

Diacritics restoration problem is defined as correct insertion of diacritic marks into a sentence without changing the context of a particular sentence. Changing a letter in a text can cause the meaning of the word to change, and therefore the meaning of the whole sentence. This kind of problem is common among languages that use Latin alphabet such as the Slovak language. Our goal is to develop an artificial neural network that can restore diacritic errors made by a human or a computer. For this purpose, we chose state of the art architecture of recurrent neural network. Our results prove that neural network is able to restore diacritics with 97 % accuracy on given Slovak corpus. The accuracy test on a text from a different genre, however, has shown a lower accuracy of 76%.
Exploring the Usage of Time-of-Flight Cameras for Contact and Remote Photoplethysmography

The heart beat is one of the basic vital signs, but the pulse wave can communicate much more information than the beat frequency of the heart. Through the heart rate variability (HRV) among other things, stress level and drowsiness can be inferred. Reliable HRV measurements are commonly obtained by electrocardiography (ECG). In this paper we analyse the correlation between HRV and pulse rate variability (PRV) obtained from contact or remote photoplethysmography (PPG) on a Time-of-Flight (ToF) camera from PMD Technologies AG. The ToF camera is independent of passive illumination as it uses an infrared (IR) light source, which is invisible to the human eye. Therefore, potential use cases include driver monitoring or convenient heart rate measurement with smart phones. We will demonstrate methods for using a ToF camera for contact and remote PPG. For contact PPG the sensor is mounted directly on the subjects body. The pulse wave is calculated as the mean amplitude intensity of all pixels. For remote PPG the sensor is mounted to measure the subjects face. The pulse wave cannot be found in every part of exposed skin. Therefore, the part of the skin with the clearest signal is localised and the pulse wave is recovered from there, by means of passband filtering and blind source separation. Our results show that contact PPG on a ToF camera has high accuracy and highly correlates with a commercial IR pulse oximetry device, as well as with an ECG grade chest belt. For remote PPG our method shows correlation with the ToF contact PPG signal.
Diagnostic efficacy of long non-coding RNA in lung cancer: a systematic review and meta-analysis

The detection of long non-coding RNA (lncRNA) is a novel method for lung cancer diagnosis. However, the diagnostic efficacy of lncRNA in different studies is inconsistent. Therefore, we conducted this meta-analysis to elucidate the diagnostic efficacy of lncRNA in identification of lung cancer including small cell lung cancer. The online PubMed, Medline, EMBASE, CNKI and Wanfang literature databases were searched to identify all related articles about the diagnostic efficacy of lncRNA for lung cancer. 28 articles including 3044 patients with lung cancer and 2598 controls were enrolled in our meta-analysis. lncRNA sustained a high diagnostic efficacy, pooled sensitivity of 0.82 (95% CI 0.79 to 0.84), specificity of 0.82 (95% CI 0.78 to 0.84) and area under the curve (AUC) of 0.88 (95% CI 0.85 to 0.91) in identification of patients with lung cancer from controls. Furthermore, the diagnostic efficacy of paralleled lncRNA was better than single lncRNA (sensitivity: 0.86 vs 0.80; specificity: 0.88 vs 0.78; AUC: 0.93 vs 0.86). MALAT1 had a better diagnostic efficacy than GAS5 (AUC: 0.90 vs 0.81; sensitivity: 0.83 vs 0.70; specificity: 0.83 vs 0.78). lncRNA in tissues was observed to achieve lower diagnostic efficacy than that in plasma or serum (AUC: 0.87 vs 0.90 vs 0.90) when stratified by sample types. In summary, our meta-analysis suggests that lncRNA might be a promising biomarker(s) for identifying lung cancer and the combination of lncRNA or with other biomarkers had a better diagnostic efficacy.
A comparative study on infusion of usual dose of oxytocin and 80 units dose of oxytocin in the prevention of postpartum hemorrhage in cesarean section

Postpartum hemorrhage is one of the main causes of mothers' death mostly in developing country. The frequency of postpartum hemorrhage after natural pregnancy and cesarean are reported 2%–4% and 6%, respectively. The main goal of this study is to compare two regimens of oxytocine: One with high dose and the other with normal one to prevent uterine atony. The study has been done via a clinical trial method on 150 pregnant women in Zahedan, Iran. The society is randomly divided into two groups. The patients having risk factors of uterine atony and postpartum hemorrhage were omitted from the list. Oxytocin with 80 and 30 dose was, respectively, infused in 500 cc Ringer serum for control and witness group immediately after infant emersion during a period of 30 min. Decline of hemoglobin 6 and 24 h after cesarean section, uterine atony, blood infusion, and extra uterotonic drug needs after oxytocin infusion have been measured for both groups. The data have been analyzed using Chi-square via SPSS software. The result indicates that there exist meaningful differences between control and witness group in uterine atony and extra utertonic drug need while and after surgery (0 as compared with 22.6% and P= 0); whereas there is no significant difference between control and trial group in decline of hemoglobin 6 and 24 h after cesarean section (P = 0.714 and 0.231, respectively). It means that oxytocin infusion with 80 dose has a good impact on the above four variances. It is recommended that oxytocin infusion with 80 dose during a period of 30 min could be a good substitution for oxytocin infusion with 30 dose for the prevention of uterine atony during and after cesarean delivery, especially when utertonic drug is not accessible and in the case of counterindication of other drugs' use.
Improving Person Re-identification by Body Parts Segmentation Generated by GAN

Person re-identification(ReID) is a task of associating persons that cross the non-overlapping camera views at different locations and times. It is a challenging task due to the large variations in person pose, background, luminance, occlusion, low resolution, etc. How to extracting a powerful features representation is the prime problem in ReID and is still unsolved. In this paper, we propose a cascade network architecture combined with a generative adversarial networks(GANs) and a convolutional neural network(CNN) to improve the performance of person re-identification. The GANs first generates the person body parts segmentation from the person image, and then inputs the segmentation label into the connected CNN together with the original person image. Finally obtain a discriminative and robust feature representation for ReID task. The body parts segmentation partitioning the person image into multiple segments, such as background, head, face, arms, lags, etc. The body parts segmentation information contains accurate borders and category attributes for body parts, which makes the our model more accurate compared to other predefined rigid parts alignment models. Experiments are conduced on the CUHK03, Market1501, DukeMTMC-ReID datasets and the results demonstrate that this approach outperforms several existing state-of-the-art methods.
Joint Alignment of Multiple Generalized Point Sets with Anisotropic Positional Uncertainty Based on Expectation Maximization

Alignment of multiple point sets is an essential problem in medical imaging and computer-assisted surgery. For example, aligning multiple point sets into one common coordinate frame is a prerequisite for statistical shape modelling (SSM). In this paper, we first formally formulate the multiple generalized point cloud registration problem in a probabilistic manner. Not only positional but also the orientational information is utilized in the registration. All the observed generalized point sets to be registered are considered to be realizations of underlyinng unknown hybrid mixture models (HMMs). By (i) utilizing more enriched information, i.e. orientational information or normal vectors (ii) treating all point sets equally, our registration algorithm is more robust to outliers and does not bias towards any point set. Assuming that the positional and orientational data are co-independent, the probability density function (PDF) of an observed hybrid point is the multiplication of Gaussian and Fisher distributions. Notably, the positional error vector is assumed to obey a multivariate Gaussian distribution to accommodate anisotropic noise. Expectation maxmization (EM) framework is utilized to jointly estimate the parameters. In the E-step, the posteriors between points and underlying mixture model components are computed. In the M-step, the constrained optimization problem of the rigid transformation matrix is re-formulated as an unconstrained one using the Rodrigues Formula of a rotation matrix. Extensive experiments are conducted on CT data of a femur bone model to compare the proposed algorithm with the state-of-the-art registration methods. The experimental results demonstrate the algorithm's better accuracy, robustness to noise and outliers, and faster convergence speed.
Chitosan-Based Nanomaterials for Drug Delivery

This review discusses different forms of nanomaterials generated from chitosan and its derivatives for controlled drug delivery. Nanomaterials are drug carriers with multiple features, including target delivery triggered by environmental, pH, thermal responses, enhanced biocompatibility, and the ability to cross the blood-brain barrier. Chitosan (CS), a natural polysaccharide largely obtained from marine crustaceans, is a promising drug delivery vector for therapeutics and diagnostics, owing to its biocompatibility, biodegradability, low toxicity, and structural variability. This review describes various approaches to obtain novel CS derivatives, including their distinct advantages, as well as different forms of nanomaterials recently developed from CS. The advanced applications of CS-based nanomaterials are presented here in terms of their specific functions. Recent studies have proven that nanotechnology combined with CS and its derivatives could potentially circumvent obstacles in the transport of drugs thereby improving the drug efficacy. CS-based nanomaterials have been shown to be highly effective in targeted drug therapy.
Modeling of solvent effects on phytocompounds’ extraction from glycyrrhizae radix

The aim of the article was to study and model solvent effects on phytocompounds' extraction from Glycyrrhizae radix for substantiation of rational choice of the extractant in the technology of drugs obtained from this type of plant raw material. The process of extraction was carried out by simple maceration for 24 h at temperature 25°C ± 1°C and extractant/plant raw material ratio 5:1 (v/w). The content of hydro-ethanolic extracts based on ethanol solutions with concentration of 22, 41, 50, 71, 82, and 96% ± 1% v/v and some other solvents was studied by reversed phase high-performance liquid chromatography. The optimal range of solvent concentration for simultaneous extraction of chalcones and glycyrrhizic acid derivatives from Glycyrrhizae radix was determined. It was found that dielectric constant of the solvent plays a key role in the distribution process of phytocompounds between the phases; there is a certain range of dielectric constant values of the solvent-water solution, within which maximum phytocompound concentration in the extract can be observed; the dependency between phytocompound concentration in the extract and dielectric constant of the solvent-water solution can be described by equation lnC = a + b/ε+d/ε2.
A TOPSIS Multi-Criteria Decision Method-Based Function-Link Fuzzy CMAC Control System Design for Uncertain Nonlinear Systems

This paper presents a more efficient control algorithm for selecting suitable firing nodes and removing unimportant input variables automatically to improve the computational efficiency, reduce the number of firing rules and achieve good performance for the nonlinear control systems. A novel function-link fuzzy cerebellar model articulation controller (FLFC) is designed for uncertain nonlinear systems based on a multiple attribute decision-marking method known as Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The proposed control system is comprised a TOPSIS FLFC (TFLFC) and a robust compensator. The parameters of the proposed TFLFC are tuned online using the adaptation laws that are derived from a Lyapunov stability theorem, so the system’s stability is guaranteed. Finally, the proposed control system is used to control an inverted pendulum system and a master-slave Duffing-Holmes chaotic system to illustrate its favorable control performance.
Recent Progress on Microelectrodes in Neural Interfaces

Brain‒machine interface (BMI) is a promising technology that looks set to contribute to the development of artificial limbs and new input devices by integrating various recent technological advances, including neural electrodes, wireless communication, signal analysis, and robot control. Neural electrodes are a key technological component of BMI, as they can record the rapid and numerous signals emitted by neurons. To receive stable, consistent, and accurate signals, electrodes are designed in accordance with various templates using diverse materials. With the development of microelectromechanical systems (MEMS) technology, electrodes have become more integrated, and their performance has gradually evolved through surface modification and advances in biotechnology. In this paper, we review the development of the extracellular/intracellular type of in vitro microelectrode array (MEA) to investigate neural interface technology and the penetrating/surface (non-penetrating) type of in vivo electrodes. We briefly examine the history and study the recently developed shapes and various uses of the electrode. Also, electrode materials and surface modification techniques are reviewed to measure high-quality neural signals that can be used in BMI.
Induced Tropia Test and Visual Acuity Testing in Nonverbal Children

ABSTRACT The objective of our study was to compare the Cardiff Acuity Card© test (CAC test) (Kay Pictures Ltd) with the induced tropia test (ITT) in nonverbal children for the detection of monocular vision deficit. This is a retrospective case note review of 34 nonverbal children, aged 12–48 months, attending the pediatric ophthalmology clinic at Children’s Hospital of Pittsburgh of UPMC between October 2014 and January 2015. 30/34 were included for analysis. Binocular visual acuity and monocular visual acuity were tested at 50cm in 30 and 17 patients, respectively. At 100cm, binocular visual acuity and monocular visual acuity were tested in 16 and 11 patients, respectively. All 30 children had successful induced tropia testing; 21 had no fixation preference and 9 had a fixation preference. Of those that had no difference on monocular visual acuity, five had a fixation preference at 50cm and three at 100cm. Out of 21 that had no fixation preference, 3 had a monocular visual acuity difference, but of only one line. Our study suggests that to obtain as much information as possible without losing the interest or cooperation of the child, it may be more beneficial to perform the CAC test with both eyes open, followed by ITT, before attempting monocular visual acuity testing with the CAC test. Obtaining visual information using ITT was much more attainable compared to monocular CAC testing. The sensitivity using CAC test to find a visual acuity discrepancy is 40% using ITT as the standard, and the specificity is 63%. If one loses the interest of the child after the ITT, at least some information will have been gleaned rather than none about monocular visual behavior. This provides a more complete, attainable approach to gathering visual information.
Influence of Positive Additive Noise on Classification Performance of Convolutional Neural Networks

Convolutional neural networks have emerged as a leading architecture in computer vision tasks. In practical applications, the input layer of the network may need to process images with added noise. In some applications, such as speech recognition, medical imaging or network intrusion detection systems, the noise will be positive and additive. We evaluate performance of convolutional neural networks on recognition of MNIST and CIFAR datasets with such noise added. Our preliminary findings indicate that convolutional networks are resilient to the noise. However, there appears to be benefit to train the network in matched setting that is with such signal-to-noise ratio in training set that will be encountered in practice.