Miracle-dz/UniFAR-FTUs huggingface dataset:
Peak-Power-Clamped Passive Q-Switching of a Thulium/Holmium Co-Doped Fiber Laser

We report on a type of peak-power-clamped passive Q-switching in a thulium/holmium co-doped fiber laser, for the first time to the best of our knowledge. Nonlinear polarization rotation was used as an artificial saturable absorber to initiate the Q-switched operation. The peak-power-clamping (PPC) effect was realized by incorporating a piece of high nonlinear fiber in the fiber resonator and launching high-enough pump power. Normal Q-switching behavior could be observed with the pump power within the range of 0.9–0.95 W, where the pulse peak power monotonically rose with the pump power increasing. However, once the pump power reached a threshold-like level, i.e., ∼1 W, the delivered Q-switched pulse peak power started to be clamped at a nearly constant value of ∼0.4 W, corresponding to an intracavity total peak power of ∼2.22 W. It indicates that, besides typical dissipative soliton resonances in mode-locked fiber lasers, PPC effect can also occur in the passively Q-switched regime with appropriate pump power and cavity nonlinearity managements.
Towards Improving the Quality of Knowledge Graphs with Data-driven Ontology Patterns and SHACL

As Linked Data available on the Web continue to grow, understanding their structure and assessing their quality remains a challenging task making such the bottleneck for their reuse. ABSTAT is an online semantic profiling tool which helps data consumers in better understanding of the data by extracting data-driven ontology patterns and statistics about the data. The SHACL Shapes Constraint Language helps users capturing quality issues in the data by means of co straints. In this paper we propose a methodology to improve the quality of different versions of the data by means of SHACL constraints learned from the semantic profiles produced by ABSTAT.
Understanding drug release from PCL/gelatin electrospun blends

Electrospinning is one of the efficient processes to fabricate polymeric fibrous scaffolds for several biomedical applications. Several studies have published to demonstrate drug release from electrospun scaffolds. Blends of natural and synthetic electrospun fibers provide excellent platform to combine mechanical and bioactive properties. Drug release from polymer blends is a complex process. Drug release from polymer can be dominated by one or more of following mechanisms: polymer erosion, relaxation, and degradation. In this study, electrospun polycaprolactone (PCL)–gelatin blends are investigated to understand release mechanism of Rhodamine B dye. Also, this article summarizes the effect of high-pressure carbon dioxide on drug loading and release from PCL–gelatin fibers. Results indicate that release media diffusion is a dominant mechanism for PCL–gelatin electrospun fibers. Thickness of electrospun mat becomes critical for blends with gelatin. As gelatin is highly soluble in water and has tendency of gelation, it affects diffusion of release media in and out of scaffold. This article is a key step forward in understanding release from electrospun blends.
A Survey on Recent OS-Level Energy Management Techniques for Mobile Processing Units

To improve mobile experience of users, recent mobile devices have adopted powerful processing units (CPUs and GPUs). Unfortunately, the processing units often consume a considerable amount of energy, which in turn shortens battery life of mobile devices. For energy reduction of the processing units, mobile devices adopt energy management techniques based on software, especially OS (Operating Systems), as well as hardware. In this survey paper, we summarize recent OS-level energy management techniques for mobile processing units. We categorize the energy management techniques into three parts, according to main operations of the summarized techniques: 1) techniques adjusting power states of processing units, 2) techniques exploiting other computing resources, and 3) techniques considering interactions between displays and processing units. We believe this comprehensive survey paper will be a useful guideline for understanding recent OS-level energy management techniques and developing more advanced OS-level techniques for energy-efficient mobile processing units.
Fish Oil Supplementation Reduces Inflammation but Does Not Restore Renal Function and Klotho Expression in an Adenine-Induced CKD Model

Background: Chronic kidney disease and inflammation promote loss of Klotho expression. Given the well-established anti-inflammatory effects of omega-3 fatty acids, we aimed to investigate the effect of fish oil supplementation in a model of CKD. Methods: Male C57BL/6 mice received supplementation with an adenine-enriched diet (AD, n = 5) or standard diet (CTL, n = 5) for 10 days. Two other experimental groups were kept under the adenine diet for 10 days. Following adenine withdrawal on the 11th day, the animals returned to a standard diet supplemented with fish oil (Post AD-Fish oil, n = 9) or not (Post AD-CTL, n = 9) for an additional period of 7 days. Results: Adenine mice exhibited significantly higher mean serum urea, creatinine, and renal expression of the pro-inflammatory markers Interleukin-6 (IL-6), C-X-C motif chemokine 10 (CXCL10), and Interleukin-1β (IL-1β), in addition to prominent renal fibrosis and reduced renal Klotho gene expression compared to the control. Post AD-Fish oil animals demonstrated a significant reduction of IL-6, C-X-C motif chemokine 9 (CXCL9), and IL-1β compared to Post AD-CTL animals. However, serum creatinine, renal fibrosis, and Klotho were not significantly different in the fish oil-treated group. Furthermore, renal histomorphological changes such as tubular dilatation and interstitial infiltration persisted despite treatment. Conclusions: Fish oil supplementation reduced renal pro-inflammatory markers but was not able to restore renal function nor Klotho expression in an adenine-induced CKD model.
Una nueva herramienta para la evaluación de la calidad perceptible en imágenes

Resumen Las imagenes digitales pueden verse afectadas por una amplia variedad de distorsiones que darian lugar como resultado una degradacion en la calidad visual. Es por ello que la evaluacion de la calidad de imagenes es un aspecto esencial dentro de las tareas de procesado. En general, para obtener una estimacion precisa de la calidad de las imagenes se utilizan pruebas subjetivas. Sin embargo, estas son generalmente costosas, tediosas y toman mucho tiempo. El objetivo es definir metricas cuantitativas que incorporen caracteristicas del sistema visual humano (SVH) de manera que se pueda evaluar de forma automatica la calidad de las imagenes. En este trabajo se presenta una herramienta de codigo abierto desarrollada en MATLAB para la evaluacion de la calidad perceptible de imagenes monocromatica. Proporciona tres tipos de medidas: objetivas, hibridas y perceptivas, habiendose estas ultimas evaluadas con observadores. La herramienta incorpora asimismo algoritmos de compresion, marcado de agua y de inclusion de ruido para su validacion.
Correlation Between β-Glucan and Neutrophil/Eosinophil Ratio of Paranasal Sinus Mucosa and Blood in the Diagnosis of Chronic Fungal Rhinosinusitis.

Objectives Chronic fungal rhinosinusitis is a major health problem because of the large impact on quality of life and difficult medical treatment. Its prevalence is increasing. There is currently an opportunity to establish the diagnosis without invasive intervention by utilizing β-glucan, which is the largest component of fungal cell wall and neutrophil/eosinophil ratio (NER). We sought to discover the correlation between β-glucan level and the NER of paranasal sinuses mucosa and blood as a potential diagnostic marker for patients with chronic fungal rhinosinusitis. Methods We conducted a cross-sectional study involving 20 subjects. Blood sampling and maxillary sinus surgery were performed, and fungal identification in the sinus mucosa was determined using the polymerase chain reaction. If a fungal species with β-glucan was found, then the examination was continued with the measurement of β-glucan by enzyme-linked immunosorbent assay, neutrophil, and eosinophil flow cytometry in sinus mucosa and blood. Results Aspergillus flavus is the most commonly found fungus. All subjects passed the positive β-glucan limit (3 80 pg/mL) of the mucosal sample and only one subject had intermediate results (60-79 pg/mL) from their blood sample. Seventeen subjects had mucosal eosinophilic inflammation whereas the blood of 12 subjects revealed neutrophilic inflammation. There was no significant difference between the level of β-glucan in blood and mucosal sinus (p = 0.886), so that β-glucan examination of blood can describe β-glucan levels in paranasal sinuses. There was a significant difference between mucosal NER and blood (p < 0.001). β-glucan level with NER in both paranasal sinus mucosa and blood had no significant correlation (p > 0.050). Conclusions β-glucan can be used to establish the diagnosis of chronic fungal rhinosinusitis. However, differential diagnosis of allergy or infection cannot be excluded by examination of blood NER. Still, it is hoped that the process of diagnosis can be obtained quickly and precisely without the need for invasive procedure although it requires more research, especially related to the diagnostic test.
Analysis of Packet Throughput in Small Cell Networks Under Clustered Dynamic TDD

Small cell networks under dynamic time-division duplex (D-TDD) transmission have emerged as a promising solution to accommodate the varied uplink (UL) and downlink (DL) traffic in next generation cellular mobile communication networks. By allowing each cell to individually configure its communication direction, D-TDD allocates resources to accommodate whichever transmission direction needs it most. However, with unaligned transmissions, the interference increases and limits the performance of mean packet throughput (MPT). In this paper, we study the small cell networks under D-TDD with cell clustering being the interference mitigation technique (clustered D-TDD). By leveraging stochastic geometry and queuing theory, we develop an analytical framework that captures both spatial and temporal randomness. We study the MPT whose analytical expression is verified via simulation, and based on the analysis, we explore the impact from different network and service parameters. In particular, numerical results show that there is an optimal cluster size for DL MPT, while UL MPT always benefits from increasing cluster size. By grouping cells into clusters, the clustered D-TDD can provide the flexible service compared with static time-division duplex (S-TDD), and provide significant improvement over a traditional D-TDD in terms of UL MPT at a small cost of DL MPT.
Joint PHY/MAC Layer Security Design Using ARQ With MRC and Null-Space Independent PAPR-Aware Artificial Noise in SISO Systems

Automatic-repeat-request (ARQ) as a MAC layer mechanism and artificial noise (AN) as a physical layer mechanism along with the help of maximal ratio combining (MRC), are jointly designed to achieve secrecy. Basically, a special AN, which does not require null-space in the channel, is designed based on the quality of service requirements and the channel condition between the legitimate parties and injected to the data packet. If the same packet is requested by the legitimate receiver (Bob), an AN canceling signal is properly designed and added to the next packet. Then, an AN-free packet is obtained by using MRC process at Bob, while deteriorating the eavesdropper’s performance. Furthermore, two simple closed-form expressions of the achievable secure throughput are derived. The first one is given in a closed-form for the case of ARQ scheme without AN, while the second one is given in an upper-bound form for the case of ARQ with AN. Moreover, this paper addresses two critical security-associated problems: 1) the joint design of secrecy, reliability, throughput, delay and the tradeoff among them, and 2) the increase in the peak-to-average power ratio (PAPR) due to the added AN. Finally, the proposed design is extended to OFDM to demonstrate its capability in not only enhancing the secrecy due to the frequency selectivity of the channel, but also in reducing the PAPR and out-of-band emission of OFDM-based waveforms, while maintaining secrecy.
Improving Dynamic Recommender System Based on Item Clustering for Preference Drifts

The recommender system is an efficient tool for online application, which exploits historical user rating on item to make recommendations on items to users. This paper aims to enhance dynamic recommender systems under volatile user preference drifts. It proposed an algorithm to solve sparse data by using Gaussian mixture model to fill in data matrix for sparsity reduction and improve more completely ratings prediction. Subsequently, it utilizes item clustering and linear regression technique to predict the future interests of users in category based and additionally uses the nearest neighbor method to prevent over-fitting. The experimental results show that the proposed approach provides the better performance on rating prediction when compared with the state-of-the-art dynamic recommendation algorithms.
Improved Immune Responses Against Zika Virus After Sequential Dengue and Zika Virus Infection in Humans

The high levels of dengue-virus (DENV) seroprevalence in areas where the Zika virus (ZIKV) is circulating and the cross-reactivity between these two viruses have raised concerns on the risk of increased ZIKV disease severity for patients with a history of previous DENV infections. To determine the role of DENV preimmunity in ZIKV infection, we analyzed the T- and B-cell responses against ZIKV in donors with or without previous DENV infection. Using peripheral blood mononuclear cells (PBMCs) from donors living in an endemic area in Colombia, we have identified, by interferon (IFN)-γ enzyme-linked immunospot (ELISPOT) assay, most of the immunodominant ZIKV T-cell epitopes in the nonstructural (NS) proteins NS1, NS3, and NS5. Analyses of the T- and B-cell responses in the same donors revealed a stronger T-cell response against peptides conserved between DENV and ZIKV, with a higher level of ZIKV-neutralizing antibodies in DENV-immune donors in comparison with DENV-naïve donors. Strikingly, the potential for antibody-mediated enhancement of ZIKV infection was reduced in donors with sequential DENV and ZIKV infection in comparison with donors with DENV infection only. Altogether, these data suggest that individuals with DENV immunity present improved immune responses against ZIKV.
ISLES 2016 and 2017-Benchmarking Ischemic Stroke Lesion Outcome Prediction Based on Multispectral MRI

Performance of models highly depend not only on the used algorithm but also the data set it was applied to. This makes the comparison of newly developed tools to previously published approaches difficult. Either researchers need to implement others' algorithms first, to establish an adequate benchmark on their data, or a direct comparison of new and old techniques is infeasible. The Ischemic Stroke Lesion Segmentation (ISLES) challenge, which has ran now consecutively for 3 years, aims to address this problem of comparability. ISLES 2016 and 2017 focused on lesion outcome prediction after ischemic stroke: By providing a uniformly pre-processed data set, researchers from all over the world could apply their algorithm directly. A total of nine teams participated in ISLES 2015, and 15 teams participated in ISLES 2016. Their performance was evaluated in a fair and transparent way to identify the state-of-the-art among all submissions. Top ranked teams almost always employed deep learning tools, which were predominately convolutional neural networks (CNNs). Despite the great efforts, lesion outcome prediction persists challenging. The annotated data set remains publicly available and new approaches can be compared directly via the online evaluation system, serving as a continuing benchmark (www.isles-challenge.org).
Physical Layer Security in Multi-Hop AF Relay Network Based on Compressed Sensing

In this letter, we consider secure communication of one source-destination pair with multi-hop amplify-and-forward relays. We utilize compressed sensing (CS) to guarantee the CS multi-hop network security. Different to the most existed works, we first demonstrate that the time slots during the whole transmission can be regarded as the measurement dimension in CS. Therefore, the physical layer security can be achieved by multi-hop cooperative wireless transmission whose equivalent channel matrix can be used as the secure measurement matrix. Increasing the number of hops will increase the secrecy rate. We also investigate the robustness of the CS multi-hop scheme in the presence of one eavesdropper.
Identification of Genes and Pathways Involved in Ovarian Epithelial Cancer by Bioinformatics Analysis

Ovarian epithelial cancer (OEC) is an often fatal disease with poor prognosis in women with high-stage disease. In contrast, ovarian low malignant potential (LMP) tumors with favorable prognosis behaves as a disease between benign and malignant tumors. The involved genes and pathways between benign-like LMP and aggressive OEC are largely unknown. This study integrated two cohorts profile datasets to investigate the potential key candidate genes and pathways associated with OEC. Gene expression in two datasets (GSE9891 and GSE12172), including 327 OECs and 48 LMP tumors, were analyzed. 559 differentially expressed genes were found to overlap, 251 up-regulated and 308 down-regulated. Subsequently, analysis of gene ontology, signaling pathway enrichment and protein-protein interaction (PPI) network was performed. Gene ontology analysis clustered the up-regulated and down-regulated genes based on significant enrichment. 282 nodes/ differentially expressed genes (DEGs) were identified from DEGs PPI network complex, and two most significant k-clique modules were identified from PPI. In a summary, using integrated bioinformatics analysis, we are able to identify biomarkers potentially significant in the pathogenesis of OEC, which can improve our understanding of the cause and molecular events. These candidate genes and pathways could be used for further confirmation, and lead to better disease diagnose and therapy.
Multi-level Sentiment Information Extraction Using the CRbSA Algorithm

Social network platforms allow the customers to feedback and complain about their opinions on products and services. Normally, users' feedbacks on social networks are unstructured data usually involving an enormous size of texts, called Social Big Data. Even though Social Big Data supports marketers by giving the information about the customers' sentiments, a lot of organizations suffer with labor intensive and time-consuming tasks in extracting the customers' satisfaction from Social Big Data manually. Therefore, an automatic process to extract the information from Social Big Data is required by marketers and decision-makers. To deal with this requirement, this paper proposes a new sentiment information extraction algorithm, called the Contrast Rule-based Sentiment Analysis algorithm that intends to extract the information automatically. We prove the validity of our proposed algorithm through comparison with the well-known sentiment information extraction algorithms, general word counting and SentiStrength. Applying on the labelled customer feedbacks on the Amazon dataset, our algorithm extracted sentiments more correctly than the general word counting and SentiStrength algorithms, especially in the negative cases. The processing time is also faster than the SentiStrength algorithm. This algorithm can be applied in a marketing system to help extract the customers' satisfaction, especially work as an alarming tool for negative comments.
Endoscopic augmentation of gastroesophageal junction using a full-thickness endoscopic suturing device

Abstract Background and study aims This preliminary study was conducted to determine the feasibility and safety of endoscopic augmentation of the gastroesophageal junction (GEJ) using the Apollo OverStitch endoscopic suturing system in patients with gastroesophageal reflux disease (GERD) symptoms. Patients and methods Endoscopic augmentation of GEJ was performed on 10 consecutive patients and the data were analyzed retrospectively. Using a double-channel gastroscope affixed to the endoscopic suturing platform, interrupted sutures were placed on the gastric side of the GEJ in 2 layers in order to create a narrowed and elongated GEJ. Results Technical success was achieved in all patients, including those with a history of previous antireflux procedures (n = 7) and those with a hiatal hernia (n = 6). The median follow-up duration was 5 mo (range: 2 – 12). The median pre-procedure GERD-Health Related Quality of Life Questionnaire improved from 20 (range: 11 – 45) to a post-procedure score of 6 (range: 3 – 25) (P = 0.001). The median duration of GERD symptom improvement after the procedure was 1 mo (range: 0.5 – 4). Adverse events were limited to 1 patient who developed nausea and vomiting, which was self-limited. Conclusions The use of a novel endoscopic suturing technique for the treatment of GERD is feasible and safe. The procedure resulted in short-term GERD symptom improvement. Further prospective studies using refined techniques are currently underway to improve durability and to prove efficacy.
Wibheda: Framework for Data Dependency-Aware Multi-Constrained Hardware-Software Partitioning in FPGA-Based SoCs for IoT Devices

The increasing popularity of FPGA-based system-on-chip (SoC) devices for Internet of Things (IoT) applications calls for hardware-software partitioning solutions optimized for performance under stringent area and power constraints. In this work, we propose Wibheda, a heuristic based framework for data dependency-aware multi-constrained hardware-software partitioning at fine-granularity that can be employed to partition designs for FPGA-based SoCs used in IoT. Wibheda, evaluated on 6 applications from the popular CHStone benchmark suite has been shown to find solutions with 98.7% accuracy within several milliseconds compared to several minutes or hours in an existing state-of-the-art work and an exhaustive approach respectively.