Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of Adherence to The Neurocritical Care Society Guidelines in Management of Generalized Convulsive Status Epilepticus at The Emergency Department of Suez Canal University Hospital

Introduction: Status epilepticus management protocols have adopted the operational defining of generalized convulsive status epilepticus (GCSE) as “more than 5 minutes of either continuous seizures or two or more seizures with incomplete recovery of consciousness in between. Subjects and methods: This study was conducted as a cross sectional observational study among 81 patients presenting with generalized convulsive status epilepticus. The study was applied by comparing the steps of management of GCSE at the Emergency Department of Suez Canal university hospital to the management guidelines of Neurocritical Care Society in evaluation and management of GCSE. Results: The study showed that (37%) of the studied patients had a history of epilepsy and (9%) of the patients had a positive family history of epilepsy. It showed that the commonest causes of GCSE were AEDs noncompliance/ Discontinuation, febrile seizures and cerebro-vascular stroke by a percentage of (30.8%, 19.7%, and 10%) respectively. It showed that adherence to the guidelines of the total studied patients who took the emergent therapy was (28.4%). Seizure termination occurred in (32%) of the patients after emergent therapy. Conclusion: The present study revealed that the median percentage of adherence to NCS 2012 Guidelines for GCSE management in the Emergency Department of Suez Canal University Hospital was (55.7%) and the percentage of adherence for Emergent AEDs therapy was (28.4%), the percentage of adherence for Urgent AEDs control therapy was (45.5%) and the percentage of adherence for Refractory GCSE was (33.3%). *Corresponding author: Mohamed Abdelfattah abdeldayem, Department of Neurology, Suez Canal University, Ismailia, Egypt, Tel: 966566223205; E-mail: mohelsamahy7@yahoo.com Received October 05, 2018; Accepted November 21, 2018; Published November 24, 2018 Citation: Al-Shamy S, Hosny AO, Ismail MT, Alsekaya MSM, Abdeldayem M (2018) Evaluation of Adherence to The Neurocritical Care Society Guidelines in Management of Generalized Convulsive Status Epilepticus at The Emergency Department of Suez Canal University Hospital. J Neurol Disord 6: 395. doi:10.4172/2329-6895.1000395 Copyright: © 2018 Al-Shamy S, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
A phase 1b study of AFM13 in combination with pembrolizumab in patients with relapsed or refractory Hodgkin lymphoma.

In relapsed/refractory Hodgkin lymphoma (R/R HL), immunotherapies such as the PD-1 inhibitor pembrolizumab have demonstrated efficacy as monotherapy and are playing an increasingly prominent role in treatment. The CD30/CD16A bispecific antibody AFM13 is an innate immune cell engager, a first-in-class, tetravalent antibody, designed to create a bridge between CD30 on HL cells and the CD16A receptor on NK cells and macrophages, to induce tumor cell killing. Early studies of AFM13 have demonstrated signs of efficacy as monotherapy for patients with R/R HL and the combination of AFM13 with pembrolizumab represents a rational new treatment modality. Here, we describe a Phase 1b, dose-escalation study to assess the safety and preliminary efficacy of AFM13 in combination with pembrolizumab in patients with R/R HL. The primary objective was estimating the MTD; the secondary objectives were to assess safety, tolerability, anti-tumor efficacy, pharmacokinetics, and pharmacodynamics. In this heavily pre-treated patient population, the combination of AFM13 and pembrolizumab was generally well tolerated, with similar safety profiles compared to the known profiles of each agent alone. AFM13 plus pembrolizumab demonstrated objective response rates of 88% at the highest treatment dose, with an 83% ORR for the overall population. Pharmacokinetic assessment of AFM13 in the combination treatment revealed a half-life of up to 20.6 hours. This proof-of-concept study holds promise as a novel immunotherapy combination worthy of further investigation. This Phase 1b study was registered at www.clinicaltrials. gov as NCT02665650.
TDAE: Autoencoder-based Automatic Feature Learning Method for the Detection of DNS tunnel

The DNS protocol is one of the most important network infrastructure protocols. The encrypted information based on this protocol will not be intercepted by the firewall, so the attacker uses this vulnerability to pass private data through the establishment of DNS tunnels and avoids the security inspection. In order to detect the DNS tunnel conveniently and effectively, we present a novel method that uses Autoencoder to learn latent representation of different datasets. Because the feature is not extracted manually, we show how Autoencoder(AE) can automatically learn the concept of semantic similarity among features of normal traffic. We propose a novel method named TDAE which can detect DNS tunnel traffics using Autoencoder algorithms. To verify the validity of our method, we select a labeled dataset and a public and unlabeled dataset as our training set. The experimental results show that the recall rate can exceed 0.9834 on the labeled dataset and 0.9313 on the SINGH-data [1].
Stereoselectivity of Electron and Energy Transfer in the Quenching of (S/R)-Ketoprofen-(S)-Tryptophan Dyad Excited State

Photoinduced elementary processes in chiral linked systems, consisting of drugs and tryptophan (Trp) residues, attract considerable attention due to several aspects. First of all, these are models that allow one to trace the full and partial charge transfer underlying the binding of drugs to enzymes and receptors. On the other hand, Trp fluorescence is widely used to establish the structure and conformational mobility of proteins due to its high sensitivity to the microenvironment. Therefore, the study of mechanisms of Trp fluorescence quenching in various systems has both fundamental and practical interest. An analysis of the photo-chemically induced dynamic nuclear polarization (CIDNP) and Trp fluorescence quenching in (R/S)-ketoprofen-(S)-tryptophan ((S/R)-KP-(S)-Trp) dyad carried out in this work allowed us to trace the intramolecular reversible electron transfer (ET) and obtain evidence in favor of the resonance energy transfer (RET). The fraction of dyad’s singlet excited state, quenched via ET, was shown to be 7.5 times greater for the (S,S)-diastereomer than for the (R,S) analog. At the same time, the ratio of the fluorescence quantum yields shows that quenching effectiveness of (S,S)-diastereomer to be 5.4 times lower than for the (R,S) analog. It means that the main mechanism of Trp fluorescence quenching in (S/R)-KP-(S)-Trp dyad is RET.
Endogenous Risk-Exposure and Systemic Instability

Most research on financial systemic stability assumes an economy in which banks are subject to exogenous shocks, but in practice, banks choose their exposure to risk. This paper studies the determinants of this endogenous risk exposure when banks are connected in a financial network. I show that there exists a network risk-taking externality: connected banks’ choices of risk exposure are strategically complementary. Banks in financial networks, particularly densely connected ones, endogenously expose to greater risks. Furthermore, they choose correlated risks, aggravating the systemic fragility. Banks, however, do have incentives to form networks to protect their charter values. The theory yields several novel perspectives on policy debates.
Small Extracellular Vesicles Isolated from Serum May Serve as Signal-Enhancers for the Monitoring of CNS Tumors

Liquid biopsy-based methods to test biomarkers (e.g., serum proteins and extracellular vesicles) may help to monitor brain tumors. In this proteomics-based study, we aimed to identify a characteristic protein fingerprint associated with central nervous system (CNS) tumors. Overall, 96 human serum samples were obtained from four patient groups, namely glioblastoma multiforme (GBM), non-small-cell lung cancer brain metastasis (BM), meningioma (M) and lumbar disc hernia patients (CTRL). After the isolation and characterization of small extracellular vesicles (sEVs) by nanoparticle tracking analysis (NTA) and atomic force microscopy (AFM), liquid chromatography -mass spectrometry (LC-MS) was performed on two different sample types (whole serum and serum sEVs). Statistical analyses (ratio, Cohen’s d, receiver operating characteristic; ROC) were carried out to compare patient groups. To recognize differences between the two sample types, pairwise comparisons (Welch’s test) and ingenuity pathway analysis (IPA) were performed. According to our knowledge, this is the first study that compares the proteome of whole serum and serum-derived sEVs. From the 311 proteins identified, 10 whole serum proteins and 17 sEV proteins showed the highest intergroup differences. Sixty-five proteins were significantly enriched in sEV samples, while 129 proteins were significantly depleted compared to whole serum. Based on principal component analysis (PCA) analyses, sEVs are more suitable to discriminate between the patient groups. Our results support that sEVs have greater potential to monitor CNS tumors, than whole serum.
Progression of Large Vessel Disease in Patients With Giant Cell Arteritis–Associated Ischemic Stroke: The Role of Vascular Imaging

Objective Giant cell arteritis (GCA) can cause ischemic stroke (IS) due to the involvement of the internal carotid and vertebral arteries. The aim of our study is to describe the pattern of stroke recurrence in patients with GCA-related IS and the role of vascular imaging in the follow-up of these patients. Methods We conducted an observational study of 2417 consecutive patients diagnosed with IS and admitted to our hospital from January 2012 to December 2018. We reviewed patients with GCA-related IS and the relationship of erythrocyte sedimentation rate, C-reactive protein, vascular status, and clinical course. Results We found 4 patients with GCA-related IS among 2417 IS patients: 1 woman (25%); median age, 77.3 years (67–85 years). Mean follow-up was 3.6 years. Initial vascular workup showed vertebral artery stenosis in all of them and internal carotid artery stenosis in 2 patients. All patients were started on treatment with full-dose prednisone, associated with methotrexate in 2 cases. Follow-up color-coded duplex sonography disclosed progression of arterial stenoses in 3 patients who suffered a recurrent IS (days after index stroke; mean, 27.67 [SD, 10.97]) despite normal C-reactive protein and erythrocyte sedimentation rate values. Conclusions Vascular imaging, especially with color-coded duplex sonography, could play a role in the follow-up of patients with GCA-related IS and identify those patients with higher risk of recurrent stroke.
Leaf Spot Attention Network for Apple Leaf Disease Identification

Although new deep learning approaches have recently been introduced for leaf disease identification, existing deep learning models such as VGG and ResNet have been used previously. Therefore, a new deep learning architecture is proposed to consider the leaf spot attention mechanism. The primary idea is that leaf disease symptoms appear in the leaf area, whereas the background region does not contain any useful information regarding leaf diseases. To realize this, two subnetworks are designed. The first is a feature segmentation subnetwork to provide more discriminative features for the separated background, leaf areas, and spot areas in the feature map. The other is a spot-aware classification subnetwork to increase the classification accuracy. To train the proposed leaf spot attention network, the feature segmentation subnetwork is first learned with a new image set, where the background, leaf area, and spot area are annotated. Subsequently, the spot-aware classification subnetwork is connected to the feature segmentation subnetwork and then trained through early and later fusions to produce the semantic-level spot feature information. The experimental results confirm that the proposed network can increase the discriminative power by modeling the leaf spot attention mechanism. The results prove that the proposed method outperforms conventional state-of-the-art deep learning models.
Performance Analysis for Drone-Assisted HetNets with Flexible Cell Association

Drone small cells (DSCs) are served as aerial base stations to complement the terrestrial cellular networks, in order to provide seamless wireless coverage and increased network capacity. In this paper, we study a drone-assisted downlink heterogeneous network (HetNet) consisting of a first tier of DSCs overlaid with a second tier of terrestrial small cells (TSCs), both of which operate on the same frequency band. By considering a flexible biased association policy, we develop an analytical framework to evaluate the network performance. After deriving the association probabilities, and the probability distribution functions (PDFs) of typical link length, we derive exact expressions for the per-tier and overall coverage probabilities. The proposed framework allows to quantify the impact on network performance of most important system parameters, such as the height of DSCs, the bias factor, and the base station density. In absence of interference management, our results show that very limited gains can be obtained in the dense network scenario and the improper deployment of DSCs can only degrade the coverage probability achieved by the single-tier TSC network. What’s more, the unbiased cell association is shown to be optimal for the overall coverage probability in the interference-limited network regime.
Sub-THz Wideband System Employing 1-bit Quantization and Temporal Oversampling

Wireless communications systems beyond 5G are foreseen to utilize the large available bandwidths above 100 GHz. However, the power consumption of analog-to-digital converters (ADCs) for such systems is expected to be prohibitively high, because it grows quadratically with the sampling rate for high amplitude resolutions. Shifting the resolution from the amplitude to the time domain, i.e., by reducing the amplitude resolution and by employing temporal oversampling w.r. t. the Nyquist rate, is expected to be more energy efficient. To this end, we propose a novel low-cost sub-terahertz system employing zero crossing modulation (ZXM) transmit signals in combination with 1-bit quantization and temporal oversampling at the receiver. We derive and evaluate new finite-state machines for efficient de-/modulation of ZXM transmit signals, i.e., for efficient bit sequence to symbol sequence de-/mapping. Furthermore, the coded performance of the system is evaluated for a wideband line-of-sight channel.
Joint CKF-PHD Filter and Map Fusion for 5G Multi-cell SLAM

5G is expected to enable simultaneous vehicle localization and environment mapping (SLAM). Furthermore, vehicular networks will be covered with 5G small cells, wherein the map information is collected at each base station (BS) and then fused so as to promote the overall performance of SLAM. In 5G multi-cell SLAM, there are challenges such as the unknown number of targets, uncertainty regarding the association between the targets and the measurements, unknown types of targets, as well as map management among BSs. To address those challenges, we propose a new method for 5G multi-cell SLAM which comprises a joint cubature Kalman filter and multi-model probability hypothesis density, and a map fusion routine. Simulation results demonstrate that the proposed method solves the aforementioned challenges and also improves vehicle state and map estimates.
Towards Slot Bonding for Adaptive MCS in IEEE 802.15.4e TSCH Networks

Low-power wireless mesh networks provide connectivity for a wide range of applications in industrial scenarios. For many years, IEEE 802. 15.4e Time-Slotted Channel Hopping (TSCH) networks have proven their efficiency in such environments, providing high reliability and low-power operation. TSCH networks run on top of one physical (PHY) layer and are thus limited by the characteristics of the chosen PHY layer in terms of, among others, data rate, reliability and energy efficiency. To tackle these limitations and to improve network performance and flexibility in those challenging industrial environments, this work explores the simultaneous use of multiple PHYs, and more specifically multiple modulation and coding schemes (MCSs), in a TSCH network. Traditionally, TSCH relies on fixed-duration slots, large enough to send a packet of any size given the fixed data rate. In order to avoid wasting airtime when simultaneously using multiple PHYs or MCSs with different data rates, we first introduce the concept of slot bonding. This allows the creation of different-sized bonded slots with a duration adapted to the data rate of each chosen PHY. Afterwards, we formally describe TSCH slot bonding using a Mixed Integer Linear Program (MILP) model. Finally, we use this model to determine the optimal MCS configuration with a short slot frame length that causes network saturation and show the scalability advantage of slot bonding in terms of packet delivery ratio.
Embedded Learning-based Model Predictive Control for Mobile Robots using Gaussian Process Regression

This paper proposes a learning-based Model Predictive Control (MPC) strategy for mobile robots. The strategy relies on Gaussian Process Regression (GPR) to improve the robot's model based on measurement data, therefore avoiding the need for an accurate model. More specifically, the kinematics are assumed to be known and GPR is employed to capture the unknown robot dynamics. Within a cascaded control structure, the resulting full system model is used to generate velocity commands by means of MPC. This allows the dynamics to be taken into account during velocity reference generation, while keeping a fast inner-loop controller to track the velocity reference. The variance provided by the Gaussian process is used to tighten constraints for robust constraint satisfaction. Incremental Sparse Spectrum Gaussian Process Regression (ISSGPR) and Real-Time Iteration (RTI) are employed to facilitate real-time online learning and ensure implementability on embedded hardware. Simulations and experiments are performed to show the effectiveness of the proposed approach. Although we focus on the application to Wheeled Mobile Robots (WMR), the ideas provided here are applicable in other contexts where the kinematics are known and there are unmodelled dynamics.
Produção de morangueiro da cv. Camino Real em sistema de túnel

The aim of this work was to evaluate the 'Camino Real' strawberry yield that is a cultivar recently introduced from California, and to compare it to 'Aromas' and 'Camarosa' under climatic conditions of the state of Rio Grande do Sul. The experiment was carried out in Pelotas, using tunnel system, fertirrigation and runners imported from Chile. The runners were transplanted to space of 35 cm between rows and between plants, in May 2006. Weekly, from August to December, data on fruit fresh matter and number of fruits produced in each experimental unit were collected. The experimental design was a randomized complete block with split plot, with four replications. The experimental unit consisted of 21 plants. The fruit yield of 'Camino Real' (1121.2 g of commercial fruits per plant per year) is similar to Aromas (1043.3 g) and to Camarosa (1038.3 g) cultivars. Their fruits are bigger and with higher fresh matter (24.6 g) than Camarosa (19.5 g) and Aromas (17.9 g) cultivars, but in small number. The highest average of fresh fruit matter of the Camino Real cultivar is 30,7g, in the 12th week of harvest.
Interspecific variation in potential water-use efficiency and its relation to plant species abundance in a water-limited grassland

Summary 1 Interspecific and temporal variations in presumed water-use efficiency, as measured by carbon isotope discrimination (Δ), were examined in a Greek semiarid herbaceous grassland, and the hypothesis tested that competition for water is a factor in determining vegetation structure. 2 Experiments were conducted for 3 years in an upland grassland consisting of exclusively C3 perennial species. The most abundant species were the grasses Poa pratensis L. , Lolium perenne L. and Festuca valida (Uechtr.) Penzes; the forbs Achillea millefolium L. , Plantago lanceolata L. and Taraxacum officinale Weber ex Wigg; and the legume Trifolium repens L. 3 Rooting depth did not explain plant Δ in this shallow-soil site, where soil moisture was concentrated in the top of the profile. 4 Although temporal variation for Δ was significant within most species, changes in ranking of species were slight throughout three growing seasons. 5 Δ Was negatively related to species biomass production, which means that species’ abundances were positively related to species’ water-use efficiencies. Δ Was positively related to leaf N content, which we interpret as an autocorrelation with water availability. 6 The results of this study are consistent with the notion that, in an environment where water is scarce, plants may compete effectively by increasing their potential water-use efficiency as measured by Δ, and that species abundance is regulated by competition-driven water depletion.
The clinical characteristics and course of COVID‐19 in hemodialysis patients

In December 2019, pneumonia cases emerging in China rapidly spread and became a global pandemic. The disease called COVID‐19 threatens health and life in patients with comorbid disease, especially in patients with kidney failure. In this study, we aimed to evaluate the clinical findings, laboratory parameters, and prognosis of COVID‐19 disease in end‐stage renal patients undergoing hemodialysis treatment.
Fruit Quality Characterization of New Sweet Cherry Cultivars as a Good Source of Bioactive Phenolic Compounds with Antioxidant and Neuroprotective Potential

Sweet cherries (Prunus avium L.) are highly appreciated fruits for their taste, color, nutritional value, and beneficial health effects. In this work, seven new cultivars of sweet cherry were investigated for their main quality traits and nutraceutical value. The phytochemical profile of three classes of phenolic compounds and the antioxidant activity of the new cultivars were investigated through high-performance liquid chromatography with diode array detection (HPLC-DAD) and spectrophotometric assays, respectively, and compared with those of commonly commercialized cultivars. Cyanidine-3-O-rutinoside was the main anthocyanin in all genotypes, and its levels in some new cultivars were about three-fold higher than in commercial ones. The ORAC-assayed antioxidant capacity was positively correlated with the total anthocyanin index. The nutraceutical value of the new cultivars was investigated in terms of antioxidant/neuroprotective capacity in neuron-like SH-SY5Y cells. Results demonstrated that the new cultivars were more effective in counteracting oxidative stress and were also able to upregulate brain-derived neurotrophic factor (BDNF), a pro-survival neurotrophin, suggesting their potential pleiotropic role in counteracting neurodegenerations.