Miracle-dz/UniFAR-FTUs huggingface dataset:
SANTIS: Sampling-Augmented Neural neTwork with Incoherent Structure for MR image reconstruction

Deep learning holds great promise in the reconstruction of undersampled Magnetic Resonance Imaging (MRI) data, providing new opportunities to escalate the performance of rapid MRI. In existing deep learning-based reconstruction methods, supervised training is performed using artifact-free reference images and their corresponding undersampled pairs. The undersampled images are generated by a fixed undersampling pattern in the training, and the trained network is then applied to reconstruct new images acquired with the same pattern in the inference. While such a training strategy can maintain a favorable reconstruction for a pre-selected undersampling pattern, the robustness of the trained network against any discrepancy of undersampling schemes is typically poor. We developed a novel deep learning-based reconstruction framework called SANTIS for efficient MR image reconstruction with improved robustness against sampling pattern discrepancy. SANTIS uses a data cycle-consistent adversarial network combining efficient end-to-end convolutional neural network mapping, data fidelity enforcement and adversarial training for reconstructing accelerated MR images more faithfully. A training strategy employing sampling augmentation with extensive variation of undersampling patterns was further introduced to promote the robustness of the trained network. Compared to conventional reconstruction and standard deep learning methods, SANTIS achieved consistent better reconstruction performance, with lower errors, greater image sharpness and higher similarity with respect to the reference regardless of the undersampling patterns during inference. This novel concept behind SANTIS can particularly be useful towards improving the robustness of deep learning-based image reconstruction against discrepancy between training and evaluation, which is currently an important but less studied open question.
Spiking Neural P Systems With Colored Spikes

Spiking neural P systems (SN P systems) are bio-inspired neural-like computing models, which are obtained by abstracting the way of biological neurons’ spiking and communication by means of spikes in central nervous systems. SN P systems performed well in describing and modeling behaviors that occur simultaneously, yet weak at modeling complex systems with the limits of using a single spike. In this paper, drawing on the idea from colored petri nets, SN P systems with colored spikes are proposed, where a finite set of colors is introduced to mark the spikes such that each spike is associated with a unique color. The updated spiking rule is applied by consuming and emitting a number of colored spikes (with the same or different colors). The computation power of the systems is investigated. Specifically, it is shown that SN P systems with colored spikes having three neurons are sufficient to compute Turing computable sets of numbers, and such system having two neurons is able to compute the family of recursive functions. These results improved the corresponding ones on the number of neurons needed to construct universal SN P systems recently appeared in [Neurocomputing, 2016, 193(12): 193–200]. To our best knowledge, this is the smallest number of neurons used to construct Turing universal SN P systems as number generator and function computing device by far.
An approach to floating knee injury in Indian Population: An analysis of 52 patients

Background Floating knee is a condition resulting from high energy trauma usually associated with minor to life threatening injuries making it challanging to treat There are no studies available in literature describing cross leg sitting and squatting after surgical management of floating knee. This study analyzes prognostic factors, plan of management, functional outcomes (special attention to squatting and cross legged sitting), complications. Materials and Methods 52 patients with floating knee injuries treated over a period of 3 years were included in this study. The study followed an algorithmic approach for the management. Femur fractures were fixed before fixing the tibia according to fracture type that was classified by Fraser classification after the stabilization of patient. The mean followup duration was 21 ± 6 months. The outcome was assessed using Karlstrom criteria after bony union. Results The study consists of majority (46) of male. Thirty three patients had some types of significantly associated injury. The mean postoperative range of motion of the knee was observed to be 97° ± 27°. Twenty one patients showed excellent results, whereas 17, 8, and 6 patients had good, fair, and poor results, respectively, as per Karlstrom criteria. Knee pain, stiffness, infection, nerve palsy, delayed union, and nonunion were some of the complications observed. Cross legged sitting was possible in 40 patients and squatting in 31. Conclusion The prognosis of floating knee injury is dependent on factors such as type of fracture, soft tissue condition, and management. Excellent outcomes following these injuries can be achieved with individualized plan of management by multidisciplinary team.
Deep Transfer Mapping for Unsupervised Writer Adaptation

Convolutional neural network (CNN) has achieved great success in handwriting recognition. However, it relies on large set of labeled data in training and its performance will deteriorate when the data distribution varies. To solve this problem, traditional methods usually consider adaptation of the single top layer of CNN. To better reduce the distribution discrepancy, in this paper, we consider adaptation of all layers of CNN including both convolutional and full layers. Four variations of transformations are designed based on different assumptions about the space relations for adaptation of convolutional layers. In order to make adaptation of multiple layers, we propose to cascade the transformations of different layers to conduct adaptation in a deep manner, and therefore this method is denoted as deep transfer mapping (DTM). DTM can capture the information from different layers and minimize the data divergence under different information abstract levels, thus it is more powerful and flexible for domain adaptation. Experiments on the online Chinese handwriting dataset (OLHWDB) demonstrate the efficiency and effectiveness of the proposed method for unsupervised writer adaptation.
A New Automated Signal Quality-Aware ECG Beat Classification Method for Unsupervised ECG Diagnosis Environments

In this paper, we propose a new automated quality-aware electrocardiogram (ECG) beat classification method for effective diagnosis of ECG arrhythmias under unsupervised healthcare environments. The proposed method consists of three major stages: 1) the ECG signal quality assessment (“acceptable” or “unacceptable”) based on our previous modified complete ensemble empirical mode decomposition and temporal features; 2) the ECG signal reconstruction and R-peak detection; and 3) the ECG beat classification including the ECG beat extraction, beat alignment, and normalized cross-correlation-based beat classification. The accuracy and robustness of the proposed method are evaluated using different normal and abnormal ECG signals taken from the standard MIT-BIH arrhythmia database. Evaluation results show that the proposed quality-aware ECG beat classification method can significantly achieve false alarm reduction ranging from 24% to 93% under noisy ECG recordings. The R-peak detector achieves the average Se = 99.67% and positive predictivity (Pp) = 93.10% and the average sensitivity (Se) = 99.65% and Pp = 98.88% without and with denoising approaches, respectively. Results further showed that the proposed ECG beat extraction approach can improve the classification accuracy by preserving the QRS complex portion and suppressing the background noises under acceptable level of noises. The quality-aware ECG beat classification methods achieve higher kappa values for the classification accuracies which can be consistent as compared with the heartbeat classification methods without the ECG quality assessment process.
Bioactivities of EF24, a Novel Curcumin Analog: A Review

Curcumin is an attractive agent due to its multiple bioactivities. However, the low oral bioavailability and efficacy profile hinders its clinical application. To improve the bioavailability, many analogs of curcumin have been developed, among which EF24 is an excellent representative. EF24 has enhanced bioavailability over curcumin and shows more potent bioactivity, including anti-cancer, anti-inflammatory, and anti-bacterial. EF24 inhibits tumor growth by inducing cell cycle arrest and apoptosis, mainly through its inhibitory effect on the nuclear factor kappa B (NF-κB) pathway and by regulating key genes through microRNA (miRNA) or the proteosomal pathway. Based on the current structure, more potent EF24 analogs have been designed and synthesized. However, some roles of EF24 remain unclear, such as whether it induces or inhibits reactive oxygen species (ROS) production and whether it stimulates or inhibits the mitogen activated kinase-like protein (MAPK) pathway. This review summarizes the known biological and pharmacological activities and mechanisms of action of EF24.
Single Nucleotide Polymorphisms in PLCE1 for Cancer Risk of Different Types: A Meta-Analysis

Background: Recent studies have investigated the relationships between PLCE1 polymorphisms and cancer susceptibility. However, some findings lack consistency. Objectives: In the current study, we conducted a meta-analysis to more accurately evaluate the relationships between PLCE1 (rs2274223, rs3765524, rs753724, rs11187842, and rs7922612) single nucleotide polymorphisms (SNPs) and risk for different types of cancer. Methods: We performed a comprehensive search strategy in PubMed, Web of Science, Medline, EMbase, and Scopus for articles available until 19 March 2018. A total of 54 case-control studies comprising 17,955 cases and 20,400 controls were included in the current meta-analysis, which together comprised a total of 32 publications. The pooled odds ratios (ORs) with 95% confidence intervals (CIs) were used to evaluate relationships between the PLCE1 polymorphisms and cancer susceptibility. All statistical analyses were performed using Stata 11 software. Results: Results of the meta-analysis demonstrated that the rs2274223 polymorphism showed a significant correlation with increased overall cancer susceptibility (AG vs. AA: OR 1.168, 95% CI 1.084–1.259; GG vs. AA: OR 1.351, 95% CI 1.163–1.570; AG+GG vs. AA: OR 1.193, 95% CI 1.103–1.290; GG vs. AA+AG: OR 1.262, 95% CI 1.102–1.446; G vs. A: OR 1.163, 95% CI 1.089–1.242). Results of subgroup analysis showed that the rs2274223 polymorphism was associated with higher risk for esophageal cancer and gastric cancer relative to colorectal cancer and head and neck cancer. In addition, the rs2274223 polymorphism was found to be associated with increased cancer risk, especially among the subgroups comprising Asians, studies with population-based controls, studies employing the TaqMan genotyping method, and studies consistent with Hardy-Weinberg equilibrium (HWE). The association between the rs3765524 polymorphism and reduced overall cancer risk was detected in one specific genetic model (CT vs. CC: OR 0.681, 95% CI 0.523–0.886). Results of subgroup analysis showed that the rs3765524 polymorphism was associated with cancer risk in a specific genetic model among the subgroups of colorectal cancer, esophageal cancer, Asians, studies with population-based controls, and studies consistent with HWE. However, relationships among the PLCE1 rs753724, rs11187842, and rs7922612 polymorphisms and tumor risk were not identified. Conclusions: Results of the current meta-analysis suggested that PLCE1 (rs2274223, rs3765524) polymorphisms are associated with cancer susceptibility.
Transfer Learning Based Intrusion Detection

In the past decades, machine learning based intrusion detection systems have been developed. This paper discloses a new aspect of machine learning based intrusion detection systems. The proposed method detects normal and anomaly behaviors in the desired network where there are not any labeled samples as training dataset. That is while a plenty of labeled samples may exist in another network that is different from the desired network. Because of the difference between two networks, their samples produce in different manners. So, direct utilizing of labeled samples of a different network as training samples does not provide acceptable accuracy to detect anomaly behaviors in the desired network. In this paper, we propose a transfer learning based intrusion detection method which transfers knowledge between the networks and eliminates the problem of providing training samples that is a costly procedure. Comparing the experimental results with the results of a basic machine learning method (SVM) and also baseline method(DAMA) shows the effectiveness of the proposed method for transferring knowledge for intrusion detection systems.
Origin and Diversification of South American Polyploid Silene Sect. Physolychnis (Caryophyllaceae) in the Andes and Patagonia

The Andes are an important biogeographic region in South America extending for about 8000 km from Venezuela to Argentina. They are – along with the Patagonian steppes – the main distribution area of ca. 18 polyploid species of Silene sect. Physolychnis. Using nuclear ITS and plastid psbE-petG and matK sequences, flow cytometric ploidy level estimations and chromosome counts, and including 13 South American species, we explored the origin and diversification of this group. Our data suggest a single, late Pliocene or early Pleistocene migration of the North American S. verecunda lineage to South America, which was followed by dispersal and diversification of this tetraploid lineage in the Andes, other Argentinian mountain ranges and the Patagonian steppes. Later in the Pleistocene South American populations hybridized with the S. uralensis lineage, which led to allopolyploidisation and origin of decaploid S. chilensis and S. echegarayi occurring at high elevations. Additionally, we show that the morphological differentiation in leaf shape correlated with divergent habitats (high elevation Andes vs. lower elevation Patagonian steppes) is also supported phylogenetically, especially in the ITS tree. Lastly, the species boundaries among the narrow-leaved Patagonian steppe species are poorly resolved and need more thorough taxonomic revision.
Correction between the Morphology of Acromion and Acromial Angle in Chinese Population: A Study on 292 Scapulas

Introduction. The acromion is a small section of the scapula which extends anteriorly from the spine of the scapula and the acromial angle (AA) is a prominent bony point at the junction of the lateral border of the acromion and the spine of the shoulder blade. As is well known, the morphology of the acromion and the acromial angle are important as their anatomical variation may contribute to shoulder pathologies. However, few people have studied the morphology and the association between the acromion and the acromial angle. The study explores the acromion and the acromial angle in the anatomical morphology and the association, providing an anatomical basis for clinical diagnosis and treatment. Material and Methods. A total of 292 dry, intact scapulae (152 right, 140 left) were used in the study. Three types of the acromion were already measured, type I(flat shape), type II (curved shape), and type III (hooked shape), respectively. Three types of the acromial angles were also measured in this study, C shape, L shape, and Double Angle shape. Results. The research result shows that C shape and L shape were the most common, while Double Angle shape was the least common. C shape was often related to type I (flat shape) and L shape was often related to type II (curved shape). Conclusions. The presented data provides precise and well-sorted information about the acromion and the acromial angle variation in Chinese population, contributing to diagnosis and treating in shoulder pathology.
Enhanced Cost Analysis of Multiple Virtual Machines Live Migration in VMware Environments

Live migration is an important feature in modern software-defined datacenters and cloud computing environments. Dynamic resource management, load balance, power saving and fault tolerance are all dependent on the live migration feature. Despite the importance of live migration, the cost of live migration cannot be ignored and may result in service availability degradation. Live migration cost includes the migration time, downtime, CPU overhead, network and power consumption. There are many research articles that discuss the problem of live migration cost with different scopes like analyzing the cost and relate it to the parameters that control it, proposing new migration algorithms that minimize the cost and also predicting the migration cost. For the best of our knowledge, most of the papers that discuss the migration cost problem focus on open source hypervisors. For the research articles focus on VMware environments, none of the published articles proposed migration time, network overhead and power consumption modeling for single and multiple VMs live migration. In this paper, we propose empirical models for the live migration time, network overhead and power consumption for single and multiple VMs migration. The proposed models are obtained using a VMware based testbed.
A Fast and Robust Approach for Human Detection in Thermal Imagery for Surveillance Using UAVs

The use of Unmanned Aerial Vehicles (UAV)s has spread in various fields such as surveillance and search and rescue purposes. This leads to many research efforts that are focusing on the detection of people using aerial images. However, these platforms have limited resources of power and bandwidth which cause many restrictions and challenges. The use of the thermal sensors offers the possibility to work day and night and the detection of the human bodies because of its distinguished thermal signature. In this paper, we propose a fast and efficient method for the detection of humans in outdoor scenes using thermal images taken from aerial platforms. We start by extracting the bright blobs based on a conjunction between a saliency map and a contrast enhancement techniques. Then, we use the Discrete Chebyshev Moments as a shape descriptor and finally, we classify the blobs into humans and non-humans. The proposed framework is first tested using a well-known thermal database that covers a wide range of lighting and weather conditions and further and then compared to an also well-known blob extractor which is the Maximally Stable Extremal Regions detector (MSER). The results highlight the effectiveness and even the superiority of the proposed method in terms of true positives, false alarms and processing time.
Sentiment Analysis Using Learning Approaches Over Emojis for Turkish Tweets

With the rise of the usage and interest on social media platforms, emojis have become an increasingly important part of the written language and one of the most important signals for micro-blog sentiment analysis. In this paper, we employed and evaluated classification models using two different representations based on bag-of-words and fastText to address the problem of sentiment analysis over emojis/emoticons for Turkish positive, negative and neutral tweets. At first, the bagof-words approach is used as a simple and efficient baseline method for tweet representation, where the classifiers such as Naive Bayes, Logistic Regression, Support Vector Machines, Decision Trees have been applied to these tweets. Secondly, we utilized fastText to represent tweets as word n-grams for sentiment analysis problem. The results show that there is no significant difference between the two models. While fastText shows 79% and the Linear Regression classifier obtains 77% F1-score for binary classification, fastText performs 62% and Linear Regression has 58% F1-score for multi-class classification. This study is considered as the first study that contributes to the literature by applying different vector representations such as bag-of-words and fastText to predict Turkish tweets over emojis. This study can also be utilized to predict emojis on social media context in the future.
Immunoglobulin E and G Levels in Predicting Minimal Change Disease before Renal Biopsy

Purpose The diagnosis of minimal change disease in adults relies mainly on renal biopsy, but this procedure is not without complications. Despite the advancements in technique of percutaneous renal biopsy, biopsy-related complications still occur. Bleeding is one of the major complications, which may lead to hemodynamic instability and, sometimes, even death. Thus, we developed a model to predict MCD for high-risk patients unsuitable for renal biopsy. Methods We enrolled 142 patients with nephrotic syndrome who received renal biopsy between October 2007 and April 2011 at one tertiary medical center in this study. Demographic, clinical, and prebiopsy laboratory variables were retrospectively recorded and analyzed. Results The overall prevalence of MCD was 26.8%. Age, hemoglobin levels, 24-hour urine protein, immunoglobulin (Ig) G, and IgE differed significantly between the MCD and non-MCD groups. Logistic regression analysis showed a significant increase in the risk of developing MCD as the number of Ig risk factors, namely, IgG < 450 mg/dl and IgE > 110 mg/dl, increased. Having both risk factors significantly increased the chances of receiving a diagnosis of MCD (by 31.84-fold, P =.007) compared with having neither. Combining the aforementioned clinical model and the 2 Ig risk factors was the best in predicting the diagnosis of MCD, with the area under a receiver-operating characteristic curve of 0.91. Conclusions Combining clinical model and this 2 Ig risk factors provides physicians simple and valuable clinical markers to diagnose MCD.
Chronic Recurrent Multifocal Osteomyelitis - A Case Series from India

Background Chronic recurrent multifocal osteomyelitis (CRMO) is a rare auto-inflammatory disease of the bone. It tends to be multifocal and usually the symptoms tend to run for months and years before diagnosis is usually made. The objective of our study was to understand the clinical presentation and short-term response to treatment of CRMO patients. Materials and Methods A retrospective analysis of patients diagnosed with CRMO between 2011 and 2016 was done. Case records of these were retrospectively reviewed for clinical features, investigations and treatment received. Results Six patients were diagnosed with CRMO. The median age of onset and time to diagnosis from onset of symptoms was 8 and 3.5 years respectively. Lower limb bones were the most commonly involved. Conclusions There is significant delay in diagnosis of CRMO and this could be because of a lack of awareness of this condition amongst clinicians. Our case series with only male affection is rather unique as compared to other case series reported in medical literature which tend to have more female predilection. Pain with or without swelling was the most common symptom. Most of patients responded to combination therapy.