Miracle-dz/UniFAR-FTUs huggingface dataset:
pH-Responsive Aqueous Bubbles Stabilized With Polymer Particles Carrying Poly(4-vinylpyridine) Colloidal Stabilizer

Free radical dispersion polymerization was conducted to synthesize near-monodispersed, micrometer-sized polystyrene (PS) particles carrying pH-responsive poly(4-vinylpyridine) (P4VP) colloidal stabilizer (P4VP-PS particles). The P4VP-PS particles were extensively characterized in terms of morphology, size, size distribution, chemical composition, surface chemistry, and pH-response using optical and scanning electron microscopies, elemental microanalysis, X-ray photoelectron spectroscopy, laser diffraction particle size analysis, and zeta potential measurement. The P4VP-PS particles can work as a pH-responsive stabilizer of aqueous bubbles by adsorption at the air-water interface. At and above pH 4.0, where the particles have partially protonated/non-protonated P4VP stabilizer with relatively hydrophobic character, particle-stabilized bubbles were formed. Optical and scanning electron microscopy studies confirmed that the P4VP-PS particles were adsorbed at the air-water interface of the bubbles in aqueous media. At and below pH 3.0, where the particles have cationic P4VP stabilizer with water-soluble character, no bubble was formed. Rapid disruption of the bubbles can be induced by decreasing the pH; the addition of acid caused the in situ protonation of pyridine groups in P4VP, which impart water-soluble character to the P4VP stabilizer, and the P4VP-PS particles were desorbed from the air-water interface. The bubble stabilization/destabilization cycles could be repeated at least five times.
Full-Duplex Non-Orthogonal Multiple Access in Cooperative Relay Sharing for 5G Systems

In this paper, a full-duplex (FD) non-orthogonal multiple access (NOMA) scheme for a cooperative relay sharing network (termed as FD-NOMA-RS) is presented, in which two source-destination pairs share a dedicated FD relay FD-R. Following the principle of uplink NOMA, both sources transmit their symbols to FD-R, forming a NOMA pair, by depending on their channel conditions with respect to FD-R. The FD-R then decodes these symbols and simultaneously transmits a superimposed composite signal to the destinations with a processing delay $\tau$ according to the principle of downlink NOMA. The ergodic sum capacity, outage probability and outage sum capacity of FD-NOMA-RS are investigated comprehensively along with analytical derivations, considering both perfect and imperfect interference cancellation. A simulation is conducted to corroborate the correctness of the analysis presented here. Moreover, the effectiveness of FD-NOMA-RS is demonstrated through analysis and simulation and then compared with that of its counterpart, the half-duplex system.
Learning with SGD and Random Features

Sketching and stochastic gradient methods are arguably the most common techniques to derive efficient large scale learning algorithms. In this paper, we investigate their application in the context of nonparametric statistical learning. More precisely, we study the estimator defined by stochastic gradient with mini batches and random features. The latter can be seen as form of nonlinear sketching and used to define approximate kernel methods. The considered estimator is not explicitly penalized/constrained and regularization is implicit. Indeed, our study highlights how different parameters, such as number of features, iterations, step-size and mini-batch size control the learning properties of the solutions. We do this by deriving optimal finite sample bounds, under standard assumptions. The obtained results are corroborated and illustrated by numerical experiments.
Korean Journal for Food Science of Animal Resources

Changes in microbial community and physicochemical traits of chicken summer sausage made from spent layer thigh added with different level (0%, 0.1%, 0.3%, and 0.5% w/w) of ground chopi (Zanthoxylum piperitum) during manufacture were analyzed. The microbial community was profiled and analyzed by sequencing 16S rRNA gene using Illumina MiSeq. Samples were taken from raw sausage batter, after 15 h of fermentation, 8 h of cooking including cooling down, and 7 d of drying. The final pH of the sausage was reduced by the addition of ground chopi. However, no clear effect on water activity was observed. Ground chopi inhibited the development of red curing color after fermentation as it exhibited antimicrobial effect. However, the effect on species richness and microbial composition after cooking was unclear. Ground chopi delayed lipid oxidation during manufacture and the effect was dependent on the addition level. Fermentation reduced the species richness with a dominancy of lactic acid bacteria. The profile of microbiota in the raw batter was different from other stages, while the closest relationship was observed after cooking and drying. Proteobacteria was predominant, followed by Firmicutes and Bacteroidetes in raw samples. Firmicutes became dominating after fermentation and so forth, whereas other predominant phylum decreased. At genus level, unclassified Lactobacillales was the most abundant group found after fermentation and so forth. Therefore, the overall microbial composition aspects were mainly controlled during fermentation by the abundance of lactic acid bacteria, while bacterial counts and lipid oxidation were controlled by cooking and the addition of ground chopi.
RST-RF: A Hybrid Model based on Rough Set Theory and Random Forest for Network Intrusion Detection

Network intrusion detection system plays an important role in defending against more and more network malicious attacks. In addition, the accuracy and efficiency of the system will be significantly improved when combing different machine learning based algorithms. To this end, we build a hybrid model for network intrusion detection system which combines improved Rough Set Theory(RST) and Random Forests(RF) algorithm, named RST-RF. These two algorithms are used separately for feature selection and classification. After evaluated our RST-RF model in a public data set NSL-KDD, our method outperforms than the state-of-art researches on accuracy. As we know, our model is the first approach which combined rough set theory and random forest together for intrusion detection.
Intestinal tuberculosis in a 55-year-old woman with a 30-year history of rheumatoid arthritis

Introduction Tuberculosis (TB) is one of the endemic diseases with a challenging diagnosis in the absence of pulmonary disease. On the other hand, rheumatoid arthritis (RA) is a systemic autoimmune disease with extra-articular manifestations that occur at any age after onset, such as nodules, Sjögren’s syndrome, anemia of chronic disease, and pulmonary manifestations, which are more frequently seen in patients with severe, active disease. Here we present a case of RA with intestinal TB. Case report A 55-year-old woman with a 30-year history of RA using prednisolone and hydroxychloroquine presented with a nonpositional hypogastric pain and a weight loss of 20 kg over 7 months. No history of biological therapy was recorded. Colonoscopy revealed an ulcerated mass that was suspicious for malignancy. The pathobiological assessments confirmed ulceration and granulation tissue formation, foci of necrotizing granulomatous inflammation in lamina propria with adjacent mild crypt regenerative changes. Also, Ziehl–Neelsen staining for acid-fast bacilli in the granulomas was positive though the polymerase chain reaction assay did not detect the Mycobacterium tuberculosis. Anti-TB medication for 2 weeks eliminated the symptoms. Conclusions Intestinal TB in patients with vague abdominal symptoms and relevant physical findings such as pain and palpable mass should be considered to prevent late or misdiagnosis.
SySeVR: A Framework for Using Deep Learning to Detect Software Vulnerabilities

The detection of software vulnerabilities (or vulnerabilities for short) is an important problem that has yet to be tackled, as manifested by the many vulnerabilities reported on a daily basis. This calls for machine learning methods for vulnerability detection. Deep learning is attractive for this purpose because it alleviates the requirement to manually define features. Despite the tremendous success of deep learning in other application domains, its applicability to vulnerability detection is not systematically understood. In order to fill this void, we propose the first systematic framework for using deep learning to detect vulnerabilities in C/C++ programs with source code. The framework, dubbed Syntax-based, Semantics-based, and Vector Representations (SySeVR), focuses on obtaining program representations that can accommodate syntax and semantic information pertinent to vulnerabilities. Our experiments with four software products demonstrate the usefulness of the framework: we detect 15 vulnerabilities that are not reported in the National Vulnerability Database. Among these 15 vulnerabilities, seven are unknown and have been reported to the vendors, and the other eight have been “silently” patched by the vendors when releasing newer versions of the pertinent software products.
Extended duration pulsed radiofrequency for the management of refractory meralgia paresthetica: a series of five cases

Meralgia paresthetica (MP) is a sensory mononeuropathy, caused by compression of the lateral femoral cutaneous nerve (LFCN) of thigh. Patients refractory to conservative management are treated with various interventional procedures. We report the first use of extended duration (8 minutes) pulsed radiofrequency of the LFCN in a case series of five patients with refractory MP. Four patients had follow up for 1–2 years, and one had 6 months follow up. All patients reported remarkable and long lasting symptom relief and an increase in daily life activities. Three patients came off medications and two patients required minimal doses of neuropathic medications. No complications were observed.
Backplay: "Man muss immer umkehren"

Model-free reinforcement learning (RL) requires a large number of trials to learn a good policy, especially in environments with sparse rewards. We explore a method to improve the sample efficiency when we have access to demonstrations. Our approach, Backplay, uses a single demonstration to construct a curriculum for a given task. Rather than starting each training episode in the environment's fixed initial state, we start the agent near the end of the demonstration and move the starting point backwards during the course of training until we reach the initial state. Our contributions are that we analytically characterize the types of environments where Backplay can improve training speed, demonstrate the effectiveness of Backplay both in large grid worlds and a complex four player zero-sum game (Pommerman), and show that Backplay compares favorably to other competitive methods known to improve sample efficiency. This includes reward shaping, behavioral cloning, and reverse curriculum generation.
Dual versus mono antiplatelet therapy for acute non-cardioembolic ischaemic stroke or transient ischaemic attack: a systematic review and meta-analysis

Objective Recent years have seen new evidence on the efficacy and safety of dual antiplatelet therapy for secondary stroke prevention. We updated a meta-analysis of randomised controlled trials evaluating dual antiplatelet versus monotherapy for patients with acute non-cardioembolic ischaemic stroke (IS) or transient ischaemic attack (TIA). Methods We searched PubMed and identified randomised controlled trials evaluating dual antiplatelet versus monotherapy for acute non-cardioembolic IS or TIA within 3 days of ictus up to May 2018. Risk ratio (RR) with 95% CI were calculated using random effects models. Clinical endpoints included stroke recurrence, composite vascular events and major bleeding. Results 18 randomised controlled trials including 15 515 patients were pooled in the meta-analysis. When compared with monotherapy among patients with acute IS or TIA, dual antiplatelet therapy reduced the risk of stroke recurrence (RR 0.69; 95% CI 0.61 to 0.78; p<0.001) and composite vascular events (RR 0.72; 95% CI 0.64 to 0.80; p<0.001). Dual therapy was associated with a significant increase in the risk of major bleeding (RR 1.77; 95% CI 1.09 to 2.87; p=0.02) when all trial data were combined. However, when all previous trials before the completion of the POINT trial were analysed, dual antiplatelet versus monotherapy was not associated with a significant increase in the risk of major bleeding (RR 1.46; 95% CI 0.77 to 2.75; p=0.25). Conclusions Among patients with acute non-cardioembolic IS or TIA within 3 days of ictus, dual antiplatelet therapy was associated with a reduction in stroke recurrence, and composite vascular events, when compared with monotherapy. However, a significant increase in the risk of major bleeding was observed.
Impact of Hypothyroidism on Outcomes of Percutaneous Coronary Intervention with Coronary Atherectomy for Calcified Coronary Lesions: A Propensity-matched Analysis

Overt and subclinical hypothyroid states have been depicted as risk factors for a more prominent level of coronary calcification. The utilization of coronary atherectomy with percutaneous coronary intervention (PCI) is on the ascent over the past few years as lesions that are more complex are now attempted with a percutaneous approach. However, the real-world data on hypothyroid status influencing PCI with coronary atherectomy outcomes is very limited. Therefore, we proposed to investigate the outcomes of percutaneous coronary intervention with coronary atherectomy in hypothyroid patients using the largest inpatient database in the United States (US), the National Inpatient Sample (NIS). To minimize the selection bias, we surveyed and compared the outcomes in both the unmatched and propensity-score matched euthyroid and hypothyroid cohorts. Concisely, this propensity-matched analysis, using the largest population-based sample in the US, has established higher cardiovascular comorbidities and worse clinical outcomes of PCI with coronary atherectomy owing to clinical hypothyroidism.
Improving 2D Face Recognition via Discriminative Face Depth Estimation

As face recognition progresses from constrained scenarios to unconstrained scenarios, new challenges such as large pose, bad illumination, and partial occlusion, are encountered. While 3D or multi-modality RGB-D sensors are helpful for face recognition systems to achieve robustness against these challenges, the requirement of new sensors limits their application scenarios. In our paper, we propose a discriminative face depth estimation approach to improve 2D face recognition accuracies under unconstrained scenarios. Our discriminative depth estimation method uses a cascaded FCN and CNN architecture, in which FCN aims at recovering the depth from an RGB image, and CNN retains the separability of individual subjects. The estimated depth information is then used as a complementary modality to RGB for face recognition tasks. Experiments on two public datasets and a dataset we collect show that the proposed face recognition method using RGB and estimated depth information can achieve better accuracy than using RGB modality alone.
Deep Content-User Embedding Model for Music Recommendation

Recently deep learning based recommendation systems have been actively explored to solve the cold-start problem using a hybrid approach. However, the majority of previous studies proposed a hybrid model where collaborative filtering and content-based filtering modules are independently trained. The end-to-end approach that takes different modality data as input and jointly trains the model can provide better optimization but it has not been fully explored yet. In this work, we propose deep content-user embedding model, a simple and intuitive architecture that combines the user-item interaction and music audio content. We evaluate the model on music recommendation and music auto-tagging tasks. The results show that the proposed model significantly outperforms the previous work. We also discuss various directions to improve the proposed model further.
Quantitative MRI evaluation of glaucomatous changes in the visual pathway

Background The aims of this study were to investigate glaucomatous morphological changes quantitatively in the visual cortex of the brain with voxel-based morphometry (VBM), a normalizing MRI technique, and to clarify the relationship between glaucomatous damage and regional changes in the visual cortex of patients with open-angle glaucoma (OAG). Methods Thirty-one patients with OAG (age: 55.9 ± 10.7, male: female = 9: 22) and 20 age-matched controls (age: 54.9 ± 9.8, male: female = 10: 10) were included in this study. The cross-sectional area (CSA) of the optic nerve was manually measured with T2-weighed MRI. Images of the visual cortex were acquired with T1-weighed 3D magnetization-prepared rapid acquisition with gradient echo (MPRAGE) sequencing, and the normalized regional visual cortex volume, i.e., gray matter density (GMD), in Brodmann areas (BA) 17, 18, and 19, was calculated with a normalizing technique based on statistic parametric mapping 8 (SPM8) analysis. We compared the regional GMD of the visual cortex in the control subjects and OAG patients. Spearman’s rank correlation analysis was used to determine the relationship between optic nerve CSA and GMD in BA 17, 18, and 19. Results We found that the normal and OAG patients differed significantly in optic nerve CSA (p < 0.001) and visual cortex GMD in BA 17 (p = 0.030), BA 18 (p = 0.003), and BA 19 (p = 0.005). In addition, we found a significant correlation between optic nerve CSA and visual cortex GMD in BA 19 (r = 0.33, p = 0.023), but not in BA 17 (r = 0.17, p = 0.237) or BA 18 (r = 0.24, p = 0.099). Conclusion Quantitative MRI parametric evaluation of GMD can detect glaucoma-associated anatomical atrophy of the visual cortex in BA 17, 18, and 19. Furthermore, GMD in BA 19 was significantly correlated to the damage level of the optic nerve, as well as the retina, in patients with OAG. This is the first demonstration of an association between the cortex of the brain responsible for higher-order visual function and glaucoma severity. Evaluation of the visual cortex with MRI is thus a very promising potential method for objective examination in OAG.
Sini Decoction Improves Adrenal Function and the Short-Term Outcome of Septic Rats through Downregulation of Adrenal Toll-Like Receptor 4 Expression

Background Sini Decoction (SND) is composed of Aconitum carmichaelii Debeaux, Zingiber officinale Roscoe, and Glycyrrhiza uralensis Fisch, having been used in China for centuries for collapsing phrase of disease. Studies reported that SND could alleviate inflammatory response, ameliorate microcirculatory disturbances, and improve shock reversal and adrenal gland glucocorticoid stress response during sepsis shock, yet the underlying mechanism is still elusive. Toll-like receptor (TLR) 4 is demonstrated to be crucially correlated with the corticosterone secretion and the impaired adrenal glucocorticoid responses in sepsis. Materials and Methods SND at dose of 10 g/kg (in low-dose SND group, LD-SND) and 20 g/kg (in high-dose SND group, HD-SND) was administered to CLP rats. Four days later, overall survival rates of rats were calculated; rat serum and adrenal glands were collected. Basic serum corticosterone levels were determined, and the increase of corticosterone after 0.8 ug/kg ACTH injection was checked to detect the adrenocortical sensitivity to ACTH. The protein and mRNA expression of TLR4 in adrenal glands were measured to study the impact of SND on TLR4 expression. mRNA levels of IL-10 and TNF-a in adrenal glands and IL-10 and TNF-a levels in serum were also determined to study the cytokines profile. Results SND improved the cumulative survival rate of CLP rats up to 4 days (P < 0.05 with HD-SND) and adrenocortical sensitivity to 0.8 ug/kg ACTH stimulation (P < 0.05 at 60 mins, 31.02 ± 19.23 ng/ml in LD-SND group and 32.18 ± 14.88 ng/ml in HD-SND group versus 5.03 ± 13.34 ng/ml in CLP group), with a significant decrease of protein (P < 0.05, 29.6% in LD-SND group and 27.8% in HD-SND group), mRNA expression of TLR4 (P < 0.05, 32.9% in LD-SND group and 36.1% in HD-SND group), mRNA expression of IL-10 (P < 0.05, 32.0% in LD-SND group and 29.6% in HD-SND group), TNF-a in adrenal glands (P < 0.05, 26.0% in LD-SND group and 25.3% in HD-SND group), and TNF-a level in serum (P < 0.05, 100.20 ± 19.41 pg/ml in LD-SND group and 92.40 ± 11.66 pg/ml in HD-SND group versus 134.40 ± 27.87 pg/ml in CLP group). Conclusion SND increased overall survival rate within 4 days and attenuated adrenal insufficiency in septic rats by downregulating TLR4 mRNA and protein expression in adrenal tissue, inhibiting adrenal production of TNF-α and IL-10, and improving adrenal responsiveness. Our results suggest that SND is able to ameliorate adrenal stress responses in a local immune-adrenal crosstalk way involving downregulated expression of TLR4 in adrenal tissue. SND might be a promising treatment for adrenal insufficiency prevention in prolonged sepsis.
Molecular-Based Detection of Leishmania infantum in Human Blood Samples in a New Focus of Visceral Leishmaniasis in Lorestan Province, Iran

Background: The fatal form of leishmaniasis is visceral form (VL), found in some of the countries in the world. Visceral leishmaniasis has been reported sporadically from all provinces in Iran, including Lorestan. This study aimed to characterize parasite species in DAT positive and some of the DAT negative human blood samples of Delfan district, Lorestan Province, central Iran. Methods: Blood samples were collected from different geographical areas of Delfan. Serum was used for DAT test and remained part of molecular study. DNA was extracted by using DNG-plus extracted kit (Cinagen, Iran). Polymerase chain reaction amplification of Leishmania kDNA and PCR-RFLP of ITS1 was done to identify Leishmania species. Some amplicons were sequenced, submitted to GenBank and analyzed by BLASTn. Results: Expected band of kDNA for L. infantum (720bp) was amplified in 16 out of 186 (8.6%) samples which showed previously anti-Leishmania antibody at different titers or were negative serologically. Using BLASTn, 93% similarity with L. infantum has been shown. The rDNA-ITS1 was amplified only in 9 samples (4.7%). RFLP pattern was similar to what expected for L. infantum. Conclusion: A new emerging hypo-endemic focus, caused by L. infantum, is going to be established in Delphan District, Lorestan Province. Further studies on vector and reservoirs are necessary for the region and other parts of Lorestan Province.
Shedding Light on the Veins - Reflected Light or Transillumination in Hand-Vein Recognition

The near-infrared light source is a crucial part of a hand-vein scanner. Depending on its position there are two main illumination types: reflected light and transillumination. Commercial scanners and all publicly available data sets use reflected light. We established two dual illumination dorsal hand-vein data sets (one of them is made publicly available) including both, reflected light and transillumination images acquired using the same subjects, hand position and environmental conditions. This enables a direct comparison of both illumination scenarios as well as cross-illumination matching. Several experiments utilising common hand-vein recognition algorithms were carried out to quantify the recognition performance in each of the illumination scenarios and in the cross-illumination matching one.